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import gradio as gr |
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from infer_onnx import TTS |
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from ruaccent import RUAccent |
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title = "GitHub with models: https://github.com/Tera2Space/RUTTS" |
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models = ["TeraTTS/natasha-g2p-vits", "TeraTTS/glados2-g2p-vits"] |
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models = {k:TTS(k) for k in models} |
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accentizer = RUAccent(workdir="./model/ruaccent", allow_cuda=False) |
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accentizer.load(omograph_model_size='medium', dict_load_startup=True) |
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def process_text(text: str) -> str: |
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text = accentizer.process_all(text) |
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return text |
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def text_to_speech(model_name, lenght_scale, text, prep_text): |
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if prep_text: |
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text = process_text(text) |
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audio = models[model_name](text, lenght_scale=lenght_scale) |
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models[model_name].save_wav(audio, 'temp.wav') |
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return 'temp.wav', f"Обработанный текст: '{text}'" |
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model_choice = gr.Dropdown(choices=list(models.keys()), value="TeraTTS/natasha-g2p-vits", label="Выберите модель") |
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input_text = gr.Textbox(label="Введите текст для синтеза речи") |
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prep_text = gr.Checkbox(label="Предобработать", info="Хотите пред обработать текст?(Ударения, ё)", value=True) |
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lenght_scale = gr.Slider(minimum=0.1, maximum=2.0, label="Lenght Scale(увеличить длину звучания) Default: 1.2", value=1.2) |
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output_audio = gr.Audio(label="Аудио", type="numpy") |
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output_text = gr.Textbox(label="Обработанный текст") |
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iface = gr.Interface(fn=text_to_speech, inputs=[model_choice, lenght_scale, input_text, prep_text], outputs=[output_audio, output_text], title=title) |
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iface.launch() |
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