mms-tts-eng
Maintainer: facebook
115
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The mms-tts-eng model is part of Facebook's Massively Multilingual Speech (MMS) project, which aims to provide speech technology across a diverse range of languages. This particular checkpoint is for the English (eng) language text-to-speech (TTS) model.
The model is based on VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech), 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. The model uses a flow-based module to predict spectrogram-based acoustic features, and a stack of transposed convolutional layers to decode the spectrogram into a waveform. It also includes a stochastic duration predictor to allow for synthesizing speech with different rhythms from the same input text.
The MMS project trains a separate VITS checkpoint for each language. This model is available through the Transformers library from version 4.33 onwards.
Model inputs and outputs
Inputs
- Text: The model takes a text sequence as input, which it uses to generate a corresponding speech waveform.
Outputs
- Audio waveform: The model outputs a speech waveform that corresponds to the input text.
Capabilities
The mms-tts-eng model can be used to generate high-quality speech audio from text input. It is capable of producing natural-sounding speech with expressive prosody and rhythm variations. This makes it suitable for applications such as text-to-speech conversion, audiobook narration, and voice assistants.
What can I use it for?
The mms-tts-eng model can be used in a variety of applications that require text-to-speech conversion, such as:
- Audiobook narration: The model can be used to generate speech audio from book text, allowing for the creation of audiobooks.
- Voice assistants: The model can be integrated into voice assistant systems to enable them to read out text or respond to user queries with synthesized speech.
- Accessibility tools: The model can be used to provide text-to-speech functionality for users with visual impairments or reading difficulties.
- Content creation: The model can be used to generate spoken versions of written content, such as news articles or blog posts, for users who prefer to consume information through audio.
Things to try
One interesting aspect of the mms-tts-eng model is its ability to generate speech with different rhythms and prosody from the same input text. This can be explored by varying the input text and observing how the model's output changes. For example, you could try generating speech for the same text with different emotional tones or styles (e.g., formal vs. casual, excited vs. calm) to see how the model adapts the intonation and timing of the speech.
Additionally, you could experiment with fine-tuning the model on a specific domain or style of speech, such as audiobook narration or voice assistant responses, to see how it performs on more specialized tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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