diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 28a4beab52c17152e1cf5971c1ceef60abe03f23..0000000000000000000000000000000000000000 Binary files a/.DS_Store and /dev/null differ diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index a6344aac8c09253b3b630fb776ae94478aa0275b..0000000000000000000000000000000000000000 --- a/.gitattributes +++ /dev/null @@ -1,35 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/pre-commit.yaml b/.github/workflows/pre-commit.yaml deleted file mode 100644 index 524f04feb484e7af753366c038bd4925f59af60a..0000000000000000000000000000000000000000 --- a/.github/workflows/pre-commit.yaml +++ /dev/null @@ -1,14 +0,0 @@ -name: pre-commit - -on: - pull_request: - push: - branches: [main] - -jobs: - pre-commit: - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v3 - - uses: pre-commit/action@v3.0.1 diff --git a/.github/workflows/sync-hf.yaml b/.github/workflows/sync-hf.yaml deleted file mode 100644 index 767d2d82a4f98b23b6d6799ebc1a23347f8d6dc3..0000000000000000000000000000000000000000 --- a/.github/workflows/sync-hf.yaml +++ /dev/null @@ -1,18 +0,0 @@ -name: Sync to HF Space - -on: - push: - branches: - - main - -jobs: - trigger_curl: - runs-on: ubuntu-latest - - steps: - - name: Send cURL POST request - run: | - curl -X POST https://mrfakename-sync-f5.hf.space/gradio_api/call/refresh \ - -s \ - -H "Content-Type: application/json" \ - -d "{\"data\": [\"${{ secrets.REFRESH_PASSWORD }}\"]}" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml deleted file mode 100644 index 9ac5ee15dbedc84f6f4aed5a6aff4cf269209077..0000000000000000000000000000000000000000 --- a/.pre-commit-config.yaml +++ /dev/null @@ -1,14 +0,0 @@ -repos: - - repo: https://github.com/astral-sh/ruff-pre-commit - # Ruff version. - rev: v0.7.0 - hooks: - # Run the linter. - - id: ruff - args: [--fix] - # Run the formatter. - - id: ruff-format - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v2.3.0 - hooks: - - id: check-yaml diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 94b3a5b19dd3cc821d7aa9d12ef084823af646f8..0000000000000000000000000000000000000000 --- a/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM pytorch/pytorch:2.4.0-cuda12.4-cudnn9-devel - -USER root - -ARG DEBIAN_FRONTEND=noninteractive - -LABEL github_repo="https://github.com/SWivid/F5-TTS" - -RUN set -x \ - && apt-get update \ - && apt-get -y install wget curl man git less openssl libssl-dev unzip unar build-essential aria2 tmux vim \ - && apt-get install -y openssh-server sox libsox-fmt-all libsox-fmt-mp3 libsndfile1-dev ffmpeg \ - && rm -rf /var/lib/apt/lists/* \ - && apt-get clean - -WORKDIR /workspace - -RUN git clone https://github.com/SWivid/F5-TTS.git \ - && cd F5-TTS \ - && pip install --no-cache-dir -r requirements.txt \ - && pip install --no-cache-dir -r requirements_eval.txt - -ENV SHELL=/bin/bash - -WORKDIR /workspace/F5-TTS diff --git a/README.md b/README.md index e26a28652832bbcfcf672ef3898a2809e9af4bea..8a9e7daa2125c66cfac963d7651e4dc0f669abf3 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ --- -title: F5-TTS +title: E2/F5 TTS emoji: 🗣️ colorFrom: green colorTo: green sdk: gradio app_file: app.py pinned: true -short_description: 'F5-TTS & E2-TTS: Zero-Shot Voice Cloning (Unofficial Demo)' -sdk_version: 4.44.1 +short_description: 'E2-TTS & F5-TTS: Zero-Shot Voice Cloning (Unofficial Demo)' +sdk_version: 5.0.2 --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/README_REPO.md b/README_REPO.md deleted file mode 100644 index ab8affa2f08d49ed5e45873ed1d5873b8e0e7e96..0000000000000000000000000000000000000000 --- a/README_REPO.md +++ /dev/null @@ -1,269 +0,0 @@ -# F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching - -[![python](https://img.shields.io/badge/Python-3.10-brightgreen)](https://github.com/SWivid/F5-TTS) -[![arXiv](https://img.shields.io/badge/arXiv-2410.06885-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2410.06885) -[![demo](https://img.shields.io/badge/GitHub-Demo%20page-orange.svg)](https://swivid.github.io/F5-TTS/) -[![hfspace](https://img.shields.io/badge/🤗-Space%20demo-yellow)](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) -[![msspace](https://img.shields.io/badge/🤖-Space%20demo-blue)](https://modelscope.cn/studios/modelscope/E2-F5-TTS) -[![lab](https://img.shields.io/badge/X--LANCE-Lab-grey?labelColor=lightgrey)](https://x-lance.sjtu.edu.cn/) -Watermark - -**F5-TTS**: Diffusion Transformer with ConvNeXt V2, faster trained and inference. - -**E2 TTS**: Flat-UNet Transformer, closest reproduction from [paper](https://arxiv.org/abs/2406.18009). - -**Sway Sampling**: Inference-time flow step sampling strategy, greatly improves performance - -### Thanks to all the contributors ! - -## Installation - -Clone the repository: - -```bash -git clone https://github.com/SWivid/F5-TTS.git -cd F5-TTS -``` - -Install torch with your CUDA version, e.g. : - -```bash -pip install torch==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 -pip install torchaudio==2.3.0+cu118 --extra-index-url https://download.pytorch.org/whl/cu118 -``` - -Install other packages: - -```bash -pip install -r requirements.txt -``` - -**[Optional]**: We provide [Dockerfile](https://github.com/SWivid/F5-TTS/blob/main/Dockerfile) and you can use the following command to build it. -```bash -docker build -t f5tts:v1 . -``` - -### Development - -When making a pull request, please use pre-commit to ensure code quality: - -```bash -pip install pre-commit -pre-commit install -``` - -This will run linters and formatters automatically before each commit. - -Manually run using: - -```bash -pre-commit run --all-files -``` - -Note: Some model components have linting exceptions for E722 to accommodate tensor notation - - -## Prepare Dataset - -Example data processing scripts for Emilia and Wenetspeech4TTS, and you may tailor your own one along with a Dataset class in `model/dataset.py`. - -```bash -# prepare custom dataset up to your need -# download corresponding dataset first, and fill in the path in scripts - -# Prepare the Emilia dataset -python scripts/prepare_emilia.py - -# Prepare the Wenetspeech4TTS dataset -python scripts/prepare_wenetspeech4tts.py -``` - -## Training & Finetuning - -Once your datasets are prepared, you can start the training process. - -```bash -# setup accelerate config, e.g. use multi-gpu ddp, fp16 -# will be to: ~/.cache/huggingface/accelerate/default_config.yaml -accelerate config -accelerate launch train.py -``` -An initial guidance on Finetuning [#57](https://github.com/SWivid/F5-TTS/discussions/57). - -Gradio UI finetuning with `finetune_gradio.py` see [#143](https://github.com/SWivid/F5-TTS/discussions/143). - -### Wandb Logging - -By default, the training script does NOT use logging (assuming you didn't manually log in using `wandb login`). - -To turn on wandb logging, you can either: - -1. Manually login with `wandb login`: Learn more [here](https://docs.wandb.ai/ref/cli/wandb-login) -2. Automatically login programmatically by setting an environment variable: Get an API KEY at https://wandb.ai/site/ and set the environment variable as follows: - -On Mac & Linux: - -``` -export WANDB_API_KEY= -``` - -On Windows: - -``` -set WANDB_API_KEY= -``` -Moreover, if you couldn't access Wandb and want to log metrics offline, you can the environment variable as follows: - -``` -export WANDB_MODE=offline -``` - -## Inference - -The pretrained model checkpoints can be reached at [🤗 Hugging Face](https://huggingface.co/SWivid/F5-TTS) and [🤖 Model Scope](https://www.modelscope.cn/models/SWivid/F5-TTS_Emilia-ZH-EN), or automatically downloaded with `inference-cli` and `gradio_app`. - -Currently support 30s for a single generation, which is the **TOTAL** length of prompt audio and the generated. Batch inference with chunks is supported by `inference-cli` and `gradio_app`. -- To avoid possible inference failures, make sure you have seen through the following instructions. -- A longer prompt audio allows shorter generated output. The part longer than 30s cannot be generated properly. Consider using a prompt audio <15s. -- Uppercased letters will be uttered letter by letter, so use lowercased letters for normal words. -- Add some spaces (blank: " ") or punctuations (e.g. "," ".") to explicitly introduce some pauses. If first few words skipped in code-switched generation (cuz different speed with different languages), this might help. - -### CLI Inference - -Either you can specify everything in `inference-cli.toml` or override with flags. Leave `--ref_text ""` will have ASR model transcribe the reference audio automatically (use extra GPU memory). If encounter network error, consider use local ckpt, just set `ckpt_file` in `inference-cli.py` - -for change model use `--ckpt_file` to specify the model you want to load, -for change vocab.txt use `--vocab_file` to provide your vocab.txt file. - -```bash -python inference-cli.py \ ---model "F5-TTS" \ ---ref_audio "tests/ref_audio/test_en_1_ref_short.wav" \ ---ref_text "Some call me nature, others call me mother nature." \ ---gen_text "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences." - -python inference-cli.py \ ---model "E2-TTS" \ ---ref_audio "tests/ref_audio/test_zh_1_ref_short.wav" \ ---ref_text "对，这就是我，万人敬仰的太乙真人。" \ ---gen_text "突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道，我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？" - -# Multi voice -python inference-cli.py -c samples/story.toml -``` - -### Gradio App -Currently supported features: -- Chunk inference -- Podcast Generation -- Multiple Speech-Type Generation - -You can launch a Gradio app (web interface) to launch a GUI for inference (will load ckpt from Huggingface, you may also use local file in `gradio_app.py`). Currently load ASR model, F5-TTS and E2 TTS all in once, thus use more GPU memory than `inference-cli`. - -```bash -python gradio_app.py -``` - -You can specify the port/host: - -```bash -python gradio_app.py --port 7860 --host 0.0.0.0 -``` - -Or launch a share link: - -```bash -python gradio_app.py --share -``` - -### Speech Editing - -To test speech editing capabilities, use the following command. - -```bash -python speech_edit.py -``` - -## Evaluation - -### Prepare Test Datasets - -1. Seed-TTS test set: Download from [seed-tts-eval](https://github.com/BytedanceSpeech/seed-tts-eval). -2. LibriSpeech test-clean: Download from [OpenSLR](http://www.openslr.org/12/). -3. Unzip the downloaded datasets and place them in the data/ directory. -4. Update the path for the test-clean data in `scripts/eval_infer_batch.py` -5. Our filtered LibriSpeech-PC 4-10s subset is already under data/ in this repo - -### Batch Inference for Test Set - -To run batch inference for evaluations, execute the following commands: - -```bash -# batch inference for evaluations -accelerate config # if not set before -bash scripts/eval_infer_batch.sh -``` - -### Download Evaluation Model Checkpoints - -1. Chinese ASR Model: [Paraformer-zh](https://huggingface.co/funasr/paraformer-zh) -2. English ASR Model: [Faster-Whisper](https://huggingface.co/Systran/faster-whisper-large-v3) -3. WavLM Model: Download from [Google Drive](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view). - -### Objective Evaluation - -Install packages for evaluation: - -```bash -pip install -r requirements_eval.txt -``` - -**Some Notes** - -For faster-whisper with CUDA 11: - -```bash -pip install --force-reinstall ctranslate2==3.24.0 -``` - -(Recommended) To avoid possible ASR failures, such as abnormal repetitions in output: - -```bash -pip install faster-whisper==0.10.1 -``` - -Update the path with your batch-inferenced results, and carry out WER / SIM evaluations: -```bash -# Evaluation for Seed-TTS test set -python scripts/eval_seedtts_testset.py - -# Evaluation for LibriSpeech-PC test-clean (cross-sentence) -python scripts/eval_librispeech_test_clean.py -``` - -## Acknowledgements - -- [E2-TTS](https://arxiv.org/abs/2406.18009) brilliant work, simple and effective -- [Emilia](https://arxiv.org/abs/2407.05361), [WenetSpeech4TTS](https://arxiv.org/abs/2406.05763) valuable datasets -- [lucidrains](https://github.com/lucidrains) initial CFM structure with also [bfs18](https://github.com/bfs18) for discussion -- [SD3](https://arxiv.org/abs/2403.03206) & [Hugging Face diffusers](https://github.com/huggingface/diffusers) DiT and MMDiT code structure -- [torchdiffeq](https://github.com/rtqichen/torchdiffeq) as ODE solver, [Vocos](https://huggingface.co/charactr/vocos-mel-24khz) as vocoder -- [FunASR](https://github.com/modelscope/FunASR), [faster-whisper](https://github.com/SYSTRAN/faster-whisper), [UniSpeech](https://github.com/microsoft/UniSpeech) for evaluation tools -- [ctc-forced-aligner](https://github.com/MahmoudAshraf97/ctc-forced-aligner) for speech edit test -- [mrfakename](https://x.com/realmrfakename) huggingface space demo ~ -- [f5-tts-mlx](https://github.com/lucasnewman/f5-tts-mlx/tree/main) Implementation with MLX framework by [Lucas Newman](https://github.com/lucasnewman) -- [F5-TTS-ONNX](https://github.com/DakeQQ/F5-TTS-ONNX) ONNX Runtime version by [DakeQQ](https://github.com/DakeQQ) - -## Citation -If our work and codebase is useful for you, please cite as: -``` -@article{chen-etal-2024-f5tts, - title={F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching}, - author={Yushen Chen and Zhikang Niu and Ziyang Ma and Keqi Deng and Chunhui Wang and Jian Zhao and Kai Yu and Xie Chen}, - journal={arXiv preprint arXiv:2410.06885}, - year={2024}, -} -``` -## License - -Our code is released under MIT License. The pre-trained models are licensed under the CC-BY-NC license due to the training data Emilia, which is an in-the-wild dataset. Sorry for any inconvenience this may cause. diff --git a/api.py b/api.py deleted file mode 100644 index 1980f85c0964444c550324c7dca2c159ce4a61f9..0000000000000000000000000000000000000000 --- a/api.py +++ /dev/null @@ -1,132 +0,0 @@ -import soundfile as sf -import torch -import tqdm -from cached_path import cached_path - -from model import DiT, UNetT -from model.utils import save_spectrogram - -from model.utils_infer import load_vocoder, load_model, infer_process, remove_silence_for_generated_wav -from model.utils import seed_everything -import random -import sys - - -class F5TTS: - def __init__( - self, - model_type="F5-TTS", - ckpt_file="", - vocab_file="", - ode_method="euler", - use_ema=True, - local_path=None, - device=None, - ): - # Initialize parameters - self.final_wave = None - self.target_sample_rate = 24000 - self.n_mel_channels = 100 - self.hop_length = 256 - self.target_rms = 0.1 - self.seed = -1 - - # Set device - self.device = device or ( - "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" - ) - - # Load models - self.load_vocoder_model(local_path) - self.load_ema_model(model_type, ckpt_file, vocab_file, ode_method, use_ema) - - def load_vocoder_model(self, local_path): - self.vocos = load_vocoder(local_path is not None, local_path, self.device) - - def load_ema_model(self, model_type, ckpt_file, vocab_file, ode_method, use_ema): - if model_type == "F5-TTS": - if not ckpt_file: - ckpt_file = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")) - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) - model_cls = DiT - elif model_type == "E2-TTS": - if not ckpt_file: - ckpt_file = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors")) - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - model_cls = UNetT - else: - raise ValueError(f"Unknown model type: {model_type}") - - self.ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file, ode_method, use_ema, self.device) - - def export_wav(self, wav, file_wave, remove_silence=False): - sf.write(file_wave, wav, self.target_sample_rate) - - if remove_silence: - remove_silence_for_generated_wav(file_wave) - - def export_spectrogram(self, spect, file_spect): - save_spectrogram(spect, file_spect) - - def infer( - self, - ref_file, - ref_text, - gen_text, - show_info=print, - progress=tqdm, - target_rms=0.1, - cross_fade_duration=0.15, - sway_sampling_coef=-1, - cfg_strength=2, - nfe_step=32, - speed=1.0, - fix_duration=None, - remove_silence=False, - file_wave=None, - file_spect=None, - seed=-1, - ): - if seed == -1: - seed = random.randint(0, sys.maxsize) - seed_everything(seed) - self.seed = seed - wav, sr, spect = infer_process( - ref_file, - ref_text, - gen_text, - self.ema_model, - show_info=show_info, - progress=progress, - target_rms=target_rms, - cross_fade_duration=cross_fade_duration, - nfe_step=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - speed=speed, - fix_duration=fix_duration, - device=self.device, - ) - - if file_wave is not None: - self.export_wav(wav, file_wave, remove_silence) - - if file_spect is not None: - self.export_spectrogram(spect, file_spect) - - return wav, sr, spect - - -if __name__ == "__main__": - f5tts = F5TTS() - - wav, sr, spect = f5tts.infer( - ref_file="tests/ref_audio/test_en_1_ref_short.wav", - ref_text="some call me nature, others call me mother nature.", - gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""", - file_wave="tests/out.wav", - file_spect="tests/out.png", - seed=-1, # random seed = -1 - ) - - print("seed :", f5tts.seed) diff --git a/app.py b/app.py index 112aaaca4bda875fb5dfdda08cfd3e75cbe77380..ae1f28b17228e26fdb958233dace3ee7d8dd05ae 100644 --- a/app.py +++ b/app.py @@ -1,593 +1,242 @@ -# ruff: noqa: E402 -# Above allows ruff to ignore E402: module level import not at top of file - +import os import re -import tempfile - -import click +import torch +import torchaudio import gradio as gr import numpy as np -import soundfile as sf -import torchaudio -from cached_path import cached_path +import tempfile +from einops import rearrange +from ema_pytorch import EMA +from vocos import Vocos from pydub import AudioSegment +from model import CFM, UNetT, DiT, MMDiT +from cached_path import cached_path +from model.utils import ( + get_tokenizer, + convert_char_to_pinyin, + save_spectrogram, +) +from transformers import pipeline +import spaces +import librosa +from txtsplit import txtsplit +from detoxify import Detoxify -try: - import spaces - - USING_SPACES = True -except ImportError: - USING_SPACES = False +device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" -def gpu_decorator(func): - if USING_SPACES: - return spaces.GPU(func) - else: - return func +model = Detoxify('original', device=device) -from model import DiT, UNetT -from model.utils import ( - save_spectrogram, +pipe = pipeline( + "automatic-speech-recognition", + model="openai/whisper-large-v3-turbo", + torch_dtype=torch.float16, + device=device, ) -from model.utils_infer import ( - load_vocoder, - load_model, - preprocess_ref_audio_text, - infer_process, - remove_silence_for_generated_wav, -) - -vocos = load_vocoder() +# --------------------- Settings -------------------- # + +target_sample_rate = 24000 +n_mel_channels = 100 +hop_length = 256 +target_rms = 0.1 +nfe_step = 32 # 16, 32 +cfg_strength = 2.0 +ode_method = 'euler' +sway_sampling_coef = -1.0 +speed = 1.0 +# fix_duration = 27 # None or float (duration in seconds) +fix_duration = None + +def load_model(exp_name, model_cls, model_cfg, ckpt_step): + checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device) + vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin") + model = CFM( + transformer=model_cls( + **model_cfg, + text_num_embeds=vocab_size, + mel_dim=n_mel_channels + ), + mel_spec_kwargs=dict( + target_sample_rate=target_sample_rate, + n_mel_channels=n_mel_channels, + hop_length=hop_length, + ), + odeint_kwargs=dict( + method=ode_method, + ), + vocab_char_map=vocab_char_map, + ).to(device) + + ema_model = EMA(model, include_online_model=False).to(device) + ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + ema_model.copy_params_from_ema_to_model() + + return ema_model, model # load models F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) -F5TTS_ema_model = load_model( - DiT, F5TTS_model_cfg, str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors")) -) - E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) -E2TTS_ema_model = load_model( - UNetT, E2TTS_model_cfg, str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors")) -) - - -@gpu_decorator -def infer(ref_audio_orig, ref_text, gen_text, model, remove_silence, cross_fade_duration=0.15, speed=1): - ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=gr.Info) - if model == "F5-TTS": +F5TTS_ema_model, F5TTS_base_model = load_model("F5TTS_Base", DiT, F5TTS_model_cfg, 1200000) +E2TTS_ema_model, E2TTS_base_model = load_model("E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000) + +@spaces.GPU +def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, progress = gr.Progress()): + print(gen_text) + if model.predict(gen_text)['toxicity'] > 0.8: + print("Flagged for toxicity:", gen_text) + raise gr.Error("Your text was flagged for toxicity, please try again with a different text.") + gr.Info("Converting audio...") + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: + aseg = AudioSegment.from_file(ref_audio_orig) + # Convert to mono + aseg = aseg.set_channels(1) + audio_duration = len(aseg) + if audio_duration > 15000: + gr.Warning("Audio is over 15s, clipping to only first 15s.") + aseg = aseg[:15000] + aseg.export(f.name, format="wav") + ref_audio = f.name + if exp_name == "F5-TTS": ema_model = F5TTS_ema_model - elif model == "E2-TTS": + base_model = F5TTS_base_model + elif exp_name == "E2-TTS": ema_model = E2TTS_ema_model - - final_wave, final_sample_rate, combined_spectrogram = infer_process( - ref_audio, - ref_text, - gen_text, - ema_model, - cross_fade_duration=cross_fade_duration, - speed=speed, - show_info=gr.Info, - progress=gr.Progress(), - ) - - # Remove silence + base_model = E2TTS_base_model + + if not ref_text.strip(): + gr.Info("No reference text provided, transcribing reference audio...") + ref_text = outputs = pipe( + ref_audio, + chunk_length_s=30, + batch_size=128, + generate_kwargs={"task": "transcribe"}, + return_timestamps=False, + )['text'].strip() + gr.Info("Finished transcription") + else: + gr.Info("Using custom reference text...") + audio, sr = torchaudio.load(ref_audio) + # Audio + if audio.shape[0] > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + rms = torch.sqrt(torch.mean(torch.square(audio))) + if rms < target_rms: + audio = audio * target_rms / rms + if sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(sr, target_sample_rate) + audio = resampler(audio) + audio = audio.to(device) + # Chunk + chunks = txtsplit(gen_text, 100, 150) # 100 chars preferred, 150 max + results = [] + generated_mel_specs = [] + for chunk in progress.tqdm(chunks): + # Prepare the text + text_list = [ref_text + chunk] + final_text_list = convert_char_to_pinyin(text_list) + + # Calculate duration + ref_audio_len = audio.shape[-1] // hop_length + # if fix_duration is not None: + # duration = int(fix_duration * target_sample_rate / hop_length) + # else: + zh_pause_punc = r"。，、；：？！" + ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text)) + gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text)) + duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) + + # inference + gr.Info(f"Generating audio using {exp_name}") + with torch.inference_mode(): + generated, _ = base_model.sample( + cond=audio, + text=final_text_list, + duration=duration, + steps=nfe_step, + cfg_strength=cfg_strength, + sway_sampling_coef=sway_sampling_coef, + ) + + generated = generated[:, ref_audio_len:, :] + generated_mel_spec = rearrange(generated, '1 n d -> 1 d n') + gr.Info("Running vocoder") + vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") + generated_wave = vocos.decode(generated_mel_spec.cpu()) + if rms < target_rms: + generated_wave = generated_wave * rms / target_rms + + # wav -> numpy + generated_wave = generated_wave.squeeze().cpu().numpy() + results.append(generated_wave) + generated_wave = np.concatenate(results) if remove_silence: - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: - sf.write(f.name, final_wave, final_sample_rate) - remove_silence_for_generated_wav(f.name) - final_wave, _ = torchaudio.load(f.name) - final_wave = final_wave.squeeze().cpu().numpy() - - # Save the spectrogram - with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: - spectrogram_path = tmp_spectrogram.name - save_spectrogram(combined_spectrogram, spectrogram_path) - - return (final_sample_rate, final_wave), spectrogram_path - - -@gpu_decorator -def generate_podcast( - script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, model, remove_silence -): - # Split the script into speaker blocks - speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE) - speaker_blocks = speaker_pattern.split(script)[1:] # Skip the first empty element - - generated_audio_segments = [] + gr.Info("Removing audio silences... This may take a moment") + non_silent_intervals = librosa.effects.split(generated_wave, top_db=30) + non_silent_wave = np.array([]) + for interval in non_silent_intervals: + start, end = interval + non_silent_wave = np.concatenate([non_silent_wave, generated_wave[start:end]]) + generated_wave = non_silent_wave - for i in range(0, len(speaker_blocks), 2): - speaker = speaker_blocks[i] - text = speaker_blocks[i + 1].strip() - # Determine which speaker is talking - if speaker == speaker1_name: - ref_audio = ref_audio1 - ref_text = ref_text1 - elif speaker == speaker2_name: - ref_audio = ref_audio2 - ref_text = ref_text2 - else: - continue # Skip if the speaker is neither speaker1 nor speaker2 + # spectogram + # with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: + # spectrogram_path = tmp_spectrogram.name + # save_spectrogram(generated_mel_spec[0].cpu().numpy(), spectrogram_path) - # Generate audio for this block - audio, _ = infer(ref_audio, ref_text, text, model, remove_silence) + return (target_sample_rate, generated_wave) - # Convert the generated audio to a numpy array - sr, audio_data = audio - - # Save the audio data as a WAV file - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: - sf.write(temp_file.name, audio_data, sr) - audio_segment = AudioSegment.from_wav(temp_file.name) - - generated_audio_segments.append(audio_segment) - - # Add a short pause between speakers - pause = AudioSegment.silent(duration=500) # 500ms pause - generated_audio_segments.append(pause) - - # Concatenate all audio segments - final_podcast = sum(generated_audio_segments) - - # Export the final podcast - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: - podcast_path = temp_file.name - final_podcast.export(podcast_path, format="wav") - - return podcast_path - - -def parse_speechtypes_text(gen_text): - # Pattern to find (Emotion) - pattern = r"\((.*?)\)" - - # Split the text by the pattern - tokens = re.split(pattern, gen_text) +with gr.Blocks() as app: + gr.Markdown(""" +# E2/F5 TTS - segments = [] +This is an unofficial E2/F5 TTS demo. This demo supports the following TTS models: - current_emotion = "Regular" +* [E2-TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) +* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) - for i in range(len(tokens)): - if i % 2 == 0: - # This is text - text = tokens[i].strip() - if text: - segments.append({"emotion": current_emotion, "text": text}) - else: - # This is emotion - emotion = tokens[i].strip() - current_emotion = emotion +This demo is based on the [F5-TTS](https://github.com/SWivid/F5-TTS) codebase, which is based on an [unofficial E2-TTS implementation](https://github.com/lucidrains/e2-tts-pytorch). - return segments +The checkpoints support English and Chinese. +If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. If you're still running into issues, please open a [community Discussion](https://huggingface.co/spaces/mrfakename/E2-F5-TTS/discussions). -with gr.Blocks() as app_credits: - gr.Markdown(""" -# Credits +The model is licensed under the CC-BY-NC license, this demo cannot be used for commercial purposes. -* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) -* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation -* [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type generation +**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.** """) -with gr.Blocks() as app_tts: - gr.Markdown("# Batched TTS") + ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") - gen_text_input = gr.Textbox(label="Text to Generate", lines=10) + gen_text_input = gr.Textbox(label="Text to Generate (longer text will use chunking)", lines=4) model_choice = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") generate_btn = gr.Button("Synthesize", variant="primary") with gr.Accordion("Advanced Settings", open=False): - ref_text_input = gr.Textbox( - label="Reference Text", - info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.", - lines=2, - ) - remove_silence = gr.Checkbox( - label="Remove Silences", - info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.", - value=False, - ) - speed_slider = gr.Slider( - label="Speed", - minimum=0.3, - maximum=2.0, - value=1.0, - step=0.1, - info="Adjust the speed of the audio.", - ) - cross_fade_duration_slider = gr.Slider( - label="Cross-Fade Duration (s)", - minimum=0.0, - maximum=1.0, - value=0.15, - step=0.01, - info="Set the duration of the cross-fade between audio clips.", - ) - + ref_text_input = gr.Textbox(label="Reference Text", info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.", lines=2) + remove_silence = gr.Checkbox(label="Remove Silences", info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.", value=True) + audio_output = gr.Audio(label="Synthesized Audio") - spectrogram_output = gr.Image(label="Spectrogram") - - generate_btn.click( - infer, - inputs=[ - ref_audio_input, - ref_text_input, - gen_text_input, - model_choice, - remove_silence, - cross_fade_duration_slider, - speed_slider, - ], - outputs=[audio_output, spectrogram_output], - ) - -with gr.Blocks() as app_podcast: - gr.Markdown("# Podcast Generation") - speaker1_name = gr.Textbox(label="Speaker 1 Name") - ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath") - ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2) - - speaker2_name = gr.Textbox(label="Speaker 2 Name") - ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath") - ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2) - - script_input = gr.Textbox( - label="Podcast Script", - lines=10, - placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...", - ) - - podcast_model_choice = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") - podcast_remove_silence = gr.Checkbox( - label="Remove Silences", - value=True, - ) - generate_podcast_btn = gr.Button("Generate Podcast", variant="primary") - podcast_output = gr.Audio(label="Generated Podcast") - - def podcast_generation( - script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence - ): - return generate_podcast( - script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence - ) - - generate_podcast_btn.click( - podcast_generation, - inputs=[ - script_input, - speaker1_name, - ref_audio_input1, - ref_text_input1, - speaker2_name, - ref_audio_input2, - ref_text_input2, - podcast_model_choice, - podcast_remove_silence, - ], - outputs=podcast_output, - ) - - -def parse_emotional_text(gen_text): - # Pattern to find (Emotion) - pattern = r"\((.*?)\)" - - # Split the text by the pattern - tokens = re.split(pattern, gen_text) - - segments = [] - - current_emotion = "Regular" - - for i in range(len(tokens)): - if i % 2 == 0: - # This is text - text = tokens[i].strip() - if text: - segments.append({"emotion": current_emotion, "text": text}) - else: - # This is emotion - emotion = tokens[i].strip() - current_emotion = emotion - - return segments - - -with gr.Blocks() as app_emotional: - # New section for emotional generation - gr.Markdown( - """ - # Multiple Speech-Type Generation - - This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified. - - **Example Input:** - - (Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?! - """ - ) - - gr.Markdown( - "Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button." - ) - - # Regular speech type (mandatory) - with gr.Row(): - regular_name = gr.Textbox(value="Regular", label="Speech Type Name", interactive=False) - regular_audio = gr.Audio(label="Regular Reference Audio", type="filepath") - regular_ref_text = gr.Textbox(label="Reference Text (Regular)", lines=2) - - # Additional speech types (up to 99 more) - max_speech_types = 100 - speech_type_names = [] - speech_type_audios = [] - speech_type_ref_texts = [] - speech_type_delete_btns = [] - - for i in range(max_speech_types - 1): - with gr.Row(): - name_input = gr.Textbox(label="Speech Type Name", visible=False) - audio_input = gr.Audio(label="Reference Audio", type="filepath", visible=False) - ref_text_input = gr.Textbox(label="Reference Text", lines=2, visible=False) - delete_btn = gr.Button("Delete", variant="secondary", visible=False) - speech_type_names.append(name_input) - speech_type_audios.append(audio_input) - speech_type_ref_texts.append(ref_text_input) - speech_type_delete_btns.append(delete_btn) - - # Button to add speech type - add_speech_type_btn = gr.Button("Add Speech Type") - - # Keep track of current number of speech types - speech_type_count = gr.State(value=0) - - # Function to add a speech type - def add_speech_type_fn(speech_type_count): - if speech_type_count < max_speech_types - 1: - speech_type_count += 1 - # Prepare updates for the components - name_updates = [] - audio_updates = [] - ref_text_updates = [] - delete_btn_updates = [] - for i in range(max_speech_types - 1): - if i < speech_type_count: - name_updates.append(gr.update(visible=True)) - audio_updates.append(gr.update(visible=True)) - ref_text_updates.append(gr.update(visible=True)) - delete_btn_updates.append(gr.update(visible=True)) - else: - name_updates.append(gr.update()) - audio_updates.append(gr.update()) - ref_text_updates.append(gr.update()) - delete_btn_updates.append(gr.update()) - else: - # Optionally, show a warning - # gr.Warning("Maximum number of speech types reached.") - name_updates = [gr.update() for _ in range(max_speech_types - 1)] - audio_updates = [gr.update() for _ in range(max_speech_types - 1)] - ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)] - delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)] - return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates - - add_speech_type_btn.click( - add_speech_type_fn, - inputs=speech_type_count, - outputs=[speech_type_count] - + speech_type_names - + speech_type_audios - + speech_type_ref_texts - + speech_type_delete_btns, - ) - - # Function to delete a speech type - def make_delete_speech_type_fn(index): - def delete_speech_type_fn(speech_type_count): - # Prepare updates - name_updates = [] - audio_updates = [] - ref_text_updates = [] - delete_btn_updates = [] - - for i in range(max_speech_types - 1): - if i == index: - name_updates.append(gr.update(visible=False, value="")) - audio_updates.append(gr.update(visible=False, value=None)) - ref_text_updates.append(gr.update(visible=False, value="")) - delete_btn_updates.append(gr.update(visible=False)) - else: - name_updates.append(gr.update()) - audio_updates.append(gr.update()) - ref_text_updates.append(gr.update()) - delete_btn_updates.append(gr.update()) - - speech_type_count = max(0, speech_type_count - 1) - - return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates - - return delete_speech_type_fn - - for i, delete_btn in enumerate(speech_type_delete_btns): - delete_fn = make_delete_speech_type_fn(i) - delete_btn.click( - delete_fn, - inputs=speech_type_count, - outputs=[speech_type_count] - + speech_type_names - + speech_type_audios - + speech_type_ref_texts - + speech_type_delete_btns, - ) - - # Text input for the prompt - gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10) - - # Model choice - model_choice_emotional = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") - - with gr.Accordion("Advanced Settings", open=False): - remove_silence_emotional = gr.Checkbox( - label="Remove Silences", - value=True, - ) - - # Generate button - generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary") - - # Output audio - audio_output_emotional = gr.Audio(label="Synthesized Audio") - - @gpu_decorator - def generate_emotional_speech( - regular_audio, - regular_ref_text, - gen_text, - *args, - ): - num_additional_speech_types = max_speech_types - 1 - speech_type_names_list = args[:num_additional_speech_types] - speech_type_audios_list = args[num_additional_speech_types : 2 * num_additional_speech_types] - speech_type_ref_texts_list = args[2 * num_additional_speech_types : 3 * num_additional_speech_types] - model_choice = args[3 * num_additional_speech_types] - remove_silence = args[3 * num_additional_speech_types + 1] - - # Collect the speech types and their audios into a dict - speech_types = {"Regular": {"audio": regular_audio, "ref_text": regular_ref_text}} - - for name_input, audio_input, ref_text_input in zip( - speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list - ): - if name_input and audio_input: - speech_types[name_input] = {"audio": audio_input, "ref_text": ref_text_input} - - # Parse the gen_text into segments - segments = parse_speechtypes_text(gen_text) - - # For each segment, generate speech - generated_audio_segments = [] - current_emotion = "Regular" - - for segment in segments: - emotion = segment["emotion"] - text = segment["text"] - - if emotion in speech_types: - current_emotion = emotion - else: - # If emotion not available, default to Regular - current_emotion = "Regular" - - ref_audio = speech_types[current_emotion]["audio"] - ref_text = speech_types[current_emotion].get("ref_text", "") - - # Generate speech for this segment - audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, 0) - sr, audio_data = audio - - generated_audio_segments.append(audio_data) - - # Concatenate all audio segments - if generated_audio_segments: - final_audio_data = np.concatenate(generated_audio_segments) - return (sr, final_audio_data) - else: - gr.Warning("No audio generated.") - return None - - generate_emotional_btn.click( - generate_emotional_speech, - inputs=[ - regular_audio, - regular_ref_text, - gen_text_input_emotional, - ] - + speech_type_names - + speech_type_audios - + speech_type_ref_texts - + [ - model_choice_emotional, - remove_silence_emotional, - ], - outputs=audio_output_emotional, - ) - - # Validation function to disable Generate button if speech types are missing - def validate_speech_types(gen_text, regular_name, *args): - num_additional_speech_types = max_speech_types - 1 - speech_type_names_list = args[:num_additional_speech_types] - - # Collect the speech types names - speech_types_available = set() - if regular_name: - speech_types_available.add(regular_name) - for name_input in speech_type_names_list: - if name_input: - speech_types_available.add(name_input) - - # Parse the gen_text to get the speech types used - segments = parse_emotional_text(gen_text) - speech_types_in_text = set(segment["emotion"] for segment in segments) - - # Check if all speech types in text are available - missing_speech_types = speech_types_in_text - speech_types_available - - if missing_speech_types: - # Disable the generate button - return gr.update(interactive=False) - else: - # Enable the generate button - return gr.update(interactive=True) - - gen_text_input_emotional.change( - validate_speech_types, - inputs=[gen_text_input_emotional, regular_name] + speech_type_names, - outputs=generate_emotional_btn, - ) -with gr.Blocks() as app: - gr.Markdown( - """ -# E2/F5 TTS + # spectrogram_output = gr.Image(label="Spectrogram") -This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models: + generate_btn.click(infer, inputs=[ref_audio_input, ref_text_input, gen_text_input, model_choice, remove_silence], outputs=[audio_output]) + gr.Markdown(""" +## Run Locally -* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) -* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) +Run this demo locally on CPU, CUDA, or MPS/Apple Silicon (requires macOS >= 14): -The checkpoints support English and Chinese. +First, ensure `ffmpeg` is installed. -If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. +```bash +git clone https://huggingface.co/spaces/mrfakename/E2-F5-TTS +cd E2-F5-TTS +python -m pip install -r requirements.txt +python app_local.py +``` -**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.** -""" - ) - gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"]) - - -@click.command() -@click.option("--port", "-p", default=None, type=int, help="Port to run the app on") -@click.option("--host", "-H", default=None, help="Host to run the app on") -@click.option( - "--share", - "-s", - default=False, - is_flag=True, - help="Share the app via Gradio share link", -) -@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access") -def main(port, host, share, api): - global app - print("Starting app...") - app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api) +""") + gr.Markdown("Unofficial demo by [mrfakename](https://x.com/realmrfakename)") -if __name__ == "__main__": - if not USING_SPACES: - main() - else: - app.queue().launch() +app.queue().launch() \ No newline at end of file diff --git a/app_local.py b/app_local.py new file mode 100644 index 0000000000000000000000000000000000000000..ed512f7c458fd9b835c7291fc272fbe9b446afdb --- /dev/null +++ b/app_local.py @@ -0,0 +1,219 @@ +print("WARNING: You are running this unofficial E2/F5 TTS demo locally, it may not be as up-to-date as the hosted version (https://huggingface.co/spaces/mrfakename/E2-F5-TTS)") + +import os +import re +import torch +import torchaudio +import gradio as gr +import numpy as np +import tempfile +from einops import rearrange +from ema_pytorch import EMA +from vocos import Vocos +from pydub import AudioSegment +from model import CFM, UNetT, DiT, MMDiT +from cached_path import cached_path +from model.utils import ( + get_tokenizer, + convert_char_to_pinyin, + save_spectrogram, +) +from transformers import pipeline +import librosa +from txtsplit import txtsplit + +device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + +pipe = pipeline( + "automatic-speech-recognition", + model="openai/whisper-large-v3-turbo", + torch_dtype=torch.float16, + device=device, +) + +# --------------------- Settings -------------------- # + +target_sample_rate = 24000 +n_mel_channels = 100 +hop_length = 256 +target_rms = 0.1 +nfe_step = 32 # 16, 32 +cfg_strength = 2.0 +ode_method = 'euler' +sway_sampling_coef = -1.0 +speed = 1.0 +# fix_duration = 27 # None or float (duration in seconds) +fix_duration = None + +def load_model(exp_name, model_cls, model_cfg, ckpt_step): + checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device) + vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin") + model = CFM( + transformer=model_cls( + **model_cfg, + text_num_embeds=vocab_size, + mel_dim=n_mel_channels + ), + mel_spec_kwargs=dict( + target_sample_rate=target_sample_rate, + n_mel_channels=n_mel_channels, + hop_length=hop_length, + ), + odeint_kwargs=dict( + method=ode_method, + ), + vocab_char_map=vocab_char_map, + ).to(device) + + ema_model = EMA(model, include_online_model=False).to(device) + ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + ema_model.copy_params_from_ema_to_model() + + return ema_model, model + +# load models +F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) +E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) + +F5TTS_ema_model, F5TTS_base_model = load_model("F5TTS_Base", DiT, F5TTS_model_cfg, 1200000) +E2TTS_ema_model, E2TTS_base_model = load_model("E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000) + +def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, progress = gr.Progress()): + print(gen_text) + gr.Info("Converting audio...") + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: + aseg = AudioSegment.from_file(ref_audio_orig) + # Convert to mono + aseg = aseg.set_channels(1) + audio_duration = len(aseg) + if audio_duration > 15000: + gr.Warning("Audio is over 15s, clipping to only first 15s.") + aseg = aseg[:15000] + aseg.export(f.name, format="wav") + ref_audio = f.name + if exp_name == "F5-TTS": + ema_model = F5TTS_ema_model + base_model = F5TTS_base_model + elif exp_name == "E2-TTS": + ema_model = E2TTS_ema_model + base_model = E2TTS_base_model + + if not ref_text.strip(): + gr.Info("No reference text provided, transcribing reference audio...") + ref_text = outputs = pipe( + ref_audio, + chunk_length_s=30, + batch_size=128, + generate_kwargs={"task": "transcribe"}, + return_timestamps=False, + )['text'].strip() + gr.Info("Finished transcription") + else: + gr.Info("Using custom reference text...") + audio, sr = torchaudio.load(ref_audio) + # Audio + if audio.shape[0] > 1: + audio = torch.mean(audio, dim=0, keepdim=True) + rms = torch.sqrt(torch.mean(torch.square(audio))) + if rms < target_rms: + audio = audio * target_rms / rms + if sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(sr, target_sample_rate) + audio = resampler(audio) + audio = audio.to(device) + # Chunk + chunks = txtsplit(gen_text, 100, 150) # 100 chars preferred, 150 max + results = [] + generated_mel_specs = [] + for chunk in progress.tqdm(chunks): + # Prepare the text + text_list = [ref_text + chunk] + final_text_list = convert_char_to_pinyin(text_list) + + # Calculate duration + ref_audio_len = audio.shape[-1] // hop_length + # if fix_duration is not None: + # duration = int(fix_duration * target_sample_rate / hop_length) + # else: + zh_pause_punc = r"。，、；：？！" + ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text)) + gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text)) + duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) + + # inference + gr.Info(f"Generating audio using {exp_name}") + with torch.inference_mode(): + generated, _ = base_model.sample( + cond=audio, + text=final_text_list, + duration=duration, + steps=nfe_step, + cfg_strength=cfg_strength, + sway_sampling_coef=sway_sampling_coef, + ) + + generated = generated[:, ref_audio_len:, :] + generated_mel_spec = rearrange(generated, '1 n d -> 1 d n') + gr.Info("Running vocoder") + vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") + generated_wave = vocos.decode(generated_mel_spec.cpu()) + if rms < target_rms: + generated_wave = generated_wave * rms / target_rms + + # wav -> numpy + generated_wave = generated_wave.squeeze().cpu().numpy() + results.append(generated_wave) + generated_wave = np.concatenate(results) + if remove_silence: + gr.Info("Removing audio silences... This may take a moment") + non_silent_intervals = librosa.effects.split(generated_wave, top_db=30) + non_silent_wave = np.array([]) + for interval in non_silent_intervals: + start, end = interval + non_silent_wave = np.concatenate([non_silent_wave, generated_wave[start:end]]) + generated_wave = non_silent_wave + + + # spectogram + # with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: + # spectrogram_path = tmp_spectrogram.name + # save_spectrogram(generated_mel_spec[0].cpu().numpy(), spectrogram_path) + + return (target_sample_rate, generated_wave) + +with gr.Blocks() as app: + gr.Markdown(""" +# E2/F5 TTS + +This is an unofficial E2/F5 TTS demo. This demo supports the following TTS models: + +* [E2-TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) +* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) + +This demo is based on the [F5-TTS](https://github.com/SWivid/F5-TTS) codebase, which is based on an [unofficial E2-TTS implementation](https://github.com/lucidrains/e2-tts-pytorch). + +The checkpoints support English and Chinese. + +If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. If you're still running into issues, please open a [community Discussion](https://huggingface.co/spaces/mrfakename/E2-F5-TTS/discussions). + +Long-form/batched inference + speech editing is coming soon! + +**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.** +""") + + ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") + gen_text_input = gr.Textbox(label="Text to Generate (longer text will use chunking)", lines=4) + model_choice = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") + generate_btn = gr.Button("Synthesize", variant="primary") + with gr.Accordion("Advanced Settings", open=False): + ref_text_input = gr.Textbox(label="Reference Text", info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.", lines=2) + remove_silence = gr.Checkbox(label="Remove Silences", info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.", value=True) + + audio_output = gr.Audio(label="Synthesized Audio") + # spectrogram_output = gr.Image(label="Spectrogram") + + generate_btn.click(infer, inputs=[ref_audio_input, ref_text_input, gen_text_input, model_choice, remove_silence], outputs=[audio_output]) + gr.Markdown("Unofficial demo by [mrfakename](https://x.com/realmrfakename)") + + +app.queue().launch() \ No newline at end of file diff --git a/cog.py b/cog.py new file mode 100644 index 0000000000000000000000000000000000000000..792605a73923438777e8630dcb9989ca2bf08ee8 --- /dev/null +++ b/cog.py @@ -0,0 +1,180 @@ +# Prediction interface for Cog ⚙️ +# https://cog.run/python + +from cog import BasePredictor, Input, Path + +import os +import re +import torch +import torchaudio +import numpy as np +import tempfile +from einops import rearrange +from ema_pytorch import EMA +from vocos import Vocos +from pydub import AudioSegment +from model import CFM, UNetT, DiT, MMDiT +from cached_path import cached_path +from model.utils import ( + get_tokenizer, + convert_char_to_pinyin, + save_spectrogram, +) +from transformers import pipeline +import librosa + +device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" + +target_sample_rate = 24000 +n_mel_channels = 100 +hop_length = 256 +target_rms = 0.1 +nfe_step = 32 # 16, 32 +cfg_strength = 2.0 +ode_method = 'euler' +sway_sampling_coef = -1.0 +speed = 1.0 +# fix_duration = 27 # None or float (duration in seconds) +fix_duration = None + + +class Predictor(BasePredictor): + def load_model(exp_name, model_cls, model_cfg, ckpt_step): + checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device) + vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin") + model = CFM( + transformer=model_cls( + **model_cfg, + text_num_embeds=vocab_size, + mel_dim=n_mel_channels + ), + mel_spec_kwargs=dict( + target_sample_rate=target_sample_rate, + n_mel_channels=n_mel_channels, + hop_length=hop_length, + ), + odeint_kwargs=dict( + method=ode_method, + ), + vocab_char_map=vocab_char_map, + ).to(device) + + ema_model = EMA(model, include_online_model=False).to(device) + ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + ema_model.copy_params_from_ema_to_model() + + return ema_model, model + def setup(self) -> None: + """Load the model into memory to make running multiple predictions efficient""" + # self.model = torch.load("./weights.pth") + print("Loading Whisper model...") + self.pipe = pipeline( + "automatic-speech-recognition", + model="openai/whisper-large-v3-turbo", + torch_dtype=torch.float16, + device=device, + ) + print("Loading F5-TTS model...") + + F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) + self.F5TTS_ema_model, self.F5TTS_base_model = self.load_model("F5TTS_Base", DiT, F5TTS_model_cfg, 1200000) + + + def predict( + self, + gen_text: str = Input(description="Text to generate"), + ref_audio_orig: Path = Input(description="Reference audio"), + remove_silence: bool = Input(description="Remove silences", default=True), + ) -> Path: + """Run a single prediction on the model""" + model_choice = "F5-TTS" + print(gen_text) + if len(gen_text) > 200: + raise gr.Error("Please keep your text under 200 chars.") + gr.Info("Converting audio...") + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: + aseg = AudioSegment.from_file(ref_audio_orig) + audio_duration = len(aseg) + if audio_duration > 15000: + gr.Warning("Audio is over 15s, clipping to only first 15s.") + aseg = aseg[:15000] + aseg.export(f.name, format="wav") + ref_audio = f.name + ema_model = self.F5TTS_ema_model + base_model = self.F5TTS_base_model + + if not ref_text.strip(): + gr.Info("No reference text provided, transcribing reference audio...") + ref_text = outputs = self.pipe( + ref_audio, + chunk_length_s=30, + batch_size=128, + generate_kwargs={"task": "transcribe"}, + return_timestamps=False, + )['text'].strip() + gr.Info("Finished transcription") + else: + gr.Info("Using custom reference text...") + audio, sr = torchaudio.load(ref_audio) + + rms = torch.sqrt(torch.mean(torch.square(audio))) + if rms < target_rms: + audio = audio * target_rms / rms + if sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(sr, target_sample_rate) + audio = resampler(audio) + audio = audio.to(device) + + # Prepare the text + text_list = [ref_text + gen_text] + final_text_list = convert_char_to_pinyin(text_list) + + # Calculate duration + ref_audio_len = audio.shape[-1] // hop_length + # if fix_duration is not None: + # duration = int(fix_duration * target_sample_rate / hop_length) + # else: + zh_pause_punc = r"。，、；：？！" + ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text)) + gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text)) + duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) + + # inference + gr.Info(f"Generating audio using F5-TTS") + with torch.inference_mode(): + generated, _ = base_model.sample( + cond=audio, + text=final_text_list, + duration=duration, + steps=nfe_step, + cfg_strength=cfg_strength, + sway_sampling_coef=sway_sampling_coef, + ) + + generated = generated[:, ref_audio_len:, :] + generated_mel_spec = rearrange(generated, '1 n d -> 1 d n') + gr.Info("Running vocoder") + vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") + generated_wave = vocos.decode(generated_mel_spec.cpu()) + if rms < target_rms: + generated_wave = generated_wave * rms / target_rms + + # wav -> numpy + generated_wave = generated_wave.squeeze().cpu().numpy() + + if remove_silence: + gr.Info("Removing audio silences... This may take a moment") + non_silent_intervals = librosa.effects.split(generated_wave, top_db=30) + non_silent_wave = np.array([]) + for interval in non_silent_intervals: + start, end = interval + non_silent_wave = np.concatenate([non_silent_wave, generated_wave[start:end]]) + generated_wave = non_silent_wave + + + # spectogram + with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav: + wav_path = tmp_wav.name + torchaudio.save(wav_path, torch.tensor(generated_wave), target_sample_rate) + + return wav_path \ No newline at end of file diff --git a/data/.DS_Store b/data/.DS_Store deleted file mode 100644 index 3383e9e073356ef0c99f2d3d82342ad2c77f384a..0000000000000000000000000000000000000000 Binary files a/data/.DS_Store and /dev/null differ diff --git a/finetune-cli.py b/finetune-cli.py deleted file mode 100644 index bc11ee2cf4ffb5f72e8f341f572658bdffb18217..0000000000000000000000000000000000000000 --- a/finetune-cli.py +++ /dev/null @@ -1,127 +0,0 @@ -import argparse -from model import CFM, UNetT, DiT, Trainer -from model.utils import get_tokenizer -from model.dataset import load_dataset -from cached_path import cached_path -import shutil -import os - -# -------------------------- Dataset Settings --------------------------- # -target_sample_rate = 24000 -n_mel_channels = 100 -hop_length = 256 - - -# -------------------------- Argument Parsing --------------------------- # -def parse_args(): - parser = argparse.ArgumentParser(description="Train CFM Model") - - parser.add_argument( - "--exp_name", type=str, default="F5TTS_Base", choices=["F5TTS_Base", "E2TTS_Base"], help="Experiment name" - ) - parser.add_argument("--dataset_name", type=str, default="Emilia_ZH_EN", help="Name of the dataset to use") - parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate for training") - parser.add_argument("--batch_size_per_gpu", type=int, default=256, help="Batch size per GPU") - parser.add_argument( - "--batch_size_type", type=str, default="frame", choices=["frame", "sample"], help="Batch size type" - ) - parser.add_argument("--max_samples", type=int, default=16, help="Max sequences per batch") - parser.add_argument("--grad_accumulation_steps", type=int, default=1, help="Gradient accumulation steps") - parser.add_argument("--max_grad_norm", type=float, default=1.0, help="Max gradient norm for clipping") - parser.add_argument("--epochs", type=int, default=10, help="Number of training epochs") - parser.add_argument("--num_warmup_updates", type=int, default=5, help="Warmup steps") - parser.add_argument("--save_per_updates", type=int, default=10, help="Save checkpoint every X steps") - parser.add_argument("--last_per_steps", type=int, default=10, help="Save last checkpoint every X steps") - parser.add_argument("--finetune", type=bool, default=True, help="Use Finetune") - - parser.add_argument( - "--tokenizer", type=str, default="pinyin", choices=["pinyin", "char", "custom"], help="Tokenizer type" - ) - parser.add_argument( - "--tokenizer_path", - type=str, - default=None, - help="Path to custom tokenizer vocab file (only used if tokenizer = 'custom')", - ) - - return parser.parse_args() - - -# -------------------------- Training Settings -------------------------- # - - -def main(): - args = parse_args() - - # Model parameters based on experiment name - if args.exp_name == "F5TTS_Base": - wandb_resume_id = None - model_cls = DiT - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) - if args.finetune: - ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt")) - elif args.exp_name == "E2TTS_Base": - wandb_resume_id = None - model_cls = UNetT - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - if args.finetune: - ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt")) - - if args.finetune: - path_ckpt = os.path.join("ckpts", args.dataset_name) - if not os.path.isdir(path_ckpt): - os.makedirs(path_ckpt, exist_ok=True) - shutil.copy2(ckpt_path, os.path.join(path_ckpt, os.path.basename(ckpt_path))) - - checkpoint_path = os.path.join("ckpts", args.dataset_name) - - # Use the tokenizer and tokenizer_path provided in the command line arguments - tokenizer = args.tokenizer - if tokenizer == "custom": - if not args.tokenizer_path: - raise ValueError("Custom tokenizer selected, but no tokenizer_path provided.") - tokenizer_path = args.tokenizer_path - else: - tokenizer_path = args.dataset_name - - vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer) - - mel_spec_kwargs = dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - ) - - e2tts = CFM( - transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels), - mel_spec_kwargs=mel_spec_kwargs, - vocab_char_map=vocab_char_map, - ) - - trainer = Trainer( - e2tts, - args.epochs, - args.learning_rate, - num_warmup_updates=args.num_warmup_updates, - save_per_updates=args.save_per_updates, - checkpoint_path=checkpoint_path, - batch_size=args.batch_size_per_gpu, - batch_size_type=args.batch_size_type, - max_samples=args.max_samples, - grad_accumulation_steps=args.grad_accumulation_steps, - max_grad_norm=args.max_grad_norm, - wandb_project="CFM-TTS", - wandb_run_name=args.exp_name, - wandb_resume_id=wandb_resume_id, - last_per_steps=args.last_per_steps, - ) - - train_dataset = load_dataset(args.dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs) - trainer.train( - train_dataset, - resumable_with_seed=666, # seed for shuffling dataset - ) - - -if __name__ == "__main__": - main() diff --git a/finetune_gradio.py b/finetune_gradio.py deleted file mode 100644 index a61d95ac2db386712fb935c5ef55fe25f2026c6a..0000000000000000000000000000000000000000 --- a/finetune_gradio.py +++ /dev/null @@ -1,944 +0,0 @@ -import os -import sys - -import tempfile -import random -from transformers import pipeline -import gradio as gr -import torch -import gc -import click -import torchaudio -from glob import glob -import librosa -import numpy as np -from scipy.io import wavfile -import shutil -import time - -import json -from model.utils import convert_char_to_pinyin -import signal -import psutil -import platform -import subprocess -from datasets.arrow_writer import ArrowWriter -from datasets import Dataset as Dataset_ -from api import F5TTS - - -training_process = None -system = platform.system() -python_executable = sys.executable or "python" -tts_api = None -last_checkpoint = "" -last_device = "" - -path_data = "data" - -device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" - -pipe = None - - -# Load metadata -def get_audio_duration(audio_path): - """Calculate the duration of an audio file.""" - audio, sample_rate = torchaudio.load(audio_path) - num_channels = audio.shape[0] - return audio.shape[1] / (sample_rate * num_channels) - - -def clear_text(text): - """Clean and prepare text by lowering the case and stripping whitespace.""" - return text.lower().strip() - - -def get_rms( - y, - frame_length=2048, - hop_length=512, - pad_mode="constant", -): # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py - padding = (int(frame_length // 2), int(frame_length // 2)) - y = np.pad(y, padding, mode=pad_mode) - - axis = -1 - # put our new within-frame axis at the end for now - out_strides = y.strides + tuple([y.strides[axis]]) - # Reduce the shape on the framing axis - x_shape_trimmed = list(y.shape) - x_shape_trimmed[axis] -= frame_length - 1 - out_shape = tuple(x_shape_trimmed) + tuple([frame_length]) - xw = np.lib.stride_tricks.as_strided(y, shape=out_shape, strides=out_strides) - if axis < 0: - target_axis = axis - 1 - else: - target_axis = axis + 1 - xw = np.moveaxis(xw, -1, target_axis) - # Downsample along the target axis - slices = [slice(None)] * xw.ndim - slices[axis] = slice(0, None, hop_length) - x = xw[tuple(slices)] - - # Calculate power - power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True) - - return np.sqrt(power) - - -class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py - def __init__( - self, - sr: int, - threshold: float = -40.0, - min_length: int = 2000, - min_interval: int = 300, - hop_size: int = 20, - max_sil_kept: int = 2000, - ): - if not min_length >= min_interval >= hop_size: - raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size") - if not max_sil_kept >= hop_size: - raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size") - min_interval = sr * min_interval / 1000 - self.threshold = 10 ** (threshold / 20.0) - self.hop_size = round(sr * hop_size / 1000) - self.win_size = min(round(min_interval), 4 * self.hop_size) - self.min_length = round(sr * min_length / 1000 / self.hop_size) - self.min_interval = round(min_interval / self.hop_size) - self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) - - def _apply_slice(self, waveform, begin, end): - if len(waveform.shape) > 1: - return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)] - else: - return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)] - - # @timeit - def slice(self, waveform): - if len(waveform.shape) > 1: - samples = waveform.mean(axis=0) - else: - samples = waveform - if samples.shape[0] <= self.min_length: - return [waveform] - rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) - sil_tags = [] - silence_start = None - clip_start = 0 - for i, rms in enumerate(rms_list): - # Keep looping while frame is silent. - if rms < self.threshold: - # Record start of silent frames. - if silence_start is None: - silence_start = i - continue - # Keep looping while frame is not silent and silence start has not been recorded. - if silence_start is None: - continue - # Clear recorded silence start if interval is not enough or clip is too short - is_leading_silence = silence_start == 0 and i > self.max_sil_kept - need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length - if not is_leading_silence and not need_slice_middle: - silence_start = None - continue - # Need slicing. Record the range of silent frames to be removed. - if i - silence_start <= self.max_sil_kept: - pos = rms_list[silence_start : i + 1].argmin() + silence_start - if silence_start == 0: - sil_tags.append((0, pos)) - else: - sil_tags.append((pos, pos)) - clip_start = pos - elif i - silence_start <= self.max_sil_kept * 2: - pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin() - pos += i - self.max_sil_kept - pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start - pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept - if silence_start == 0: - sil_tags.append((0, pos_r)) - clip_start = pos_r - else: - sil_tags.append((min(pos_l, pos), max(pos_r, pos))) - clip_start = max(pos_r, pos) - else: - pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start - pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept - if silence_start == 0: - sil_tags.append((0, pos_r)) - else: - sil_tags.append((pos_l, pos_r)) - clip_start = pos_r - silence_start = None - # Deal with trailing silence. - total_frames = rms_list.shape[0] - if silence_start is not None and total_frames - silence_start >= self.min_interval: - silence_end = min(total_frames, silence_start + self.max_sil_kept) - pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start - sil_tags.append((pos, total_frames + 1)) - # Apply and return slices. - ####音频+起始时间+终止时间 - if len(sil_tags) == 0: - return [[waveform, 0, int(total_frames * self.hop_size)]] - else: - chunks = [] - if sil_tags[0][0] > 0: - chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)]) - for i in range(len(sil_tags) - 1): - chunks.append( - [ - self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]), - int(sil_tags[i][1] * self.hop_size), - int(sil_tags[i + 1][0] * self.hop_size), - ] - ) - if sil_tags[-1][1] < total_frames: - chunks.append( - [ - self._apply_slice(waveform, sil_tags[-1][1], total_frames), - int(sil_tags[-1][1] * self.hop_size), - int(total_frames * self.hop_size), - ] - ) - return chunks - - -# terminal -def terminate_process_tree(pid, including_parent=True): - try: - parent = psutil.Process(pid) - except psutil.NoSuchProcess: - # Process already terminated - return - - children = parent.children(recursive=True) - for child in children: - try: - os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL - except OSError: - pass - if including_parent: - try: - os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL - except OSError: - pass - - -def terminate_process(pid): - if system == "Windows": - cmd = f"taskkill /t /f /pid {pid}" - os.system(cmd) - else: - terminate_process_tree(pid) - - -def start_training( - dataset_name="", - exp_name="F5TTS_Base", - learning_rate=1e-4, - batch_size_per_gpu=400, - batch_size_type="frame", - max_samples=64, - grad_accumulation_steps=1, - max_grad_norm=1.0, - epochs=11, - num_warmup_updates=200, - save_per_updates=400, - last_per_steps=800, - finetune=True, -): - global training_process, tts_api - - if tts_api is not None: - del tts_api - gc.collect() - torch.cuda.empty_cache() - tts_api = None - - path_project = os.path.join(path_data, dataset_name + "_pinyin") - - if not os.path.isdir(path_project): - yield ( - f"There is not project with name {dataset_name}", - gr.update(interactive=True), - gr.update(interactive=False), - ) - return - - file_raw = os.path.join(path_project, "raw.arrow") - if not os.path.isfile(file_raw): - yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False) - return - - # Check if a training process is already running - if training_process is not None: - return "Train run already!", gr.update(interactive=False), gr.update(interactive=True) - - yield "start train", gr.update(interactive=False), gr.update(interactive=False) - - # Command to run the training script with the specified arguments - cmd = ( - f"accelerate launch finetune-cli.py --exp_name {exp_name} " - f"--learning_rate {learning_rate} " - f"--batch_size_per_gpu {batch_size_per_gpu} " - f"--batch_size_type {batch_size_type} " - f"--max_samples {max_samples} " - f"--grad_accumulation_steps {grad_accumulation_steps} " - f"--max_grad_norm {max_grad_norm} " - f"--epochs {epochs} " - f"--num_warmup_updates {num_warmup_updates} " - f"--save_per_updates {save_per_updates} " - f"--last_per_steps {last_per_steps} " - f"--dataset_name {dataset_name}" - ) - if finetune: - cmd += f" --finetune {finetune}" - - print(cmd) - - try: - # Start the training process - training_process = subprocess.Popen(cmd, shell=True) - - time.sleep(5) - yield "train start", gr.update(interactive=False), gr.update(interactive=True) - - # Wait for the training process to finish - training_process.wait() - time.sleep(1) - - if training_process is None: - text_info = "train stop" - else: - text_info = "train complete !" - - except Exception as e: # Catch all exceptions - # Ensure that we reset the training process variable in case of an error - text_info = f"An error occurred: {str(e)}" - - training_process = None - - yield text_info, gr.update(interactive=True), gr.update(interactive=False) - - -def stop_training(): - global training_process - if training_process is None: - return "Train not run !", gr.update(interactive=True), gr.update(interactive=False) - terminate_process_tree(training_process.pid) - training_process = None - return "train stop", gr.update(interactive=True), gr.update(interactive=False) - - -def create_data_project(name): - name += "_pinyin" - os.makedirs(os.path.join(path_data, name), exist_ok=True) - os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True) - - -def transcribe(file_audio, language="english"): - global pipe - - if pipe is None: - pipe = pipeline( - "automatic-speech-recognition", - model="openai/whisper-large-v3-turbo", - torch_dtype=torch.float16, - device=device, - ) - - text_transcribe = pipe( - file_audio, - chunk_length_s=30, - batch_size=128, - generate_kwargs={"task": "transcribe", "language": language}, - return_timestamps=False, - )["text"].strip() - return text_transcribe - - -def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()): - name_project += "_pinyin" - path_project = os.path.join(path_data, name_project) - path_dataset = os.path.join(path_project, "dataset") - path_project_wavs = os.path.join(path_project, "wavs") - file_metadata = os.path.join(path_project, "metadata.csv") - - if audio_files is None: - return "You need to load an audio file." - - if os.path.isdir(path_project_wavs): - shutil.rmtree(path_project_wavs) - - if os.path.isfile(file_metadata): - os.remove(file_metadata) - - os.makedirs(path_project_wavs, exist_ok=True) - - if user: - file_audios = [ - file - for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac") - for file in glob(os.path.join(path_dataset, format)) - ] - if file_audios == []: - return "No audio file was found in the dataset." - else: - file_audios = audio_files - - alpha = 0.5 - _max = 1.0 - slicer = Slicer(24000) - - num = 0 - error_num = 0 - data = "" - for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))): - audio, _ = librosa.load(file_audio, sr=24000, mono=True) - - list_slicer = slicer.slice(audio) - for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"): - name_segment = os.path.join(f"segment_{num}") - file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav") - - tmp_max = np.abs(chunk).max() - if tmp_max > 1: - chunk /= tmp_max - chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk - wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16)) - - try: - text = transcribe(file_segment, language) - text = text.lower().strip().replace('"', "") - - data += f"{name_segment}|{text}\n" - - num += 1 - except: # noqa: E722 - error_num += 1 - - with open(file_metadata, "w", encoding="utf-8") as f: - f.write(data) - - if error_num != []: - error_text = f"\nerror files : {error_num}" - else: - error_text = "" - - return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}" - - -def format_seconds_to_hms(seconds): - hours = int(seconds / 3600) - minutes = int((seconds % 3600) / 60) - seconds = seconds % 60 - return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds)) - - -def create_metadata(name_project, progress=gr.Progress()): - name_project += "_pinyin" - path_project = os.path.join(path_data, name_project) - path_project_wavs = os.path.join(path_project, "wavs") - file_metadata = os.path.join(path_project, "metadata.csv") - file_raw = os.path.join(path_project, "raw.arrow") - file_duration = os.path.join(path_project, "duration.json") - file_vocab = os.path.join(path_project, "vocab.txt") - - if not os.path.isfile(file_metadata): - return "The file was not found in " + file_metadata - - with open(file_metadata, "r", encoding="utf-8") as f: - data = f.read() - - audio_path_list = [] - text_list = [] - duration_list = [] - - count = data.split("\n") - lenght = 0 - result = [] - error_files = [] - for line in progress.tqdm(data.split("\n"), total=count): - sp_line = line.split("|") - if len(sp_line) != 2: - continue - name_audio, text = sp_line[:2] - - file_audio = os.path.join(path_project_wavs, name_audio + ".wav") - - if not os.path.isfile(file_audio): - error_files.append(file_audio) - continue - - duraction = get_audio_duration(file_audio) - if duraction < 2 and duraction > 15: - continue - if len(text) < 4: - continue - - text = clear_text(text) - text = convert_char_to_pinyin([text], polyphone=True)[0] - - audio_path_list.append(file_audio) - duration_list.append(duraction) - text_list.append(text) - - result.append({"audio_path": file_audio, "text": text, "duration": duraction}) - - lenght += duraction - - if duration_list == []: - error_files_text = "\n".join(error_files) - return f"Error: No audio files found in the specified path : \n{error_files_text}" - - min_second = round(min(duration_list), 2) - max_second = round(max(duration_list), 2) - - with ArrowWriter(path=file_raw, writer_batch_size=1) as writer: - for line in progress.tqdm(result, total=len(result), desc="prepare data"): - writer.write(line) - - with open(file_duration, "w", encoding="utf-8") as f: - json.dump({"duration": duration_list}, f, ensure_ascii=False) - - file_vocab_finetune = "data/Emilia_ZH_EN_pinyin/vocab.txt" - if not os.path.isfile(file_vocab_finetune): - return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!" - shutil.copy2(file_vocab_finetune, file_vocab) - - if error_files != []: - error_text = "error files\n" + "\n".join(error_files) - else: - error_text = "" - - return f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\n{error_text}" - - -def check_user(value): - return gr.update(visible=not value), gr.update(visible=value) - - -def calculate_train( - name_project, - batch_size_type, - max_samples, - learning_rate, - num_warmup_updates, - save_per_updates, - last_per_steps, - finetune, -): - name_project += "_pinyin" - path_project = os.path.join(path_data, name_project) - file_duraction = os.path.join(path_project, "duration.json") - - if not os.path.isfile(file_duraction): - return ( - 1000, - max_samples, - num_warmup_updates, - save_per_updates, - last_per_steps, - "project not found !", - learning_rate, - ) - - with open(file_duraction, "r") as file: - data = json.load(file) - - duration_list = data["duration"] - - samples = len(duration_list) - - if torch.cuda.is_available(): - gpu_properties = torch.cuda.get_device_properties(0) - total_memory = gpu_properties.total_memory / (1024**3) - elif torch.backends.mps.is_available(): - total_memory = psutil.virtual_memory().available / (1024**3) - - if batch_size_type == "frame": - batch = int(total_memory * 0.5) - batch = (lambda num: num + 1 if num % 2 != 0 else num)(batch) - batch_size_per_gpu = int(38400 / batch) - else: - batch_size_per_gpu = int(total_memory / 8) - batch_size_per_gpu = (lambda num: num + 1 if num % 2 != 0 else num)(batch_size_per_gpu) - batch = batch_size_per_gpu - - if batch_size_per_gpu <= 0: - batch_size_per_gpu = 1 - - if samples < 64: - max_samples = int(samples * 0.25) - else: - max_samples = 64 - - num_warmup_updates = int(samples * 0.05) - save_per_updates = int(samples * 0.10) - last_per_steps = int(save_per_updates * 5) - - max_samples = (lambda num: num + 1 if num % 2 != 0 else num)(max_samples) - num_warmup_updates = (lambda num: num + 1 if num % 2 != 0 else num)(num_warmup_updates) - save_per_updates = (lambda num: num + 1 if num % 2 != 0 else num)(save_per_updates) - last_per_steps = (lambda num: num + 1 if num % 2 != 0 else num)(last_per_steps) - - if finetune: - learning_rate = 1e-5 - else: - learning_rate = 7.5e-5 - - return batch_size_per_gpu, max_samples, num_warmup_updates, save_per_updates, last_per_steps, samples, learning_rate - - -def extract_and_save_ema_model(checkpoint_path: str, new_checkpoint_path: str) -> None: - try: - checkpoint = torch.load(checkpoint_path) - print("Original Checkpoint Keys:", checkpoint.keys()) - - ema_model_state_dict = checkpoint.get("ema_model_state_dict", None) - - if ema_model_state_dict is not None: - new_checkpoint = {"ema_model_state_dict": ema_model_state_dict} - torch.save(new_checkpoint, new_checkpoint_path) - return f"New checkpoint saved at: {new_checkpoint_path}" - else: - return "No 'ema_model_state_dict' found in the checkpoint." - - except Exception as e: - return f"An error occurred: {e}" - - -def vocab_check(project_name): - name_project = project_name + "_pinyin" - path_project = os.path.join(path_data, name_project) - - file_metadata = os.path.join(path_project, "metadata.csv") - - file_vocab = "data/Emilia_ZH_EN_pinyin/vocab.txt" - if not os.path.isfile(file_vocab): - return f"the file {file_vocab} not found !" - - with open(file_vocab, "r", encoding="utf-8") as f: - data = f.read() - - vocab = data.split("\n") - - if not os.path.isfile(file_metadata): - return f"the file {file_metadata} not found !" - - with open(file_metadata, "r", encoding="utf-8") as f: - data = f.read() - - miss_symbols = [] - miss_symbols_keep = {} - for item in data.split("\n"): - sp = item.split("|") - if len(sp) != 2: - continue - - text = sp[1].lower().strip() - - for t in text: - if t not in vocab and t not in miss_symbols_keep: - miss_symbols.append(t) - miss_symbols_keep[t] = t - if miss_symbols == []: - info = "You can train using your language !" - else: - info = f"The following symbols are missing in your language : {len(miss_symbols)}\n\n" + "\n".join(miss_symbols) - - return info - - -def get_random_sample_prepare(project_name): - name_project = project_name + "_pinyin" - path_project = os.path.join(path_data, name_project) - file_arrow = os.path.join(path_project, "raw.arrow") - if not os.path.isfile(file_arrow): - return "", None - dataset = Dataset_.from_file(file_arrow) - random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0]) - text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]" - audio_path = random_sample["audio_path"][0] - return text, audio_path - - -def get_random_sample_transcribe(project_name): - name_project = project_name + "_pinyin" - path_project = os.path.join(path_data, name_project) - file_metadata = os.path.join(path_project, "metadata.csv") - if not os.path.isfile(file_metadata): - return "", None - - data = "" - with open(file_metadata, "r", encoding="utf-8") as f: - data = f.read() - - list_data = [] - for item in data.split("\n"): - sp = item.split("|") - if len(sp) != 2: - continue - list_data.append([os.path.join(path_project, "wavs", sp[0] + ".wav"), sp[1]]) - - if list_data == []: - return "", None - - random_item = random.choice(list_data) - - return random_item[1], random_item[0] - - -def get_random_sample_infer(project_name): - text, audio = get_random_sample_transcribe(project_name) - return ( - text, - text, - audio, - ) - - -def infer(file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step): - global last_checkpoint, last_device, tts_api - - if not os.path.isfile(file_checkpoint): - return None - - if training_process is not None: - device_test = "cpu" - else: - device_test = None - - if last_checkpoint != file_checkpoint or last_device != device_test: - if last_checkpoint != file_checkpoint: - last_checkpoint = file_checkpoint - if last_device != device_test: - last_device = device_test - - tts_api = F5TTS(model_type=exp_name, ckpt_file=file_checkpoint, device=device_test) - - print("update", device_test, file_checkpoint) - - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: - tts_api.infer(gen_text=gen_text, ref_text=ref_text, ref_file=ref_audio, nfe_step=nfe_step, file_wave=f.name) - return f.name - - -with gr.Blocks() as app: - with gr.Row(): - project_name = gr.Textbox(label="project name", value="my_speak") - bt_create = gr.Button("create new project") - - bt_create.click(fn=create_data_project, inputs=[project_name]) - - with gr.Tabs(): - with gr.TabItem("transcribe Data"): - ch_manual = gr.Checkbox(label="user", value=False) - - mark_info_transcribe = gr.Markdown( - """```plaintext - Place your 'wavs' folder and 'metadata.csv' file in the {your_project_name}' directory. - - my_speak/ - │ - └── dataset/ - ├── audio1.wav - └── audio2.wav - ... - ```""", - visible=False, - ) - - audio_speaker = gr.File(label="voice", type="filepath", file_count="multiple") - txt_lang = gr.Text(label="Language", value="english") - bt_transcribe = bt_create = gr.Button("transcribe") - txt_info_transcribe = gr.Text(label="info", value="") - bt_transcribe.click( - fn=transcribe_all, - inputs=[project_name, audio_speaker, txt_lang, ch_manual], - outputs=[txt_info_transcribe], - ) - ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe]) - - random_sample_transcribe = gr.Button("random sample") - - with gr.Row(): - random_text_transcribe = gr.Text(label="Text") - random_audio_transcribe = gr.Audio(label="Audio", type="filepath") - - random_sample_transcribe.click( - fn=get_random_sample_transcribe, - inputs=[project_name], - outputs=[random_text_transcribe, random_audio_transcribe], - ) - - with gr.TabItem("prepare Data"): - gr.Markdown( - """```plaintext - place all your wavs folder and your metadata.csv file in {your name project} - my_speak/ - │ - ├── wavs/ - │ ├── audio1.wav - │ └── audio2.wav - | ... - │ - └── metadata.csv - - file format metadata.csv - - audio1|text1 - audio2|text1 - ... - - ```""" - ) - - bt_prepare = bt_create = gr.Button("prepare") - txt_info_prepare = gr.Text(label="info", value="") - bt_prepare.click(fn=create_metadata, inputs=[project_name], outputs=[txt_info_prepare]) - - random_sample_prepare = gr.Button("random sample") - - with gr.Row(): - random_text_prepare = gr.Text(label="Pinyin") - random_audio_prepare = gr.Audio(label="Audio", type="filepath") - - random_sample_prepare.click( - fn=get_random_sample_prepare, inputs=[project_name], outputs=[random_text_prepare, random_audio_prepare] - ) - - with gr.TabItem("train Data"): - with gr.Row(): - bt_calculate = bt_create = gr.Button("Auto Settings") - ch_finetune = bt_create = gr.Checkbox(label="finetune", value=True) - lb_samples = gr.Label(label="samples") - batch_size_type = gr.Radio(label="Batch Size Type", choices=["frame", "sample"], value="frame") - - with gr.Row(): - exp_name = gr.Radio(label="Model", choices=["F5TTS_Base", "E2TTS_Base"], value="F5TTS_Base") - learning_rate = gr.Number(label="Learning Rate", value=1e-5, step=1e-5) - - with gr.Row(): - batch_size_per_gpu = gr.Number(label="Batch Size per GPU", value=1000) - max_samples = gr.Number(label="Max Samples", value=64) - - with gr.Row(): - grad_accumulation_steps = gr.Number(label="Gradient Accumulation Steps", value=1) - max_grad_norm = gr.Number(label="Max Gradient Norm", value=1.0) - - with gr.Row(): - epochs = gr.Number(label="Epochs", value=10) - num_warmup_updates = gr.Number(label="Warmup Updates", value=5) - - with gr.Row(): - save_per_updates = gr.Number(label="Save per Updates", value=10) - last_per_steps = gr.Number(label="Last per Steps", value=50) - - with gr.Row(): - start_button = gr.Button("Start Training") - stop_button = gr.Button("Stop Training", interactive=False) - - txt_info_train = gr.Text(label="info", value="") - start_button.click( - fn=start_training, - inputs=[ - project_name, - exp_name, - learning_rate, - batch_size_per_gpu, - batch_size_type, - max_samples, - grad_accumulation_steps, - max_grad_norm, - epochs, - num_warmup_updates, - save_per_updates, - last_per_steps, - ch_finetune, - ], - outputs=[txt_info_train, start_button, stop_button], - ) - stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button]) - bt_calculate.click( - fn=calculate_train, - inputs=[ - project_name, - batch_size_type, - max_samples, - learning_rate, - num_warmup_updates, - save_per_updates, - last_per_steps, - ch_finetune, - ], - outputs=[ - batch_size_per_gpu, - max_samples, - num_warmup_updates, - save_per_updates, - last_per_steps, - lb_samples, - learning_rate, - ], - ) - - with gr.TabItem("reduse checkpoint"): - txt_path_checkpoint = gr.Text(label="path checkpoint :") - txt_path_checkpoint_small = gr.Text(label="path output :") - txt_info_reduse = gr.Text(label="info", value="") - reduse_button = gr.Button("reduse") - reduse_button.click( - fn=extract_and_save_ema_model, - inputs=[txt_path_checkpoint, txt_path_checkpoint_small], - outputs=[txt_info_reduse], - ) - - with gr.TabItem("vocab check experiment"): - check_button = gr.Button("check vocab") - txt_info_check = gr.Text(label="info", value="") - check_button.click(fn=vocab_check, inputs=[project_name], outputs=[txt_info_check]) - - with gr.TabItem("test model"): - exp_name = gr.Radio(label="Model", choices=["F5-TTS", "E2-TTS"], value="F5-TTS") - nfe_step = gr.Number(label="n_step", value=32) - file_checkpoint_pt = gr.Textbox(label="Checkpoint", value="") - - random_sample_infer = gr.Button("random sample") - - ref_text = gr.Textbox(label="ref text") - ref_audio = gr.Audio(label="audio ref", type="filepath") - gen_text = gr.Textbox(label="gen text") - random_sample_infer.click( - fn=get_random_sample_infer, inputs=[project_name], outputs=[ref_text, gen_text, ref_audio] - ) - check_button_infer = gr.Button("infer") - gen_audio = gr.Audio(label="audio gen", type="filepath") - - check_button_infer.click( - fn=infer, - inputs=[file_checkpoint_pt, exp_name, ref_text, ref_audio, gen_text, nfe_step], - outputs=[gen_audio], - ) - - -@click.command() -@click.option("--port", "-p", default=None, type=int, help="Port to run the app on") -@click.option("--host", "-H", default=None, help="Host to run the app on") -@click.option( - "--share", - "-s", - default=False, - is_flag=True, - help="Share the app via Gradio share link", -) -@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access") -def main(port, host, share, api): - global app - print("Starting app...") - app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api) - - -if __name__ == "__main__": - main() diff --git a/gradio_app.py b/gradio_app.py deleted file mode 100644 index 492793dd0f8f3269a3eff388296a96ed3a9c33c9..0000000000000000000000000000000000000000 --- a/gradio_app.py +++ /dev/null @@ -1,824 +0,0 @@ -import os -import re -import torch -import torchaudio -import gradio as gr -import numpy as np -import tempfile -from einops import rearrange -from vocos import Vocos -from pydub import AudioSegment, silence -from model import CFM, UNetT, DiT, MMDiT -from cached_path import cached_path -from model.utils import ( - load_checkpoint, - get_tokenizer, - convert_char_to_pinyin, - save_spectrogram, -) -from transformers import pipeline -import librosa -import click -import soundfile as sf - -try: - import spaces - USING_SPACES = True -except ImportError: - USING_SPACES = False - -def gpu_decorator(func): - if USING_SPACES: - return spaces.GPU(func) - else: - return func - - - -SPLIT_WORDS = [ - "but", "however", "nevertheless", "yet", "still", - "therefore", "thus", "hence", "consequently", - "moreover", "furthermore", "additionally", - "meanwhile", "alternatively", "otherwise", - "namely", "specifically", "for example", "such as", - "in fact", "indeed", "notably", - "in contrast", "on the other hand", "conversely", - "in conclusion", "to summarize", "finally" -] - -device = ( - "cuda" - if torch.cuda.is_available() - else "mps" if torch.backends.mps.is_available() else "cpu" -) - -print(f"Using {device} device") - -pipe = pipeline( - "automatic-speech-recognition", - model="openai/whisper-large-v3-turbo", - torch_dtype=torch.float16, - device=device, -) -vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") - -# --------------------- Settings -------------------- # - -target_sample_rate = 24000 -n_mel_channels = 100 -hop_length = 256 -target_rms = 0.1 -nfe_step = 32 # 16, 32 -cfg_strength = 2.0 -ode_method = "euler" -sway_sampling_coef = -1.0 -speed = 1.0 -# fix_duration = 27 # None or float (duration in seconds) -fix_duration = None - - -def load_model(repo_name, exp_name, model_cls, model_cfg, ckpt_step): - ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors")) - # ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors - vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin") - model = CFM( - transformer=model_cls( - **model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels - ), - mel_spec_kwargs=dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - ), - odeint_kwargs=dict( - method=ode_method, - ), - vocab_char_map=vocab_char_map, - ).to(device) - - model = load_checkpoint(model, ckpt_path, device, use_ema = True) - - return model - - -# load models -F5TTS_model_cfg = dict( - dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4 -) -E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - -F5TTS_ema_model = load_model( - "F5-TTS", "F5TTS_Base", DiT, F5TTS_model_cfg, 1200000 -) -E2TTS_ema_model = load_model( - "E2-TTS", "E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000 -) - -def split_text_into_batches(text, max_chars=200, split_words=SPLIT_WORDS): - if len(text.encode('utf-8')) <= max_chars: - return [text] - if text[-1] not in ['。', '.', '!', '！', '?', '？']: - text += '.' - - sentences = re.split('([。.!?！？])', text) - sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2])] - - batches = [] - current_batch = "" - - def split_by_words(text): - words = text.split() - current_word_part = "" - word_batches = [] - for word in words: - if len(current_word_part.encode('utf-8')) + len(word.encode('utf-8')) + 1 <= max_chars: - current_word_part += word + ' ' - else: - if current_word_part: - # Try to find a suitable split word - for split_word in split_words: - split_index = current_word_part.rfind(' ' + split_word + ' ') - if split_index != -1: - word_batches.append(current_word_part[:split_index].strip()) - current_word_part = current_word_part[split_index:].strip() + ' ' - break - else: - # If no suitable split word found, just append the current part - word_batches.append(current_word_part.strip()) - current_word_part = "" - current_word_part += word + ' ' - if current_word_part: - word_batches.append(current_word_part.strip()) - return word_batches - - for sentence in sentences: - if len(current_batch.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars: - current_batch += sentence - else: - # If adding this sentence would exceed the limit - if current_batch: - batches.append(current_batch) - current_batch = "" - - # If the sentence itself is longer than max_chars, split it - if len(sentence.encode('utf-8')) > max_chars: - # First, try to split by colon - colon_parts = sentence.split(':') - if len(colon_parts) > 1: - for part in colon_parts: - if len(part.encode('utf-8')) <= max_chars: - batches.append(part) - else: - # If colon part is still too long, split by comma - comma_parts = re.split('[,，]', part) - if len(comma_parts) > 1: - current_comma_part = "" - for comma_part in comma_parts: - if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars: - current_comma_part += comma_part + ',' - else: - if current_comma_part: - batches.append(current_comma_part.rstrip(',')) - current_comma_part = comma_part + ',' - if current_comma_part: - batches.append(current_comma_part.rstrip(',')) - else: - # If no comma, split by words - batches.extend(split_by_words(part)) - else: - # If no colon, split by comma - comma_parts = re.split('[,，]', sentence) - if len(comma_parts) > 1: - current_comma_part = "" - for comma_part in comma_parts: - if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars: - current_comma_part += comma_part + ',' - else: - if current_comma_part: - batches.append(current_comma_part.rstrip(',')) - current_comma_part = comma_part + ',' - if current_comma_part: - batches.append(current_comma_part.rstrip(',')) - else: - # If no comma, split by words - batches.extend(split_by_words(sentence)) - else: - current_batch = sentence - - if current_batch: - batches.append(current_batch) - - return batches - -def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, progress=gr.Progress()): - if exp_name == "F5-TTS": - ema_model = F5TTS_ema_model - elif exp_name == "E2-TTS": - ema_model = E2TTS_ema_model - - audio, sr = ref_audio - if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - - rms = torch.sqrt(torch.mean(torch.square(audio))) - if rms < target_rms: - audio = audio * target_rms / rms - if sr != target_sample_rate: - resampler = torchaudio.transforms.Resample(sr, target_sample_rate) - audio = resampler(audio) - audio = audio.to(device) - - generated_waves = [] - spectrograms = [] - - for i, gen_text in enumerate(progress.tqdm(gen_text_batches)): - # Prepare the text - if len(ref_text[-1].encode('utf-8')) == 1: - ref_text = ref_text + " " - text_list = [ref_text + gen_text] - final_text_list = convert_char_to_pinyin(text_list) - - # Calculate duration - ref_audio_len = audio.shape[-1] // hop_length - zh_pause_punc = r"。，、；：？！" - ref_text_len = len(ref_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, ref_text)) - gen_text_len = len(gen_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, gen_text)) - duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) - - # inference - with torch.inference_mode(): - generated, _ = ema_model.sample( - cond=audio, - text=final_text_list, - duration=duration, - steps=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - ) - - generated = generated[:, ref_audio_len:, :] - generated_mel_spec = rearrange(generated, "1 n d -> 1 d n") - generated_wave = vocos.decode(generated_mel_spec.cpu()) - if rms < target_rms: - generated_wave = generated_wave * rms / target_rms - - # wav -> numpy - generated_wave = generated_wave.squeeze().cpu().numpy() - - generated_waves.append(generated_wave) - spectrograms.append(generated_mel_spec[0].cpu().numpy()) - - # Combine all generated waves - final_wave = np.concatenate(generated_waves) - - # Remove silence - if remove_silence: - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: - sf.write(f.name, final_wave, target_sample_rate) - aseg = AudioSegment.from_file(f.name) - non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500) - non_silent_wave = AudioSegment.silent(duration=0) - for non_silent_seg in non_silent_segs: - non_silent_wave += non_silent_seg - aseg = non_silent_wave - aseg.export(f.name, format="wav") - final_wave, _ = torchaudio.load(f.name) - final_wave = final_wave.squeeze().cpu().numpy() - - # Create a combined spectrogram - combined_spectrogram = np.concatenate(spectrograms, axis=1) - - with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: - spectrogram_path = tmp_spectrogram.name - save_spectrogram(combined_spectrogram, spectrogram_path) - - return (target_sample_rate, final_wave), spectrogram_path - -def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, custom_split_words=''): - if not custom_split_words.strip(): - custom_words = [word.strip() for word in custom_split_words.split(',')] - global SPLIT_WORDS - SPLIT_WORDS = custom_words - - print(gen_text) - - gr.Info("Converting audio...") - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: - aseg = AudioSegment.from_file(ref_audio_orig) - - non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500) - non_silent_wave = AudioSegment.silent(duration=0) - for non_silent_seg in non_silent_segs: - non_silent_wave += non_silent_seg - aseg = non_silent_wave - - audio_duration = len(aseg) - if audio_duration > 15000: - gr.Warning("Audio is over 15s, clipping to only first 15s.") - aseg = aseg[:15000] - aseg.export(f.name, format="wav") - ref_audio = f.name - - if not ref_text.strip(): - gr.Info("No reference text provided, transcribing reference audio...") - ref_text = pipe( - ref_audio, - chunk_length_s=30, - batch_size=128, - generate_kwargs={"task": "transcribe"}, - return_timestamps=False, - )["text"].strip() - gr.Info("Finished transcription") - else: - gr.Info("Using custom reference text...") - - # Split the input text into batches - audio, sr = torchaudio.load(ref_audio) - max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (30 - audio.shape[-1] / sr)) - gen_text_batches = split_text_into_batches(gen_text, max_chars=max_chars) - print('ref_text', ref_text) - for i, gen_text in enumerate(gen_text_batches): - print(f'gen_text {i}', gen_text) - - gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches") - return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence) - -def generate_podcast(script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, exp_name, remove_silence): - # Split the script into speaker blocks - speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE) - speaker_blocks = speaker_pattern.split(script)[1:] # Skip the first empty element - - generated_audio_segments = [] - - for i in range(0, len(speaker_blocks), 2): - speaker = speaker_blocks[i] - text = speaker_blocks[i+1].strip() - - # Determine which speaker is talking - if speaker == speaker1_name: - ref_audio = ref_audio1 - ref_text = ref_text1 - elif speaker == speaker2_name: - ref_audio = ref_audio2 - ref_text = ref_text2 - else: - continue # Skip if the speaker is neither speaker1 nor speaker2 - - # Generate audio for this block - audio, _ = infer(ref_audio, ref_text, text, exp_name, remove_silence) - - # Convert the generated audio to a numpy array - sr, audio_data = audio - - # Save the audio data as a WAV file - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: - sf.write(temp_file.name, audio_data, sr) - audio_segment = AudioSegment.from_wav(temp_file.name) - - generated_audio_segments.append(audio_segment) - - # Add a short pause between speakers - pause = AudioSegment.silent(duration=500) # 500ms pause - generated_audio_segments.append(pause) - - # Concatenate all audio segments - final_podcast = sum(generated_audio_segments) - - # Export the final podcast - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: - podcast_path = temp_file.name - final_podcast.export(podcast_path, format="wav") - - return podcast_path - -def parse_speechtypes_text(gen_text): - # Pattern to find (Emotion) - pattern = r'\((.*?)\)' - - # Split the text by the pattern - tokens = re.split(pattern, gen_text) - - segments = [] - - current_emotion = 'Regular' - - for i in range(len(tokens)): - if i % 2 == 0: - # This is text - text = tokens[i].strip() - if text: - segments.append({'emotion': current_emotion, 'text': text}) - else: - # This is emotion - emotion = tokens[i].strip() - current_emotion = emotion - - return segments - -def update_speed(new_speed): - global speed - speed = new_speed - return f"Speed set to: {speed}" - -with gr.Blocks() as app_credits: - gr.Markdown(""" -# Credits - -* [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) -* [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation -""") -with gr.Blocks() as app_tts: - gr.Markdown("# Batched TTS") - ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") - gen_text_input = gr.Textbox(label="Text to Generate", lines=10) - model_choice = gr.Radio( - choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS" - ) - generate_btn = gr.Button("Synthesize", variant="primary") - with gr.Accordion("Advanced Settings", open=False): - ref_text_input = gr.Textbox( - label="Reference Text", - info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.", - lines=2, - ) - remove_silence = gr.Checkbox( - label="Remove Silences", - info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.", - value=True, - ) - split_words_input = gr.Textbox( - label="Custom Split Words", - info="Enter custom words to split on, separated by commas. Leave blank to use default list.", - lines=2, - ) - speed_slider = gr.Slider( - label="Speed", - minimum=0.3, - maximum=2.0, - value=speed, - step=0.1, - info="Adjust the speed of the audio.", - ) - speed_slider.change(update_speed, inputs=speed_slider) - - audio_output = gr.Audio(label="Synthesized Audio") - spectrogram_output = gr.Image(label="Spectrogram") - - generate_btn.click( - infer, - inputs=[ - ref_audio_input, - ref_text_input, - gen_text_input, - model_choice, - remove_silence, - split_words_input, - ], - outputs=[audio_output, spectrogram_output], - ) - -with gr.Blocks() as app_podcast: - gr.Markdown("# Podcast Generation") - speaker1_name = gr.Textbox(label="Speaker 1 Name") - ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath") - ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2) - - speaker2_name = gr.Textbox(label="Speaker 2 Name") - ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath") - ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2) - - script_input = gr.Textbox(label="Podcast Script", lines=10, - placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...") - - podcast_model_choice = gr.Radio( - choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS" - ) - podcast_remove_silence = gr.Checkbox( - label="Remove Silences", - value=True, - ) - generate_podcast_btn = gr.Button("Generate Podcast", variant="primary") - podcast_output = gr.Audio(label="Generated Podcast") - - def podcast_generation(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence): - return generate_podcast(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence) - - generate_podcast_btn.click( - podcast_generation, - inputs=[ - script_input, - speaker1_name, - ref_audio_input1, - ref_text_input1, - speaker2_name, - ref_audio_input2, - ref_text_input2, - podcast_model_choice, - podcast_remove_silence, - ], - outputs=podcast_output, - ) - -def parse_emotional_text(gen_text): - # Pattern to find (Emotion) - pattern = r'\((.*?)\)' - - # Split the text by the pattern - tokens = re.split(pattern, gen_text) - - segments = [] - - current_emotion = 'Regular' - - for i in range(len(tokens)): - if i % 2 == 0: - # This is text - text = tokens[i].strip() - if text: - segments.append({'emotion': current_emotion, 'text': text}) - else: - # This is emotion - emotion = tokens[i].strip() - current_emotion = emotion - - return segments - -with gr.Blocks() as app_emotional: - # New section for emotional generation - gr.Markdown( - """ - # Multiple Speech-Type Generation - - This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified. - - **Example Input:** - - (Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?! - """ - ) - - gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.") - - # Regular speech type (mandatory) - with gr.Row(): - regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False) - regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath') - regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2) - - # Additional speech types (up to 9 more) - max_speech_types = 10 - speech_type_names = [] - speech_type_audios = [] - speech_type_ref_texts = [] - speech_type_delete_btns = [] - - for i in range(max_speech_types - 1): - with gr.Row(): - name_input = gr.Textbox(label='Speech Type Name', visible=False) - audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False) - ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False) - delete_btn = gr.Button("Delete", variant="secondary", visible=False) - speech_type_names.append(name_input) - speech_type_audios.append(audio_input) - speech_type_ref_texts.append(ref_text_input) - speech_type_delete_btns.append(delete_btn) - - # Button to add speech type - add_speech_type_btn = gr.Button("Add Speech Type") - - # Keep track of current number of speech types - speech_type_count = gr.State(value=0) - - # Function to add a speech type - def add_speech_type_fn(speech_type_count): - if speech_type_count < max_speech_types - 1: - speech_type_count += 1 - # Prepare updates for the components - name_updates = [] - audio_updates = [] - ref_text_updates = [] - delete_btn_updates = [] - for i in range(max_speech_types - 1): - if i < speech_type_count: - name_updates.append(gr.update(visible=True)) - audio_updates.append(gr.update(visible=True)) - ref_text_updates.append(gr.update(visible=True)) - delete_btn_updates.append(gr.update(visible=True)) - else: - name_updates.append(gr.update()) - audio_updates.append(gr.update()) - ref_text_updates.append(gr.update()) - delete_btn_updates.append(gr.update()) - else: - # Optionally, show a warning - # gr.Warning("Maximum number of speech types reached.") - name_updates = [gr.update() for _ in range(max_speech_types - 1)] - audio_updates = [gr.update() for _ in range(max_speech_types - 1)] - ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)] - delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)] - return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates - - add_speech_type_btn.click( - add_speech_type_fn, - inputs=speech_type_count, - outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns - ) - - # Function to delete a speech type - def make_delete_speech_type_fn(index): - def delete_speech_type_fn(speech_type_count): - # Prepare updates - name_updates = [] - audio_updates = [] - ref_text_updates = [] - delete_btn_updates = [] - - for i in range(max_speech_types - 1): - if i == index: - name_updates.append(gr.update(visible=False, value='')) - audio_updates.append(gr.update(visible=False, value=None)) - ref_text_updates.append(gr.update(visible=False, value='')) - delete_btn_updates.append(gr.update(visible=False)) - else: - name_updates.append(gr.update()) - audio_updates.append(gr.update()) - ref_text_updates.append(gr.update()) - delete_btn_updates.append(gr.update()) - - speech_type_count = max(0, speech_type_count - 1) - - return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates - - return delete_speech_type_fn - - for i, delete_btn in enumerate(speech_type_delete_btns): - delete_fn = make_delete_speech_type_fn(i) - delete_btn.click( - delete_fn, - inputs=speech_type_count, - outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns - ) - - # Text input for the prompt - gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10) - - # Model choice - model_choice_emotional = gr.Radio( - choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS" - ) - - with gr.Accordion("Advanced Settings", open=False): - remove_silence_emotional = gr.Checkbox( - label="Remove Silences", - value=True, - ) - - # Generate button - generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary") - - # Output audio - audio_output_emotional = gr.Audio(label="Synthesized Audio") - - def generate_emotional_speech( - regular_audio, - regular_ref_text, - gen_text, - *args, - ): - num_additional_speech_types = max_speech_types - 1 - speech_type_names_list = args[:num_additional_speech_types] - speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types] - speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types] - model_choice = args[3 * num_additional_speech_types] - remove_silence = args[3 * num_additional_speech_types + 1] - - # Collect the speech types and their audios into a dict - speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}} - - for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list): - if name_input and audio_input: - speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input} - - # Parse the gen_text into segments - segments = parse_speechtypes_text(gen_text) - - # For each segment, generate speech - generated_audio_segments = [] - current_emotion = 'Regular' - - for segment in segments: - emotion = segment['emotion'] - text = segment['text'] - - if emotion in speech_types: - current_emotion = emotion - else: - # If emotion not available, default to Regular - current_emotion = 'Regular' - - ref_audio = speech_types[current_emotion]['audio'] - ref_text = speech_types[current_emotion].get('ref_text', '') - - # Generate speech for this segment - audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, "") - sr, audio_data = audio - - generated_audio_segments.append(audio_data) - - # Concatenate all audio segments - if generated_audio_segments: - final_audio_data = np.concatenate(generated_audio_segments) - return (sr, final_audio_data) - else: - gr.Warning("No audio generated.") - return None - - generate_emotional_btn.click( - generate_emotional_speech, - inputs=[ - regular_audio, - regular_ref_text, - gen_text_input_emotional, - ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [ - model_choice_emotional, - remove_silence_emotional, - ], - outputs=audio_output_emotional, - ) - - # Validation function to disable Generate button if speech types are missing - def validate_speech_types( - gen_text, - regular_name, - *args - ): - num_additional_speech_types = max_speech_types - 1 - speech_type_names_list = args[:num_additional_speech_types] - - # Collect the speech types names - speech_types_available = set() - if regular_name: - speech_types_available.add(regular_name) - for name_input in speech_type_names_list: - if name_input: - speech_types_available.add(name_input) - - # Parse the gen_text to get the speech types used - segments = parse_emotional_text(gen_text) - speech_types_in_text = set(segment['emotion'] for segment in segments) - - # Check if all speech types in text are available - missing_speech_types = speech_types_in_text - speech_types_available - - if missing_speech_types: - # Disable the generate button - return gr.update(interactive=False) - else: - # Enable the generate button - return gr.update(interactive=True) - - gen_text_input_emotional.change( - validate_speech_types, - inputs=[gen_text_input_emotional, regular_name] + speech_type_names, - outputs=generate_emotional_btn - ) -with gr.Blocks() as app: - gr.Markdown( - """ -# E2/F5 TTS - -This is a local web UI for F5 TTS with advanced batch processing support. This app supports the following TTS models: - -* [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) -* [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) - -The checkpoints support English and Chinese. - -If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. - -**NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.** -""" - ) - gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"]) - -@click.command() -@click.option("--port", "-p", default=None, type=int, help="Port to run the app on") -@click.option("--host", "-H", default=None, help="Host to run the app on") -@click.option( - "--share", - "-s", - default=False, - is_flag=True, - help="Share the app via Gradio share link", -) -@click.option("--api", "-a", default=True, is_flag=True, help="Allow API access") -def main(port, host, share, api): - global app - print(f"Starting app...") - app.queue(api_open=api).launch( - server_name=host, server_port=port, share=share, show_api=api - ) - - -if __name__ == "__main__": - main() diff --git a/inference-cli.py b/inference-cli.py deleted file mode 100644 index 1e74eec2d22bc14ef63e733962740bf0fc023efc..0000000000000000000000000000000000000000 --- a/inference-cli.py +++ /dev/null @@ -1,170 +0,0 @@ -import argparse -import codecs -import re -from pathlib import Path - -import numpy as np -import soundfile as sf -import tomli -from cached_path import cached_path - -from model import DiT, UNetT -from model.utils_infer import ( - load_vocoder, - load_model, - preprocess_ref_audio_text, - infer_process, - remove_silence_for_generated_wav, -) - - -parser = argparse.ArgumentParser( - prog="python3 inference-cli.py", - description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.", - epilog="Specify options above to override one or more settings from config.", -) -parser.add_argument( - "-c", - "--config", - help="Configuration file. Default=cli-config.toml", - default="inference-cli.toml", -) -parser.add_argument( - "-m", - "--model", - help="F5-TTS | E2-TTS", -) -parser.add_argument( - "-p", - "--ckpt_file", - help="The Checkpoint .pt", -) -parser.add_argument( - "-v", - "--vocab_file", - help="The vocab .txt", -) -parser.add_argument("-r", "--ref_audio", type=str, help="Reference audio file < 15 seconds.") -parser.add_argument("-s", "--ref_text", type=str, default="666", help="Subtitle for the reference audio.") -parser.add_argument( - "-t", - "--gen_text", - type=str, - help="Text to generate.", -) -parser.add_argument( - "-f", - "--gen_file", - type=str, - help="File with text to generate. Ignores --text", -) -parser.add_argument( - "-o", - "--output_dir", - type=str, - help="Path to output folder..", -) -parser.add_argument( - "--remove_silence", - help="Remove silence.", -) -parser.add_argument( - "--load_vocoder_from_local", - action="store_true", - help="load vocoder from local. Default: ../checkpoints/charactr/vocos-mel-24khz", -) -args = parser.parse_args() - -config = tomli.load(open(args.config, "rb")) - -ref_audio = args.ref_audio if args.ref_audio else config["ref_audio"] -ref_text = args.ref_text if args.ref_text != "666" else config["ref_text"] -gen_text = args.gen_text if args.gen_text else config["gen_text"] -gen_file = args.gen_file if args.gen_file else config["gen_file"] -if gen_file: - gen_text = codecs.open(gen_file, "r", "utf-8").read() -output_dir = args.output_dir if args.output_dir else config["output_dir"] -model = args.model if args.model else config["model"] -ckpt_file = args.ckpt_file if args.ckpt_file else "" -vocab_file = args.vocab_file if args.vocab_file else "" -remove_silence = args.remove_silence if args.remove_silence else config["remove_silence"] -wave_path = Path(output_dir) / "out.wav" -spectrogram_path = Path(output_dir) / "out.png" -vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz" - -vocos = load_vocoder(is_local=args.load_vocoder_from_local, local_path=vocos_local_path) - - -# load models -if model == "F5-TTS": - model_cls = DiT - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) - if ckpt_file == "": - repo_name = "F5-TTS" - exp_name = "F5TTS_Base" - ckpt_step = 1200000 - ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors")) - # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors; local path - -elif model == "E2-TTS": - model_cls = UNetT - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - if ckpt_file == "": - repo_name = "E2-TTS" - exp_name = "E2TTS_Base" - ckpt_step = 1200000 - ckpt_file = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors")) - # ckpt_file = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors; local path - -print(f"Using {model}...") -ema_model = load_model(model_cls, model_cfg, ckpt_file, vocab_file) - - -def main_process(ref_audio, ref_text, text_gen, model_obj, remove_silence): - main_voice = {"ref_audio": ref_audio, "ref_text": ref_text} - if "voices" not in config: - voices = {"main": main_voice} - else: - voices = config["voices"] - voices["main"] = main_voice - for voice in voices: - voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text( - voices[voice]["ref_audio"], voices[voice]["ref_text"] - ) - print("Voice:", voice) - print("Ref_audio:", voices[voice]["ref_audio"]) - print("Ref_text:", voices[voice]["ref_text"]) - - generated_audio_segments = [] - reg1 = r"(?=\[\w+\])" - chunks = re.split(reg1, text_gen) - reg2 = r"\[(\w+)\]" - for text in chunks: - match = re.match(reg2, text) - if match: - voice = match[1] - else: - print("No voice tag found, using main.") - voice = "main" - if voice not in voices: - print(f"Voice {voice} not found, using main.") - voice = "main" - text = re.sub(reg2, "", text) - gen_text = text.strip() - ref_audio = voices[voice]["ref_audio"] - ref_text = voices[voice]["ref_text"] - print(f"Voice: {voice}") - audio, final_sample_rate, spectragram = infer_process(ref_audio, ref_text, gen_text, model_obj) - generated_audio_segments.append(audio) - - if generated_audio_segments: - final_wave = np.concatenate(generated_audio_segments) - with open(wave_path, "wb") as f: - sf.write(f.name, final_wave, final_sample_rate) - # Remove silence - if remove_silence: - remove_silence_for_generated_wav(f.name) - print(f.name) - - -main_process(ref_audio, ref_text, gen_text, ema_model, remove_silence) diff --git a/inference-cli.toml b/inference-cli.toml deleted file mode 100644 index b6bea1c7c170744fbdddd3a0899d808bff8f1d08..0000000000000000000000000000000000000000 --- a/inference-cli.toml +++ /dev/null @@ -1,10 +0,0 @@ -# F5-TTS | E2-TTS -model = "F5-TTS" -ref_audio = "tests/ref_audio/test_en_1_ref_short.wav" -# If an empty "", transcribes the reference audio automatically. -ref_text = "Some call me nature, others call me mother nature." -gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences." -# File with text to generate. Ignores the text above. -gen_file = "" -remove_silence = false -output_dir = "tests" \ No newline at end of file diff --git a/model/__init__.py b/model/__init__.py index 4b4f031397a3b21cd54675fc3d28421eb2b36c42..d505b15dcaafb8aca9b6ae6cb0a3de3ba31b45fc 100644 --- a/model/__init__.py +++ b/model/__init__.py @@ -5,6 +5,3 @@ from model.backbones.dit import DiT from model.backbones.mmdit import MMDiT from model.trainer import Trainer - - -__all__ = ["CFM", "UNetT", "DiT", "MMDiT", "Trainer"] diff --git a/model/backbones/dit.py b/model/backbones/dit.py index b8e6dc3f8ce2ff1f007b04b182b64a9a8e03a09c..5654191fd7c5820788ed794861682f2c16c03e4f 100644 --- a/model/backbones/dit.py +++ b/model/backbones/dit.py @@ -13,6 +13,8 @@ import torch from torch import nn import torch.nn.functional as F +from einops import repeat + from x_transformers.x_transformers import RotaryEmbedding from model.modules import ( @@ -21,16 +23,14 @@ from model.modules import ( ConvPositionEmbedding, DiTBlock, AdaLayerNormZero_Final, - precompute_freqs_cis, - get_pos_embed_indices, + precompute_freqs_cis, get_pos_embed_indices, ) # Text embedding - class TextEmbedding(nn.Module): - def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2): + def __init__(self, text_num_embeds, text_dim, conv_layers = 0, conv_mult = 2): super().__init__() self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token @@ -38,22 +38,20 @@ class TextEmbedding(nn.Module): self.extra_modeling = True self.precompute_max_pos = 4096 # ~44s of 24khz audio self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False) - self.text_blocks = nn.Sequential( - *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)] - ) + self.text_blocks = nn.Sequential(*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]) else: self.extra_modeling = False - def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722 + def forward(self, text: int['b nt'], seq_len, drop_text = False): + batch, text_len = text.shape[0], text.shape[1] text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx() text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens - batch, text_len = text.shape[0], text.shape[1] - text = F.pad(text, (0, seq_len - text_len), value=0) + text = F.pad(text, (0, seq_len - text_len), value = 0) if drop_text: # cfg for text text = torch.zeros_like(text) - text = self.text_embed(text) # b n -> b n d + text = self.text_embed(text) # b n -> b n d # possible extra modeling if self.extra_modeling: @@ -71,91 +69,88 @@ class TextEmbedding(nn.Module): # noised input audio and context mixing embedding - class InputEmbedding(nn.Module): def __init__(self, mel_dim, text_dim, out_dim): super().__init__() self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim) - self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim) + self.conv_pos_embed = ConvPositionEmbedding(dim = out_dim) - def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False): # noqa: F722 + def forward(self, x: float['b n d'], cond: float['b n d'], text_embed: float['b n d'], drop_audio_cond = False): if drop_audio_cond: # cfg for cond audio cond = torch.zeros_like(cond) - x = self.proj(torch.cat((x, cond, text_embed), dim=-1)) + x = self.proj(torch.cat((x, cond, text_embed), dim = -1)) x = self.conv_pos_embed(x) + x return x - + # Transformer backbone using DiT blocks - class DiT(nn.Module): - def __init__( - self, - *, - dim, - depth=8, - heads=8, - dim_head=64, - dropout=0.1, - ff_mult=4, - mel_dim=100, - text_num_embeds=256, - text_dim=None, - conv_layers=0, - long_skip_connection=False, + def __init__(self, *, + dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4, + mel_dim = 100, text_num_embeds = 256, text_dim = None, conv_layers = 0, + long_skip_connection = False, ): super().__init__() self.time_embed = TimestepEmbedding(dim) if text_dim is None: text_dim = mel_dim - self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers) + self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers = conv_layers) self.input_embed = InputEmbedding(mel_dim, text_dim, dim) self.rotary_embed = RotaryEmbedding(dim_head) self.dim = dim self.depth = depth - + self.transformer_blocks = nn.ModuleList( - [DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)] + [ + DiTBlock( + dim = dim, + heads = heads, + dim_head = dim_head, + ff_mult = ff_mult, + dropout = dropout + ) + for _ in range(depth) + ] ) - self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None - + self.long_skip_connection = nn.Linear(dim * 2, dim, bias = False) if long_skip_connection else None + self.norm_out = AdaLayerNormZero_Final(dim) # final modulation self.proj_out = nn.Linear(dim, mel_dim) def forward( self, - x: float["b n d"], # nosied input audio # noqa: F722 - cond: float["b n d"], # masked cond audio # noqa: F722 - text: int["b nt"], # text # noqa: F722 - time: float["b"] | float[""], # time step # noqa: F821 F722 + x: float['b n d'], # nosied input audio + cond: float['b n d'], # masked cond audio + text: int['b nt'], # text + time: float['b'] | float[''], # time step drop_audio_cond, # cfg for cond audio - drop_text, # cfg for text - mask: bool["b n"] | None = None, # noqa: F722 + drop_text, # cfg for text + mask: bool['b n'] | None = None, ): batch, seq_len = x.shape[0], x.shape[1] if time.ndim == 0: - time = time.repeat(batch) - + time = repeat(time, ' -> b', b = batch) + # t: conditioning time, c: context (text + masked cond audio), x: noised input audio t = self.time_embed(time) - text_embed = self.text_embed(text, seq_len, drop_text=drop_text) - x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond) - + text_embed = self.text_embed(text, seq_len, drop_text = drop_text) + x = self.input_embed(x, cond, text_embed, drop_audio_cond = drop_audio_cond) + rope = self.rotary_embed.forward_from_seq_len(seq_len) if self.long_skip_connection is not None: residual = x for block in self.transformer_blocks: - x = block(x, t, mask=mask, rope=rope) + x = block(x, t, mask = mask, rope = rope) if self.long_skip_connection is not None: - x = self.long_skip_connection(torch.cat((x, residual), dim=-1)) + x = self.long_skip_connection(torch.cat((x, residual), dim = -1)) x = self.norm_out(x, t) output = self.proj_out(x) diff --git a/model/backbones/mmdit.py b/model/backbones/mmdit.py index 86313b136205788d7599896f8c36d2498c17ce6c..f1b3b7d53effb436fee783147a9c391734f89615 100644 --- a/model/backbones/mmdit.py +++ b/model/backbones/mmdit.py @@ -12,6 +12,8 @@ from __future__ import annotations import torch from torch import nn +from einops import repeat + from x_transformers.x_transformers import RotaryEmbedding from model.modules import ( @@ -19,14 +21,12 @@ from model.modules import ( ConvPositionEmbedding, MMDiTBlock, AdaLayerNormZero_Final, - precompute_freqs_cis, - get_pos_embed_indices, + precompute_freqs_cis, get_pos_embed_indices, ) # text embedding - class TextEmbedding(nn.Module): def __init__(self, out_dim, text_num_embeds): super().__init__() @@ -35,7 +35,7 @@ class TextEmbedding(nn.Module): self.precompute_max_pos = 1024 self.register_buffer("freqs_cis", precompute_freqs_cis(out_dim, self.precompute_max_pos), persistent=False) - def forward(self, text: int["b nt"], drop_text=False) -> int["b nt d"]: # noqa: F722 + def forward(self, text: int['b nt'], drop_text = False) -> int['b nt d']: text = text + 1 if drop_text: text = torch.zeros_like(text) @@ -54,37 +54,27 @@ class TextEmbedding(nn.Module): # noised input & masked cond audio embedding - class AudioEmbedding(nn.Module): def __init__(self, in_dim, out_dim): super().__init__() self.linear = nn.Linear(2 * in_dim, out_dim) self.conv_pos_embed = ConvPositionEmbedding(out_dim) - def forward(self, x: float["b n d"], cond: float["b n d"], drop_audio_cond=False): # noqa: F722 + def forward(self, x: float['b n d'], cond: float['b n d'], drop_audio_cond = False): if drop_audio_cond: cond = torch.zeros_like(cond) - x = torch.cat((x, cond), dim=-1) + x = torch.cat((x, cond), dim = -1) x = self.linear(x) x = self.conv_pos_embed(x) + x return x - + # Transformer backbone using MM-DiT blocks - class MMDiT(nn.Module): - def __init__( - self, - *, - dim, - depth=8, - heads=8, - dim_head=64, - dropout=0.1, - ff_mult=4, - text_num_embeds=256, - mel_dim=100, + def __init__(self, *, + dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4, + text_num_embeds = 256, mel_dim = 100, ): super().__init__() @@ -96,16 +86,16 @@ class MMDiT(nn.Module): self.dim = dim self.depth = depth - + self.transformer_blocks = nn.ModuleList( [ MMDiTBlock( - dim=dim, - heads=heads, - dim_head=dim_head, - dropout=dropout, - ff_mult=ff_mult, - context_pre_only=i == depth - 1, + dim = dim, + heads = heads, + dim_head = dim_head, + dropout = dropout, + ff_mult = ff_mult, + context_pre_only = i == depth - 1, ) for i in range(depth) ] @@ -115,30 +105,30 @@ class MMDiT(nn.Module): def forward( self, - x: float["b n d"], # nosied input audio # noqa: F722 - cond: float["b n d"], # masked cond audio # noqa: F722 - text: int["b nt"], # text # noqa: F722 - time: float["b"] | float[""], # time step # noqa: F821 F722 + x: float['b n d'], # nosied input audio + cond: float['b n d'], # masked cond audio + text: int['b nt'], # text + time: float['b'] | float[''], # time step drop_audio_cond, # cfg for cond audio - drop_text, # cfg for text - mask: bool["b n"] | None = None, # noqa: F722 + drop_text, # cfg for text + mask: bool['b n'] | None = None, ): batch = x.shape[0] if time.ndim == 0: - time = time.repeat(batch) + time = repeat(time, ' -> b', b = batch) # t: conditioning (time), c: context (text + masked cond audio), x: noised input audio t = self.time_embed(time) - c = self.text_embed(text, drop_text=drop_text) - x = self.audio_embed(x, cond, drop_audio_cond=drop_audio_cond) + c = self.text_embed(text, drop_text = drop_text) + x = self.audio_embed(x, cond, drop_audio_cond = drop_audio_cond) seq_len = x.shape[1] text_len = text.shape[1] rope_audio = self.rotary_embed.forward_from_seq_len(seq_len) rope_text = self.rotary_embed.forward_from_seq_len(text_len) - + for block in self.transformer_blocks: - c, x = block(x, c, t, mask=mask, rope=rope_audio, c_rope=rope_text) + c, x = block(x, c, t, mask = mask, rope = rope_audio, c_rope = rope_text) x = self.norm_out(x, t) output = self.proj_out(x) diff --git a/model/backbones/unett.py b/model/backbones/unett.py index ac1d3d3563fdd95e34f904a3c19fcdca4b1a3efa..b1881bf96462d859e92cd2ab2a7aefb4e364c52e 100644 --- a/model/backbones/unett.py +++ b/model/backbones/unett.py @@ -14,6 +14,8 @@ import torch from torch import nn import torch.nn.functional as F +from einops import repeat, pack, unpack + from x_transformers import RMSNorm from x_transformers.x_transformers import RotaryEmbedding @@ -24,16 +26,14 @@ from model.modules import ( Attention, AttnProcessor, FeedForward, - precompute_freqs_cis, - get_pos_embed_indices, + precompute_freqs_cis, get_pos_embed_indices, ) # Text embedding - class TextEmbedding(nn.Module): - def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2): + def __init__(self, text_num_embeds, text_dim, conv_layers = 0, conv_mult = 2): super().__init__() self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token @@ -41,22 +41,20 @@ class TextEmbedding(nn.Module): self.extra_modeling = True self.precompute_max_pos = 4096 # ~44s of 24khz audio self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False) - self.text_blocks = nn.Sequential( - *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)] - ) + self.text_blocks = nn.Sequential(*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]) else: self.extra_modeling = False - def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722 + def forward(self, text: int['b nt'], seq_len, drop_text = False): + batch, text_len = text.shape[0], text.shape[1] text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx() text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens - batch, text_len = text.shape[0], text.shape[1] - text = F.pad(text, (0, seq_len - text_len), value=0) + text = F.pad(text, (0, seq_len - text_len), value = 0) if drop_text: # cfg for text text = torch.zeros_like(text) - text = self.text_embed(text) # b n -> b n d + text = self.text_embed(text) # b n -> b n d # possible extra modeling if self.extra_modeling: @@ -74,40 +72,28 @@ class TextEmbedding(nn.Module): # noised input audio and context mixing embedding - class InputEmbedding(nn.Module): def __init__(self, mel_dim, text_dim, out_dim): super().__init__() self.proj = nn.Linear(mel_dim * 2 + text_dim, out_dim) - self.conv_pos_embed = ConvPositionEmbedding(dim=out_dim) + self.conv_pos_embed = ConvPositionEmbedding(dim = out_dim) - def forward(self, x: float["b n d"], cond: float["b n d"], text_embed: float["b n d"], drop_audio_cond=False): # noqa: F722 + def forward(self, x: float['b n d'], cond: float['b n d'], text_embed: float['b n d'], drop_audio_cond = False): if drop_audio_cond: # cfg for cond audio cond = torch.zeros_like(cond) - x = self.proj(torch.cat((x, cond, text_embed), dim=-1)) + x = self.proj(torch.cat((x, cond, text_embed), dim = -1)) x = self.conv_pos_embed(x) + x return x # Flat UNet Transformer backbone - class UNetT(nn.Module): - def __init__( - self, - *, - dim, - depth=8, - heads=8, - dim_head=64, - dropout=0.1, - ff_mult=4, - mel_dim=100, - text_num_embeds=256, - text_dim=None, - conv_layers=0, - skip_connect_type: Literal["add", "concat", "none"] = "concat", + def __init__(self, *, + dim, depth = 8, heads = 8, dim_head = 64, dropout = 0.1, ff_mult = 4, + mel_dim = 100, text_num_embeds = 256, text_dim = None, conv_layers = 0, + skip_connect_type: Literal['add', 'concat', 'none'] = 'concat', ): super().__init__() assert depth % 2 == 0, "UNet-Transformer's depth should be even." @@ -115,7 +101,7 @@ class UNetT(nn.Module): self.time_embed = TimestepEmbedding(dim) if text_dim is None: text_dim = mel_dim - self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers=conv_layers) + self.text_embed = TextEmbedding(text_num_embeds, text_dim, conv_layers = conv_layers) self.input_embed = InputEmbedding(mel_dim, text_dim, dim) self.rotary_embed = RotaryEmbedding(dim_head) @@ -124,7 +110,7 @@ class UNetT(nn.Module): self.dim = dim self.skip_connect_type = skip_connect_type - needs_skip_proj = skip_connect_type == "concat" + needs_skip_proj = skip_connect_type == 'concat' self.depth = depth self.layers = nn.ModuleList([]) @@ -134,57 +120,53 @@ class UNetT(nn.Module): attn_norm = RMSNorm(dim) attn = Attention( - processor=AttnProcessor(), - dim=dim, - heads=heads, - dim_head=dim_head, - dropout=dropout, - ) + processor = AttnProcessor(), + dim = dim, + heads = heads, + dim_head = dim_head, + dropout = dropout, + ) ff_norm = RMSNorm(dim) - ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") - - skip_proj = nn.Linear(dim * 2, dim, bias=False) if needs_skip_proj and is_later_half else None - - self.layers.append( - nn.ModuleList( - [ - skip_proj, - attn_norm, - attn, - ff_norm, - ff, - ] - ) - ) + ff = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh") + + skip_proj = nn.Linear(dim * 2, dim, bias = False) if needs_skip_proj and is_later_half else None + + self.layers.append(nn.ModuleList([ + skip_proj, + attn_norm, + attn, + ff_norm, + ff, + ])) self.norm_out = RMSNorm(dim) self.proj_out = nn.Linear(dim, mel_dim) def forward( self, - x: float["b n d"], # nosied input audio # noqa: F722 - cond: float["b n d"], # masked cond audio # noqa: F722 - text: int["b nt"], # text # noqa: F722 - time: float["b"] | float[""], # time step # noqa: F821 F722 + x: float['b n d'], # nosied input audio + cond: float['b n d'], # masked cond audio + text: int['b nt'], # text + time: float['b'] | float[''], # time step drop_audio_cond, # cfg for cond audio - drop_text, # cfg for text - mask: bool["b n"] | None = None, # noqa: F722 + drop_text, # cfg for text + mask: bool['b n'] | None = None, ): batch, seq_len = x.shape[0], x.shape[1] if time.ndim == 0: - time = time.repeat(batch) - + time = repeat(time, ' -> b', b = batch) + # t: conditioning time, c: context (text + masked cond audio), x: noised input audio t = self.time_embed(time) - text_embed = self.text_embed(text, seq_len, drop_text=drop_text) - x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond) + text_embed = self.text_embed(text, seq_len, drop_text = drop_text) + x = self.input_embed(x, cond, text_embed, drop_audio_cond = drop_audio_cond) # postfix time t to input x, [b n d] -> [b n+1 d] - x = torch.cat([t.unsqueeze(1), x], dim=1) # pack t to x + x, ps = pack((t, x), 'b * d') if mask is not None: mask = F.pad(mask, (1, 0), value=1) - + rope = self.rotary_embed.forward_from_seq_len(seq_len + 1) # flat unet transformer @@ -202,18 +184,18 @@ class UNetT(nn.Module): if is_later_half: skip = skips.pop() - if skip_connect_type == "concat": - x = torch.cat((x, skip), dim=-1) + if skip_connect_type == 'concat': + x = torch.cat((x, skip), dim = -1) x = maybe_skip_proj(x) - elif skip_connect_type == "add": + elif skip_connect_type == 'add': x = x + skip # attention and feedforward blocks - x = attn(attn_norm(x), rope=rope, mask=mask) + x + x = attn(attn_norm(x), rope = rope, mask = mask) + x x = ff(ff_norm(x)) + x assert len(skips) == 0 - x = self.norm_out(x)[:, 1:, :] # unpack t from x + _, x = unpack(self.norm_out(x), ps, 'b * d') return self.proj_out(x) diff --git a/model/cfm.py b/model/cfm.py index e2d7f726ff6bc2ec06f371c84b82376955d3709f..05ac4353db69b77aeef27ccd62633177137b0aca 100644 --- a/model/cfm.py +++ b/model/cfm.py @@ -18,34 +18,34 @@ from torch.nn.utils.rnn import pad_sequence from torchdiffeq import odeint +from einops import rearrange + from model.modules import MelSpec + from model.utils import ( - default, - exists, - list_str_to_idx, - list_str_to_tensor, - lens_to_mask, - mask_from_frac_lengths, -) + default, exists, + list_str_to_idx, list_str_to_tensor, + lens_to_mask, mask_from_frac_lengths, +) class CFM(nn.Module): def __init__( self, transformer: nn.Module, - sigma=0.0, + sigma = 0., odeint_kwargs: dict = dict( # atol = 1e-5, # rtol = 1e-5, - method="euler" # 'midpoint' + method = 'euler' # 'midpoint' ), - audio_drop_prob=0.3, - cond_drop_prob=0.2, - num_channels=None, + audio_drop_prob = 0.3, + cond_drop_prob = 0.2, + num_channels = None, mel_spec_module: nn.Module | None = None, mel_spec_kwargs: dict = dict(), - frac_lengths_mask: tuple[float, float] = (0.7, 1.0), - vocab_char_map: dict[str:int] | None = None, + frac_lengths_mask: tuple[float, float] = (0.7, 1.), + vocab_char_map: dict[str: int] | None = None ): super().__init__() @@ -81,37 +81,33 @@ class CFM(nn.Module): @torch.no_grad() def sample( self, - cond: float["b n d"] | float["b nw"], # noqa: F722 - text: int["b nt"] | list[str], # noqa: F722 - duration: int | int["b"], # noqa: F821 + cond: float['b n d'] | float['b nw'], + text: int['b nt'] | list[str], + duration: int | int['b'], *, - lens: int["b"] | None = None, # noqa: F821 - steps=32, - cfg_strength=1.0, - sway_sampling_coef=None, + lens: int['b'] | None = None, + steps = 32, + cfg_strength = 1., + sway_sampling_coef = None, seed: int | None = None, - max_duration=4096, - vocoder: Callable[[float["b d n"]], float["b nw"]] | None = None, # noqa: F722 - no_ref_audio=False, - duplicate_test=False, - t_inter=0.1, - edit_mask=None, + max_duration = 4096, + vocoder: Callable[[float['b d n']], float['b nw']] | None = None, + no_ref_audio = False, + duplicate_test = False, + t_inter = 0.1, ): self.eval() - if next(self.parameters()).dtype == torch.float16: - cond = cond.half() - # raw wave if cond.ndim == 2: cond = self.mel_spec(cond) - cond = cond.permute(0, 2, 1) + cond = rearrange(cond, 'b d n -> b n d') assert cond.shape[-1] == self.num_channels batch, cond_seq_len, device = *cond.shape[:2], cond.device if not exists(lens): - lens = torch.full((batch,), cond_seq_len, device=device, dtype=torch.long) + lens = torch.full((batch,), cond_seq_len, device = device, dtype = torch.long) # text @@ -123,37 +119,30 @@ class CFM(nn.Module): assert text.shape[0] == batch if exists(text): - text_lens = (text != -1).sum(dim=-1) - lens = torch.maximum(text_lens, lens) # make sure lengths are at least those of the text characters + text_lens = (text != -1).sum(dim = -1) + lens = torch.maximum(text_lens, lens) # make sure lengths are at least those of the text characters # duration cond_mask = lens_to_mask(lens) - if edit_mask is not None: - cond_mask = cond_mask & edit_mask if isinstance(duration, int): - duration = torch.full((batch,), duration, device=device, dtype=torch.long) + duration = torch.full((batch,), duration, device = device, dtype = torch.long) - duration = torch.maximum(lens + 1, duration) # just add one token so something is generated - duration = duration.clamp(max=max_duration) + duration = torch.maximum(lens + 1, duration) # just add one token so something is generated + duration = duration.clamp(max = max_duration) max_duration = duration.amax() - + # duplicate test corner for inner time step oberservation if duplicate_test: - test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2 * cond_seq_len), value=0.0) - - cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value=0.0) - cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value=False) - cond_mask = cond_mask.unsqueeze(-1) - step_cond = torch.where( - cond_mask, cond, torch.zeros_like(cond) - ) # allow direct control (cut cond audio) with lens passed in + test_cond = F.pad(cond, (0, 0, cond_seq_len, max_duration - 2*cond_seq_len), value = 0.) + + cond = F.pad(cond, (0, 0, 0, max_duration - cond_seq_len), value = 0.) + cond_mask = F.pad(cond_mask, (0, max_duration - cond_mask.shape[-1]), value = False) + cond_mask = rearrange(cond_mask, '... -> ... 1') + step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond)) # allow direct control (cut cond audio) with lens passed in - if batch > 1: - mask = lens_to_mask(duration) - else: # save memory and speed up, as single inference need no mask currently - mask = None + mask = lens_to_mask(duration) # test for no ref audio if no_ref_audio: @@ -166,15 +155,11 @@ class CFM(nn.Module): # step_cond = torch.where(cond_mask, cond, torch.zeros_like(cond)) # predict flow - pred = self.transformer( - x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=False, drop_text=False - ) + pred = self.transformer(x = x, cond = step_cond, text = text, time = t, mask = mask, drop_audio_cond = False, drop_text = False) if cfg_strength < 1e-5: return pred - - null_pred = self.transformer( - x=x, cond=step_cond, text=text, time=t, mask=mask, drop_audio_cond=True, drop_text=True - ) + + null_pred = self.transformer(x = x, cond = step_cond, text = text, time = t, mask = mask, drop_audio_cond = True, drop_text = True) return pred + (pred - null_pred) * cfg_strength # noise input @@ -184,8 +169,8 @@ class CFM(nn.Module): for dur in duration: if exists(seed): torch.manual_seed(seed) - y0.append(torch.randn(dur, self.num_channels, device=self.device, dtype=step_cond.dtype)) - y0 = pad_sequence(y0, padding_value=0, batch_first=True) + y0.append(torch.randn(dur, self.num_channels, device = self.device)) + y0 = pad_sequence(y0, padding_value = 0, batch_first = True) t_start = 0 @@ -195,37 +180,37 @@ class CFM(nn.Module): y0 = (1 - t_start) * y0 + t_start * test_cond steps = int(steps * (1 - t_start)) - t = torch.linspace(t_start, 1, steps, device=self.device, dtype=step_cond.dtype) + t = torch.linspace(t_start, 1, steps, device = self.device) if sway_sampling_coef is not None: t = t + sway_sampling_coef * (torch.cos(torch.pi / 2 * t) - 1 + t) trajectory = odeint(fn, y0, t, **self.odeint_kwargs) - + sampled = trajectory[-1] out = sampled out = torch.where(cond_mask, cond, out) if exists(vocoder): - out = out.permute(0, 2, 1) + out = rearrange(out, 'b n d -> b d n') out = vocoder(out) return out, trajectory def forward( self, - inp: float["b n d"] | float["b nw"], # mel or raw wave # noqa: F722 - text: int["b nt"] | list[str], # noqa: F722 + inp: float['b n d'] | float['b nw'], # mel or raw wave + text: int['b nt'] | list[str], *, - lens: int["b"] | None = None, # noqa: F821 + lens: int['b'] | None = None, noise_scheduler: str | None = None, ): # handle raw wave if inp.ndim == 2: inp = self.mel_spec(inp) - inp = inp.permute(0, 2, 1) + inp = rearrange(inp, 'b d n -> b n d') assert inp.shape[-1] == self.num_channels - batch, seq_len, dtype, device, _σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma + batch, seq_len, dtype, device, σ1 = *inp.shape[:2], inp.dtype, self.device, self.sigma # handle text as string if isinstance(text, list): @@ -237,12 +222,12 @@ class CFM(nn.Module): # lens and mask if not exists(lens): - lens = torch.full((batch,), seq_len, device=device) - - mask = lens_to_mask(lens, length=seq_len) # useless here, as collate_fn will pad to max length in batch + lens = torch.full((batch,), seq_len, device = device) + + mask = lens_to_mask(lens, length = seq_len) # useless here, as collate_fn will pad to max length in batch # get a random span to mask out for training conditionally - frac_lengths = torch.zeros((batch,), device=self.device).float().uniform_(*self.frac_lengths_mask) + frac_lengths = torch.zeros((batch,), device = self.device).float().uniform_(*self.frac_lengths_mask) rand_span_mask = mask_from_frac_lengths(lens, frac_lengths) if exists(mask): @@ -255,16 +240,19 @@ class CFM(nn.Module): x0 = torch.randn_like(x1) # time step - time = torch.rand((batch,), dtype=dtype, device=self.device) + time = torch.rand((batch,), dtype = dtype, device = self.device) # TODO. noise_scheduler # sample xt (φ_t(x) in the paper) - t = time.unsqueeze(-1).unsqueeze(-1) + t = rearrange(time, 'b -> b 1 1') φ = (1 - t) * x0 + t * x1 flow = x1 - x0 # only predict what is within the random mask span for infilling - cond = torch.where(rand_span_mask[..., None], torch.zeros_like(x1), x1) + cond = torch.where( + rand_span_mask[..., None], + torch.zeros_like(x1), x1 + ) # transformer and cfg training with a drop rate drop_audio_cond = random() < self.audio_drop_prob # p_drop in voicebox paper @@ -273,15 +261,13 @@ class CFM(nn.Module): drop_text = True else: drop_text = False - + # if want rigourously mask out padding, record in collate_fn in dataset.py, and pass in here # adding mask will use more memory, thus also need to adjust batchsampler with scaled down threshold for long sequences - pred = self.transformer( - x=φ, cond=cond, text=text, time=time, drop_audio_cond=drop_audio_cond, drop_text=drop_text - ) + pred = self.transformer(x = φ, cond = cond, text = text, time = time, drop_audio_cond = drop_audio_cond, drop_text = drop_text) # flow matching loss - loss = F.mse_loss(pred, flow, reduction="none") + loss = F.mse_loss(pred, flow, reduction = 'none') loss = loss[rand_span_mask] return loss.mean(), cond, pred diff --git a/model/dataset.py b/model/dataset.py index c293fe23a55e712e7ac1a89fc4bcfcfc54faafa7..d1af96ae82a776dd666aa791cf9cf82656649948 100644 --- a/model/dataset.py +++ b/model/dataset.py @@ -6,67 +6,65 @@ import torch import torch.nn.functional as F from torch.utils.data import Dataset, Sampler import torchaudio -from datasets import load_from_disk +from datasets import load_dataset, load_from_disk from datasets import Dataset as Dataset_ -from torch import nn + +from einops import rearrange from model.modules import MelSpec -from model.utils import default class HFDataset(Dataset): def __init__( self, hf_dataset: Dataset, - target_sample_rate=24_000, - n_mel_channels=100, - hop_length=256, + target_sample_rate = 24_000, + n_mel_channels = 100, + hop_length = 256, ): self.data = hf_dataset self.target_sample_rate = target_sample_rate self.hop_length = hop_length - self.mel_spectrogram = MelSpec( - target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length - ) - + self.mel_spectrogram = MelSpec(target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length) + def get_frame_len(self, index): row = self.data[index] - audio = row["audio"]["array"] - sample_rate = row["audio"]["sampling_rate"] + audio = row['audio']['array'] + sample_rate = row['audio']['sampling_rate'] return audio.shape[-1] / sample_rate * self.target_sample_rate / self.hop_length def __len__(self): return len(self.data) - + def __getitem__(self, index): row = self.data[index] - audio = row["audio"]["array"] + audio = row['audio']['array'] # logger.info(f"Audio shape: {audio.shape}") - sample_rate = row["audio"]["sampling_rate"] + sample_rate = row['audio']['sampling_rate'] duration = audio.shape[-1] / sample_rate if duration > 30 or duration < 0.3: return self.__getitem__((index + 1) % len(self.data)) - + audio_tensor = torch.from_numpy(audio).float() - + if sample_rate != self.target_sample_rate: resampler = torchaudio.transforms.Resample(sample_rate, self.target_sample_rate) audio_tensor = resampler(audio_tensor) - - audio_tensor = audio_tensor.unsqueeze(0) # 't -> 1 t') - + + audio_tensor = rearrange(audio_tensor, 't -> 1 t') + mel_spec = self.mel_spectrogram(audio_tensor) - - mel_spec = mel_spec.squeeze(0) # '1 d t -> d t' - - text = row["text"] - + + mel_spec = rearrange(mel_spec, '1 d t -> d t') + + text = row['text'] + return dict( - mel_spec=mel_spec, - text=text, + mel_spec = mel_spec, + text = text, ) @@ -74,39 +72,28 @@ class CustomDataset(Dataset): def __init__( self, custom_dataset: Dataset, - durations=None, - target_sample_rate=24_000, - hop_length=256, - n_mel_channels=100, - preprocessed_mel=False, - mel_spec_module: nn.Module | None = None, + durations = None, + target_sample_rate = 24_000, + hop_length = 256, + n_mel_channels = 100, + preprocessed_mel = False, ): self.data = custom_dataset self.durations = durations self.target_sample_rate = target_sample_rate self.hop_length = hop_length self.preprocessed_mel = preprocessed_mel - if not preprocessed_mel: - self.mel_spectrogram = default( - mel_spec_module, - MelSpec( - target_sample_rate=target_sample_rate, - hop_length=hop_length, - n_mel_channels=n_mel_channels, - ), - ) + self.mel_spectrogram = MelSpec(target_sample_rate=target_sample_rate, hop_length=hop_length, n_mel_channels=n_mel_channels) def get_frame_len(self, index): - if ( - self.durations is not None - ): # Please make sure the separately provided durations are correct, otherwise 99.99% OOM + if self.durations is not None: # Please make sure the separately provided durations are correct, otherwise 99.99% OOM return self.durations[index] * self.target_sample_rate / self.hop_length return self.data[index]["duration"] * self.target_sample_rate / self.hop_length - + def __len__(self): return len(self.data) - + def __getitem__(self, index): row = self.data[index] audio_path = row["audio_path"] @@ -118,57 +105,48 @@ class CustomDataset(Dataset): else: audio, source_sample_rate = torchaudio.load(audio_path) - if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) if duration > 30 or duration < 0.3: return self.__getitem__((index + 1) % len(self.data)) - + if source_sample_rate != self.target_sample_rate: resampler = torchaudio.transforms.Resample(source_sample_rate, self.target_sample_rate) audio = resampler(audio) - + mel_spec = self.mel_spectrogram(audio) - mel_spec = mel_spec.squeeze(0) # '1 d t -> d t') - + mel_spec = rearrange(mel_spec, '1 d t -> d t') + return dict( - mel_spec=mel_spec, - text=text, + mel_spec = mel_spec, + text = text, ) - + # Dynamic Batch Sampler - class DynamicBatchSampler(Sampler[list[int]]): - """Extension of Sampler that will do the following: - 1. Change the batch size (essentially number of sequences) - in a batch to ensure that the total number of frames are less - than a certain threshold. - 2. Make sure the padding efficiency in the batch is high. + """ Extension of Sampler that will do the following: + 1. Change the batch size (essentially number of sequences) + in a batch to ensure that the total number of frames are less + than a certain threshold. + 2. Make sure the padding efficiency in the batch is high. """ - def __init__( - self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False - ): + def __init__(self, sampler: Sampler[int], frames_threshold: int, max_samples=0, random_seed=None, drop_last: bool = False): self.sampler = sampler self.frames_threshold = frames_threshold self.max_samples = max_samples indices, batches = [], [] data_source = self.sampler.data_source - - for idx in tqdm( - self.sampler, desc="Sorting with sampler... if slow, check whether dataset is provided with duration" - ): + + for idx in tqdm(self.sampler, desc=f"Sorting with sampler... if slow, check whether dataset is provided with duration"): indices.append((idx, data_source.get_frame_len(idx))) - indices.sort(key=lambda elem: elem[1]) + indices.sort(key=lambda elem : elem[1]) batch = [] batch_frames = 0 - for idx, frame_len in tqdm( - indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu" - ): + for idx, frame_len in tqdm(indices, desc=f"Creating dynamic batches with {frames_threshold} audio frames per gpu"): if batch_frames + frame_len <= self.frames_threshold and (max_samples == 0 or len(batch) < max_samples): batch.append(idx) batch_frames += frame_len @@ -204,91 +182,61 @@ class DynamicBatchSampler(Sampler[list[int]]): # Load dataset - def load_dataset( - dataset_name: str, - tokenizer: str = "pinyin", - dataset_type: str = "CustomDataset", - audio_type: str = "raw", - mel_spec_module: nn.Module | None = None, - mel_spec_kwargs: dict = dict(), -) -> CustomDataset | HFDataset: - """ - dataset_type - "CustomDataset" if you want to use tokenizer name and default data path to load for train_dataset - - "CustomDatasetPath" if you just want to pass the full path to a preprocessed dataset without relying on tokenizer - """ - + dataset_name: str, + tokenizer: str, + dataset_type: str = "CustomDataset", + audio_type: str = "raw", + mel_spec_kwargs: dict = dict() + ) -> CustomDataset | HFDataset: + print("Loading dataset ...") if dataset_type == "CustomDataset": if audio_type == "raw": try: train_dataset = load_from_disk(f"data/{dataset_name}_{tokenizer}/raw") - except: # noqa: E722 + except: train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/raw.arrow") preprocessed_mel = False elif audio_type == "mel": train_dataset = Dataset_.from_file(f"data/{dataset_name}_{tokenizer}/mel.arrow") preprocessed_mel = True - with open(f"data/{dataset_name}_{tokenizer}/duration.json", "r", encoding="utf-8") as f: - data_dict = json.load(f) - durations = data_dict["duration"] - train_dataset = CustomDataset( - train_dataset, - durations=durations, - preprocessed_mel=preprocessed_mel, - mel_spec_module=mel_spec_module, - **mel_spec_kwargs, - ) - - elif dataset_type == "CustomDatasetPath": - try: - train_dataset = load_from_disk(f"{dataset_name}/raw") - except: # noqa: E722 - train_dataset = Dataset_.from_file(f"{dataset_name}/raw.arrow") - - with open(f"{dataset_name}/duration.json", "r", encoding="utf-8") as f: + with open(f"data/{dataset_name}_{tokenizer}/duration.json", 'r', encoding='utf-8') as f: data_dict = json.load(f) durations = data_dict["duration"] - train_dataset = CustomDataset( - train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs - ) + train_dataset = CustomDataset(train_dataset, durations=durations, preprocessed_mel=preprocessed_mel, **mel_spec_kwargs) elif dataset_type == "HFDataset": - print( - "Should manually modify the path of huggingface dataset to your need.\n" - + "May also the corresponding script cuz different dataset may have different format." - ) + print("Should manually modify the path of huggingface dataset to your need.\n" + + "May also the corresponding script cuz different dataset may have different format.") pre, post = dataset_name.split("_") - train_dataset = HFDataset( - load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir="./data"), - ) + train_dataset = HFDataset(load_dataset(f"{pre}/{pre}", split=f"train.{post}", cache_dir="./data"),) return train_dataset # collation - def collate_fn(batch): - mel_specs = [item["mel_spec"].squeeze(0) for item in batch] + mel_specs = [item['mel_spec'].squeeze(0) for item in batch] mel_lengths = torch.LongTensor([spec.shape[-1] for spec in mel_specs]) max_mel_length = mel_lengths.amax() padded_mel_specs = [] for spec in mel_specs: # TODO. maybe records mask for attention here padding = (0, max_mel_length - spec.size(-1)) - padded_spec = F.pad(spec, padding, value=0) + padded_spec = F.pad(spec, padding, value = 0) padded_mel_specs.append(padded_spec) - + mel_specs = torch.stack(padded_mel_specs) - text = [item["text"] for item in batch] + text = [item['text'] for item in batch] text_lengths = torch.LongTensor([len(item) for item in text]) return dict( - mel=mel_specs, - mel_lengths=mel_lengths, - text=text, - text_lengths=text_lengths, + mel = mel_specs, + mel_lengths = mel_lengths, + text = text, + text_lengths = text_lengths, ) diff --git a/model/ecapa_tdnn.py b/model/ecapa_tdnn.py index 6bc431eb9e2fc6173e6009ef3b0326a40618b1ec..30b611eda2dd8dc8fed4f59997e976181c07f78e 100644 --- a/model/ecapa_tdnn.py +++ b/model/ecapa_tdnn.py @@ -9,14 +9,13 @@ import torch.nn as nn import torch.nn.functional as F -""" Res2Conv1d + BatchNorm1d + ReLU -""" - +''' Res2Conv1d + BatchNorm1d + ReLU +''' class Res2Conv1dReluBn(nn.Module): - """ + ''' in_channels == out_channels == channels - """ + ''' def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True, scale=4): super().__init__() @@ -52,9 +51,8 @@ class Res2Conv1dReluBn(nn.Module): return out -""" Conv1d + BatchNorm1d + ReLU -""" - +''' Conv1d + BatchNorm1d + ReLU +''' class Conv1dReluBn(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=True): @@ -66,9 +64,8 @@ class Conv1dReluBn(nn.Module): return self.bn(F.relu(self.conv(x))) -""" The SE connection of 1D case. -""" - +''' The SE connection of 1D case. +''' class SE_Connect(nn.Module): def __init__(self, channels, se_bottleneck_dim=128): @@ -85,8 +82,8 @@ class SE_Connect(nn.Module): return out -""" SE-Res2Block of the ECAPA-TDNN architecture. -""" +''' SE-Res2Block of the ECAPA-TDNN architecture. +''' # def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale): # return nn.Sequential( @@ -96,7 +93,6 @@ class SE_Connect(nn.Module): # SE_Connect(channels) # ) - class SE_Res2Block(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, scale, se_bottleneck_dim): super().__init__() @@ -126,9 +122,8 @@ class SE_Res2Block(nn.Module): return x + residual -""" Attentive weighted mean and standard deviation pooling. -""" - +''' Attentive weighted mean and standard deviation pooling. +''' class AttentiveStatsPool(nn.Module): def __init__(self, in_dim, attention_channels=128, global_context_att=False): @@ -143,6 +138,7 @@ class AttentiveStatsPool(nn.Module): self.linear2 = nn.Conv1d(attention_channels, in_dim, kernel_size=1) # equals V and k in the paper def forward(self, x): + if self.global_context_att: context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x) context_std = torch.sqrt(torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x) @@ -155,52 +151,38 @@ class AttentiveStatsPool(nn.Module): # alpha = F.relu(self.linear1(x_in)) alpha = torch.softmax(self.linear2(alpha), dim=2) mean = torch.sum(alpha * x, dim=2) - residuals = torch.sum(alpha * (x**2), dim=2) - mean**2 + residuals = torch.sum(alpha * (x ** 2), dim=2) - mean ** 2 std = torch.sqrt(residuals.clamp(min=1e-9)) return torch.cat([mean, std], dim=1) class ECAPA_TDNN(nn.Module): - def __init__( - self, - feat_dim=80, - channels=512, - emb_dim=192, - global_context_att=False, - feat_type="wavlm_large", - sr=16000, - feature_selection="hidden_states", - update_extract=False, - config_path=None, - ): + def __init__(self, feat_dim=80, channels=512, emb_dim=192, global_context_att=False, + feat_type='wavlm_large', sr=16000, feature_selection="hidden_states", update_extract=False, config_path=None): super().__init__() self.feat_type = feat_type self.feature_selection = feature_selection self.update_extract = update_extract self.sr = sr - - torch.hub._validate_not_a_forked_repo = lambda a, b, c: True + + torch.hub._validate_not_a_forked_repo=lambda a,b,c: True try: local_s3prl_path = os.path.expanduser("~/.cache/torch/hub/s3prl_s3prl_main") - self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source="local", config_path=config_path) - except: # noqa: E722 - self.feature_extract = torch.hub.load("s3prl/s3prl", feat_type) + self.feature_extract = torch.hub.load(local_s3prl_path, feat_type, source='local', config_path=config_path) + except: + self.feature_extract = torch.hub.load('s3prl/s3prl', feat_type) - if len(self.feature_extract.model.encoder.layers) == 24 and hasattr( - self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention" - ): + if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"): self.feature_extract.model.encoder.layers[23].self_attn.fp32_attention = False - if len(self.feature_extract.model.encoder.layers) == 24 and hasattr( - self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention" - ): + if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"): self.feature_extract.model.encoder.layers[11].self_attn.fp32_attention = False self.feat_num = self.get_feat_num() self.feature_weight = nn.Parameter(torch.zeros(self.feat_num)) - if feat_type != "fbank" and feat_type != "mfcc": - freeze_list = ["final_proj", "label_embs_concat", "mask_emb", "project_q", "quantizer"] + if feat_type != 'fbank' and feat_type != 'mfcc': + freeze_list = ['final_proj', 'label_embs_concat', 'mask_emb', 'project_q', 'quantizer'] for name, param in self.feature_extract.named_parameters(): for freeze_val in freeze_list: if freeze_val in name: @@ -216,46 +198,18 @@ class ECAPA_TDNN(nn.Module): self.channels = [channels] * 4 + [1536] self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2) - self.layer2 = SE_Res2Block( - self.channels[0], - self.channels[1], - kernel_size=3, - stride=1, - padding=2, - dilation=2, - scale=8, - se_bottleneck_dim=128, - ) - self.layer3 = SE_Res2Block( - self.channels[1], - self.channels[2], - kernel_size=3, - stride=1, - padding=3, - dilation=3, - scale=8, - se_bottleneck_dim=128, - ) - self.layer4 = SE_Res2Block( - self.channels[2], - self.channels[3], - kernel_size=3, - stride=1, - padding=4, - dilation=4, - scale=8, - se_bottleneck_dim=128, - ) + self.layer2 = SE_Res2Block(self.channels[0], self.channels[1], kernel_size=3, stride=1, padding=2, dilation=2, scale=8, se_bottleneck_dim=128) + self.layer3 = SE_Res2Block(self.channels[1], self.channels[2], kernel_size=3, stride=1, padding=3, dilation=3, scale=8, se_bottleneck_dim=128) + self.layer4 = SE_Res2Block(self.channels[2], self.channels[3], kernel_size=3, stride=1, padding=4, dilation=4, scale=8, se_bottleneck_dim=128) # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1) cat_channels = channels * 3 self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1) - self.pooling = AttentiveStatsPool( - self.channels[-1], attention_channels=128, global_context_att=global_context_att - ) + self.pooling = AttentiveStatsPool(self.channels[-1], attention_channels=128, global_context_att=global_context_att) self.bn = nn.BatchNorm1d(self.channels[-1] * 2) self.linear = nn.Linear(self.channels[-1] * 2, emb_dim) + def get_feat_num(self): self.feature_extract.eval() wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)] @@ -272,12 +226,12 @@ class ECAPA_TDNN(nn.Module): x = self.feature_extract([sample for sample in x]) else: with torch.no_grad(): - if self.feat_type == "fbank" or self.feat_type == "mfcc": + if self.feat_type == 'fbank' or self.feat_type == 'mfcc': x = self.feature_extract(x) + 1e-6 # B x feat_dim x time_len else: x = self.feature_extract([sample for sample in x]) - if self.feat_type == "fbank": + if self.feat_type == 'fbank': x = x.log() if self.feat_type != "fbank" and self.feat_type != "mfcc": @@ -309,22 +263,6 @@ class ECAPA_TDNN(nn.Module): return out -def ECAPA_TDNN_SMALL( - feat_dim, - emb_dim=256, - feat_type="wavlm_large", - sr=16000, - feature_selection="hidden_states", - update_extract=False, - config_path=None, -): - return ECAPA_TDNN( - feat_dim=feat_dim, - channels=512, - emb_dim=emb_dim, - feat_type=feat_type, - sr=sr, - feature_selection=feature_selection, - update_extract=update_extract, - config_path=config_path, - ) +def ECAPA_TDNN_SMALL(feat_dim, emb_dim=256, feat_type='wavlm_large', sr=16000, feature_selection="hidden_states", update_extract=False, config_path=None): + return ECAPA_TDNN(feat_dim=feat_dim, channels=512, emb_dim=emb_dim, + feat_type=feat_type, sr=sr, feature_selection=feature_selection, update_extract=update_extract, config_path=config_path) diff --git a/model/modules.py b/model/modules.py index c026eff106bb58db9ca5106d4bcc88346609af03..fa2e3b1d274046fd8520fd188be36b92c9fad883 100644 --- a/model/modules.py +++ b/model/modules.py @@ -16,45 +16,45 @@ from torch import nn import torch.nn.functional as F import torchaudio +from einops import rearrange from x_transformers.x_transformers import apply_rotary_pos_emb # raw wav to mel spec - class MelSpec(nn.Module): def __init__( self, - filter_length=1024, - hop_length=256, - win_length=1024, - n_mel_channels=100, - target_sample_rate=24_000, - normalize=False, - power=1, - norm=None, - center=True, + filter_length = 1024, + hop_length = 256, + win_length = 1024, + n_mel_channels = 100, + target_sample_rate = 24_000, + normalize = False, + power = 1, + norm = None, + center = True, ): super().__init__() self.n_mel_channels = n_mel_channels self.mel_stft = torchaudio.transforms.MelSpectrogram( - sample_rate=target_sample_rate, - n_fft=filter_length, - win_length=win_length, - hop_length=hop_length, - n_mels=n_mel_channels, - power=power, - center=center, - normalized=normalize, - norm=norm, + sample_rate = target_sample_rate, + n_fft = filter_length, + win_length = win_length, + hop_length = hop_length, + n_mels = n_mel_channels, + power = power, + center = center, + normalized = normalize, + norm = norm, ) - self.register_buffer("dummy", torch.tensor(0), persistent=False) + self.register_buffer('dummy', torch.tensor(0), persistent = False) def forward(self, inp): if len(inp.shape) == 3: - inp = inp.squeeze(1) # 'b 1 nw -> b nw' + inp = rearrange(inp, 'b 1 nw -> b nw') assert len(inp.shape) == 2 @@ -62,13 +62,12 @@ class MelSpec(nn.Module): self.to(inp.device) mel = self.mel_stft(inp) - mel = mel.clamp(min=1e-5).log() + mel = mel.clamp(min = 1e-5).log() return mel - + # sinusoidal position embedding - class SinusPositionEmbedding(nn.Module): def __init__(self, dim): super().__init__() @@ -86,37 +85,35 @@ class SinusPositionEmbedding(nn.Module): # convolutional position embedding - class ConvPositionEmbedding(nn.Module): - def __init__(self, dim, kernel_size=31, groups=16): + def __init__(self, dim, kernel_size = 31, groups = 16): super().__init__() assert kernel_size % 2 != 0 self.conv1d = nn.Sequential( - nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Conv1d(dim, dim, kernel_size, groups = groups, padding = kernel_size // 2), nn.Mish(), - nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2), + nn.Conv1d(dim, dim, kernel_size, groups = groups, padding = kernel_size // 2), nn.Mish(), ) - def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722 + def forward(self, x: float['b n d'], mask: bool['b n'] | None = None): if mask is not None: mask = mask[..., None] - x = x.masked_fill(~mask, 0.0) + x = x.masked_fill(~mask, 0.) - x = x.permute(0, 2, 1) + x = rearrange(x, 'b n d -> b d n') x = self.conv1d(x) - out = x.permute(0, 2, 1) + out = rearrange(x, 'b d n -> b n d') if mask is not None: - out = out.masked_fill(~mask, 0.0) + out = out.masked_fill(~mask, 0.) return out # rotary positional embedding related - -def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0): +def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.): # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning # has some connection to NTK literature # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ @@ -129,14 +126,12 @@ def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_resca freqs_sin = torch.sin(freqs) # imaginary part return torch.cat([freqs_cos, freqs_sin], dim=-1) - -def get_pos_embed_indices(start, length, max_pos, scale=1.0): +def get_pos_embed_indices(start, length, max_pos, scale=1.): # length = length if isinstance(length, int) else length.max() scale = scale * torch.ones_like(start, dtype=torch.float32) # in case scale is a scalar - pos = ( - start.unsqueeze(1) - + (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long() - ) + pos = start.unsqueeze(1) + ( + torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * + scale.unsqueeze(1)).long() # avoid extra long error. pos = torch.where(pos < max_pos, pos, max_pos - 1) return pos @@ -144,7 +139,6 @@ def get_pos_embed_indices(start, length, max_pos, scale=1.0): # Global Response Normalization layer (Instance Normalization ?) - class GRN(nn.Module): def __init__(self, dim): super().__init__() @@ -160,7 +154,6 @@ class GRN(nn.Module): # ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py # ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108 - class ConvNeXtV2Block(nn.Module): def __init__( self, @@ -170,9 +163,7 @@ class ConvNeXtV2Block(nn.Module): ): super().__init__() padding = (dilation * (7 - 1)) // 2 - self.dwconv = nn.Conv1d( - dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation - ) # depthwise conv + self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation) # depthwise conv self.norm = nn.LayerNorm(dim, eps=1e-6) self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers self.act = nn.GELU() @@ -195,7 +186,6 @@ class ConvNeXtV2Block(nn.Module): # AdaLayerNormZero # return with modulated x for attn input, and params for later mlp modulation - class AdaLayerNormZero(nn.Module): def __init__(self, dim): super().__init__() @@ -205,7 +195,7 @@ class AdaLayerNormZero(nn.Module): self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) - def forward(self, x, emb=None): + def forward(self, x, emb = None): emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) @@ -216,7 +206,6 @@ class AdaLayerNormZero(nn.Module): # AdaLayerNormZero for final layer # return only with modulated x for attn input, cuz no more mlp modulation - class AdaLayerNormZero_Final(nn.Module): def __init__(self, dim): super().__init__() @@ -236,16 +225,22 @@ class AdaLayerNormZero_Final(nn.Module): # FeedForward - class FeedForward(nn.Module): - def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"): + def __init__(self, dim, dim_out = None, mult = 4, dropout = 0., approximate: str = 'none'): super().__init__() inner_dim = int(dim * mult) dim_out = dim_out if dim_out is not None else dim activation = nn.GELU(approximate=approximate) - project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation) - self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + activation + ) + self.ff = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) def forward(self, x): return self.ff(x) @@ -254,7 +249,6 @@ class FeedForward(nn.Module): # Attention with possible joint part # modified from diffusers/src/diffusers/models/attention_processor.py - class Attention(nn.Module): def __init__( self, @@ -263,8 +257,8 @@ class Attention(nn.Module): heads: int = 8, dim_head: int = 64, dropout: float = 0.0, - context_dim: Optional[int] = None, # if not None -> joint attention - context_pre_only=None, + context_dim: Optional[int] = None, # if not None -> joint attention + context_pre_only = None, ): super().__init__() @@ -300,21 +294,20 @@ class Attention(nn.Module): def forward( self, - x: float["b n d"], # noised input x # noqa: F722 - c: float["b n d"] = None, # context c # noqa: F722 - mask: bool["b n"] | None = None, # noqa: F722 - rope=None, # rotary position embedding for x - c_rope=None, # rotary position embedding for c + x: float['b n d'], # noised input x + c: float['b n d'] = None, # context c + mask: bool['b n'] | None = None, + rope = None, # rotary position embedding for x + c_rope = None, # rotary position embedding for c ) -> torch.Tensor: if c is not None: - return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope) + return self.processor(self, x, c = c, mask = mask, rope = rope, c_rope = c_rope) else: - return self.processor(self, x, mask=mask, rope=rope) + return self.processor(self, x, mask = mask, rope = rope) # Attention processor - class AttnProcessor: def __init__(self): pass @@ -322,10 +315,11 @@ class AttnProcessor: def __call__( self, attn: Attention, - x: float["b n d"], # noised input x # noqa: F722 - mask: bool["b n"] | None = None, # noqa: F722 - rope=None, # rotary position embedding + x: float['b n d'], # noised input x + mask: bool['b n'] | None = None, + rope = None, # rotary position embedding ) -> torch.FloatTensor: + batch_size = x.shape[0] # `sample` projections. @@ -336,7 +330,7 @@ class AttnProcessor: # apply rotary position embedding if rope is not None: freqs, xpos_scale = rope - q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.) query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) @@ -351,7 +345,7 @@ class AttnProcessor: # mask. e.g. inference got a batch with different target durations, mask out the padding if mask is not None: attn_mask = mask - attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = rearrange(attn_mask, 'b n -> b 1 1 n') attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) else: attn_mask = None @@ -366,16 +360,15 @@ class AttnProcessor: x = attn.to_out[1](x) if mask is not None: - mask = mask.unsqueeze(-1) - x = x.masked_fill(~mask, 0.0) + mask = rearrange(mask, 'b n -> b n 1') + x = x.masked_fill(~mask, 0.) return x - + # Joint Attention processor for MM-DiT # modified from diffusers/src/diffusers/models/attention_processor.py - class JointAttnProcessor: def __init__(self): pass @@ -383,11 +376,11 @@ class JointAttnProcessor: def __call__( self, attn: Attention, - x: float["b n d"], # noised input x # noqa: F722 - c: float["b nt d"] = None, # context c, here text # noqa: F722 - mask: bool["b n"] | None = None, # noqa: F722 - rope=None, # rotary position embedding for x - c_rope=None, # rotary position embedding for c + x: float['b n d'], # noised input x + c: float['b nt d'] = None, # context c, here text + mask: bool['b n'] | None = None, + rope = None, # rotary position embedding for x + c_rope = None, # rotary position embedding for c ) -> torch.FloatTensor: residual = x @@ -406,12 +399,12 @@ class JointAttnProcessor: # apply rope for context and noised input independently if rope is not None: freqs, xpos_scale = rope - q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.) query = apply_rotary_pos_emb(query, freqs, q_xpos_scale) key = apply_rotary_pos_emb(key, freqs, k_xpos_scale) if c_rope is not None: freqs, xpos_scale = c_rope - q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0) + q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale ** -1.) if xpos_scale is not None else (1., 1.) c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale) c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale) @@ -428,8 +421,8 @@ class JointAttnProcessor: # mask. e.g. inference got a batch with different target durations, mask out the padding if mask is not None: - attn_mask = F.pad(mask, (0, c.shape[1]), value=True) # no mask for c (text) - attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n' + attn_mask = F.pad(mask, (0, c.shape[1]), value = True) # no mask for c (text) + attn_mask = rearrange(attn_mask, 'b n -> b 1 1 n') attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2]) else: attn_mask = None @@ -440,8 +433,8 @@ class JointAttnProcessor: # Split the attention outputs. x, c = ( - x[:, : residual.shape[1]], - x[:, residual.shape[1] :], + x[:, :residual.shape[1]], + x[:, residual.shape[1]:], ) # linear proj @@ -452,8 +445,8 @@ class JointAttnProcessor: c = attn.to_out_c(c) if mask is not None: - mask = mask.unsqueeze(-1) - x = x.masked_fill(~mask, 0.0) + mask = rearrange(mask, 'b n -> b n 1') + x = x.masked_fill(~mask, 0.) # c = c.masked_fill(~mask, 0.) # no mask for c (text) return x, c @@ -461,24 +454,24 @@ class JointAttnProcessor: # DiT Block - class DiTBlock(nn.Module): - def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1): - super().__init__() + def __init__(self, dim, heads, dim_head, ff_mult = 4, dropout = 0.1): + super().__init__() + self.attn_norm = AdaLayerNormZero(dim) self.attn = Attention( - processor=AttnProcessor(), - dim=dim, - heads=heads, - dim_head=dim_head, - dropout=dropout, - ) - + processor = AttnProcessor(), + dim = dim, + heads = heads, + dim_head = dim_head, + dropout = dropout, + ) + self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) - self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + self.ff = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh") - def forward(self, x, t, mask=None, rope=None): # x: noised input, t: time embedding + def forward(self, x, t, mask = None, rope = None): # x: noised input, t: time embedding # pre-norm & modulation for attention input norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t) @@ -487,7 +480,7 @@ class DiTBlock(nn.Module): # process attention output for input x x = x + gate_msa.unsqueeze(1) * attn_output - + norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] ff_output = self.ff(norm) x = x + gate_mlp.unsqueeze(1) * ff_output @@ -497,9 +490,8 @@ class DiTBlock(nn.Module): # MMDiT Block https://arxiv.org/abs/2403.03206 - class MMDiTBlock(nn.Module): - r""" + r""" modified from diffusers/src/diffusers/models/attention.py notes. @@ -508,33 +500,33 @@ class MMDiTBlock(nn.Module): context_pre_only: last layer only do prenorm + modulation cuz no more ffn """ - def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False): + def __init__(self, dim, heads, dim_head, ff_mult = 4, dropout = 0.1, context_pre_only = False): super().__init__() self.context_pre_only = context_pre_only - + self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim) self.attn_norm_x = AdaLayerNormZero(dim) self.attn = Attention( - processor=JointAttnProcessor(), - dim=dim, - heads=heads, - dim_head=dim_head, - dropout=dropout, - context_dim=dim, - context_pre_only=context_pre_only, - ) + processor = JointAttnProcessor(), + dim = dim, + heads = heads, + dim_head = dim_head, + dropout = dropout, + context_dim = dim, + context_pre_only = context_pre_only, + ) if not context_pre_only: self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) - self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + self.ff_c = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh") else: self.ff_norm_c = None self.ff_c = None self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) - self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh") + self.ff_x = FeedForward(dim = dim, mult = ff_mult, dropout = dropout, approximate = "tanh") - def forward(self, x, c, t, mask=None, rope=None, c_rope=None): # x: noised input, c: context, t: time embedding + def forward(self, x, c, t, mask = None, rope = None, c_rope = None): # x: noised input, c: context, t: time embedding # pre-norm & modulation for attention input if self.context_pre_only: norm_c = self.attn_norm_c(c, t) @@ -548,7 +540,7 @@ class MMDiTBlock(nn.Module): # process attention output for context c if self.context_pre_only: c = None - else: # if not last layer + else: # if not last layer c = c + c_gate_msa.unsqueeze(1) * c_attn_output norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None] @@ -557,7 +549,7 @@ class MMDiTBlock(nn.Module): # process attention output for input x x = x + x_gate_msa.unsqueeze(1) * x_attn_output - + norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None] x_ff_output = self.ff_x(norm_x) x = x + x_gate_mlp.unsqueeze(1) * x_ff_output @@ -567,15 +559,17 @@ class MMDiTBlock(nn.Module): # time step conditioning embedding - class TimestepEmbedding(nn.Module): def __init__(self, dim, freq_embed_dim=256): super().__init__() self.time_embed = SinusPositionEmbedding(freq_embed_dim) - self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)) + self.time_mlp = nn.Sequential( + nn.Linear(freq_embed_dim, dim), + nn.SiLU(), + nn.Linear(dim, dim) + ) - def forward(self, timestep: float["b"]): # noqa: F821 + def forward(self, timestep: float['b']): time_hidden = self.time_embed(timestep) - time_hidden = time_hidden.to(timestep.dtype) time = self.time_mlp(time_hidden) # b d return time diff --git a/model/trainer.py b/model/trainer.py index 5ab9ba2a6ce2b9a92050070232faab014633e31f..4914f300ea65796444dcd64982393878fc708cf6 100644 --- a/model/trainer.py +++ b/model/trainer.py @@ -10,6 +10,8 @@ from torch.optim import AdamW from torch.utils.data import DataLoader, Dataset, SequentialSampler from torch.optim.lr_scheduler import LinearLR, SequentialLR +from einops import rearrange + from accelerate import Accelerator from accelerate.utils import DistributedDataParallelKwargs @@ -22,69 +24,66 @@ from model.dataset import DynamicBatchSampler, collate_fn # trainer - class Trainer: def __init__( self, model: CFM, epochs, learning_rate, - num_warmup_updates=20000, - save_per_updates=1000, - checkpoint_path=None, - batch_size=32, + num_warmup_updates = 20000, + save_per_updates = 1000, + checkpoint_path = None, + batch_size = 32, batch_size_type: str = "sample", - max_samples=32, - grad_accumulation_steps=1, - max_grad_norm=1.0, + max_samples = 32, + grad_accumulation_steps = 1, + max_grad_norm = 1.0, noise_scheduler: str | None = None, duration_predictor: torch.nn.Module | None = None, - wandb_project="test_e2-tts", - wandb_run_name="test_run", + wandb_project = "test_e2-tts", + wandb_run_name = "test_run", wandb_resume_id: str = None, - last_per_steps=None, + last_per_steps = None, accelerate_kwargs: dict = dict(), - ema_kwargs: dict = dict(), - bnb_optimizer: bool = False, + ema_kwargs: dict = dict() ): - ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) - - logger = "wandb" if wandb.api.api_key else None - print(f"Using logger: {logger}") + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters = True) self.accelerator = Accelerator( - log_with=logger, - kwargs_handlers=[ddp_kwargs], - gradient_accumulation_steps=grad_accumulation_steps, - **accelerate_kwargs, + log_with = "wandb", + kwargs_handlers = [ddp_kwargs], + gradient_accumulation_steps = grad_accumulation_steps, + **accelerate_kwargs ) - - if logger == "wandb": - if exists(wandb_resume_id): - init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}} - else: - init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}} - self.accelerator.init_trackers( - project_name=wandb_project, - init_kwargs=init_kwargs, - config={ - "epochs": epochs, + + if exists(wandb_resume_id): + init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name, 'id': wandb_resume_id}} + else: + init_kwargs={"wandb": {"resume": "allow", "name": wandb_run_name}} + self.accelerator.init_trackers( + project_name = wandb_project, + init_kwargs=init_kwargs, + config={"epochs": epochs, "learning_rate": learning_rate, - "num_warmup_updates": num_warmup_updates, + "num_warmup_updates": num_warmup_updates, "batch_size": batch_size, "batch_size_type": batch_size_type, "max_samples": max_samples, "grad_accumulation_steps": grad_accumulation_steps, "max_grad_norm": max_grad_norm, "gpus": self.accelerator.num_processes, - "noise_scheduler": noise_scheduler, - }, + "noise_scheduler": noise_scheduler} ) self.model = model if self.is_main: - self.ema_model = EMA(model, include_online_model=False, **ema_kwargs) + self.ema_model = EMA( + model, + include_online_model = False, + **ema_kwargs + ) self.ema_model.to(self.accelerator.device) @@ -92,7 +91,7 @@ class Trainer: self.num_warmup_updates = num_warmup_updates self.save_per_updates = save_per_updates self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps) - self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts") + self.checkpoint_path = default(checkpoint_path, 'ckpts/test_e2-tts') self.batch_size = batch_size self.batch_size_type = batch_size_type @@ -104,13 +103,10 @@ class Trainer: self.duration_predictor = duration_predictor - if bnb_optimizer: - import bitsandbytes as bnb - - self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate) - else: - self.optimizer = AdamW(model.parameters(), lr=learning_rate) - self.model, self.optimizer = self.accelerator.prepare(self.model, self.optimizer) + self.optimizer = AdamW(model.parameters(), lr=learning_rate) + self.model, self.optimizer = self.accelerator.prepare( + self.model, self.optimizer + ) @property def is_main(self): @@ -120,112 +116,76 @@ class Trainer: self.accelerator.wait_for_everyone() if self.is_main: checkpoint = dict( - model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(), - optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(), - ema_model_state_dict=self.ema_model.state_dict(), - scheduler_state_dict=self.scheduler.state_dict(), - step=step, + model_state_dict = self.accelerator.unwrap_model(self.model).state_dict(), + optimizer_state_dict = self.accelerator.unwrap_model(self.optimizer).state_dict(), + ema_model_state_dict = self.ema_model.state_dict(), + scheduler_state_dict = self.scheduler.state_dict(), + step = step ) if not os.path.exists(self.checkpoint_path): os.makedirs(self.checkpoint_path) - if last: + if last == True: self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt") print(f"Saved last checkpoint at step {step}") else: self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt") def load_checkpoint(self): - if ( - not exists(self.checkpoint_path) - or not os.path.exists(self.checkpoint_path) - or not os.listdir(self.checkpoint_path) - ): + if not exists(self.checkpoint_path) or not os.path.exists(self.checkpoint_path) or not os.listdir(self.checkpoint_path): return 0 - + self.accelerator.wait_for_everyone() if "model_last.pt" in os.listdir(self.checkpoint_path): latest_checkpoint = "model_last.pt" else: - latest_checkpoint = sorted( - [f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")], - key=lambda x: int("".join(filter(str.isdigit, x))), - )[-1] + latest_checkpoint = sorted(os.listdir(self.checkpoint_path), key=lambda x: int(''.join(filter(str.isdigit, x))))[-1] # checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location=self.accelerator.device) # rather use accelerator.load_state ಥ_ಥ - checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", weights_only=True, map_location="cpu") + checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu") + self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint['model_state_dict']) + self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint['optimizer_state_dict']) if self.is_main: - self.ema_model.load_state_dict(checkpoint["ema_model_state_dict"]) - - if "step" in checkpoint: - self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"]) - self.accelerator.unwrap_model(self.optimizer).load_state_dict(checkpoint["optimizer_state_dict"]) - if self.scheduler: - self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) - step = checkpoint["step"] - else: - checkpoint["model_state_dict"] = { - k.replace("ema_model.", ""): v - for k, v in checkpoint["ema_model_state_dict"].items() - if k not in ["initted", "step"] - } - self.accelerator.unwrap_model(self.model).load_state_dict(checkpoint["model_state_dict"]) - step = 0 - - del checkpoint - gc.collect() + self.ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + + if self.scheduler: + self.scheduler.load_state_dict(checkpoint['scheduler_state_dict']) + + step = checkpoint['step'] + del checkpoint; gc.collect() return step def train(self, train_dataset: Dataset, num_workers=16, resumable_with_seed: int = None): + if exists(resumable_with_seed): generator = torch.Generator() generator.manual_seed(resumable_with_seed) - else: + else: generator = None if self.batch_size_type == "sample": - train_dataloader = DataLoader( - train_dataset, - collate_fn=collate_fn, - num_workers=num_workers, - pin_memory=True, - persistent_workers=True, - batch_size=self.batch_size, - shuffle=True, - generator=generator, - ) + train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, + batch_size=self.batch_size, shuffle=True, generator=generator) elif self.batch_size_type == "frame": self.accelerator.even_batches = False sampler = SequentialSampler(train_dataset) - batch_sampler = DynamicBatchSampler( - sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False - ) - train_dataloader = DataLoader( - train_dataset, - collate_fn=collate_fn, - num_workers=num_workers, - pin_memory=True, - persistent_workers=True, - batch_sampler=batch_sampler, - ) + batch_sampler = DynamicBatchSampler(sampler, self.batch_size, max_samples=self.max_samples, random_seed=resumable_with_seed, drop_last=False) + train_dataloader = DataLoader(train_dataset, collate_fn=collate_fn, num_workers=num_workers, pin_memory=True, + batch_sampler=batch_sampler) else: - raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but received {self.batch_size_type}") - + raise ValueError(f"batch_size_type must be either 'sample' or 'frame', but recieved {self.batch_size_type}") + # accelerator.prepare() dispatches batches to devices; # which means the length of dataloader calculated before, should consider the number of devices - warmup_steps = ( - self.num_warmup_updates * self.accelerator.num_processes - ) # consider a fixed warmup steps while using accelerate multi-gpu ddp - # otherwise by default with split_batches=False, warmup steps change with num_processes + warmup_steps = self.num_warmup_updates * self.accelerator.num_processes # consider a fixed warmup steps while using accelerate multi-gpu ddp + # otherwise by default with split_batches=False, warmup steps change with num_processes total_steps = len(train_dataloader) * self.epochs / self.grad_accumulation_steps decay_steps = total_steps - warmup_steps warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps) decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps) - self.scheduler = SequentialLR( - self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps] - ) - train_dataloader, self.scheduler = self.accelerator.prepare( - train_dataloader, self.scheduler - ) # actual steps = 1 gpu steps / gpus + self.scheduler = SequentialLR(self.optimizer, + schedulers=[warmup_scheduler, decay_scheduler], + milestones=[warmup_steps]) + train_dataloader, self.scheduler = self.accelerator.prepare(train_dataloader, self.scheduler) # actual steps = 1 gpu steps / gpus start_step = self.load_checkpoint() global_step = start_step @@ -240,36 +200,23 @@ class Trainer: for epoch in range(skipped_epoch, self.epochs): self.model.train() if exists(resumable_with_seed) and epoch == skipped_epoch: - progress_bar = tqdm( - skipped_dataloader, - desc=f"Epoch {epoch+1}/{self.epochs}", - unit="step", - disable=not self.accelerator.is_local_main_process, - initial=skipped_batch, - total=orig_epoch_step, - ) + progress_bar = tqdm(skipped_dataloader, desc=f"Epoch {epoch+1}/{self.epochs}", unit="step", disable=not self.accelerator.is_local_main_process, + initial=skipped_batch, total=orig_epoch_step) else: - progress_bar = tqdm( - train_dataloader, - desc=f"Epoch {epoch+1}/{self.epochs}", - unit="step", - disable=not self.accelerator.is_local_main_process, - ) + progress_bar = tqdm(train_dataloader, desc=f"Epoch {epoch+1}/{self.epochs}", unit="step", disable=not self.accelerator.is_local_main_process) for batch in progress_bar: with self.accelerator.accumulate(self.model): - text_inputs = batch["text"] - mel_spec = batch["mel"].permute(0, 2, 1) + text_inputs = batch['text'] + mel_spec = rearrange(batch['mel'], 'b d n -> b n d') mel_lengths = batch["mel_lengths"] # TODO. add duration predictor training if self.duration_predictor is not None and self.accelerator.is_local_main_process: - dur_loss = self.duration_predictor(mel_spec, lens=batch.get("durations")) + dur_loss = self.duration_predictor(mel_spec, lens=batch.get('durations')) self.accelerator.log({"duration loss": dur_loss.item()}, step=global_step) - loss, cond, pred = self.model( - mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler - ) + loss, cond, pred = self.model(mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler) self.accelerator.backward(loss) if self.max_grad_norm > 0 and self.accelerator.sync_gradients: @@ -286,15 +233,13 @@ class Trainer: if self.accelerator.is_local_main_process: self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step) - + progress_bar.set_postfix(step=str(global_step), loss=loss.item()) - + if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0: self.save_checkpoint(global_step) - + if global_step % self.last_per_steps == 0: self.save_checkpoint(global_step, last=True) - - self.save_checkpoint(global_step, last=True) - + self.accelerator.end_training() diff --git a/model/utils.py b/model/utils.py index 2253cb812e945a75da858b29c0ccdb482f23fafb..8546f472484c88aeca12f78cb9c32274794e7879 100644 --- a/model/utils.py +++ b/model/utils.py @@ -1,6 +1,7 @@ from __future__ import annotations import os +import re import math import random import string @@ -8,7 +9,6 @@ from tqdm import tqdm from collections import defaultdict import matplotlib - matplotlib.use("Agg") import matplotlib.pylab as plt @@ -17,8 +17,17 @@ import torch.nn.functional as F from torch.nn.utils.rnn import pad_sequence import torchaudio +import einx +from einops import rearrange, reduce + import jieba from pypinyin import lazy_pinyin, Style +import zhconv +from zhon.hanzi import punctuation +from jiwer import compute_measures + +from funasr import AutoModel +from faster_whisper import WhisperModel from model.ecapa_tdnn import ECAPA_TDNN_SMALL from model.modules import MelSpec @@ -26,102 +35,106 @@ from model.modules import MelSpec # seed everything - -def seed_everything(seed=0): +def seed_everything(seed = 0): random.seed(seed) - os.environ["PYTHONHASHSEED"] = str(seed) + os.environ['PYTHONHASHSEED'] = str(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False - # helpers - def exists(v): return v is not None - def default(v, d): return v if exists(v) else d - # tensor helpers +def lens_to_mask( + t: int['b'], + length: int | None = None +) -> bool['b n']: -def lens_to_mask(t: int["b"], length: int | None = None) -> bool["b n"]: # noqa: F722 F821 if not exists(length): length = t.amax() - seq = torch.arange(length, device=t.device) - return seq[None, :] < t[:, None] - - -def mask_from_start_end_indices(seq_len: int["b"], start: int["b"], end: int["b"]): # noqa: F722 F821 - max_seq_len = seq_len.max().item() - seq = torch.arange(max_seq_len, device=start.device).long() - start_mask = seq[None, :] >= start[:, None] - end_mask = seq[None, :] < end[:, None] - return start_mask & end_mask + seq = torch.arange(length, device = t.device) + return einx.less('n, b -> b n', seq, t) +def mask_from_start_end_indices( + seq_len: int['b'], + start: int['b'], + end: int['b'] +): + max_seq_len = seq_len.max().item() + seq = torch.arange(max_seq_len, device = start.device).long() + return einx.greater_equal('n, b -> b n', seq, start) & einx.less('n, b -> b n', seq, end) -def mask_from_frac_lengths(seq_len: int["b"], frac_lengths: float["b"]): # noqa: F722 F821 +def mask_from_frac_lengths( + seq_len: int['b'], + frac_lengths: float['b'] +): lengths = (frac_lengths * seq_len).long() max_start = seq_len - lengths rand = torch.rand_like(frac_lengths) - start = (max_start * rand).long().clamp(min=0) + start = (max_start * rand).long().clamp(min = 0) end = start + lengths return mask_from_start_end_indices(seq_len, start, end) +def maybe_masked_mean( + t: float['b n d'], + mask: bool['b n'] = None +) -> float['b d']: -def maybe_masked_mean(t: float["b n d"], mask: bool["b n"] = None) -> float["b d"]: # noqa: F722 if not exists(mask): - return t.mean(dim=1) + return t.mean(dim = 1) - t = torch.where(mask[:, :, None], t, torch.tensor(0.0, device=t.device)) - num = t.sum(dim=1) - den = mask.float().sum(dim=1) + t = einx.where('b n, b n d, -> b n d', mask, t, 0.) + num = reduce(t, 'b n d -> b d', 'sum') + den = reduce(mask.float(), 'b n -> b', 'sum') - return num / den.clamp(min=1.0) + return einx.divide('b d, b -> b d', num, den.clamp(min = 1.)) # simple utf-8 tokenizer, since paper went character based -def list_str_to_tensor(text: list[str], padding_value=-1) -> int["b nt"]: # noqa: F722 - list_tensors = [torch.tensor([*bytes(t, "UTF-8")]) for t in text] # ByT5 style - text = pad_sequence(list_tensors, padding_value=padding_value, batch_first=True) +def list_str_to_tensor( + text: list[str], + padding_value = -1 +) -> int['b nt']: + list_tensors = [torch.tensor([*bytes(t, 'UTF-8')]) for t in text] # ByT5 style + text = pad_sequence(list_tensors, padding_value = padding_value, batch_first = True) return text - # char tokenizer, based on custom dataset's extracted .txt file def list_str_to_idx( text: list[str] | list[list[str]], vocab_char_map: dict[str, int], # {char: idx} - padding_value=-1, -) -> int["b nt"]: # noqa: F722 + padding_value = -1 +) -> int['b nt']: list_idx_tensors = [torch.tensor([vocab_char_map.get(c, 0) for c in t]) for t in text] # pinyin or char style - text = pad_sequence(list_idx_tensors, padding_value=padding_value, batch_first=True) + text = pad_sequence(list_idx_tensors, padding_value = padding_value, batch_first = True) return text # Get tokenizer - def get_tokenizer(dataset_name, tokenizer: str = "pinyin"): - """ + ''' tokenizer - "pinyin" do g2p for only chinese characters, need .txt vocab_file - "char" for char-wise tokenizer, need .txt vocab_file - "byte" for utf-8 tokenizer - - "custom" if you're directly passing in a path to the vocab.txt you want to use vocab_size - if use "pinyin", all available pinyin types, common alphabets (also those with accent) and symbols - if use "char", derived from unfiltered character & symbol counts of custom dataset - - if use "byte", set to 256 (unicode byte range) - """ + - if use "byte", set to 256 (unicode byte range) + ''' if tokenizer in ["pinyin", "char"]: - with open(f"data/{dataset_name}_{tokenizer}/vocab.txt", "r", encoding="utf-8") as f: + with open (f"data/{dataset_name}_{tokenizer}/vocab.txt", "r") as f: vocab_char_map = {} for i, char in enumerate(f): vocab_char_map[char[:-1]] = i @@ -131,31 +144,20 @@ def get_tokenizer(dataset_name, tokenizer: str = "pinyin"): elif tokenizer == "byte": vocab_char_map = None vocab_size = 256 - elif tokenizer == "custom": - with open(dataset_name, "r", encoding="utf-8") as f: - vocab_char_map = {} - for i, char in enumerate(f): - vocab_char_map[char[:-1]] = i - vocab_size = len(vocab_char_map) return vocab_char_map, vocab_size # convert char to pinyin - -def convert_char_to_pinyin(text_list, polyphone=True): +def convert_char_to_pinyin(text_list, polyphone = True): final_text_list = [] - god_knows_why_en_testset_contains_zh_quote = str.maketrans( - {"“": '"', "”": '"', "‘": "'", "’": "'"} - ) # in case librispeech (orig no-pc) test-clean - custom_trans = str.maketrans({";": ","}) # add custom trans here, to address oov + god_knows_why_en_testset_contains_zh_quote = str.maketrans({'“': '"', '”': '"', '‘': "'", '’': "'"}) # in case librispeech (orig no-pc) test-clean for text in text_list: char_list = [] text = text.translate(god_knows_why_en_testset_contains_zh_quote) - text = text.translate(custom_trans) for seg in jieba.cut(text): - seg_byte_len = len(bytes(seg, "UTF-8")) + seg_byte_len = len(bytes(seg, 'UTF-8')) if seg_byte_len == len(seg): # if pure alphabets and symbols if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"": char_list.append(" ") @@ -184,7 +186,7 @@ def convert_char_to_pinyin(text_list, polyphone=True): # save spectrogram def save_spectrogram(spectrogram, path): plt.figure(figsize=(12, 4)) - plt.imshow(spectrogram, origin="lower", aspect="auto") + plt.imshow(spectrogram, origin='lower', aspect='auto') plt.colorbar() plt.savefig(path) plt.close() @@ -192,15 +194,13 @@ def save_spectrogram(spectrogram, path): # seedtts testset metainfo: utt, prompt_text, prompt_wav, gt_text, gt_wav def get_seedtts_testset_metainfo(metalst): - f = open(metalst) - lines = f.readlines() - f.close() + f = open(metalst); lines = f.readlines(); f.close() metainfo = [] for line in lines: - if len(line.strip().split("|")) == 5: - utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|") - elif len(line.strip().split("|")) == 4: - utt, prompt_text, prompt_wav, gt_text = line.strip().split("|") + if len(line.strip().split('|')) == 5: + utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split('|') + elif len(line.strip().split('|')) == 4: + utt, prompt_text, prompt_wav, gt_text = line.strip().split('|') gt_wav = os.path.join(os.path.dirname(metalst), "wavs", utt + ".wav") if not os.path.isabs(prompt_wav): prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav) @@ -210,20 +210,18 @@ def get_seedtts_testset_metainfo(metalst): # librispeech test-clean metainfo: gen_utt, ref_txt, ref_wav, gen_txt, gen_wav def get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path): - f = open(metalst) - lines = f.readlines() - f.close() + f = open(metalst); lines = f.readlines(); f.close() metainfo = [] for line in lines: - ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t") + ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split('\t') # ref_txt = ref_txt[0] + ref_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc) - ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-") - ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac") + ref_spk_id, ref_chaptr_id, _ = ref_utt.split('-') + ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + '.flac') # gen_txt = gen_txt[0] + gen_txt[1:].lower() + '.' # if use librispeech test-clean (no-pc) - gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-") - gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac") + gen_spk_id, gen_chaptr_id, _ = gen_utt.split('-') + gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + '.flac') metainfo.append((gen_utt, ref_txt, ref_wav, " " + gen_txt, gen_wav)) @@ -235,30 +233,21 @@ def padded_mel_batch(ref_mels): max_mel_length = torch.LongTensor([mel.shape[-1] for mel in ref_mels]).amax() padded_ref_mels = [] for mel in ref_mels: - padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value=0) + padded_ref_mel = F.pad(mel, (0, max_mel_length - mel.shape[-1]), value = 0) padded_ref_mels.append(padded_ref_mel) padded_ref_mels = torch.stack(padded_ref_mels) - padded_ref_mels = padded_ref_mels.permute(0, 2, 1) + padded_ref_mels = rearrange(padded_ref_mels, 'b d n -> b n d') return padded_ref_mels # get prompts from metainfo containing: utt, prompt_text, prompt_wav, gt_text, gt_wav - def get_inference_prompt( - metainfo, - speed=1.0, - tokenizer="pinyin", - polyphone=True, - target_sample_rate=24000, - n_mel_channels=100, - hop_length=256, - target_rms=0.1, - use_truth_duration=False, - infer_batch_size=1, - num_buckets=200, - min_secs=3, - max_secs=40, + metainfo, + speed = 1., tokenizer = "pinyin", polyphone = True, + target_sample_rate = 24000, n_mel_channels = 100, hop_length = 256, target_rms = 0.1, + use_truth_duration = False, + infer_batch_size = 1, num_buckets = 200, min_secs = 3, max_secs = 40, ): prompts_all = [] @@ -266,15 +255,13 @@ def get_inference_prompt( max_tokens = max_secs * target_sample_rate // hop_length batch_accum = [0] * num_buckets - utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = ( - [[] for _ in range(num_buckets)] for _ in range(6) - ) + utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = \ + ([[] for _ in range(num_buckets)] for _ in range(6)) - mel_spectrogram = MelSpec( - target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length - ) + mel_spectrogram = MelSpec(target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length) for utt, prompt_text, prompt_wav, gt_text, gt_wav in tqdm(metainfo, desc="Processing prompts..."): + # Audio ref_audio, ref_sr = torchaudio.load(prompt_wav) ref_rms = torch.sqrt(torch.mean(torch.square(ref_audio))) @@ -286,11 +273,9 @@ def get_inference_prompt( ref_audio = resampler(ref_audio) # Text - if len(prompt_text[-1].encode("utf-8")) == 1: - prompt_text = prompt_text + " " text = [prompt_text + gt_text] if tokenizer == "pinyin": - text_list = convert_char_to_pinyin(text, polyphone=polyphone) + text_list = convert_char_to_pinyin(text, polyphone = polyphone) else: text_list = text @@ -306,19 +291,19 @@ def get_inference_prompt( # # test vocoder resynthesis # ref_audio = gt_audio else: - ref_text_len = len(prompt_text.encode("utf-8")) - gen_text_len = len(gt_text.encode("utf-8")) + zh_pause_punc = r"。，、；：？！" + ref_text_len = len(prompt_text) + len(re.findall(zh_pause_punc, prompt_text)) + gen_text_len = len(gt_text) + len(re.findall(zh_pause_punc, gt_text)) total_mel_len = ref_mel_len + int(ref_mel_len / ref_text_len * gen_text_len / speed) # to mel spectrogram ref_mel = mel_spectrogram(ref_audio) - ref_mel = ref_mel.squeeze(0) + ref_mel = rearrange(ref_mel, '1 d n -> d n') # deal with batch assert infer_batch_size > 0, "infer_batch_size should be greater than 0." - assert ( - min_tokens <= total_mel_len <= max_tokens - ), f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]." + assert min_tokens <= total_mel_len <= max_tokens, \ + f"Audio {utt} has duration {total_mel_len*hop_length//target_sample_rate}s out of range [{min_secs}, {max_secs}]." bucket_i = math.floor((total_mel_len - min_tokens) / (max_tokens - min_tokens + 1) * num_buckets) utts[bucket_i].append(utt) @@ -332,39 +317,28 @@ def get_inference_prompt( if batch_accum[bucket_i] >= infer_batch_size: # print(f"\n{len(ref_mels[bucket_i][0][0])}\n{ref_mel_lens[bucket_i]}\n{total_mel_lens[bucket_i]}") - prompts_all.append( - ( - utts[bucket_i], - ref_rms_list[bucket_i], - padded_mel_batch(ref_mels[bucket_i]), - ref_mel_lens[bucket_i], - total_mel_lens[bucket_i], - final_text_list[bucket_i], - ) - ) + prompts_all.append(( + utts[bucket_i], + ref_rms_list[bucket_i], + padded_mel_batch(ref_mels[bucket_i]), + ref_mel_lens[bucket_i], + total_mel_lens[bucket_i], + final_text_list[bucket_i] + )) batch_accum[bucket_i] = 0 - ( - utts[bucket_i], - ref_rms_list[bucket_i], - ref_mels[bucket_i], - ref_mel_lens[bucket_i], - total_mel_lens[bucket_i], - final_text_list[bucket_i], - ) = [], [], [], [], [], [] + utts[bucket_i], ref_rms_list[bucket_i], ref_mels[bucket_i], ref_mel_lens[bucket_i], total_mel_lens[bucket_i], final_text_list[bucket_i] = [], [], [], [], [], [] # add residual for bucket_i, bucket_frames in enumerate(batch_accum): if bucket_frames > 0: - prompts_all.append( - ( - utts[bucket_i], - ref_rms_list[bucket_i], - padded_mel_batch(ref_mels[bucket_i]), - ref_mel_lens[bucket_i], - total_mel_lens[bucket_i], - final_text_list[bucket_i], - ) - ) + prompts_all.append(( + utts[bucket_i], + ref_rms_list[bucket_i], + padded_mel_batch(ref_mels[bucket_i]), + ref_mel_lens[bucket_i], + total_mel_lens[bucket_i], + final_text_list[bucket_i] + )) # not only leave easy work for last workers random.seed(666) random.shuffle(prompts_all) @@ -375,7 +349,6 @@ def get_inference_prompt( # get wav_res_ref_text of seed-tts test metalst # https://github.com/BytedanceSpeech/seed-tts-eval - def get_seed_tts_test(metalst, gen_wav_dir, gpus): f = open(metalst) lines = f.readlines() @@ -383,14 +356,14 @@ def get_seed_tts_test(metalst, gen_wav_dir, gpus): test_set_ = [] for line in tqdm(lines): - if len(line.strip().split("|")) == 5: - utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split("|") - elif len(line.strip().split("|")) == 4: - utt, prompt_text, prompt_wav, gt_text = line.strip().split("|") + if len(line.strip().split('|')) == 5: + utt, prompt_text, prompt_wav, gt_text, gt_wav = line.strip().split('|') + elif len(line.strip().split('|')) == 4: + utt, prompt_text, prompt_wav, gt_text = line.strip().split('|') - if not os.path.exists(os.path.join(gen_wav_dir, utt + ".wav")): + if not os.path.exists(os.path.join(gen_wav_dir, utt + '.wav')): continue - gen_wav = os.path.join(gen_wav_dir, utt + ".wav") + gen_wav = os.path.join(gen_wav_dir, utt + '.wav') if not os.path.isabs(prompt_wav): prompt_wav = os.path.join(os.path.dirname(metalst), prompt_wav) @@ -399,69 +372,63 @@ def get_seed_tts_test(metalst, gen_wav_dir, gpus): num_jobs = len(gpus) if num_jobs == 1: return [(gpus[0], test_set_)] - + wav_per_job = len(test_set_) // num_jobs + 1 test_set = [] for i in range(num_jobs): - test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job])) + test_set.append((gpus[i], test_set_[i*wav_per_job:(i+1)*wav_per_job])) return test_set # get librispeech test-clean cross sentence test - -def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth=False): +def get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path, eval_ground_truth = False): f = open(metalst) lines = f.readlines() f.close() test_set_ = [] for line in tqdm(lines): - ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split("\t") + ref_utt, ref_dur, ref_txt, gen_utt, gen_dur, gen_txt = line.strip().split('\t') if eval_ground_truth: - gen_spk_id, gen_chaptr_id, _ = gen_utt.split("-") - gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + ".flac") + gen_spk_id, gen_chaptr_id, _ = gen_utt.split('-') + gen_wav = os.path.join(librispeech_test_clean_path, gen_spk_id, gen_chaptr_id, gen_utt + '.flac') else: - if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + ".wav")): + if not os.path.exists(os.path.join(gen_wav_dir, gen_utt + '.wav')): raise FileNotFoundError(f"Generated wav not found: {gen_utt}") - gen_wav = os.path.join(gen_wav_dir, gen_utt + ".wav") + gen_wav = os.path.join(gen_wav_dir, gen_utt + '.wav') - ref_spk_id, ref_chaptr_id, _ = ref_utt.split("-") - ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + ".flac") + ref_spk_id, ref_chaptr_id, _ = ref_utt.split('-') + ref_wav = os.path.join(librispeech_test_clean_path, ref_spk_id, ref_chaptr_id, ref_utt + '.flac') test_set_.append((gen_wav, ref_wav, gen_txt)) num_jobs = len(gpus) if num_jobs == 1: return [(gpus[0], test_set_)] - + wav_per_job = len(test_set_) // num_jobs + 1 test_set = [] for i in range(num_jobs): - test_set.append((gpus[i], test_set_[i * wav_per_job : (i + 1) * wav_per_job])) + test_set.append((gpus[i], test_set_[i*wav_per_job:(i+1)*wav_per_job])) return test_set # load asr model - -def load_asr_model(lang, ckpt_dir=""): +def load_asr_model(lang, ckpt_dir = ""): if lang == "zh": - from funasr import AutoModel - model = AutoModel( - model=os.path.join(ckpt_dir, "paraformer-zh"), - # vad_model = os.path.join(ckpt_dir, "fsmn-vad"), + model = os.path.join(ckpt_dir, "paraformer-zh"), + # vad_model = os.path.join(ckpt_dir, "fsmn-vad"), # punc_model = os.path.join(ckpt_dir, "ct-punc"), - # spk_model = os.path.join(ckpt_dir, "cam++"), + # spk_model = os.path.join(ckpt_dir, "cam++"), disable_update=True, - ) # following seed-tts setting + ) # following seed-tts setting elif lang == "en": - from faster_whisper import WhisperModel - model_size = "large-v3" if ckpt_dir == "" else ckpt_dir model = WhisperModel(model_size, device="cuda", compute_type="float16") return model @@ -469,50 +436,41 @@ def load_asr_model(lang, ckpt_dir=""): # WER Evaluation, the way Seed-TTS does - def run_asr_wer(args): rank, lang, test_set, ckpt_dir = args if lang == "zh": - import zhconv - torch.cuda.set_device(rank) elif lang == "en": os.environ["CUDA_VISIBLE_DEVICES"] = str(rank) else: - raise NotImplementedError( - "lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now." - ) + raise NotImplementedError("lang support only 'zh' (funasr paraformer-zh), 'en' (faster-whisper-large-v3), for now.") - asr_model = load_asr_model(lang, ckpt_dir=ckpt_dir) - - from zhon.hanzi import punctuation + asr_model = load_asr_model(lang, ckpt_dir = ckpt_dir) punctuation_all = punctuation + string.punctuation wers = [] - from jiwer import compute_measures - for gen_wav, prompt_wav, truth in tqdm(test_set): if lang == "zh": res = asr_model.generate(input=gen_wav, batch_size_s=300, disable_pbar=True) hypo = res[0]["text"] - hypo = zhconv.convert(hypo, "zh-cn") + hypo = zhconv.convert(hypo, 'zh-cn') elif lang == "en": segments, _ = asr_model.transcribe(gen_wav, beam_size=5, language="en") - hypo = "" + hypo = '' for segment in segments: - hypo = hypo + " " + segment.text + hypo = hypo + ' ' + segment.text # raw_truth = truth # raw_hypo = hypo for x in punctuation_all: - truth = truth.replace(x, "") - hypo = hypo.replace(x, "") + truth = truth.replace(x, '') + hypo = hypo.replace(x, '') - truth = truth.replace(" ", " ") - hypo = hypo.replace(" ", " ") + truth = truth.replace(' ', ' ') + hypo = hypo.replace(' ', ' ') if lang == "zh": truth = " ".join([x for x in truth]) @@ -536,22 +494,22 @@ def run_asr_wer(args): # SIM Evaluation - def run_sim(args): rank, test_set, ckpt_dir = args device = f"cuda:{rank}" - model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type="wavlm_large", config_path=None) - state_dict = torch.load(ckpt_dir, weights_only=True, map_location=lambda storage, loc: storage) - model.load_state_dict(state_dict["model"], strict=False) + model = ECAPA_TDNN_SMALL(feat_dim=1024, feat_type='wavlm_large', config_path=None) + state_dict = torch.load(ckpt_dir, map_location=lambda storage, loc: storage) + model.load_state_dict(state_dict['model'], strict=False) - use_gpu = True if torch.cuda.is_available() else False + use_gpu=True if torch.cuda.is_available() else False if use_gpu: model = model.cuda(device) model.eval() sim_list = [] for wav1, wav2, truth in tqdm(test_set): + wav1, sr1 = torchaudio.load(wav1) wav2, sr2 = torchaudio.load(wav2) @@ -566,55 +524,22 @@ def run_sim(args): with torch.no_grad(): emb1 = model(wav1) emb2 = model(wav2) - + sim = F.cosine_similarity(emb1, emb2)[0].item() # print(f"VSim score between two audios: {sim:.4f} (-1.0, 1.0).") sim_list.append(sim) - + return sim_list # filter func for dirty data with many repetitions - -def repetition_found(text, length=2, tolerance=10): +def repetition_found(text, length = 2, tolerance = 10): pattern_count = defaultdict(int) for i in range(len(text) - length + 1): - pattern = text[i : i + length] + pattern = text[i:i + length] pattern_count[pattern] += 1 for pattern, count in pattern_count.items(): if count > tolerance: return True return False - - -# load model checkpoint for inference - - -def load_checkpoint(model, ckpt_path, device, use_ema=True): - if device == "cuda": - model = model.half() - - ckpt_type = ckpt_path.split(".")[-1] - if ckpt_type == "safetensors": - from safetensors.torch import load_file - - checkpoint = load_file(ckpt_path) - else: - checkpoint = torch.load(ckpt_path, weights_only=True) - - if use_ema: - if ckpt_type == "safetensors": - checkpoint = {"ema_model_state_dict": checkpoint} - checkpoint["model_state_dict"] = { - k.replace("ema_model.", ""): v - for k, v in checkpoint["ema_model_state_dict"].items() - if k not in ["initted", "step"] - } - model.load_state_dict(checkpoint["model_state_dict"]) - else: - if ckpt_type == "safetensors": - checkpoint = {"model_state_dict": checkpoint} - model.load_state_dict(checkpoint["model_state_dict"]) - - return model.to(device) diff --git a/model/utils_infer.py b/model/utils_infer.py deleted file mode 100644 index 75f0cd39e72fb52556ef988bd16bae8652fe4382..0000000000000000000000000000000000000000 --- a/model/utils_infer.py +++ /dev/null @@ -1,357 +0,0 @@ -# A unified script for inference process -# Make adjustments inside functions, and consider both gradio and cli scripts if need to change func output format - -import re -import tempfile - -import numpy as np -import torch -import torchaudio -import tqdm -from pydub import AudioSegment, silence -from transformers import pipeline -from vocos import Vocos - -from model import CFM -from model.utils import ( - load_checkpoint, - get_tokenizer, - convert_char_to_pinyin, -) - - -device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" - -vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") - - -# ----------------------------------------- - -target_sample_rate = 24000 -n_mel_channels = 100 -hop_length = 256 -target_rms = 0.1 -cross_fade_duration = 0.15 -ode_method = "euler" -nfe_step = 32 # 16, 32 -cfg_strength = 2.0 -sway_sampling_coef = -1.0 -speed = 1.0 -fix_duration = None - -# ----------------------------------------- - - -# chunk text into smaller pieces - - -def chunk_text(text, max_chars=135): - """ - Splits the input text into chunks, each with a maximum number of characters. - - Args: - text (str): The text to be split. - max_chars (int): The maximum number of characters per chunk. - - Returns: - List[str]: A list of text chunks. - """ - chunks = [] - current_chunk = "" - # Split the text into sentences based on punctuation followed by whitespace - sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text) - - for sentence in sentences: - if len(current_chunk.encode("utf-8")) + len(sentence.encode("utf-8")) <= max_chars: - current_chunk += sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence - else: - if current_chunk: - chunks.append(current_chunk.strip()) - current_chunk = sentence + " " if sentence and len(sentence[-1].encode("utf-8")) == 1 else sentence - - if current_chunk: - chunks.append(current_chunk.strip()) - - return chunks - - -# load vocoder -def load_vocoder(is_local=False, local_path="", device=device): - if is_local: - print(f"Load vocos from local path {local_path}") - vocos = Vocos.from_hparams(f"{local_path}/config.yaml") - state_dict = torch.load(f"{local_path}/pytorch_model.bin", map_location=device) - vocos.load_state_dict(state_dict) - vocos.eval() - else: - print("Download Vocos from huggingface charactr/vocos-mel-24khz") - vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") - return vocos - - -# load asr pipeline - -asr_pipe = None - - -def initialize_asr_pipeline(device=device): - global asr_pipe - asr_pipe = pipeline( - "automatic-speech-recognition", - model="openai/whisper-large-v3-turbo", - torch_dtype=torch.float16, - device=device, - ) - - -# load model for inference - - -def load_model(model_cls, model_cfg, ckpt_path, vocab_file="", ode_method=ode_method, use_ema=True, device=device): - if vocab_file == "": - vocab_file = "Emilia_ZH_EN" - tokenizer = "pinyin" - else: - tokenizer = "custom" - - print("\nvocab : ", vocab_file) - print("tokenizer : ", tokenizer) - print("model : ", ckpt_path, "\n") - - vocab_char_map, vocab_size = get_tokenizer(vocab_file, tokenizer) - model = CFM( - transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels), - mel_spec_kwargs=dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - ), - odeint_kwargs=dict( - method=ode_method, - ), - vocab_char_map=vocab_char_map, - ).to(device) - - model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema) - - return model - - -# preprocess reference audio and text - - -def preprocess_ref_audio_text(ref_audio_orig, ref_text, show_info=print, device=device): - show_info("Converting audio...") - with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: - aseg = AudioSegment.from_file(ref_audio_orig) - - non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000) - non_silent_wave = AudioSegment.silent(duration=0) - for non_silent_seg in non_silent_segs: - non_silent_wave += non_silent_seg - aseg = non_silent_wave - - audio_duration = len(aseg) - if audio_duration > 15000: - show_info("Audio is over 15s, clipping to only first 15s.") - aseg = aseg[:15000] - aseg.export(f.name, format="wav") - ref_audio = f.name - - if not ref_text.strip(): - global asr_pipe - if asr_pipe is None: - initialize_asr_pipeline(device=device) - show_info("No reference text provided, transcribing reference audio...") - ref_text = asr_pipe( - ref_audio, - chunk_length_s=30, - batch_size=128, - generate_kwargs={"task": "transcribe"}, - return_timestamps=False, - )["text"].strip() - show_info("Finished transcription") - else: - show_info("Using custom reference text...") - - # Add the functionality to ensure it ends with ". " - if not ref_text.endswith(". ") and not ref_text.endswith("。"): - if ref_text.endswith("."): - ref_text += " " - else: - ref_text += ". " - - return ref_audio, ref_text - - -# infer process: chunk text -> infer batches [i.e. infer_batch_process()] - - -def infer_process( - ref_audio, - ref_text, - gen_text, - model_obj, - show_info=print, - progress=tqdm, - target_rms=target_rms, - cross_fade_duration=cross_fade_duration, - nfe_step=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - speed=speed, - fix_duration=fix_duration, - device=device, -): - # Split the input text into batches - audio, sr = torchaudio.load(ref_audio) - max_chars = int(len(ref_text.encode("utf-8")) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr)) - gen_text_batches = chunk_text(gen_text, max_chars=max_chars) - for i, gen_text in enumerate(gen_text_batches): - print(f"gen_text {i}", gen_text) - - show_info(f"Generating audio in {len(gen_text_batches)} batches...") - return infer_batch_process( - (audio, sr), - ref_text, - gen_text_batches, - model_obj, - progress=progress, - target_rms=target_rms, - cross_fade_duration=cross_fade_duration, - nfe_step=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - speed=speed, - fix_duration=fix_duration, - device=device, - ) - - -# infer batches - - -def infer_batch_process( - ref_audio, - ref_text, - gen_text_batches, - model_obj, - progress=tqdm, - target_rms=0.1, - cross_fade_duration=0.15, - nfe_step=32, - cfg_strength=2.0, - sway_sampling_coef=-1, - speed=1, - fix_duration=None, - device=None, -): - audio, sr = ref_audio - if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) - - rms = torch.sqrt(torch.mean(torch.square(audio))) - if rms < target_rms: - audio = audio * target_rms / rms - if sr != target_sample_rate: - resampler = torchaudio.transforms.Resample(sr, target_sample_rate) - audio = resampler(audio) - audio = audio.to(device) - - generated_waves = [] - spectrograms = [] - - if len(ref_text[-1].encode("utf-8")) == 1: - ref_text = ref_text + " " - for i, gen_text in enumerate(progress.tqdm(gen_text_batches)): - # Prepare the text - text_list = [ref_text + gen_text] - final_text_list = convert_char_to_pinyin(text_list) - - ref_audio_len = audio.shape[-1] // hop_length - if fix_duration is not None: - duration = int(fix_duration * target_sample_rate / hop_length) - else: - # Calculate duration - ref_text_len = len(ref_text.encode("utf-8")) - gen_text_len = len(gen_text.encode("utf-8")) - duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) - - # inference - with torch.inference_mode(): - generated, _ = model_obj.sample( - cond=audio, - text=final_text_list, - duration=duration, - steps=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - ) - - generated = generated.to(torch.float32) - generated = generated[:, ref_audio_len:, :] - generated_mel_spec = generated.permute(0, 2, 1) - generated_wave = vocos.decode(generated_mel_spec.cpu()) - if rms < target_rms: - generated_wave = generated_wave * rms / target_rms - - # wav -> numpy - generated_wave = generated_wave.squeeze().cpu().numpy() - - generated_waves.append(generated_wave) - spectrograms.append(generated_mel_spec[0].cpu().numpy()) - - # Combine all generated waves with cross-fading - if cross_fade_duration <= 0: - # Simply concatenate - final_wave = np.concatenate(generated_waves) - else: - final_wave = generated_waves[0] - for i in range(1, len(generated_waves)): - prev_wave = final_wave - next_wave = generated_waves[i] - - # Calculate cross-fade samples, ensuring it does not exceed wave lengths - cross_fade_samples = int(cross_fade_duration * target_sample_rate) - cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave)) - - if cross_fade_samples <= 0: - # No overlap possible, concatenate - final_wave = np.concatenate([prev_wave, next_wave]) - continue - - # Overlapping parts - prev_overlap = prev_wave[-cross_fade_samples:] - next_overlap = next_wave[:cross_fade_samples] - - # Fade out and fade in - fade_out = np.linspace(1, 0, cross_fade_samples) - fade_in = np.linspace(0, 1, cross_fade_samples) - - # Cross-faded overlap - cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in - - # Combine - new_wave = np.concatenate( - [prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:]] - ) - - final_wave = new_wave - - # Create a combined spectrogram - combined_spectrogram = np.concatenate(spectrograms, axis=1) - - return final_wave, target_sample_rate, combined_spectrogram - - -# remove silence from generated wav - - -def remove_silence_for_generated_wav(filename): - aseg = AudioSegment.from_file(filename) - non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500) - non_silent_wave = AudioSegment.silent(duration=0) - for non_silent_seg in non_silent_segs: - non_silent_wave += non_silent_seg - aseg = non_silent_wave - aseg.export(filename, format="wav") diff --git a/packages.txt b/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..a9f1eea092d5e971b5475b82ee835cec7f196bad --- /dev/null +++ b/packages.txt @@ -0,0 +1 @@ +ffmpeg \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index f87fb0f1450276f1fe95ea228233356699d1f52b..f606bec637312baf3dedec028c2179d167516e93 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,22 +1,27 @@ accelerate>=0.33.0 -bitsandbytes>0.37.0 -cached_path -click datasets +einops>=0.8.0 +einx>=0.3.0 ema_pytorch>=0.5.2 -gradio +faster_whisper +funasr jieba +jiwer librosa matplotlib -numpy<=1.26.4 -pydub pypinyin -safetensors -soundfile -tomli +torch>=2.0 +torchaudio>=2.3.0 torchdiffeq tqdm>=4.65.0 transformers vocos wandb x_transformers>=1.31.14 +zhconv +zhon +cached_path +pydub +txtsplit +detoxify +soundfile \ No newline at end of file diff --git a/requirements_eval.txt b/requirements_eval.txt deleted file mode 100644 index 1d34c5fc01cbc67093f033af169fc4d5bbf8eff1..0000000000000000000000000000000000000000 --- a/requirements_eval.txt +++ /dev/null @@ -1,5 +0,0 @@ -faster_whisper -funasr -jiwer -zhconv -zhon diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index 4c3887643968cf109c507894b69cc41be3a4c430..0000000000000000000000000000000000000000 --- a/ruff.toml +++ /dev/null @@ -1,10 +0,0 @@ -line-length = 120 -target-version = "py310" - -[lint] -# Only ignore variables with names starting with "_". -dummy-variable-rgx = "^_.*$" - -[lint.isort] -force-single-line = true -lines-after-imports = 2 diff --git a/samples/country.flac b/samples/country.flac deleted file mode 100644 index ad6985b710ec754b0d40e79be6cc3563b32abf15..0000000000000000000000000000000000000000 Binary files a/samples/country.flac and /dev/null differ diff --git a/samples/main.flac b/samples/main.flac deleted file mode 100644 index 671aded6b10fc6f10d27d6b0764a109f0e7fadca..0000000000000000000000000000000000000000 Binary files a/samples/main.flac and /dev/null differ diff --git a/samples/story.toml b/samples/story.toml deleted file mode 100644 index 93022c41b6036eb7d31f87883102f0d2b13b0f86..0000000000000000000000000000000000000000 --- a/samples/story.toml +++ /dev/null @@ -1,19 +0,0 @@ -# F5-TTS | E2-TTS -model = "F5-TTS" -ref_audio = "samples/main.flac" -# If an empty "", transcribes the reference audio automatically. -ref_text = "" -gen_text = "" -# File with text to generate. Ignores the text above. -gen_file = "samples/story.txt" -remove_silence = true -output_dir = "samples" - -[voices.town] -ref_audio = "samples/town.flac" -ref_text = "" - -[voices.country] -ref_audio = "samples/country.flac" -ref_text = "" - diff --git a/samples/story.txt b/samples/story.txt deleted file mode 100644 index bda1f2ba1b967d2e63fdaac3b987fcb54574d76f..0000000000000000000000000000000000000000 --- a/samples/story.txt +++ /dev/null @@ -1 +0,0 @@ -A Town Mouse and a Country Mouse were acquaintances, and the Country Mouse one day invited his friend to come and see him at his home in the fields. The Town Mouse came, and they sat down to a dinner of barleycorns and roots, the latter of which had a distinctly earthy flavour. The fare was not much to the taste of the guest, and presently he broke out with [town] “My poor dear friend, you live here no better than the ants. Now, you should just see how I fare! My larder is a regular horn of plenty. You must come and stay with me, and I promise you you shall live on the fat of the land.” [main] So when he returned to town he took the Country Mouse with him, and showed him into a larder containing flour and oatmeal and figs and honey and dates. The Country Mouse had never seen anything like it, and sat down to enjoy the luxuries his friend provided: but before they had well begun, the door of the larder opened and someone came in. The two Mice scampered off and hid themselves in a narrow and exceedingly uncomfortable hole. Presently, when all was quiet, they ventured out again; but someone else came in, and off they scuttled again. This was too much for the visitor. [country] “Goodbye,” [main] said he, [country] “I’m off. You live in the lap of luxury, I can see, but you are surrounded by dangers; whereas at home I can enjoy my simple dinner of roots and corn in peace.” \ No newline at end of file diff --git a/samples/town.flac b/samples/town.flac deleted file mode 100644 index 39c258582ccbedc48b82c988e7808c114a682292..0000000000000000000000000000000000000000 Binary files a/samples/town.flac and /dev/null differ diff --git a/scripts/count_max_epoch.py b/scripts/count_max_epoch.py index 7cd7332dfdc66b1c20bed369aaa6c6bec8c8e0cc..2a4f3e7cf7a4042fa98f167907af9462c01f5398 100644 --- a/scripts/count_max_epoch.py +++ b/scripts/count_max_epoch.py @@ -1,7 +1,6 @@ -"""ADAPTIVE BATCH SIZE""" - -print("Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in") -print(" -> least padding, gather wavs with accumulated frames in a batch\n") +'''ADAPTIVE BATCH SIZE''' +print('Adaptive batch size: using grouping batch sampler, frames_per_gpu fixed fed in') +print(' -> least padding, gather wavs with accumulated frames in a batch\n') # data total_hours = 95282 diff --git a/scripts/count_params_gflops.py b/scripts/count_params_gflops.py index 7fc493a8d5eb3827fa6af3fe55ebcaae4ee626c7..737c6dcefa65d3392cbf5361179119a1612f8ba4 100644 --- a/scripts/count_params_gflops.py +++ b/scripts/count_params_gflops.py @@ -1,15 +1,13 @@ -import sys -import os - +import sys, os sys.path.append(os.getcwd()) -from model import M2_TTS, DiT +from model import M2_TTS, UNetT, DiT, MMDiT import torch import thop -""" ~155M """ +''' ~155M ''' # transformer = UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4) # transformer = UNetT(dim = 768, depth = 20, heads = 12, ff_mult = 4, text_dim = 512, conv_layers = 4) # transformer = DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2) @@ -17,11 +15,11 @@ import thop # transformer = DiT(dim = 768, depth = 18, heads = 12, ff_mult = 2, text_dim = 512, conv_layers = 4, long_skip_connection = True) # transformer = MMDiT(dim = 512, depth = 16, heads = 16, ff_mult = 2) -""" ~335M """ +''' ~335M ''' # FLOPs: 622.1 G, Params: 333.2 M # transformer = UNetT(dim = 1024, depth = 24, heads = 16, ff_mult = 4) # FLOPs: 363.4 G, Params: 335.8 M -transformer = DiT(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) +transformer = DiT(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4) model = M2_TTS(transformer=transformer) @@ -32,8 +30,6 @@ duration = 20 frame_length = int(duration * target_sample_rate / hop_length) text_length = 150 -flops, params = thop.profile( - model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long)) -) +flops, params = thop.profile(model, inputs=(torch.randn(1, frame_length, n_mel_channels), torch.zeros(1, text_length, dtype=torch.long))) print(f"FLOPs: {flops / 1e9} G") print(f"Params: {params / 1e6} M") diff --git a/scripts/eval_infer_batch.sh b/scripts/eval_infer_batch.sh deleted file mode 100644 index 45b07174700604a292a397e975c6787fe8f67eaa..0000000000000000000000000000000000000000 --- a/scripts/eval_infer_batch.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -# e.g. F5-TTS, 16 NFE -accelerate launch scripts/eval_infer_batch.py -n "F5TTS_Base" -t "seedtts_test_zh" -nfe 16 -accelerate launch scripts/eval_infer_batch.py -n "F5TTS_Base" -t "seedtts_test_en" -nfe 16 -accelerate launch scripts/eval_infer_batch.py -n "F5TTS_Base" -t "ls_pc_test_clean" -nfe 16 - -# e.g. Vanilla E2 TTS, 32 NFE -accelerate launch scripts/eval_infer_batch.py -n "E2TTS_Base" -t "seedtts_test_zh" -o "midpoint" -ss 0 -accelerate launch scripts/eval_infer_batch.py -n "E2TTS_Base" -t "seedtts_test_en" -o "midpoint" -ss 0 -accelerate launch scripts/eval_infer_batch.py -n "E2TTS_Base" -t "ls_pc_test_clean" -o "midpoint" -ss 0 - -# etc. diff --git a/scripts/eval_librispeech_test_clean.py b/scripts/eval_librispeech_test_clean.py index a1ce8b7b36f79392dd2ab91c8909766d70fadf7f..2f5820f7a87913002196d3539b115d3ed3b61f5c 100644 --- a/scripts/eval_librispeech_test_clean.py +++ b/scripts/eval_librispeech_test_clean.py @@ -1,8 +1,6 @@ # Evaluate with Librispeech test-clean, ~3s prompt to generate 4-10s audio (the way of valle/voicebox evaluation) -import sys -import os - +import sys, os sys.path.append(os.getcwd()) import multiprocessing as mp @@ -21,7 +19,7 @@ metalst = "data/librispeech_pc_test_clean_cross_sentence.lst" librispeech_test_clean_path = "/LibriSpeech/test-clean" # test-clean path gen_wav_dir = "PATH_TO_GENERATED" # generated wavs -gpus = [0, 1, 2, 3, 4, 5, 6, 7] +gpus = [0,1,2,3,4,5,6,7] test_set = get_librispeech_test(metalst, gen_wav_dir, gpus, librispeech_test_clean_path) ## In LibriSpeech, some speakers utilized varying voice characteristics for different characters in the book, @@ -48,7 +46,7 @@ if eval_task == "wer": for wers_ in results: wers.extend(wers_) - wer = round(np.mean(wers) * 100, 3) + wer = round(np.mean(wers)*100, 3) print(f"\nTotal {len(wers)} samples") print(f"WER : {wer}%") @@ -64,6 +62,6 @@ if eval_task == "sim": for sim_ in results: sim_list.extend(sim_) - sim = round(sum(sim_list) / len(sim_list), 3) + sim = round(sum(sim_list)/len(sim_list), 3) print(f"\nTotal {len(sim_list)} samples") print(f"SIM : {sim}") diff --git a/scripts/eval_seedtts_testset.py b/scripts/eval_seedtts_testset.py index e70534e11165cc56bb3663dd5205aa792af61934..c50bd501dd7fdf820a1da88aaefc71515d8b0054 100644 --- a/scripts/eval_seedtts_testset.py +++ b/scripts/eval_seedtts_testset.py @@ -1,8 +1,6 @@ # Evaluate with Seed-TTS testset -import sys -import os - +import sys, os sys.path.append(os.getcwd()) import multiprocessing as mp @@ -16,21 +14,21 @@ from model.utils import ( eval_task = "wer" # sim | wer -lang = "zh" # zh | en +lang = "zh" # zh | en metalst = f"data/seedtts_testset/{lang}/meta.lst" # seed-tts testset # gen_wav_dir = f"data/seedtts_testset/{lang}/wavs" # ground truth wavs -gen_wav_dir = "PATH_TO_GENERATED" # generated wavs +gen_wav_dir = f"PATH_TO_GENERATED" # generated wavs # NOTE. paraformer-zh result will be slightly different according to the number of gpus, cuz batchsize is different -# zh 1.254 seems a result of 4 workers wer_seed_tts -gpus = [0, 1, 2, 3, 4, 5, 6, 7] +# zh 1.254 seems a result of 4 workers wer_seed_tts +gpus = [0,1,2,3,4,5,6,7] test_set = get_seed_tts_test(metalst, gen_wav_dir, gpus) local = False if local: # use local custom checkpoint dir if lang == "zh": - asr_ckpt_dir = "../checkpoints/funasr" # paraformer-zh dir under funasr + asr_ckpt_dir = "../checkpoints/funasr" # paraformer-zh dir under funasr elif lang == "en": asr_ckpt_dir = "../checkpoints/Systran/faster-whisper-large-v3" else: @@ -50,7 +48,7 @@ if eval_task == "wer": for wers_ in results: wers.extend(wers_) - wer = round(np.mean(wers) * 100, 3) + wer = round(np.mean(wers)*100, 3) print(f"\nTotal {len(wers)} samples") print(f"WER : {wer}%") @@ -66,6 +64,6 @@ if eval_task == "sim": for sim_ in results: sim_list.extend(sim_) - sim = round(sum(sim_list) / len(sim_list), 3) + sim = round(sum(sim_list)/len(sim_list), 3) print(f"\nTotal {len(sim_list)} samples") print(f"SIM : {sim}") diff --git a/scripts/prepare_csv_wavs.py b/scripts/prepare_csv_wavs.py deleted file mode 100644 index 6e56774dcb9cb33f27964c48fbb877eeea08f01a..0000000000000000000000000000000000000000 --- a/scripts/prepare_csv_wavs.py +++ /dev/null @@ -1,138 +0,0 @@ -import sys -import os - -sys.path.append(os.getcwd()) - -from pathlib import Path -import json -import shutil -import argparse - -import csv -import torchaudio -from tqdm import tqdm -from datasets.arrow_writer import ArrowWriter - -from model.utils import ( - convert_char_to_pinyin, -) - -PRETRAINED_VOCAB_PATH = Path(__file__).parent.parent / "data/Emilia_ZH_EN_pinyin/vocab.txt" - - -def is_csv_wavs_format(input_dataset_dir): - fpath = Path(input_dataset_dir) - metadata = fpath / "metadata.csv" - wavs = fpath / "wavs" - return metadata.exists() and metadata.is_file() and wavs.exists() and wavs.is_dir() - - -def prepare_csv_wavs_dir(input_dir): - assert is_csv_wavs_format(input_dir), f"not csv_wavs format: {input_dir}" - input_dir = Path(input_dir) - metadata_path = input_dir / "metadata.csv" - audio_path_text_pairs = read_audio_text_pairs(metadata_path.as_posix()) - - sub_result, durations = [], [] - vocab_set = set() - polyphone = True - for audio_path, text in audio_path_text_pairs: - if not Path(audio_path).exists(): - print(f"audio {audio_path} not found, skipping") - continue - audio_duration = get_audio_duration(audio_path) - # assume tokenizer = "pinyin" ("pinyin" | "char") - text = convert_char_to_pinyin([text], polyphone=polyphone)[0] - sub_result.append({"audio_path": audio_path, "text": text, "duration": audio_duration}) - durations.append(audio_duration) - vocab_set.update(list(text)) - - return sub_result, durations, vocab_set - - -def get_audio_duration(audio_path): - audio, sample_rate = torchaudio.load(audio_path) - num_channels = audio.shape[0] - return audio.shape[1] / (sample_rate * num_channels) - - -def read_audio_text_pairs(csv_file_path): - audio_text_pairs = [] - - parent = Path(csv_file_path).parent - with open(csv_file_path, mode="r", newline="", encoding="utf-8") as csvfile: - reader = csv.reader(csvfile, delimiter="|") - next(reader) # Skip the header row - for row in reader: - if len(row) >= 2: - audio_file = row[0].strip() # First column: audio file path - text = row[1].strip() # Second column: text - audio_file_path = parent / audio_file - audio_text_pairs.append((audio_file_path.as_posix(), text)) - - return audio_text_pairs - - -def save_prepped_dataset(out_dir, result, duration_list, text_vocab_set, is_finetune): - out_dir = Path(out_dir) - # save preprocessed dataset to disk - out_dir.mkdir(exist_ok=True, parents=True) - print(f"\nSaving to {out_dir} ...") - - # dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list}) # oom - # dataset.save_to_disk(f"data/{dataset_name}/raw", max_shard_size="2GB") - raw_arrow_path = out_dir / "raw.arrow" - with ArrowWriter(path=raw_arrow_path.as_posix(), writer_batch_size=1) as writer: - for line in tqdm(result, desc="Writing to raw.arrow ..."): - writer.write(line) - - # dup a json separately saving duration in case for DynamicBatchSampler ease - dur_json_path = out_dir / "duration.json" - with open(dur_json_path.as_posix(), "w", encoding="utf-8") as f: - json.dump({"duration": duration_list}, f, ensure_ascii=False) - - # vocab map, i.e. tokenizer - # add alphabets and symbols (optional, if plan to ft on de/fr etc.) - # if tokenizer == "pinyin": - # text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)]) - voca_out_path = out_dir / "vocab.txt" - with open(voca_out_path.as_posix(), "w") as f: - for vocab in sorted(text_vocab_set): - f.write(vocab + "\n") - - if is_finetune: - file_vocab_finetune = PRETRAINED_VOCAB_PATH.as_posix() - shutil.copy2(file_vocab_finetune, voca_out_path) - else: - with open(voca_out_path, "w") as f: - for vocab in sorted(text_vocab_set): - f.write(vocab + "\n") - - dataset_name = out_dir.stem - print(f"\nFor {dataset_name}, sample count: {len(result)}") - print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}") - print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours") - - -def prepare_and_save_set(inp_dir, out_dir, is_finetune: bool = True): - if is_finetune: - assert PRETRAINED_VOCAB_PATH.exists(), f"pretrained vocab.txt not found: {PRETRAINED_VOCAB_PATH}" - sub_result, durations, vocab_set = prepare_csv_wavs_dir(inp_dir) - save_prepped_dataset(out_dir, sub_result, durations, vocab_set, is_finetune) - - -def cli(): - # finetune: python scripts/prepare_csv_wavs.py /path/to/input_dir /path/to/output_dir_pinyin - # pretrain: python scripts/prepare_csv_wavs.py /path/to/output_dir_pinyin --pretrain - parser = argparse.ArgumentParser(description="Prepare and save dataset.") - parser.add_argument("inp_dir", type=str, help="Input directory containing the data.") - parser.add_argument("out_dir", type=str, help="Output directory to save the prepared data.") - parser.add_argument("--pretrain", action="store_true", help="Enable for new pretrain, otherwise is a fine-tune") - - args = parser.parse_args() - - prepare_and_save_set(args.inp_dir, args.out_dir, is_finetune=not args.pretrain) - - -if __name__ == "__main__": - cli() diff --git a/scripts/prepare_emilia.py b/scripts/prepare_emilia.py index 6461f30ac4904904e397fe57b8cde495976de554..f268e724382c02a86a3233f8ad62ed4acf391793 100644 --- a/scripts/prepare_emilia.py +++ b/scripts/prepare_emilia.py @@ -4,9 +4,7 @@ # generate audio text map for Emilia ZH & EN # evaluate for vocab size -import sys -import os - +import sys, os sys.path.append(os.getcwd()) from pathlib import Path @@ -14,6 +12,7 @@ import json from tqdm import tqdm from concurrent.futures import ProcessPoolExecutor +from datasets import Dataset from datasets.arrow_writer import ArrowWriter from model.utils import ( @@ -22,89 +21,13 @@ from model.utils import ( ) -out_zh = { - "ZH_B00041_S06226", - "ZH_B00042_S09204", - "ZH_B00065_S09430", - "ZH_B00065_S09431", - "ZH_B00066_S09327", - "ZH_B00066_S09328", -} +out_zh = {"ZH_B00041_S06226", "ZH_B00042_S09204", "ZH_B00065_S09430", "ZH_B00065_S09431", "ZH_B00066_S09327", "ZH_B00066_S09328"} zh_filters = ["い", "て"] # seems synthesized audios, or heavily code-switched out_en = { - "EN_B00013_S00913", - "EN_B00042_S00120", - "EN_B00055_S04111", - "EN_B00061_S00693", - "EN_B00061_S01494", - "EN_B00061_S03375", - "EN_B00059_S00092", - "EN_B00111_S04300", - "EN_B00100_S03759", - "EN_B00087_S03811", - "EN_B00059_S00950", - "EN_B00089_S00946", - "EN_B00078_S05127", - "EN_B00070_S04089", - "EN_B00074_S09659", - "EN_B00061_S06983", - "EN_B00061_S07060", - "EN_B00059_S08397", - "EN_B00082_S06192", - "EN_B00091_S01238", - "EN_B00089_S07349", - "EN_B00070_S04343", - "EN_B00061_S02400", - "EN_B00076_S01262", - "EN_B00068_S06467", - "EN_B00076_S02943", - "EN_B00064_S05954", - "EN_B00061_S05386", - "EN_B00066_S06544", - "EN_B00076_S06944", - "EN_B00072_S08620", - "EN_B00076_S07135", - "EN_B00076_S09127", - "EN_B00065_S00497", - "EN_B00059_S06227", - "EN_B00063_S02859", - "EN_B00075_S01547", - "EN_B00061_S08286", - "EN_B00079_S02901", - "EN_B00092_S03643", - "EN_B00096_S08653", - "EN_B00063_S04297", - "EN_B00063_S04614", - "EN_B00079_S04698", - "EN_B00104_S01666", - "EN_B00061_S09504", - "EN_B00061_S09694", - "EN_B00065_S05444", - "EN_B00063_S06860", - "EN_B00065_S05725", - "EN_B00069_S07628", - "EN_B00083_S03875", - "EN_B00071_S07665", - "EN_B00071_S07665", - "EN_B00062_S04187", - "EN_B00065_S09873", - "EN_B00065_S09922", - "EN_B00084_S02463", - "EN_B00067_S05066", - "EN_B00106_S08060", - "EN_B00073_S06399", - "EN_B00073_S09236", - "EN_B00087_S00432", - "EN_B00085_S05618", - "EN_B00064_S01262", - "EN_B00072_S01739", - "EN_B00059_S03913", - "EN_B00069_S04036", - "EN_B00067_S05623", - "EN_B00060_S05389", - "EN_B00060_S07290", - "EN_B00062_S08995", + "EN_B00013_S00913", "EN_B00042_S00120", "EN_B00055_S04111", "EN_B00061_S00693", "EN_B00061_S01494", "EN_B00061_S03375", + + "EN_B00059_S00092", "EN_B00111_S04300", "EN_B00100_S03759", "EN_B00087_S03811", "EN_B00059_S00950", "EN_B00089_S00946", "EN_B00078_S05127", "EN_B00070_S04089", "EN_B00074_S09659", "EN_B00061_S06983", "EN_B00061_S07060", "EN_B00059_S08397", "EN_B00082_S06192", "EN_B00091_S01238", "EN_B00089_S07349", "EN_B00070_S04343", "EN_B00061_S02400", "EN_B00076_S01262", "EN_B00068_S06467", "EN_B00076_S02943", "EN_B00064_S05954", "EN_B00061_S05386", "EN_B00066_S06544", "EN_B00076_S06944", "EN_B00072_S08620", "EN_B00076_S07135", "EN_B00076_S09127", "EN_B00065_S00497", "EN_B00059_S06227", "EN_B00063_S02859", "EN_B00075_S01547", "EN_B00061_S08286", "EN_B00079_S02901", "EN_B00092_S03643", "EN_B00096_S08653", "EN_B00063_S04297", "EN_B00063_S04614", "EN_B00079_S04698", "EN_B00104_S01666", "EN_B00061_S09504", "EN_B00061_S09694", "EN_B00065_S05444", "EN_B00063_S06860", "EN_B00065_S05725", "EN_B00069_S07628", "EN_B00083_S03875", "EN_B00071_S07665", "EN_B00071_S07665", "EN_B00062_S04187", "EN_B00065_S09873", "EN_B00065_S09922", "EN_B00084_S02463", "EN_B00067_S05066", "EN_B00106_S08060", "EN_B00073_S06399", "EN_B00073_S09236", "EN_B00087_S00432", "EN_B00085_S05618", "EN_B00064_S01262", "EN_B00072_S01739", "EN_B00059_S03913", "EN_B00069_S04036", "EN_B00067_S05623", "EN_B00060_S05389", "EN_B00060_S07290", "EN_B00062_S08995", } en_filters = ["ا", "い", "て"] @@ -120,24 +43,18 @@ def deal_with_audio_dir(audio_dir): for line in tqdm(lines, desc=f"{audio_jsonl.stem}"): obj = json.loads(line) text = obj["text"] - if obj["language"] == "zh": + if obj['language'] == "zh": if obj["wav"].split("/")[1] in out_zh or any(f in text for f in zh_filters) or repetition_found(text): bad_case_zh += 1 continue else: - text = text.translate( - str.maketrans({",": "，", "!": "！", "?": "？"}) - ) # not "。" cuz much code-switched - if obj["language"] == "en": - if ( - obj["wav"].split("/")[1] in out_en - or any(f in text for f in en_filters) - or repetition_found(text, length=4) - ): + text = text.translate(str.maketrans({',': '，', '!': '！', '?': '？'})) # not "。" cuz much code-switched + if obj['language'] == "en": + if obj["wav"].split("/")[1] in out_en or any(f in text for f in en_filters) or repetition_found(text, length=4): bad_case_en += 1 continue if tokenizer == "pinyin": - text = convert_char_to_pinyin([text], polyphone=polyphone)[0] + text = convert_char_to_pinyin([text], polyphone = polyphone)[0] duration = obj["duration"] sub_result.append({"audio_path": str(audio_dir.parent / obj["wav"]), "text": text, "duration": duration}) durations.append(duration) @@ -179,11 +96,11 @@ def main(): # dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list}) # oom # dataset.save_to_disk(f"data/{dataset_name}/raw", max_shard_size="2GB") with ArrowWriter(path=f"data/{dataset_name}/raw.arrow") as writer: - for line in tqdm(result, desc="Writing to raw.arrow ..."): + for line in tqdm(result, desc=f"Writing to raw.arrow ..."): writer.write(line) # dup a json separately saving duration in case for DynamicBatchSampler ease - with open(f"data/{dataset_name}/duration.json", "w", encoding="utf-8") as f: + with open(f"data/{dataset_name}/duration.json", 'w', encoding='utf-8') as f: json.dump({"duration": duration_list}, f, ensure_ascii=False) # vocab map, i.e. tokenizer @@ -197,13 +114,12 @@ def main(): print(f"\nFor {dataset_name}, sample count: {len(result)}") print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}") print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours") - if "ZH" in langs: - print(f"Bad zh transcription case: {total_bad_case_zh}") - if "EN" in langs: - print(f"Bad en transcription case: {total_bad_case_en}\n") + if "ZH" in langs: print(f"Bad zh transcription case: {total_bad_case_zh}") + if "EN" in langs: print(f"Bad en transcription case: {total_bad_case_en}\n") if __name__ == "__main__": + max_workers = 32 tokenizer = "pinyin" # "pinyin" | "char" diff --git a/scripts/prepare_wenetspeech4tts.py b/scripts/prepare_wenetspeech4tts.py index 2763fda980caa11eca2d0e7c8f7d11ee6d4a1cb3..0403ad7f08313ccc25b8903b8f8578c8cddc70cb 100644 --- a/scripts/prepare_wenetspeech4tts.py +++ b/scripts/prepare_wenetspeech4tts.py @@ -1,9 +1,7 @@ # generate audio text map for WenetSpeech4TTS # evaluate for vocab size -import sys -import os - +import sys, os sys.path.append(os.getcwd()) import json @@ -25,7 +23,7 @@ def deal_with_sub_path_files(dataset_path, sub_path): audio_paths, texts, durations = [], [], [] for text_file in tqdm(text_files): - with open(os.path.join(text_dir, text_file), "r", encoding="utf-8") as file: + with open(os.path.join(text_dir, text_file), 'r', encoding='utf-8') as file: first_line = file.readline().split("\t") audio_nm = first_line[0] audio_path = os.path.join(audio_dir, audio_nm + ".wav") @@ -34,7 +32,7 @@ def deal_with_sub_path_files(dataset_path, sub_path): audio_paths.append(audio_path) if tokenizer == "pinyin": - texts.extend(convert_char_to_pinyin([text], polyphone=polyphone)) + texts.extend(convert_char_to_pinyin([text], polyphone = polyphone)) elif tokenizer == "char": texts.append(text) @@ -48,7 +46,7 @@ def main(): assert tokenizer in ["pinyin", "char"] audio_path_list, text_list, duration_list = [], [], [] - + executor = ProcessPoolExecutor(max_workers=max_workers) futures = [] for dataset_path in dataset_paths: @@ -70,10 +68,8 @@ def main(): dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list}) dataset.save_to_disk(f"data/{dataset_name}_{tokenizer}/raw", max_shard_size="2GB") # arrow format - with open(f"data/{dataset_name}_{tokenizer}/duration.json", "w", encoding="utf-8") as f: - json.dump( - {"duration": duration_list}, f, ensure_ascii=False - ) # dup a json separately saving duration in case for DynamicBatchSampler ease + with open(f"data/{dataset_name}_{tokenizer}/duration.json", 'w', encoding='utf-8') as f: + json.dump({"duration": duration_list}, f, ensure_ascii=False) # dup a json separately saving duration in case for DynamicBatchSampler ease print("\nEvaluating vocab size (all characters and symbols / all phonemes) ...") text_vocab_set = set() @@ -89,21 +85,22 @@ def main(): f.write(vocab + "\n") print(f"\nFor {dataset_name}, sample count: {len(text_list)}") print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}\n") - + if __name__ == "__main__": + max_workers = 32 tokenizer = "pinyin" # "pinyin" | "char" polyphone = True dataset_choice = 1 # 1: Premium, 2: Standard, 3: Basic - dataset_name = ["WenetSpeech4TTS_Premium", "WenetSpeech4TTS_Standard", "WenetSpeech4TTS_Basic"][dataset_choice - 1] + dataset_name = ["WenetSpeech4TTS_Premium", "WenetSpeech4TTS_Standard", "WenetSpeech4TTS_Basic"][dataset_choice-1] dataset_paths = [ "/WenetSpeech4TTS/Basic", "/WenetSpeech4TTS/Standard", "/WenetSpeech4TTS/Premium", - ][-dataset_choice:] + ][-dataset_choice:] print(f"\nChoose Dataset: {dataset_name}\n") main() @@ -112,8 +109,8 @@ if __name__ == "__main__": # WenetSpeech4TTS Basic Standard Premium # samples count 3932473 1941220 407494 # pinyin vocab size 1349 1348 1344 (no polyphone) - # - - 1459 (polyphone) + # - - 1459 (polyphone) # char vocab size 5264 5219 5042 - + # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme) # please be careful if using pretrained model, make sure the vocab.txt is same diff --git a/speech_edit.py b/speech_edit.py deleted file mode 100644 index 82f7cc9b217386e367c265036f361e89f79c501c..0000000000000000000000000000000000000000 --- a/speech_edit.py +++ /dev/null @@ -1,189 +0,0 @@ -import os - -import torch -import torch.nn.functional as F -import torchaudio -from vocos import Vocos - -from model import CFM, UNetT, DiT -from model.utils import ( - load_checkpoint, - get_tokenizer, - convert_char_to_pinyin, - save_spectrogram, -) - -device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" - - -# --------------------- Dataset Settings -------------------- # - -target_sample_rate = 24000 -n_mel_channels = 100 -hop_length = 256 -target_rms = 0.1 - -tokenizer = "pinyin" -dataset_name = "Emilia_ZH_EN" - - -# ---------------------- infer setting ---------------------- # - -seed = None # int | None - -exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base -ckpt_step = 1200000 - -nfe_step = 32 # 16, 32 -cfg_strength = 2.0 -ode_method = "euler" # euler | midpoint -sway_sampling_coef = -1.0 -speed = 1.0 - -if exp_name == "F5TTS_Base": - model_cls = DiT - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) - -elif exp_name == "E2TTS_Base": - model_cls = UNetT - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - -ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.safetensors" -output_dir = "tests" - -# [leverage https://github.com/MahmoudAshraf97/ctc-forced-aligner to get char level alignment] -# pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git -# [write the origin_text into a file, e.g. tests/test_edit.txt] -# ctc-forced-aligner --audio_path "tests/ref_audio/test_en_1_ref_short.wav" --text_path "tests/test_edit.txt" --language "zho" --romanize --split_size "char" -# [result will be saved at same path of audio file] -# [--language "zho" for Chinese, "eng" for English] -# [if local ckpt, set --alignment_model "../checkpoints/mms-300m-1130-forced-aligner"] - -audio_to_edit = "tests/ref_audio/test_en_1_ref_short.wav" -origin_text = "Some call me nature, others call me mother nature." -target_text = "Some call me optimist, others call me realist." -parts_to_edit = [ - [1.42, 2.44], - [4.04, 4.9], -] # stard_ends of "nature" & "mother nature", in seconds -fix_duration = [ - 1.2, - 1, -] # fix duration for "optimist" & "realist", in seconds - -# audio_to_edit = "tests/ref_audio/test_zh_1_ref_short.wav" -# origin_text = "对，这就是我，万人敬仰的太乙真人。" -# target_text = "对，那就是你，万人敬仰的太白金星。" -# parts_to_edit = [[0.84, 1.4], [1.92, 2.4], [4.26, 6.26], ] -# fix_duration = None # use origin text duration - - -# -------------------------------------------------# - -use_ema = True - -if not os.path.exists(output_dir): - os.makedirs(output_dir) - -# Vocoder model -local = False -if local: - vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz" - vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml") - state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device) - vocos.load_state_dict(state_dict) - - vocos.eval() -else: - vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") - -# Tokenizer -vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer) - -# Model -model = CFM( - transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels), - mel_spec_kwargs=dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - ), - odeint_kwargs=dict( - method=ode_method, - ), - vocab_char_map=vocab_char_map, -).to(device) - -model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema) - -# Audio -audio, sr = torchaudio.load(audio_to_edit) -if audio.shape[0] > 1: - audio = torch.mean(audio, dim=0, keepdim=True) -rms = torch.sqrt(torch.mean(torch.square(audio))) -if rms < target_rms: - audio = audio * target_rms / rms -if sr != target_sample_rate: - resampler = torchaudio.transforms.Resample(sr, target_sample_rate) - audio = resampler(audio) -offset = 0 -audio_ = torch.zeros(1, 0) -edit_mask = torch.zeros(1, 0, dtype=torch.bool) -for part in parts_to_edit: - start, end = part - part_dur = end - start if fix_duration is None else fix_duration.pop(0) - part_dur = part_dur * target_sample_rate - start = start * target_sample_rate - audio_ = torch.cat((audio_, audio[:, round(offset) : round(start)], torch.zeros(1, round(part_dur))), dim=-1) - edit_mask = torch.cat( - ( - edit_mask, - torch.ones(1, round((start - offset) / hop_length), dtype=torch.bool), - torch.zeros(1, round(part_dur / hop_length), dtype=torch.bool), - ), - dim=-1, - ) - offset = end * target_sample_rate -# audio = torch.cat((audio_, audio[:, round(offset):]), dim = -1) -edit_mask = F.pad(edit_mask, (0, audio.shape[-1] // hop_length - edit_mask.shape[-1] + 1), value=True) -audio = audio.to(device) -edit_mask = edit_mask.to(device) - -# Text -text_list = [target_text] -if tokenizer == "pinyin": - final_text_list = convert_char_to_pinyin(text_list) -else: - final_text_list = [text_list] -print(f"text : {text_list}") -print(f"pinyin: {final_text_list}") - -# Duration -ref_audio_len = 0 -duration = audio.shape[-1] // hop_length - -# Inference -with torch.inference_mode(): - generated, trajectory = model.sample( - cond=audio, - text=final_text_list, - duration=duration, - steps=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - seed=seed, - edit_mask=edit_mask, - ) -print(f"Generated mel: {generated.shape}") - -# Final result -generated = generated.to(torch.float32) -generated = generated[:, ref_audio_len:, :] -generated_mel_spec = generated.permute(0, 2, 1) -generated_wave = vocos.decode(generated_mel_spec.cpu()) -if rms < target_rms: - generated_wave = generated_wave * rms / target_rms - -save_spectrogram(generated_mel_spec[0].cpu().numpy(), f"{output_dir}/speech_edit_out.png") -torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave, target_sample_rate) -print(f"Generated wav: {generated_wave.shape}") diff --git a/scripts/eval_infer_batch.py b/test_infer_batch.py similarity index 55% rename from scripts/eval_infer_batch.py rename to test_infer_batch.py index 3ca4a2809322454dfbd2c8029a308bcc8416bd86..19dba50112b5aad6304c83b8c3d0388d147fa21c 100644 --- a/scripts/eval_infer_batch.py +++ b/test_infer_batch.py @@ -1,8 +1,4 @@ -import sys import os - -sys.path.append(os.getcwd()) - import time import random from tqdm import tqdm @@ -11,14 +7,15 @@ import argparse import torch import torchaudio from accelerate import Accelerator +from einops import rearrange +from ema_pytorch import EMA from vocos import Vocos from model import CFM, UNetT, DiT from model.utils import ( - load_checkpoint, - get_tokenizer, - get_seedtts_testset_metainfo, - get_librispeech_test_clean_metainfo, + get_tokenizer, + get_seedtts_testset_metainfo, + get_librispeech_test_clean_metainfo, get_inference_prompt, ) @@ -40,16 +37,16 @@ tokenizer = "pinyin" parser = argparse.ArgumentParser(description="batch inference") -parser.add_argument("-s", "--seed", default=None, type=int) -parser.add_argument("-d", "--dataset", default="Emilia_ZH_EN") -parser.add_argument("-n", "--expname", required=True) -parser.add_argument("-c", "--ckptstep", default=1200000, type=int) +parser.add_argument('-s', '--seed', default=None, type=int) +parser.add_argument('-d', '--dataset', default="Emilia_ZH_EN") +parser.add_argument('-n', '--expname', required=True) +parser.add_argument('-c', '--ckptstep', default=1200000, type=int) -parser.add_argument("-nfe", "--nfestep", default=32, type=int) -parser.add_argument("-o", "--odemethod", default="euler") -parser.add_argument("-ss", "--swaysampling", default=-1, type=float) +parser.add_argument('-nfe', '--nfestep', default=32, type=int) +parser.add_argument('-o', '--odemethod', default="euler") +parser.add_argument('-ss', '--swaysampling', default=-1, type=float) -parser.add_argument("-t", "--testset", required=True) +parser.add_argument('-t', '--testset', required=True) args = parser.parse_args() @@ -58,7 +55,7 @@ seed = args.seed dataset_name = args.dataset exp_name = args.expname ckpt_step = args.ckptstep -ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt" +checkpoint = torch.load(f"ckpts/{exp_name}/model_{ckpt_step}.pt", map_location=device) nfe_step = args.nfestep ode_method = args.odemethod @@ -68,26 +65,26 @@ testset = args.testset infer_batch_size = 1 # max frames. 1 for ddp single inference (recommended) -cfg_strength = 2.0 -speed = 1.0 +cfg_strength = 2. +speed = 1. use_truth_duration = False no_ref_audio = False if exp_name == "F5TTS_Base": model_cls = DiT - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) + model_cfg = dict(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4) elif exp_name == "E2TTS_Base": model_cls = UNetT - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) + model_cfg = dict(dim = 1024, depth = 24, heads = 16, ff_mult = 4) if testset == "ls_pc_test_clean": metalst = "data/librispeech_pc_test_clean_cross_sentence.lst" librispeech_test_clean_path = "/LibriSpeech/test-clean" # test-clean path metainfo = get_librispeech_test_clean_metainfo(metalst, librispeech_test_clean_path) - + elif testset == "seedtts_test_zh": metalst = "data/seedtts_testset/zh/meta.lst" metainfo = get_seedtts_testset_metainfo(metalst) @@ -98,16 +95,13 @@ elif testset == "seedtts_test_en": # path to save genereted wavs -if seed is None: - seed = random.randint(-10000, 10000) -output_dir = ( - f"results/{exp_name}_{ckpt_step}/{testset}/" - f"seed{seed}_{ode_method}_nfe{nfe_step}" - f"{f'_ss{sway_sampling_coef}' if sway_sampling_coef else ''}" - f"_cfg{cfg_strength}_speed{speed}" - f"{'_gt-dur' if use_truth_duration else ''}" +if seed is None: seed = random.randint(-10000, 10000) +output_dir = f"results/{exp_name}_{ckpt_step}/{testset}/" \ + f"seed{seed}_{ode_method}_nfe{nfe_step}" \ + f"{f'_ss{sway_sampling_coef}' if sway_sampling_coef else ''}" \ + f"_cfg{cfg_strength}_speed{speed}" \ + f"{'_gt-dur' if use_truth_duration else ''}" \ f"{'_no-ref-audio' if no_ref_audio else ''}" -) # -------------------------------------------------# @@ -115,15 +109,15 @@ output_dir = ( use_ema = True prompts_all = get_inference_prompt( - metainfo, - speed=speed, - tokenizer=tokenizer, - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - target_rms=target_rms, - use_truth_duration=use_truth_duration, - infer_batch_size=infer_batch_size, + metainfo, + speed = speed, + tokenizer = tokenizer, + target_sample_rate = target_sample_rate, + n_mel_channels = n_mel_channels, + hop_length = hop_length, + target_rms = target_rms, + use_truth_duration = use_truth_duration, + infer_batch_size = infer_batch_size, ) # Vocoder model @@ -131,7 +125,7 @@ local = False if local: vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz" vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml") - state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", weights_only=True, map_location=device) + state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device) vocos.load_state_dict(state_dict) vocos.eval() else: @@ -142,19 +136,28 @@ vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer) # Model model = CFM( - transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels), - mel_spec_kwargs=dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, + transformer = model_cls( + **model_cfg, + text_num_embeds = vocab_size, + mel_dim = n_mel_channels ), - odeint_kwargs=dict( - method=ode_method, + mel_spec_kwargs = dict( + target_sample_rate = target_sample_rate, + n_mel_channels = n_mel_channels, + hop_length = hop_length, ), - vocab_char_map=vocab_char_map, + odeint_kwargs = dict( + method = ode_method, + ), + vocab_char_map = vocab_char_map, ).to(device) -model = load_checkpoint(model, ckpt_path, device, use_ema=use_ema) +if use_ema == True: + ema_model = EMA(model, include_online_model = False).to(device) + ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + ema_model.copy_params_from_ema_to_model() +else: + model.load_state_dict(checkpoint['model_state_dict']) if not os.path.exists(output_dir) and accelerator.is_main_process: os.makedirs(output_dir) @@ -164,29 +167,30 @@ accelerator.wait_for_everyone() start = time.time() with accelerator.split_between_processes(prompts_all) as prompts: + for prompt in tqdm(prompts, disable=not accelerator.is_local_main_process): utts, ref_rms_list, ref_mels, ref_mel_lens, total_mel_lens, final_text_list = prompt ref_mels = ref_mels.to(device) - ref_mel_lens = torch.tensor(ref_mel_lens, dtype=torch.long).to(device) - total_mel_lens = torch.tensor(total_mel_lens, dtype=torch.long).to(device) - + ref_mel_lens = torch.tensor(ref_mel_lens, dtype = torch.long).to(device) + total_mel_lens = torch.tensor(total_mel_lens, dtype = torch.long).to(device) + # Inference with torch.inference_mode(): generated, _ = model.sample( - cond=ref_mels, - text=final_text_list, - duration=total_mel_lens, - lens=ref_mel_lens, - steps=nfe_step, - cfg_strength=cfg_strength, - sway_sampling_coef=sway_sampling_coef, - no_ref_audio=no_ref_audio, - seed=seed, + cond = ref_mels, + text = final_text_list, + duration = total_mel_lens, + lens = ref_mel_lens, + steps = nfe_step, + cfg_strength = cfg_strength, + sway_sampling_coef = sway_sampling_coef, + no_ref_audio = no_ref_audio, + seed = seed, ) # Final result for i, gen in enumerate(generated): - gen = gen[ref_mel_lens[i] : total_mel_lens[i], :].unsqueeze(0) - gen_mel_spec = gen.permute(0, 2, 1) + gen = gen[ref_mel_lens[i]:total_mel_lens[i], :].unsqueeze(0) + gen_mel_spec = rearrange(gen, '1 n d -> 1 d n') generated_wave = vocos.decode(gen_mel_spec.cpu()) if ref_rms_list[i] < target_rms: generated_wave = generated_wave * ref_rms_list[i] / target_rms diff --git a/test_infer_batch.sh b/test_infer_batch.sh new file mode 100644 index 0000000000000000000000000000000000000000..c9c7a1998532e4a7b0d1a26bb98a835ba1d20c2a --- /dev/null +++ b/test_infer_batch.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +# e.g. F5-TTS, 16 NFE +accelerate launch test_infer_batch.py -n "F5TTS_Base" -t "seedtts_test_zh" -nfe 16 +accelerate launch test_infer_batch.py -n "F5TTS_Base" -t "seedtts_test_en" -nfe 16 +accelerate launch test_infer_batch.py -n "F5TTS_Base" -t "ls_pc_test_clean" -nfe 16 + +# e.g. Vanilla E2 TTS, 32 NFE +accelerate launch test_infer_batch.py -n "E2TTS_Base" -t "seedtts_test_zh" -o "midpoint" -ss 0 +accelerate launch test_infer_batch.py -n "E2TTS_Base" -t "seedtts_test_en" -o "midpoint" -ss 0 +accelerate launch test_infer_batch.py -n "E2TTS_Base" -t "ls_pc_test_clean" -o "midpoint" -ss 0 + +# etc. diff --git a/test_infer_single.py b/test_infer_single.py new file mode 100644 index 0000000000000000000000000000000000000000..d2e82827fcc4c4e801d08dea95def0b455dd3931 --- /dev/null +++ b/test_infer_single.py @@ -0,0 +1,162 @@ +import os +import re + +import torch +import torchaudio +from einops import rearrange +from ema_pytorch import EMA +from vocos import Vocos + +from model import CFM, UNetT, DiT, MMDiT +from model.utils import ( + get_tokenizer, + convert_char_to_pinyin, + save_spectrogram, +) + +device = "cuda" if torch.cuda.is_available() else "cpu" + + +# --------------------- Dataset Settings -------------------- # + +target_sample_rate = 24000 +n_mel_channels = 100 +hop_length = 256 +target_rms = 0.1 + +tokenizer = "pinyin" +dataset_name = "Emilia_ZH_EN" + + +# ---------------------- infer setting ---------------------- # + +seed = None # int | None + +exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base +ckpt_step = 1200000 + +nfe_step = 32 # 16, 32 +cfg_strength = 2. +ode_method = 'euler' # euler | midpoint +sway_sampling_coef = -1. +speed = 1. +fix_duration = 27 # None (will linear estimate. if code-switched, consider fix) | float (total in seconds, include ref audio) + +if exp_name == "F5TTS_Base": + model_cls = DiT + model_cfg = dict(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4) + +elif exp_name == "E2TTS_Base": + model_cls = UNetT + model_cfg = dict(dim = 1024, depth = 24, heads = 16, ff_mult = 4) + +checkpoint = torch.load(f"ckpts/{exp_name}/model_{ckpt_step}.pt", map_location=device) +output_dir = "tests" + +ref_audio = "tests/ref_audio/test_en_1_ref_short.wav" +ref_text = "Some call me nature, others call me mother nature." +gen_text = "I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences." + +# ref_audio = "tests/ref_audio/test_zh_1_ref_short.wav" +# ref_text = "对，这就是我，万人敬仰的太乙真人。" +# gen_text = "突然，身边一阵笑声。我看着他们，意气风发地挺直了胸膛，甩了甩那稍显肉感的双臂，轻笑道：\"我身上的肉，是为了掩饰我爆棚的魅力，否则，岂不吓坏了你们呢？\"" + + +# -------------------------------------------------# + +use_ema = True + +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# Vocoder model +local = False +if local: + vocos_local_path = "../checkpoints/charactr/vocos-mel-24khz" + vocos = Vocos.from_hparams(f"{vocos_local_path}/config.yaml") + state_dict = torch.load(f"{vocos_local_path}/pytorch_model.bin", map_location=device) + vocos.load_state_dict(state_dict) + vocos.eval() +else: + vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") + +# Tokenizer +vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer) + +# Model +model = CFM( + transformer = model_cls( + **model_cfg, + text_num_embeds = vocab_size, + mel_dim = n_mel_channels + ), + mel_spec_kwargs = dict( + target_sample_rate = target_sample_rate, + n_mel_channels = n_mel_channels, + hop_length = hop_length, + ), + odeint_kwargs = dict( + method = ode_method, + ), + vocab_char_map = vocab_char_map, +).to(device) + +if use_ema == True: + ema_model = EMA(model, include_online_model = False).to(device) + ema_model.load_state_dict(checkpoint['ema_model_state_dict']) + ema_model.copy_params_from_ema_to_model() +else: + model.load_state_dict(checkpoint['model_state_dict']) + +# Audio +audio, sr = torchaudio.load(ref_audio) +rms = torch.sqrt(torch.mean(torch.square(audio))) +if rms < target_rms: + audio = audio * target_rms / rms +if sr != target_sample_rate: + resampler = torchaudio.transforms.Resample(sr, target_sample_rate) + audio = resampler(audio) +audio = audio.to(device) + +# Text +text_list = [ref_text + gen_text] +if tokenizer == "pinyin": + final_text_list = convert_char_to_pinyin(text_list) +else: + final_text_list = [text_list] +print(f"text : {text_list}") +print(f"pinyin: {final_text_list}") + +# Duration +ref_audio_len = audio.shape[-1] // hop_length +if fix_duration is not None: + duration = int(fix_duration * target_sample_rate / hop_length) +else: # simple linear scale calcul + zh_pause_punc = r"。，、；：？！" + ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text)) + gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text)) + duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) + +# Inference +with torch.inference_mode(): + generated, trajectory = model.sample( + cond = audio, + text = final_text_list, + duration = duration, + steps = nfe_step, + cfg_strength = cfg_strength, + sway_sampling_coef = sway_sampling_coef, + seed = seed, + ) +print(f"Generated mel: {generated.shape}") + +# Final result +generated = generated[:, ref_audio_len:, :] +generated_mel_spec = rearrange(generated, '1 n d -> 1 d n') +generated_wave = vocos.decode(generated_mel_spec.cpu()) +if rms < target_rms: + generated_wave = generated_wave * rms / target_rms + +save_spectrogram(generated_mel_spec[0].cpu().numpy(), f"{output_dir}/test_single.png") +torchaudio.save(f"{output_dir}/test_single.wav", generated_wave, target_sample_rate) +print(f"Generated wav: {generated_wave.shape}") diff --git a/test_train.py b/test_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e056175a7315e9e4aafd111e19de047abe642e01 --- /dev/null +++ b/test_train.py @@ -0,0 +1,91 @@ +from model import CFM, UNetT, DiT, MMDiT, Trainer +from model.utils import get_tokenizer +from model.dataset import load_dataset + + +# -------------------------- Dataset Settings --------------------------- # + +target_sample_rate = 24000 +n_mel_channels = 100 +hop_length = 256 + +tokenizer = "pinyin" +dataset_name = "Emilia_ZH_EN" + + +# -------------------------- Training Settings -------------------------- # + +exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base + +learning_rate = 7.5e-5 + +batch_size_per_gpu = 38400 # 8 GPUs, 8 * 38400 = 307200 +batch_size_type = "frame" # "frame" or "sample" +max_samples = 64 # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models +grad_accumulation_steps = 1 # note: updates = steps / grad_accumulation_steps +max_grad_norm = 1. + +epochs = 11 # use linear decay, thus epochs control the slope +num_warmup_updates = 20000 # warmup steps +save_per_updates = 50000 # save checkpoint per steps +last_per_steps = 5000 # save last checkpoint per steps + +# model params +if exp_name == "F5TTS_Base": + wandb_resume_id = None + model_cls = DiT + model_cfg = dict(dim = 1024, depth = 22, heads = 16, ff_mult = 2, text_dim = 512, conv_layers = 4) +elif exp_name == "E2TTS_Base": + wandb_resume_id = None + model_cls = UNetT + model_cfg = dict(dim = 1024, depth = 24, heads = 16, ff_mult = 4) + + +# ----------------------------------------------------------------------- # + +def main(): + + vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer) + + mel_spec_kwargs = dict( + target_sample_rate = target_sample_rate, + n_mel_channels = n_mel_channels, + hop_length = hop_length, + ) + + e2tts = CFM( + transformer = model_cls( + **model_cfg, + text_num_embeds = vocab_size, + mel_dim = n_mel_channels + ), + mel_spec_kwargs = mel_spec_kwargs, + vocab_char_map = vocab_char_map, + ) + + trainer = Trainer( + e2tts, + epochs, + learning_rate, + num_warmup_updates = num_warmup_updates, + save_per_updates = save_per_updates, + checkpoint_path = f'ckpts/{exp_name}', + batch_size = batch_size_per_gpu, + batch_size_type = batch_size_type, + max_samples = max_samples, + grad_accumulation_steps = grad_accumulation_steps, + max_grad_norm = max_grad_norm, + wandb_project = "CFM-TTS", + wandb_run_name = exp_name, + wandb_resume_id = wandb_resume_id, + last_per_steps = last_per_steps, + ) + + train_dataset = load_dataset(dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs) + trainer.train(train_dataset, + resumable_with_seed = 666 # seed for shuffling dataset + ) + + +if __name__ == '__main__': + main() diff --git a/train.py b/train.py deleted file mode 100644 index b48b0f916fafb202f916b81f7f7d199ce4414970..0000000000000000000000000000000000000000 --- a/train.py +++ /dev/null @@ -1,92 +0,0 @@ -from model import CFM, UNetT, DiT, Trainer -from model.utils import get_tokenizer -from model.dataset import load_dataset - - -# -------------------------- Dataset Settings --------------------------- # - -target_sample_rate = 24000 -n_mel_channels = 100 -hop_length = 256 - -tokenizer = "pinyin" # 'pinyin', 'char', or 'custom' -tokenizer_path = None # if tokenizer = 'custom', define the path to the tokenizer you want to use (should be vocab.txt) -dataset_name = "Emilia_ZH_EN" - -# -------------------------- Training Settings -------------------------- # - -exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base - -learning_rate = 7.5e-5 - -batch_size_per_gpu = 38400 # 8 GPUs, 8 * 38400 = 307200 -batch_size_type = "frame" # "frame" or "sample" -max_samples = 64 # max sequences per batch if use frame-wise batch_size. we set 32 for small models, 64 for base models -grad_accumulation_steps = 1 # note: updates = steps / grad_accumulation_steps -max_grad_norm = 1.0 - -epochs = 11 # use linear decay, thus epochs control the slope -num_warmup_updates = 20000 # warmup steps -save_per_updates = 50000 # save checkpoint per steps -last_per_steps = 5000 # save last checkpoint per steps - -# model params -if exp_name == "F5TTS_Base": - wandb_resume_id = None - model_cls = DiT - model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) -elif exp_name == "E2TTS_Base": - wandb_resume_id = None - model_cls = UNetT - model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) - - -# ----------------------------------------------------------------------- # - - -def main(): - if tokenizer == "custom": - tokenizer_path = tokenizer_path - else: - tokenizer_path = dataset_name - vocab_char_map, vocab_size = get_tokenizer(tokenizer_path, tokenizer) - - mel_spec_kwargs = dict( - target_sample_rate=target_sample_rate, - n_mel_channels=n_mel_channels, - hop_length=hop_length, - ) - - model = CFM( - transformer=model_cls(**model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels), - mel_spec_kwargs=mel_spec_kwargs, - vocab_char_map=vocab_char_map, - ) - - trainer = Trainer( - model, - epochs, - learning_rate, - num_warmup_updates=num_warmup_updates, - save_per_updates=save_per_updates, - checkpoint_path=f"ckpts/{exp_name}", - batch_size=batch_size_per_gpu, - batch_size_type=batch_size_type, - max_samples=max_samples, - grad_accumulation_steps=grad_accumulation_steps, - max_grad_norm=max_grad_norm, - wandb_project="CFM-TTS", - wandb_run_name=exp_name, - wandb_resume_id=wandb_resume_id, - last_per_steps=last_per_steps, - ) - - train_dataset = load_dataset(dataset_name, tokenizer, mel_spec_kwargs=mel_spec_kwargs) - trainer.train( - train_dataset, - resumable_with_seed=666, # seed for shuffling dataset - ) - - -if __name__ == "__main__": - main()