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import os |
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from paddlespeech.cli.tts.infer import TTSExecutor |
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""" |
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PaddleSpeech |
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声码器说明：这里预制了三种声码器【PWGan】【WaveRnn】【HifiGan】, 三种声码器效果和生成时间有比较大的差距，请跟进自己的需要进行选择。不过只选择了前两种，因为WaveRNN太慢了 |
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| 声码器 | 音频质量 | 生成速度 | |
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| :----: | :----: | :----: | |
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| PWGan | 中等 | 中等 | |
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| WaveRnn | 高 | 非常慢（耐心等待） | |
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| HifiGan | 低 | 快 | |
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这些PaddleSpeech中的样例主要按数据集分类，我们主要使用的TTS数据集有： |
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CSMCS (普通话单发音人) |
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AISHELL3 (普通话多发音人) |
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LJSpeech (英文单发音人) |
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VCTK (英文多发音人) |
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PaddleSpeech 的 TTS 模型具有以下映射关系： |
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tts0 - Tacotron2 |
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tts1 - TransformerTTS |
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tts2 - SpeedySpeech |
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tts3 - FastSpeech2 |
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voc0 - WaveFlow |
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voc1 - Parallel WaveGAN |
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voc2 - MelGAN |
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voc3 - MultiBand MelGAN |
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voc4 - Style MelGAN |
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voc5 - HiFiGAN |
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vc0 - Tacotron2 Voice Clone with GE2E |
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vc1 - FastSpeech2 Voice Clone with GE2E |
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以下是 PaddleSpeech 提供的可以被命令行和 python API 使用的预训练模型列表： |
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- 声学模型 |
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| 模型 | 语言 | |
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| :--- | :---: | |
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| speedyspeech_csmsc | zh | |
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| fastspeech2_csmsc | zh | |
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| fastspeech2_ljspeech | en | |
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| fastspeech2_aishell3 | zh | |
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| fastspeech2_vctk | en | |
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| fastspeech2_cnndecoder_csmsc | zh | |
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| fastspeech2_mix | mix | |
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| tacotron2_csmsc | zh | |
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| tacotron2_ljspeech | en | |
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| fastspeech2_male | zh | |
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| fastspeech2_male | en | |
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| fastspeech2_male | mix | |
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| fastspeech2_canton | canton | |
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- 声码器 |
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| 模型 | 语言 | |
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| :--- | :---: | |
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| pwgan_csmsc | zh | |
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| pwgan_ljspeech | en | |
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| pwgan_aishell3 | zh | |
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| pwgan_vctk | en | |
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| mb_melgan_csmsc | zh | |
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| style_melgan_csmsc | zh | |
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| hifigan_csmsc | zh | |
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| hifigan_ljspeech | en | |
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| hifigan_aishell3 | zh | |
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| hifigan_vctk | en | |
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| wavernn_csmsc | zh | |
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| pwgan_male | zh | |
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| hifigan_male | zh | |
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""" |
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class PaddleTTS: |
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def __init__(self) -> None: |
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pass |
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def predict(self, text, am, voc, spk_id = 174, lang = 'zh', male=False, save_path = 'output.wav'): |
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self.tts = TTSExecutor() |
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use_onnx = True |
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voc = voc.lower() |
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am = am.lower() |
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if male: |
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assert voc in ["pwgan", "hifigan"], "male voc must be 'pwgan' or 'hifigan'" |
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wav_file = self.tts( |
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text = text, |
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output = save_path, |
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am='fastspeech2_male', |
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voc= voc + '_male', |
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lang=lang, |
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use_onnx=use_onnx |
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) |
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return wav_file |
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assert am in ['tacotron2', 'fastspeech2'], "am must be 'tacotron2' or 'fastspeech2'" |
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if lang == 'mix': |
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am = 'fastspeech2_mix' |
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voc += '_csmsc' |
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elif lang == 'en': |
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am += '_ljspeech' |
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voc += '_ljspeech' |
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elif lang == 'zh': |
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assert voc in ['wavernn', 'pwgan', 'hifigan', 'style_melgan', 'mb_melgan'], "voc must be 'wavernn' or 'pwgan' or 'hifigan' or 'style_melgan' or 'mb_melgan'" |
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am += '_csmsc' |
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voc += '_csmsc' |
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elif lang == 'canton': |
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am = 'fastspeech2_canton' |
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voc = 'pwgan_aishell3' |
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spk_id = 10 |
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print("am:", am, "voc:", voc, "lang:", lang, "male:", male, "spk_id:", spk_id) |
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try: |
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cmd = f'paddlespeech tts --am {am} --voc {voc} --input "{text}" --output {save_path} --lang {lang} --spk_id {spk_id} --use_onnx {use_onnx}' |
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os.system(cmd) |
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wav_file = save_path |
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except: |
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wav_file = self.tts( |
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text = text, |
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output = save_path, |
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am = am, |
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voc = voc, |
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lang = lang, |
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spk_id = spk_id, |
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use_onnx=use_onnx |
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) |
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return wav_file |
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if __name__ == "__main__": |
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tts = PaddleTTS() |
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tts.predict("Hello world", 'FastSpeech2', 'PWGan', spk_id=174, lang='en', male=False, save_path='output.wav') |
