app.py

# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Liu Yue)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import importlib
import sys

dynamic_modules_file = '/usr/local/lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py'

def modify_dynamic_modules_file():
    if os.path.exists(dynamic_modules_file):
        with open(dynamic_modules_file, 'r') as file:
            lines = file.readlines()
        with open(dynamic_modules_file, 'w') as file:
            for line in lines:
                if "from huggingface_hub import cached_download" in line:
                    file.write("from huggingface_hub import hf_hub_download, model_info\n")
                else:
                    file.write(line)

import os
import sys
import argparse
import gradio as gr
import numpy as np
import torch
import torchaudio
import random
import librosa
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))

from modelscope import snapshot_download
snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')
os.system('cd pretrained_models/CosyVoice-ttsfrd/ && pip install ttsfrd_dependency-0.1-py3-none-any.whl && pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl && apt install -y unzip && unzip resource.zip -d .')

from cosyvoice.cli.cosyvoice import CosyVoice2
from cosyvoice.utils.file_utils import load_wav, logging
from cosyvoice.utils.common import set_all_random_seed

inference_mode_list = ['3s极速复刻', '自然语言控制']
instruct_dict = {'3s极速复刻': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 输入prompt文本\n3. 点击生成音频按钮',
                 '自然语言控制': '1. 选择prompt音频文件，或录入prompt音频，注意不超过30s，若同时提供，优先选择prompt音频文件\n2. 输入instruct文本\n3. 点击生成音频按钮'}
stream_mode_list = [('否', False), ('是', True)]
max_val = 0.8


def generate_seed():
    seed = random.randint(1, 100000000)
    return {
        "__type__": "update",
        "value": seed
    }


def postprocess(speech, top_db=60, hop_length=220, win_length=440):
    speech, _ = librosa.effects.trim(
        speech, top_db=top_db,
        frame_length=win_length,
        hop_length=hop_length
    )
    if speech.abs().max() > max_val:
        speech = speech / speech.abs().max() * max_val
    speech = torch.concat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1)
    return speech


def change_instruction(mode_checkbox_group):
    return instruct_dict[mode_checkbox_group]

def prompt_wav_recognition(prompt_wav):
    res = asr_model.generate(input=prompt_wav,
                             language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
                             use_itn=True,
    )
    text = res[0]["text"].split('|>')[-1]
    return text

def generate_audio(tts_text, mode_checkbox_group, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
                   seed, stream):
    sft_dropdown, speed = '', 1.0
    if prompt_wav_upload is not None:
        prompt_wav = prompt_wav_upload
    elif prompt_wav_record is not None:
        prompt_wav = prompt_wav_record
    else:
        prompt_wav = None
    # if instruct mode, please make sure that model is iic/CosyVoice-300M-Instruct and not cross_lingual mode
    if mode_checkbox_group in ['自然语言控制']:
        if instruct_text == '':
            gr.Warning('您正在使用自然语言控制模式, 请输入instruct文本')
            yield (target_sr, default_data)
        if prompt_wav is None:
            gr.Info('您正在使用自然语言控制模式, 请输入prompt音频')
    # if cross_lingual mode, please make sure that model is iic/CosyVoice-300M and tts_text prompt_text are different language
    if mode_checkbox_group in ['跨语种复刻']:
        if cosyvoice.frontend.instruct is True:
            gr.Warning('您正在使用跨语种复刻模式, {}模型不支持此模式, 请使用iic/CosyVoice-300M模型'.format(args.model_dir))
            yield (target_sr, default_data)
        if instruct_text != '':
            gr.Info('您正在使用跨语种复刻模式, instruct文本会被忽略')
        if prompt_wav is None:
            gr.Warning('您正在使用跨语种复刻模式, 请提供prompt音频')
            yield (target_sr, default_data)
        gr.Info('您正在使用跨语种复刻模式, 请确保合成文本和prompt文本为不同语言')
    # if in zero_shot cross_lingual, please make sure that prompt_text and prompt_wav meets requirements
    if mode_checkbox_group in ['3s极速复刻', '跨语种复刻']:
        if prompt_wav is None:
            gr.Warning('prompt音频为空，您是否忘记输入prompt音频？')
            yield (target_sr, default_data)
        if torchaudio.info(prompt_wav).sample_rate < prompt_sr:
            gr.Warning('prompt音频采样率{}低于{}'.format(torchaudio.info(prompt_wav).sample_rate, prompt_sr))
            yield (target_sr, default_data)
    # sft mode only use sft_dropdown
    if mode_checkbox_group in ['预训练音色']:
        if instruct_text != '' or prompt_wav is not None or prompt_text != '':
            gr.Info('您正在使用预训练音色模式，prompt文本/prompt音频/instruct文本会被忽略！')
    # zero_shot mode only use prompt_wav prompt text
    if mode_checkbox_group in ['3s极速复刻']:
        if prompt_text == '':
            gr.Warning('prompt文本为空，您是否忘记输入prompt文本？')
            yield (target_sr, default_data)
        if instruct_text != '':
            gr.Info('您正在使用3s极速复刻模式，预训练音色/instruct文本会被忽略！')
        info = torchaudio.info(prompt_wav)
        if info.num_frames / info.sample_rate > 10:
            gr.Warning('请限制输入音频在10s内，避免推理效果过低')
            yield (target_sr, default_data)

    if mode_checkbox_group == '预训练音色':
        logging.info('get sft inference request')
        set_all_random_seed(seed)
        for i in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed):
            yield (target_sr, i['tts_speech'].numpy().flatten())
    elif mode_checkbox_group == '3s极速复刻':
        logging.info('get zero_shot inference request')
        prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
        set_all_random_seed(seed)
        for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
            yield (target_sr, i['tts_speech'].numpy().flatten())
    elif mode_checkbox_group == '跨语种复刻':
        logging.info('get cross_lingual inference request')
        prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
        set_all_random_seed(seed)
        for i in cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream, speed=speed):
            yield (target_sr, i['tts_speech'].numpy().flatten())
    else:
        logging.info('get instruct inference request')
        logging.info('get instruct inference request')
        prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
        set_all_random_seed(seed)
        for i in cosyvoice.inference_instruct2(tts_text, instruct_text, prompt_speech_16k, stream=stream, speed=speed):
            yield (target_sr, i['tts_speech'].numpy().flatten())


def main():
    with gr.Blocks() as demo:
        gr.Markdown("### 代码库 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) \
                    预训练模型 [CosyVoice2-0.5B](https://www.modelscope.cn/models/iic/CosyVoice2-0.5B) \
                    [CosyVoice-300M](https://www.modelscope.cn/models/iic/CosyVoice-300M) \
                    [CosyVoice-300M-Instruct](https://www.modelscope.cn/models/iic/CosyVoice-300M-Instruct) \
                    [CosyVoice-300M-SFT](https://www.modelscope.cn/models/iic/CosyVoice-300M-SFT)")
        gr.Markdown("#### 请输入需要合成的文本，选择推理模式，并按照提示步骤进行操作")

        tts_text = gr.Textbox(label="输入合成文本", lines=1, value="CosyVoice迎来全面升级，提供更准、更稳、更快、 更好的语音生成能力。CosyVoice is undergoing a comprehensive upgrade, providing more accurate, stable, faster, and better voice generation capabilities.")
        with gr.Row():
            mode_checkbox_group = gr.Radio(choices=inference_mode_list, label='选择推理模式', value=inference_mode_list[0])
            instruction_text = gr.Text(label="操作步骤", value=instruct_dict[inference_mode_list[0]], scale=0.5)
            stream = gr.Radio(choices=stream_mode_list, label='是否流式推理', value=stream_mode_list[0][1])
            with gr.Column(scale=0.25):
                seed_button = gr.Button(value="\U0001F3B2")
                seed = gr.Number(value=0, label="随机推理种子")

        with gr.Row():
            prompt_wav_upload = gr.Audio(sources='upload', type='filepath', label='选择prompt音频文件，注意采样率不低于16khz')
            prompt_wav_record = gr.Audio(sources='microphone', type='filepath', label='录制prompt音频文件')
        prompt_text = gr.Textbox(label="prompt文本", lines=1, placeholder="请输入prompt文本，支持自动识别，您可以自行修正识别结果...", value='')
        instruct_text = gr.Textbox(label="输入instruct文本", lines=1, placeholder="请输入instruct文本.例如:用四川话说这句话。", value='')

        generate_button = gr.Button("生成音频")

        audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=True)

        seed_button.click(generate_seed, inputs=[], outputs=seed)
        generate_button.click(generate_audio,
                              inputs=[tts_text, mode_checkbox_group, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
                                      seed, stream],
                              outputs=[audio_output])
        mode_checkbox_group.change(fn=change_instruction, inputs=[mode_checkbox_group], outputs=[instruction_text])
        prompt_wav_upload.change(fn=prompt_wav_recognition, inputs=[prompt_wav_upload], outputs=[prompt_text])
        prompt_wav_record.change(fn=prompt_wav_recognition, inputs=[prompt_wav_record], outputs=[prompt_text])
    demo.queue(max_size=4, default_concurrency_limit=2).launch(server_port=50000)


if __name__ == '__main__':
    load_jit = True if os.environ.get('jit') == '1' else False
    load_onnx = True if os.environ.get('onnx') == '1' else False
    load_trt = True if os.environ.get('trt') == '1' else False
    logging.info('cosyvoice args load_jit {} load_onnx {} load_trt {}'.format(load_jit, load_onnx, load_trt))
    cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=load_jit, load_onnx=load_onnx, load_trt=load_trt)
    sft_spk = cosyvoice.list_avaliable_spks()
    prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
    for stream in [True, False]:
        for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=stream)):
            continue
    prompt_sr, target_sr = 16000, 24000
    default_data = np.zeros(target_sr)

    model_dir = "iic/SenseVoiceSmall"
    asr_model = AutoModel(
        model=model_dir,
        disable_update=True,
        log_level='DEBUG',
        device="cuda:0")
    main()