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VITS

Overview

The VITS model was proposed in Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech by Jaehyeon Kim, Jungil Kong, Juhee Son.

VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior.

A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

The model is trained end-to-end with a combination of losses derived from variational lower bound and adversarial training. To improve the expressiveness of the model, normalizing flows are applied to the conditional prior distribution. During inference, the text encodings are up-sampled based on the duration prediction module, and then mapped into the waveform using a cascade of the flow module and HiFi-GAN decoder. Due to the stochastic nature of the duration predictor, the model is non-deterministic, and thus requires a fixed seed to generate the same speech waveform.

The abstract from the paper is the following:

Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In this work, we present a parallel end-to-end TTS method that generates more natural sounding audio than current two-stage models. Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling. We also propose a stochastic duration predictor to synthesize speech with diverse rhythms from input text. With the uncertainty modeling over latent variables and the stochastic duration predictor, our method expresses the natural one-to-many relationship in which a text input can be spoken in multiple ways with different pitches and rhythms. A subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a single speaker dataset, shows that our method outperforms the best publicly available TTS systems and achieves a MOS comparable to ground truth.

This model can also be used with TTS checkpoints from Massively Multilingual Speech (MMS) as these checkpoints use the same architecture and a slightly modified tokenizer.

This model was contributed by Matthijs and sanchit-gandhi. The original code can be found here.

Usage examples

Both the VITS and MMS-TTS checkpoints can be used with the same API. Since the flow-based model is non-deterministic, it is good practice to set a seed to ensure reproducibility of the outputs. For languages with a Roman alphabet, such as English or French, the tokenizer can be used directly to pre-process the text inputs. The following code example runs a forward pass using the MMS-TTS English checkpoint:

import torch
from transformers import VitsTokenizer, VitsModel, set_seed

tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
model = VitsModel.from_pretrained("facebook/mms-tts-eng")

inputs = tokenizer(text="Hello - my dog is cute", return_tensors="pt")

set_seed(555)  # make deterministic

with torch.no_grad():
   outputs = model(**inputs)

waveform = outputs.waveform[0]

The resulting waveform can be saved as a .wav file:

import scipy

scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=waveform)

Or displayed in a Jupyter Notebook / Google Colab:

from IPython.display import Audio

Audio(waveform, rate=model.config.sampling_rate)

For certain languages with a non-Roman alphabet, such as Arabic, Mandarin or Hindi, the uroman perl package is required to pre-process the text inputs to the Roman alphabet.

You can check whether you require the uroman package for your language by inspecting the is_uroman attribute of the pre-trained tokenizer:

from transformers import VitsTokenizer

tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
print(tokenizer.is_uroman)

If required, you should apply the uroman package to your text inputs prior to passing them to the VitsTokenizer, since currently the tokenizer does not support performing the pre-processing itself.

To do this, first clone the uroman repository to your local machine and set the bash variable UROMAN to the local path:

git clone https://github.com/isi-nlp/uroman.git
cd uroman
export UROMAN=$(pwd)

You can then pre-process the text input using the following code snippet. You can either rely on using the bash variable UROMAN to point to the uroman repository, or you can pass the uroman directory as an argument to the uromaize function:

import torch
from transformers import VitsTokenizer, VitsModel, set_seed
import os
import subprocess

tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-kor")
model = VitsModel.from_pretrained("facebook/mms-tts-kor")

def uromanize(input_string, uroman_path):
    """Convert non-Roman strings to Roman using the `uroman` perl package."""
    script_path = os.path.join(uroman_path, "bin", "uroman.pl")

    command = ["perl", script_path]

    process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    # Execute the perl command
    stdout, stderr = process.communicate(input=input_string.encode())

    if process.returncode != 0:
        raise ValueError(f"Error {process.returncode}: {stderr.decode()}")

    # Return the output as a string and skip the new-line character at the end
    return stdout.decode()[:-1]

text = "이봐 무슨 일이야"
uromaized_text = uromanize(text, uroman_path=os.environ["UROMAN"])

inputs = tokenizer(text=uromaized_text, return_tensors="pt")

set_seed(555)  # make deterministic
with torch.no_grad():
   outputs = model(inputs["input_ids"])

waveform = outputs.waveform[0]

VitsConfig

autodoc VitsConfig

VitsTokenizer

autodoc VitsTokenizer - call - save_vocabulary

VitsModel

autodoc VitsModel - forward

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