.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_intermediate_text_to_speech_with_torchaudio.py: Text-to-speech with torchaudio ============================== **Author**: `Yao-Yuan Yang `__, `Moto Hira `__ .. code-block:: default # %matplotlib inline Overview -------- This tutorial shows how to build text-to-speech pipeline, using the pretrained Tacotron2 in torchaudio. The text-to-speech pipeline goes as follows: 1. Text preprocessing First, the input text is encoded into a list of symbols. In this tutorial, we will use English characters and phonemes as the symbols. 2. Spectrogram generation From the encoded text, a spectrogram is generated. We use ``Tacotron2`` model for this. 3. Time-domain conversion The last step is converting the spectrogram into the waveform. The process to generate speech from spectrogram is also called Vocoder. In this tutorial, three different vocoders are used, ```WaveRNN`` `__, ```Griffin-Lim`` `__, and ```Nvidia's WaveGlow`` `__. The following figure illustrates the whole process. .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/tacotron2_tts_pipeline.png Preparation ----------- First, we install the necessary dependencies. In addition to ``torchaudio``, ``DeepPhonemizer`` is required to perform phoneme-based encoding. .. code-block:: default # When running this example in notebook, install DeepPhonemizer # !pip3 install deep_phonemizer import torch import torchaudio import matplotlib.pyplot as plt import IPython print(torch.__version__) print(torchaudio.__version__) torch.random.manual_seed(0) device = "cuda" if torch.cuda.is_available() else "cpu" Text Processing --------------- Character-based encoding ~~~~~~~~~~~~~~~~~~~~~~~~ In this section, we will go through how the character-based encoding works. Since the pre-trained Tacotron2 model expects specific set of symbol tables, the same functionalities available in ``torchaudio``. This section is more for the explanation of the basis of encoding. Firstly, we define the set of symbols. For example, we can use ``'_-!\'(),.:;? abcdefghijklmnopqrstuvwxyz'``. Then, we will map the each character of the input text into the index of the corresponding symbol in the table. The following is an example of such processing. In the example, symbols that are not in the table are ignored. .. code-block:: default symbols = '_-!\'(),.:;? abcdefghijklmnopqrstuvwxyz' look_up = {s: i for i, s in enumerate(symbols)} symbols = set(symbols) def text_to_sequence(text): text = text.lower() return [look_up[s] for s in text if s in symbols] text = "Hello world! Text to speech!" print(text_to_sequence(text)) As mentioned in the above, the symbol table and indices must match what the pretrained Tacotron2 model expects. ``torchaudio`` provides the transform along with the pretrained model. For example, you can instantiate and use such transform as follow. .. code-block:: default processor = torchaudio.pipelines.TACOTRON2_WAVERNN_CHAR_LJSPEECH.get_text_processor() text = "Hello world! Text to speech!" processed, lengths = processor(text) print(processed) print(lengths) The ``processor`` object takes either a text or list of texts as inputs. When a list of texts are provided, the returned ``lengths`` variable represents the valid length of each processed tokens in the output batch. The intermediate representation can be retrieved as follow. .. code-block:: default print([processor.tokens[i] for i in processed[0, :lengths[0]]]) Phoneme-based encoding ~~~~~~~~~~~~~~~~~~~~~~ Phoneme-based encoding is similar to character-based encoding, but it uses a symbol table based on phonemes and a G2P (Grapheme-to-Phoneme) model. The detail of the G2P model is out of scope of this tutorial, we will just look at what the conversion looks like. Similar to the case of character-based encoding, the encoding process is expected to match what a pretrained Tacotron2 model is trained on. ``torchaudio`` has an interface to create the process. The following code illustrates how to make and use the process. Behind the scene, a G2P model is created using ``DeepPhonemizer`` package, and the pretrained weights published by the author of ``DeepPhonemizer`` is fetched. .. code-block:: default bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH processor = bundle.get_text_processor() text = "Hello world! Text to speech!" with torch.inference_mode(): processed, lengths = processor(text) print(processed) print(lengths) Notice that the encoded values are different from the example of character-based encoding. The intermediate representation looks like the following. .. code-block:: default print([processor.tokens[i] for i in processed[0, :lengths[0]]]) Spectrogram Generation ---------------------- ``Tacotron2`` is the model we use to generate spectrogram from the encoded text. For the detail of the model, please refer to `the paper `__. It is easy to instantiate a Tacotron2 model with pretrained weight, however, note that the input to Tacotron2 models are processed by the matching text processor. ``torchaudio`` bundles the matching models and processors together so that it is easy to create the pipeline. (For the available bundles, and its usage, please refer to `the documentation `__.) .. code-block:: default bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH processor = bundle.get_text_processor() tacotron2 = bundle.get_tacotron2().to(device) text = "Hello world! Text to speech!" with torch.inference_mode(): processed, lengths = processor(text) processed = processed.to(device) lengths = lengths.to(device) spec, _, _ = tacotron2.infer(processed, lengths) plt.imshow(spec[0].cpu().detach()) Note that ``Tacotron2.infer`` method perfoms multinomial sampling, therefor, the process of generating the spectrogram incurs randomness. .. code-block:: default for _ in range(3): with torch.inference_mode(): spec, spec_lengths, _ = tacotron2.infer(processed, lengths) plt.imshow(spec[0].cpu().detach()) plt.show() Waveform Generation ------------------- Once the spectrogram is generated, the last process is to recover the waveform from the spectrogram. ``torchaudio`` provides vocoders based on ``GriffinLim`` and ``WaveRNN``. WaveRNN ~~~~~~~ Continuing from the previous section, we can instantiate the matching WaveRNN model from the same bundle. .. code-block:: default bundle = torchaudio.pipelines.TACOTRON2_WAVERNN_PHONE_LJSPEECH processor = bundle.get_text_processor() tacotron2 = bundle.get_tacotron2().to(device) vocoder = bundle.get_vocoder().to(device) text = "Hello world! Text to speech!" with torch.inference_mode(): processed, lengths = processor(text) processed = processed.to(device) lengths = lengths.to(device) spec, spec_lengths, _ = tacotron2.infer(processed, lengths) waveforms, lengths = vocoder(spec, spec_lengths) torchaudio.save("output_wavernn.wav", waveforms[0:1].cpu(), sample_rate=vocoder.sample_rate) IPython.display.display(IPython.display.Audio("output_wavernn.wav")) Griffin-Lim ~~~~~~~~~~~ Using the Griffin-Lim vocoder is same as WaveRNN. You can instantiate the vocode object with ``get_vocoder`` method and pass the spectrogram. .. code-block:: default bundle = torchaudio.pipelines.TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH processor = bundle.get_text_processor() tacotron2 = bundle.get_tacotron2().to(device) vocoder = bundle.get_vocoder().to(device) with torch.inference_mode(): processed, lengths = processor(text) processed = processed.to(device) lengths = lengths.to(device) spec, spec_lengths, _ = tacotron2.infer(processed, lengths) waveforms, lengths = vocoder(spec, spec_lengths) torchaudio.save("output_griffinlim.wav", waveforms[0:1].cpu(), sample_rate=vocoder.sample_rate) IPython.display.display(IPython.display.Audio("output_griffinlim.wav")) Waveglow ~~~~~~~~ Waveglow is a vocoder published by Nvidia. The pretrained weight is publishe on Torch Hub. One can instantiate the model using ``torch.hub`` module. .. code-block:: default waveglow = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_waveglow', model_math='fp32') waveglow = waveglow.remove_weightnorm(waveglow) waveglow = waveglow.to(device) waveglow.eval() with torch.no_grad(): waveforms = waveglow.infer(spec) torchaudio.save("output_waveglow.wav", waveforms[0:1].cpu(), sample_rate=22050) IPython.display.display(IPython.display.Audio("output_waveglow.wav")) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.000 seconds) .. _sphx_glr_download_intermediate_text_to_speech_with_torchaudio.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: text_to_speech_with_torchaudio.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: text_to_speech_with_torchaudio.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_