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https://github.com/2noise/ChatTTS.git
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源文雨
102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
import random
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from typing import Optional
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import gradio as gr
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import numpy as np
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from tools.audio import unsafe_float_to_int16
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from tools.logger import get_logger
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logger = get_logger(" WebUI ")
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from tools.seeder import TorchSeedContext
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import ChatTTS
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chat = ChatTTS.Chat(get_logger("ChatTTS"))
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custom_path: Optional[str] = None
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# 音色选项：用于预置合适的音色
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voices = {
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"Default": {"seed": 2},
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"Timbre1": {"seed": 1111},
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"Timbre2": {"seed": 2222},
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"Timbre3": {"seed": 3333},
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"Timbre4": {"seed": 4444},
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"Timbre5": {"seed": 5555},
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"Timbre6": {"seed": 6666},
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"Timbre7": {"seed": 7777},
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"Timbre8": {"seed": 8888},
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"Timbre9": {"seed": 9999},
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}
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def generate_seed():
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return gr.update(value=random.randint(1, 100000000))
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# 返回选择音色对应的seed
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def on_voice_change(vocie_selection):
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return voices.get(vocie_selection)['seed']
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def reload_chat(coef: Optional[str]) -> str:
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global custom_path
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chat.unload()
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gr.Info("Model unloaded.")
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try:
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if len(coef) != 230:
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gr.Warning("Ingore invalid DVAE coefficient.")
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coef = None
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if custom_path == None:
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ret = chat.load_models(coef=coef)
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else:
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logger.info('local model path: %s', custom_path)
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ret = chat.load_models('custom', custom_path=custom_path, coef=coef)
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if not ret:
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raise gr.Error("Unable to load model.")
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gr.Info("Reload succeess.")
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return chat.coef
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except Exception as e:
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raise gr.Error(str(e))
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def refine_text(text, text_seed_input, refine_text_flag):
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if not refine_text_flag:
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return text
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global chat
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params_refine_text = {'prompt': '[oral_2][laugh_0][break_6]'}
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with TorchSeedContext(text_seed_input):
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text = chat.infer(text,
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skip_refine_text=False,
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refine_text_only=True,
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params_refine_text=params_refine_text,
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)
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return text[0] if isinstance(text, list) else text
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def generate_audio(text, temperature, top_P, top_K, audio_seed_input, stream):
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if not text: return None
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global chat
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with TorchSeedContext(audio_seed_input):
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rand_spk = chat.sample_random_speaker()
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params_infer_code = {
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'spk_emb': rand_spk,
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'temperature': temperature,
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'top_P': top_P,
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'top_K': top_K,
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}
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wav = chat.infer(
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text,
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skip_refine_text=True,
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params_infer_code=params_infer_code,
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stream=stream,
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)
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if stream:
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for gen in wav:
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yield 24000, unsafe_float_to_int16(gen[0][0])
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return
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yield 24000, unsafe_float_to_int16(np.array(wav[0]).flatten())
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