tacotron2_pipeline_tutorial.py

"""
Text-to-Speech with Tacotron2
=============================

**Author** `Yao-Yuan Yang <https://github.com/yangarbiter>`__,
`Moto Hira <moto@fb.com>`__

"""

import IPython
import matplotlib
import matplotlib.pyplot as plt

######################################################################
# 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 <https://pytorch.org/hub/nvidia_deeplearningexamples_tacotron2/>`__.
#
#
# 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.

######################################################################
# Preparation
# -----------
#
# First, we install the necessary dependencies. In addition to
# ``torchaudio``, ``DeepPhonemizer`` is required to perform phoneme-based
# encoding.
#

# %%
#  .. code-block:: bash
#
#      %%bash
#      pip3 install deep_phonemizer

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)


######################################################################
# 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.
#

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.
#

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.
#

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.
#

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.
#

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 <https://arxiv.org/abs/1712.05884>`__.
#
# 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`.
#

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.
#

fig, ax = plt.subplots(3, 1, figsize=(16, 4.3 * 3))
for i in range(3):
    with torch.inference_mode():
        spec, spec_lengths, _ = tacotron2.infer(processed, lengths)
    print(spec[0].shape)
    ax[i].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.
#

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)

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)


######################################################################
# 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.
#

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)


######################################################################
# 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.
#

# 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)