.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "tutorials/tacotron2_pipeline_tutorial.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_tutorials_tacotron2_pipeline_tutorial.py: Text-to-Speech with Tacotron2 ============================= **Author**: `Yao-Yuan Yang `__, `Moto Hira `__ .. GENERATED FROM PYTHON SOURCE LINES 9-14 .. code-block:: default import IPython import matplotlib import matplotlib.pyplot as plt .. GENERATED FROM PYTHON SOURCE LINES 15-49 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, :py:class:`~torchaudio.models.WaveRNN`, :py:class:`~torchaudio.transforms.GriffinLim`, and `Nvidia's WaveGlow `__. The following figure illustrates the whole process. .. image:: https://download.pytorch.org/torchaudio/tutorial-assets/tacotron2_tts_pipeline.png All the related components are bundled in :py:class:`torchaudio.pipelines.Tacotron2TTSBundle`, but this tutorial will also cover the process under the hood. .. GENERATED FROM PYTHON SOURCE LINES 51-58 Preparation ----------- First, we install the necessary dependencies. In addition to ``torchaudio``, ``DeepPhonemizer`` is required to perform phoneme-based encoding. .. GENERATED FROM PYTHON SOURCE LINES 60-64 .. code-block:: bash %%bash pip3 install deep_phonemizer .. GENERATED FROM PYTHON SOURCE LINES 64-78 .. code-block:: default import torch import torchaudio matplotlib.rcParams["figure.figsize"] = [16.0, 4.8] torch.random.manual_seed(0) device = "cuda" if torch.cuda.is_available() else "cpu" print(torch.__version__) print(torchaudio.__version__) print(device) .. rst-class:: sphx-glr-script-out .. code-block:: none 1.13.0 0.13.0 cpu .. GENERATED FROM PYTHON SOURCE LINES 79-82 Text Processing --------------- .. GENERATED FROM PYTHON SOURCE LINES 85-103 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. .. GENERATED FROM PYTHON SOURCE LINES 103-118 .. 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)) .. rst-class:: sphx-glr-script-out .. code-block:: none [19, 16, 23, 23, 26, 11, 34, 26, 29, 23, 15, 2, 11, 31, 16, 35, 31, 11, 31, 26, 11, 30, 27, 16, 16, 14, 19, 2] .. GENERATED FROM PYTHON SOURCE LINES 119-124 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. .. GENERATED FROM PYTHON SOURCE LINES 124-134 .. 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) .. rst-class:: sphx-glr-script-out .. code-block:: none tensor([[19, 16, 23, 23, 26, 11, 34, 26, 29, 23, 15, 2, 11, 31, 16, 35, 31, 11, 31, 26, 11, 30, 27, 16, 16, 14, 19, 2]]) tensor([28], dtype=torch.int32) .. GENERATED FROM PYTHON SOURCE LINES 135-142 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. .. GENERATED FROM PYTHON SOURCE LINES 142-146 .. code-block:: default print([processor.tokens[i] for i in processed[0, : lengths[0]]]) .. rst-class:: sphx-glr-script-out .. code-block:: none ['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd', '!', ' ', 't', 'e', 'x', 't', ' ', 't', 'o', ' ', 's', 'p', 'e', 'e', 'c', 'h', '!'] .. GENERATED FROM PYTHON SOURCE LINES 147-166 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. .. GENERATED FROM PYTHON SOURCE LINES 166-179 .. 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) .. rst-class:: sphx-glr-script-out .. code-block:: none 0%| | 0.00/63.6M [00:00`__. It is easy to instantiate a Tacotron2 model with pretrained weight, however, note that the input to Tacotron2 models need to be processed by the matching text processor. :py:class:`torchaudio.pipelines.Tacotron2TTSBundle` 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 :py:class:`~torchaudio.pipelines.Tacotron2TTSBundle`. .. GENERATED FROM PYTHON SOURCE LINES 207-224 .. 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()) .. image-sg:: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_001.png :alt: tacotron2 pipeline tutorial :srcset: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading: "https://download.pytorch.org/torchaudio/models/tacotron2_english_phonemes_1500_epochs_wavernn_ljspeech.pth" to /root/.cache/torch/hub/checkpoints/tacotron2_english_phonemes_1500_epochs_wavernn_ljspeech.pth 0%| | 0.00/107M [00:00

.. GENERATED FROM PYTHON SOURCE LINES 281-289 Griffin-Lim ~~~~~~~~~~~ Using the Griffin-Lim vocoder is same as WaveRNN. You can instantiate the vocode object with :py:func:`~torchaudio.pipelines.Tacotron2TTSBundle.get_vocoder` method and pass the spectrogram. .. GENERATED FROM PYTHON SOURCE LINES 289-310 .. 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) fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9)) ax1.imshow(spec[0].cpu().detach()) ax2.plot(waveforms[0].cpu().detach()) IPython.display.Audio(waveforms[0:1].cpu(), rate=vocoder.sample_rate) .. image-sg:: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_004.png :alt: tacotron2 pipeline tutorial :srcset: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading: "https://download.pytorch.org/torchaudio/models/tacotron2_english_phonemes_1500_epochs_ljspeech.pth" to /root/.cache/torch/hub/checkpoints/tacotron2_english_phonemes_1500_epochs_ljspeech.pth 0%| | 0.00/107M [00:00

.. GENERATED FROM PYTHON SOURCE LINES 311-318 Waveglow ~~~~~~~~ Waveglow is a vocoder published by Nvidia. The pretrained weights are published on Torch Hub. One can instantiate the model using ``torch.hub`` module. .. GENERATED FROM PYTHON SOURCE LINES 318-347 .. code-block:: default # Workaround to load model mapped on GPU # https://stackoverflow.com/a/61840832 waveglow = torch.hub.load( "NVIDIA/DeepLearningExamples:torchhub", "nvidia_waveglow", model_math="fp32", pretrained=False, ) checkpoint = torch.hub.load_state_dict_from_url( "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth", # noqa: E501 progress=False, map_location=device, ) state_dict = {key.replace("module.", ""): value for key, value in checkpoint["state_dict"].items()} waveglow.load_state_dict(state_dict) waveglow = waveglow.remove_weightnorm(waveglow) waveglow = waveglow.to(device) waveglow.eval() with torch.no_grad(): waveforms = waveglow.infer(spec) fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9)) ax1.imshow(spec[0].cpu().detach()) ax2.plot(waveforms[0].cpu().detach()) IPython.display.Audio(waveforms[0:1].cpu(), rate=22050) .. image-sg:: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_005.png :alt: tacotron2 pipeline tutorial :srcset: /tutorials/images/sphx_glr_tacotron2_pipeline_tutorial_005.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /usr/local/envs/python3.8/lib/python3.8/site-packages/torch/hub.py:267: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour warnings.warn( Downloading: "https://github.com/NVIDIA/DeepLearningExamples/zipball/torchhub" to /root/.cache/torch/hub/torchhub.zip /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/common.py:13: UserWarning: pytorch_quantization module not found, quantization will not be available warnings.warn( /root/.cache/torch/hub/NVIDIA_DeepLearningExamples_torchhub/PyTorch/Classification/ConvNets/image_classification/models/efficientnet.py:17: UserWarning: pytorch_quantization module not found, quantization will not be available warnings.warn( Downloading: "https://api.ngc.nvidia.com/v2/models/nvidia/waveglowpyt_fp32/versions/1/files/nvidia_waveglowpyt_fp32_20190306.pth" to /root/.cache/torch/hub/checkpoints/nvidia_waveglowpyt_fp32_20190306.pth .. raw:: html


.. rst-class:: sphx-glr-timing **Total running time of the script:** ( 2 minutes 32.893 seconds) .. _sphx_glr_download_tutorials_tacotron2_pipeline_tutorial.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: tacotron2_pipeline_tutorial.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: tacotron2_pipeline_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_