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The text processor encodes the input texts character-by-character. You can find the training script `here `__. The default parameters were used. Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage. Example - "Hello world! T T S stands for Text to Speech!" .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH.png :alt: Spectrogram generated by Tacotron2 .. raw:: html Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired," .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH_v2.png :alt: Spectrogram generated by Tacotron2 .. raw:: html z3tacotron2_english_phonemes_1500_epochs_ljspeech.pth`aPhoneme-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs and :py:class:`~torchaudio.transforms.GriffinLim` as vocoder. The text processor encodes the input texts based on phoneme. It uses `DeepPhonemizer `__ to convert graphemes to phonemes. The model (*en_us_cmudict_forward*) was trained on `CMUDict `__. You can find the training script `here `__. The text processor is set to the *"english_phonemes"*. Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage. Example - "Hello world! T T S stands for Text to Speech!" .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH.png :alt: Spectrogram generated by Tacotron2 .. raw:: html Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired," .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH_v2.png :alt: Spectrogram generated by Tacotron2 .. raw:: html z=tacotron2_english_characters_1500_epochs_wavernn_ljspeech.pthz%wavernn_10k_epochs_8bits_ljspeech.pth)rrrraCharacter-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs and :py:class:`~torchaudio.models.WaveRNN` vocoder trained on 8 bits depth waveform of *LJSpeech* :cite:`ljspeech17` for 10,000 epochs. The text processor encodes the input texts character-by-character. You can find the training script `here `__. The following parameters were used; ``win_length=1100``, ``hop_length=275``, ``n_fft=2048``, ``mel_fmin=40``, and ``mel_fmax=11025``. You can find the training script `here `__. Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage. Example - "Hello world! T T S stands for Text to Speech!" .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH.png :alt: Spectrogram generated by Tacotron2 .. raw:: html Example - "The examination and testimony of the experts enabled the Commission to conclude that five shots may have been fired," .. image:: https://download.pytorch.org/torchaudio/doc-assets/TACOTRON2_WAVERNN_CHAR_LJSPEECH_v2.png :alt: Spectrogram generated by Tacotron2 .. raw:: html z;tacotron2_english_phonemes_1500_epochs_wavernn_ljspeech.pthaPhoneme-based TTS pipeline with :py:class:`~torchaudio.models.Tacotron2` trained on *LJSpeech* :cite:`ljspeech17` for 1,500 epochs, and :py:class:`~torchaudio.models.WaveRNN` vocoder trained on 8 bits depth waveform of *LJSpeech* :cite:`ljspeech17` for 10,000 epochs. The text processor encodes the input texts based on phoneme. It uses `DeepPhonemizer `__ to convert graphemes to phonemes. The model (*en_us_cmudict_forward*) was trained on `CMUDict `__. You can find the training script for Tacotron2 `here `__. The following parameters were used; ``win_length=1100``, ``hop_length=275``, ``n_fft=2048``, ``mel_fmin=40``, and ``mel_fmax=11025``. You can find the training script for WaveRNN `here `__. Please refer to :func:`torchaudio.pipelines.Tacotron2TTSBundle` for the usage. Example - "Hello world! 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