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webui-函数.py
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import os
import sys
import argparse
import gradio as gr
import numpy as np
import torch
import random
import librosa
import numpy as np
import scipy.io.wavfile
import tempfile # 用于生成临时文件
# 设置路径和导入 CosyVoice 库
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
from cosyvoice.utils.common import set_all_random_seed
# 定义全局变量
inference_mode_list = ['预训练音色', '3s极速复刻', '跨语种复刻', '自然语言控制']
stream_mode_list = [('否', False), ('是', True)]
max_val = 0.8
target_sr = 22050 # 假定目标采样率
prompt_sr = 16000 # 假定提示音频采样率
# 初始化全局变量和模型
def initialize_globals(model_dir):
global cosyvoice, sft_spk
cosyvoice = CosyVoice(model_dir)
sft_spk = cosyvoice.list_avaliable_spks()
def generate_seed():
"""生成随机种子"""
seed = random.randint(1, 100000000)
return {"__type__": "update", "value": seed}
def postprocess(speech, top_db=60, hop_length=220, win_length=440):
"""音频后处理"""
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
return torch.cat([speech, torch.zeros(1, int(target_sr * 0.2))], dim=1)
def change_instruction(mode):
"""根据选择的推理模式返回对应的操作说明"""
return instruct_dict[mode]
def validate_inputs(mode, prompt_wav, instruct_text, prompt_text):
"""验证输入的有效性"""
if mode == '自然语言控制' and instruct_text == '':
return False, "请输入instruct文本"
if mode == '跨语种复刻' and prompt_wav is None:
return False, "请提供prompt音频"
if mode == '3s极速复刻' and prompt_text == '':
return False, "prompt文本为空，您是否忘记输入prompt文本？"
return True, None
def inference(mode, tts_text, prompt_wav, instruct_text, sft_dropdown, seed, stream, speed):
"""执行推理过程并生成音频"""
set_all_random_seed(seed)
if mode == '预训练音色':
for audio in cosyvoice.inference_sft(tts_text, sft_dropdown, stream=stream, speed=speed):
yield (audio['tts_speech'].numpy().flatten()) # 分段返回 numpy 数组格式音频
elif mode in ['3s极速复刻', '跨语种复刻']:
prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
inference_method = cosyvoice.inference_zero_shot if mode == '3s极速复刻' else cosyvoice.inference_cross_lingual
for audio in inference_method(tts_text, prompt_text, prompt_speech_16k, stream=stream, speed=speed):
yield (audio['tts_speech'].numpy().flatten()) # 分段返回 numpy 数组格式音频
elif mode == '自然语言控制':
for audio in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed):
yield (audio['tts_speech'].numpy().flatten()) # 分段返回 numpy 数组格式音频
# 在代码开头定义操作说明字典
instruct_dict = {
'预训练音色': '选择预训练音色并输入合成文本，点击生成音频。',
'3s极速复刻': '上传prompt音频并输入prompt文本，点击生成音频。',
'跨语种复刻': '上传prompt音频并输入prompt文本，点击生成音频。',
'自然语言控制': '输入instruct文本，并选择对应的推理模式，点击生成音频。'
}
def create_ui(args):
"""创建 Gradio 用户界面"""
with gr.Blocks() as demo:
gr.Markdown("### 代码库 [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) ...")
tts_text = gr.Textbox(label="输入合成文本", lines=1,
value="我是通义实验室语音团队全新推出的生成式语音大模型，提供舒适自然的语音合成能力。")
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=1)
sft_dropdown = gr.Dropdown(choices=sft_spk, label='选择预训练音色', value=sft_spk[0], scale=0.25)
stream = gr.Radio(choices=stream_mode_list, label='是否流式推理', value=stream_mode_list[0][1])
speed = gr.Number(value=1, label="速度调节(仅支持非流式推理)", minimum=0.5, maximum=2.0, step=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="输入prompt文本", lines=1, placeholder="请输入prompt文本，需与prompt音频内容一致...", value='')
instruct_text = gr.Textbox(label="输入instruct文本", lines=1, placeholder="请输入instruct文本.", value='')
generate_button = gr.Button("生成音频")
audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=True) # 自动播放关闭流式
# 按钮的点击事件
seed_button.click(generate_seed, inputs=[], outputs=seed)
generate_button.click(generate_audio,
inputs=[tts_text, mode_checkbox_group, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
seed, stream, speed],
outputs=[audio_output])
mode_checkbox_group.change(fn=change_instruction, inputs=[mode_checkbox_group], outputs=[instruction_text])
demo.queue(max_size=4, default_concurrency_limit=2)
demo.launch(server_name='0.0.0.0', server_port=args.port)
def generate_audio(tts_text, mode, sft_dropdown, prompt_text, prompt_wav_upload, prompt_wav_record, instruct_text,
seed, stream, speed):
"""生成音频并返回实时生成的音频块"""
prompt_wav = prompt_wav_upload if prompt_wav_upload is not None else prompt_wav_record
is_valid, warning_msg = validate_inputs(mode, prompt_wav, instruct_text, prompt_text)
if not is_valid:
yield gr.Warning(warning_msg) # 返回警告信息
# 推理并生成音频
for audio in inference(mode, tts_text, prompt_wav, instruct_text, sft_dropdown, seed, stream, speed):
if isinstance(audio, tuple):
audio = audio[1] # 如果返回的是元组，获取音频数据部分
# 将生成的音频数据实时返回，而不是生成整个文件
yield (22050, audio) # 假定 target_sr = 22050
def main():
"""主函数"""
parser = argparse.ArgumentParser()
parser.add_argument('--port', type=int, default=8000)
parser.add_argument('--model_dir', type=str, default='pretrained_models/CosyVoice-300M',
help='local path or modelscope repo id')
args = parser.parse_args()
initialize_globals(args.model_dir) # 初始化全局变量和模型
global default_data # 需要在函数中修改全局变量
default_data = np.zeros(target_sr) # 创建默认音频数据
create_ui(args) # 将 args 传递给 create_ui 函数
if __name__ == '__main__':
main() # 调用主函数