Update requirements.txt

.gitattributes

@@ -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
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.github/workflows/pre-commit.yaml

@@ -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/[email protected]

.github/workflows/sync-hf.yaml

@@ -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 }}\"]}"

.pre-commit-config.yaml

@@ -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

Dockerfile

@@ -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

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

README_REPO.md

@@ -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/)
-<img src="https://github.com/user-attachments/assets/12d7749c-071a-427c-81bf-b87b91def670" alt="Watermark" style="width: 40px; height: auto">
-
-**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=<YOUR WANDB API KEY>
-```
-
-On Windows:
-
-```
-set WANDB_API_KEY=<YOUR 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.

api.py

@@ -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)

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()

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()

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

finetune-cli.py

@@ -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()

finetune_gradio.py

@@ -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()

gradio_app.py

@@ -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()

inference-cli.py

@@ -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)

inference-cli.toml

@@ -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"

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"]

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)

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)

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)

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

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,
  )

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)

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

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()

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)

model/utils_infer.py

@@ -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")

packages.txt

@@ -0,0 +1 @@
+ffmpeg

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

requirements_eval.txt

@@ -1,5 +0,0 @@
-faster_whisper
-funasr
-jiwer
-zhconv
-zhon

ruff.toml

@@ -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

samples/story.toml

@@ -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 = ""
-

samples/story.txt

@@ -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.”

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

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")

scripts/eval_infer_batch.sh

@@ -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.

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 = "<SOME_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}")

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}")

scripts/prepare_csv_wavs.py

@@ -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()

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"

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 = [
  "<SOME_PATH>/WenetSpeech4TTS/Basic",
  "<SOME_PATH>/WenetSpeech4TTS/Standard",
  "<SOME_PATH>/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

scripts/eval_infer_batch.py → 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 = "<SOME_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

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.

speech_edit.py → test_infer_single.py

@@ -1,19 +1,20 @@
 import os
+import re
 
 import torch
-import torch.nn.functional as F
 import torchaudio
+from einops import rearrange
+from ema_pytorch import EMA
 from vocos import Vocos
 
-from model import CFM, UNetT, DiT
+from model import CFM, UNetT, DiT, MMDiT
 from model.utils import (
- load_checkpoint,
- get_tokenizer,
- convert_char_to_pinyin,
+ 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"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
 
 # --------------------- Dataset Settings -------------------- #
@@ -35,47 +36,30 @@ 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
+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)
+ 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)
 
-ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.safetensors"
+checkpoint = torch.load(f"ckpts/{exp_name}/model_{ckpt_step}.pt", map_location=device)
 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
+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？\""
 
 
 # -------------------------------------------------#
@@ -90,9 +74,8 @@ 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:
  vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
@@ -102,55 +85,41 @@ 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
+ ),
+ 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,
+ odeint_kwargs = dict(
+ method = ode_method,
  ),
- vocab_char_map=vocab_char_map,
+ 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'])
 
 # Audio
-audio, sr = torchaudio.load(audio_to_edit)
-if audio.shape[0] > 1:
- audio = torch.mean(audio, dim=0, keepdim=True)
+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)
-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]
+text_list = [ref_text + gen_text]
 if tokenizer == "pinyin":
  final_text_list = convert_char_to_pinyin(text_list)
 else:
@@ -159,31 +128,35 @@ print(f"text : {text_list}")
 print(f"pinyin: {final_text_list}")
 
 # Duration
-ref_audio_len = 0
-duration = audio.shape[-1] // hop_length
+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,
- edit_mask=edit_mask,
+ 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.to(torch.float32)
 generated = generated[:, ref_audio_len:, :]
-generated_mel_spec = generated.permute(0, 2, 1)
+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}/speech_edit_out.png")
-torchaudio.save(f"{output_dir}/speech_edit_out.wav", generated_wave, target_sample_rate)
+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}")

train.py → test_train.py

@@ -1,4 +1,4 @@
-from model import CFM, UNetT, DiT, Trainer
+from model import CFM, UNetT, DiT, MMDiT, Trainer
 from model.utils import get_tokenizer
 from model.dataset import load_dataset
 
@@ -9,10 +9,10 @@ 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)
+tokenizer = "pinyin"
 dataset_name = "Emilia_ZH_EN"
 
+
 # -------------------------- Training Settings -------------------------- #
 
 exp_name = "F5TTS_Base" # F5TTS_Base | E2TTS_Base
@@ -23,7 +23,7 @@ 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
+max_grad_norm = 1.
 
 epochs = 11 # use linear decay, thus epochs control the slope
 num_warmup_updates = 20000 # warmup steps
@@ -34,59 +34,58 @@ last_per_steps = 5000 # save last checkpoint per steps
 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)
+ 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)
+ 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,
- )
+ vocab_char_map, vocab_size = get_tokenizer(dataset_name, tokenizer)
 
- 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,
+ 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(
- model,
- epochs,
+ 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,
+ 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
- )
+ trainer.train(train_dataset, 
+ resumable_with_seed = 666 # seed for shuffling dataset
+ )
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
  main()