Python PyTorch Tacotron2TTSBundle用法及代码示例

本文简要介绍python语言中 torchaudio.pipelines.Tacotron2TTSBundle 的用法。

用法:

class torchaudio.pipelines.Tacotron2TTSBundle

捆绑相关信息以使用预训练 Tacotron2 和声码器的数据类。

该类提供了用于实例化预训练模型的接口以及检索预训练权重所需的信息以及与模型一起使用的附加数据。

Torchaudio 库实例化了这个类的对象，每个对象代表一个不同的预训练模型。客户端代码应通过这些实例访问预训练模型。

请参阅下面的用法和可用值。

示例 - 使用 Tacotron2 和 WaveRNN 的基于字符的 TTS 管道

>>> import torchaudio
>>>
>>> text = "Hello, T T S !"
>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_CHAR_LJSPEECH
>>>
>>> # Build processor, Tacotron2 and WaveRNN model
>>> processor = bundle.get_text_processor()
>>> tacotron2 = bundle.get_tacotron2()
Downloading:
100%|███████████████████████████████| 107M/107M [00:01<00:00, 87.9MB/s]
>>> vocoder = bundle.get_vocoder()
Downloading:
100%|███████████████████████████████| 16.7M/16.7M [00:00<00:00, 78.1MB/s]
>>>
>>> # Encode text
>>> input, lengths = processor(text)
>>>
>>> # Generate (mel-scale) spectrogram
>>> specgram, lengths, _ = tacotron2.infer(input, lengths)
>>>
>>> # Convert spectrogram to waveform
>>> waveforms, lengths = vocoder(specgram, lengths)
>>>
>>> torchaudio.save('hello-tts.wav', waveforms[0], vocoder.sample_rate)

示例 - 使用 Tacotron2 和 WaveRNN 的基于音素的 TTS 管道

>>>
>>> # Note:
>>> #     This bundle uses pre-trained DeepPhonemizer as
>>> #     the text pre-processor.
>>> #     Please install deep-phonemizer.
>>> #     See https://github.com/as-ideas/DeepPhonemizer
>>> #     The pretrained weight is automatically downloaded.
>>>
>>> import torchaudio
>>>
>>> text = "Hello, TTS!"
>>> bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONEME_LJSPEECH
>>>
>>> # Build processor, Tacotron2 and WaveRNN model
>>> processor = bundle.get_text_processor()
Downloading:
100%|███████████████████████████████| 63.6M/63.6M [00:04<00:00, 15.3MB/s]
>>> tacotron2 = bundle.get_tacotron2()
Downloading:
100%|███████████████████████████████| 107M/107M [00:01<00:00, 87.9MB/s]
>>> vocoder = bundle.get_vocoder()
Downloading:
100%|███████████████████████████████| 16.7M/16.7M [00:00<00:00, 78.1MB/s]
>>>
>>> # Encode text
>>> input, lengths = processor(text)
>>>
>>> # Generate (mel-scale) spectrogram
>>> specgram, lengths, _ = tacotron2.infer(input, lengths)
>>>
>>> # Convert spectrogram to waveform
>>> waveforms, lengths = vocoder(specgram, lengths)
>>>
>>> torchaudio.save('hello-tts.wav', waveforms[0], vocoder.sample_rate)