# 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 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()