Introduction to Facebook Massively Multilingual Speech (MMS): English Text-to-Speech

Unreal Speech

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Introduction to Facebook Massively Multilingual Speech (MMS): English Text-to-Speech

Introduction

Massively Multilingual Speech (MMS) represents a groundbreaking development in speech technology, spearheaded by Facebook's AI research team. The English Text-to-Speech (TTS) model, which is a part of this project, stands as a testament to the significant advancements in language technology. This blog post delves into the intricacies of the MMS: English TTS model, exploring its applications, limitations, and practical usage.

Understanding the Model

The MMS: English TTS model employs VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech), an end-to-end speech synthesis model that predicts speech waveforms from text inputs. This model is unique in its structure, combining a variational autoencoder with a posterior encoder, decoder, and conditional prior, alongside a flow-based module for predicting spectrogram-based acoustic features. The key features of this model include:

  • Transformer-based text encoder: Converts input text into a spectrogram.
  • Stochastic duration predictor: Allows for varying speech rhythms from the same text.
  • HiFi-GAN vocoder: Decodes the spectrogram into speech.
  • Non-deterministic nature: Requires a fixed seed for consistent output.


Applications of MMS: English TTS

  1. Multilingual Speech Synthesis: As part of the broader MMS project, this model aids in delivering high-quality speech synthesis across multiple languages.
  2. Assistive Technologies: Enhancing accessibility for visually impaired users or those with reading difficulties.
  3. Content Creation: Useful for generating voiceovers or audio content from written text.
  4. Educational Tools: Assisting in language learning and reading comprehension.
  5. Voice Assistants and Chatbots: Enabling more natural and varied responses in AI-driven communication.

Limitations of the Model

While the MMS: English TTS model is highly advanced, it is not without its limitations:

  • Stochastic Output: The non-deterministic nature may lead to variations in output, which can be a challenge for consistency in applications.
  • Resource Intensity: The model's complexity could require substantial computational resources.
  • Language Limitation: This specific model is focused on English, necessitating separate models for other languages.
  • License Restrictions: The model is licensed under CC-BY-NC 4.0, which may limit its commercial use.

How to Use the MMS: English TTS Model

The model is accessible through the Transformers library (version 4.33 onwards). To use the model, follow these steps:

Install the Transformers Library:


pip install --upgrade transformers accelerate

Run the Model:


from transformers import VitsModel, AutoTokenizer
import torch

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

text = "Your text here"
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
    output = model(**inputs).waveform

Saving or Playing the Output:

  • To save as a .wav file:

import scipy
scipy.io.wavfile.write("output.wav", rate=model.config.sampling_rate, data=output.float().numpy())

To play in a Jupyter Notebook or Google Colab:


from IPython.display import Audio
Audio(output.numpy(), rate=model.config.sampling_rate)

Full Example:


# Step 1: Install the Necessary Libraries
# Run this command in your Python environment
# pip install --upgrade transformers accelerate

# Step 2: Import Libraries and Load the Model
from transformers import VitsModel, AutoTokenizer
import torch

# Load model and tokenizer
model = VitsModel.from_pretrained("facebook/mms-tts-eng")
tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-eng")

# Step 3: Prepare Your Text
text = "Your text here"  # Replace with your text
inputs = tokenizer(text, return_tensors="pt")

# Step 4: Generate the Speech
with torch.no_grad():
    output = model(**inputs).waveform

# Step 5: Save or Play the Output
# To save as a .wav file
import scipy.io.wavfile

scipy.io.wavfile.write("output.wav", rate=model.config.sampling_rate, data=output.float().numpy())

# To play in a Jupyter Notebook (optional)
# from IPython.display import Audio
# Audio(output.numpy(), rate=model.config.sampling_rate)

Conclusion

The MMS: English TTS model by Facebook's AI team is a remarkable step in the domain of speech synthesis, particularly in its application across multiple languages. Its innovative architecture and the flexibility it offers in speech synthesis make it a valuable tool in various fields, from education to technology. Despite its limitations, the potential applications of this model in enhancing communication and accessibility are immense. With advancements like these, the future of speech technology looks more inclusive and versatile than ever.