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| import random | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| from tools.logger import get_logger | |
| logger = get_logger(" WebUI ") | |
| import ChatTTS | |
| chat = ChatTTS.Chat(get_logger("ChatTTS")) | |
| # 音色选项：用于预置合适的音色 | |
| voices = { | |
| "默认": {"seed": 2}, | |
| "音色1": {"seed": 1111}, | |
| "音色2": {"seed": 2222}, | |
| "音色3": {"seed": 3333}, | |
| "音色4": {"seed": 4444}, | |
| "音色5": {"seed": 5555}, | |
| "音色6": {"seed": 6666}, | |
| "音色7": {"seed": 7777}, | |
| "音色8": {"seed": 8888}, | |
| "音色9": {"seed": 9999}, | |
| "音色10": {"seed": 11111}, | |
| } | |
| def generate_seed(): | |
| return gr.update(value=random.randint(1, 100000000)) | |
| # 返回选择音色对应的seed | |
| def on_voice_change(vocie_selection): | |
| return voices.get(vocie_selection)['seed'] | |
| def refine_text(text, audio_seed_input, text_seed_input, refine_text_flag): | |
| if not refine_text_flag: | |
| return text | |
| global chat | |
| torch.manual_seed(audio_seed_input) | |
| params_refine_text = {'prompt': '[oral_2][laugh_0][break_6]'} | |
| torch.manual_seed(text_seed_input) | |
| text = chat.infer(text, | |
| skip_refine_text=False, | |
| refine_text_only=True, | |
| params_refine_text=params_refine_text, | |
| ) | |
| return text[0] if isinstance(text, list) else text | |
| def generate_audio(text, temperature, top_P, top_K, audio_seed_input, text_seed_input, stream): | |
| if not text: return None | |
| global chat | |
| torch.manual_seed(audio_seed_input) | |
| rand_spk = chat.sample_random_speaker() | |
| params_infer_code = { | |
| 'spk_emb': rand_spk, | |
| 'temperature': temperature, | |
| 'top_P': top_P, | |
| 'top_K': top_K, | |
| } | |
| torch.manual_seed(text_seed_input) | |
| wav = chat.infer( | |
| text, | |
| skip_refine_text=True, | |
| params_infer_code=params_infer_code, | |
| stream=stream, | |
| ) | |
| if stream: | |
| for gen in wav: | |
| wavs = [np.array([[]])] | |
| wavs[0] = np.hstack([wavs[0], np.array(gen[0])]) | |
| audio = wavs[0][0] | |
| # normalize | |
| am = np.abs(audio).max() * 32768 | |
| if am > 32768: | |
| am = 32768 * 32768 / am | |
| np.multiply(audio, am, audio) | |
| audio = audio.astype(np.int16) | |
| yield 24000, audio | |
| return | |
| audio_data = np.array(wav[0]).flatten() | |
| # normalize | |
| am = np.abs(audio_data).max() * 32768 | |
| if am > 32768: | |
| am = 32768 * 32768 / am | |
| np.multiply(audio_data, am, audio_data) | |
| audio_data = audio_data.astype(np.int16) | |
| sample_rate = 24000 | |
| yield sample_rate, audio_data | |