# 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 threading import torch os.system('nvidia-smi') # os.system('apt update -y && apt-get install -y apt-utils && apt install -y unzip') print(torch.backends.cudnn.version()) import importlib import sys dynamic_modules_file1 = '/home/user/.pyenv/versions/3.10.16/lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py' dynamic_modules_file2 = '/usr/local/lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py' def modify_dynamic_modules_file(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) modify_dynamic_modules_file(dynamic_modules_file1) modify_dynamic_modules_file(dynamic_modules_file2) import sys import argparse import gradio as gr import numpy as np import torchaudio import random import librosa import spaces 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 huggingface_hub import snapshot_download snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B') snapshot_download('kemuriririn/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') snapshot_download('FunAudioLLM/SenseVoiceSmall', local_dir='pretrained_models/SenseVoiceSmall') 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 && 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 Voice Clone'] instruct_dict = {'3s Voice Clone': '1. Upload prompt wav file (or record from mic), no longer than 30s, wav file will be used if provided at the same time\n2. Input prompt transcription\n3. click \'Speech Synthesis\' button'} stream_mode_list = [('No', False), ('Yes', True)] max_val = 0.8 cosyvoice_instance = None asr_model = None cosyvoice_lock = threading.Lock() @spaces.GPU def get_cosyvoice(): global cosyvoice_instance, model_dir 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 with cosyvoice_lock: if cosyvoice_instance is not None: return cosyvoice_instance else: logging.info('cosyvoice args load_jit {} load_onnx {} load_trt {}'.format(load_jit, load_onnx, load_trt)) cosyvoice_instance= CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=load_jit, load_onnx=load_onnx, load_trt=load_trt) return cosyvoice_instance @spaces.GPU def infer_zeroshot(tts_text, prompt_text, prompt_speech_16k, stream, speed): cosyvoice = get_cosyvoice() if cosyvoice.frontend.instruct is True: logging.warning('CosyVoice2-0.5B does not support zero-shot inference, please use CosyVoice-300M or CosyVoice-300M-Instruct.') return for i in cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed): yield i def get_asr(): global asr_model if asr_model is not None: return asr_model else: logging.info('asr model load') model_dir = "pretrained_models/SenseVoiceSmall" asr_model = AutoModel( model=model_dir, disable_update=True, log_level='DEBUG', device="cuda:0") return asr_model 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 @spaces.GPU def prompt_wav_recognition(prompt_wav): res = get_asr().generate(input=prompt_wav, language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" use_itn=True, ) text = res[0]["text"].split('|>')[-1] return text @spaces.GPU def generate_audio(tts_text, prompt_text, prompt_wav_upload, prompt_wav_record, seed, stream): 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 prompt_text == '': gr.Warning('Empty prompt found, please check the prompt text.') yield (target_sr, default_data) return if prompt_wav is None: gr.Warning('Empty prompt found, please upload or record audio.') yield (target_sr, default_data) return info = torchaudio.info(prompt_wav) if info.num_frames / info.sample_rate > 10: gr.Warning('Please use prompt audio shorter than 10s.') yield (target_sr, default_data) return if torchaudio.info(prompt_wav).sample_rate < prompt_sr: gr.Warning('Prompt wav sample rate {}, lower than {}.'.format(torchaudio.info(prompt_wav).sample_rate, prompt_sr)) yield (target_sr, default_data) return prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr)) set_all_random_seed(seed) for i in infer_zeroshot(tts_text, prompt_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("### 3s Voice Clone") gr.Markdown("#### Clone any voice with just 3 seconds of audio. Upload or record audio, input transcription, and click 'Speech Synthesis'.") tts_text = gr.Textbox(label="Text to synthesize", lines=1, value="CosyVoice is undergoing a comprehensive upgrade, providing more accurate, stable, faster, and better voice generation capabilities.") with gr.Row(): prompt_wav_upload = gr.Audio(sources='upload', type='filepath', label='Prompt wav file (sample rate >= 16kHz)') prompt_wav_record = gr.Audio(sources='microphone', type='filepath', label='Record prompt from your microphone') prompt_text = gr.Textbox(label="Prompt Transcription", lines=1, placeholder="Prompt transcription (auto ASR, you can correct the recognition results)", value='') with gr.Row(): stream = gr.Radio(choices=stream_mode_list, label='Streaming or not', value=stream_mode_list[0][1]) with gr.Column(scale=0.25): seed_button = gr.Button(value="\U0001F3B2") seed = gr.Number(value=0, label="Random Seed") generate_button = gr.Button("Speech Synthesis") audio_output = gr.Audio(label="Audio Output", autoplay=True, streaming=False) seed_button.click(generate_seed, inputs=[], outputs=seed) generate_button.click(generate_audio, inputs=[tts_text, prompt_text, prompt_wav_upload, prompt_wav_record, seed, stream], outputs=[audio_output]) 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.launch(max_threads=4) if __name__ == '__main__': # 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(infer_zeroshot('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) main()