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Parler-TTS - Hugging Face's open-source text-to-speech model

Parler-TTS is an open-source text-to-speech (TTS) model developed by Hugging Face. It can mimic the style of a specific speaker (gender, tone, speaking style, etc.) based on input prompts, generating high-quality, audible audio...

What is Parler-TTS?

Parler-TTS, developed by Hugging Face, is an open-source text-to-speech (TTS) model that mimics a specific speaker's style (gender, tone, speaking style, etc.) based on input prompts, generating high-quality, natural-sounding speech. This lightweight TTS model is completely open-source, with all datasets, preprocessing, training code, and weights publicly available, aiming to promote innovative development of high-quality, controllable TTS models. Parler-TTS's architecture is based on MusicGen, including a text encoder, decoder, and audio codec, and optimizes sound generation by integrating text descriptions and adding embedding layers.

Parler-TTS official website entrance

Features of Parler-TTS

  • High-quality speech generationParler-TTS can generate high-quality, natural-sounding speech based on text input, mimicking different speaking styles such as gender, pitch, and expression.
  • Diverse voice output stylesThrough detailed text descriptions, users can control the generated voice style, including features such as the speaker's age, emotion, speed, and environment.
  • Open source architectureParler-TTS is based on the MusicGen architecture and includes a text encoder, decoder, and audio codec, allowing researchers and developers to freely access and modify the code to suit different needs and applications.
  • Easy to install and useParler-TTS provides a simple installation command that allows users to install it with a single command. It also provides easy-to-understand code examples, making it easy for even beginners to get started quickly.
  • Custom training and fine-tuningUsers can train and fine-tune Parler-TTS based on their own datasets to generate speech with specific styles or accents.
  • Ethics and privacy protectionParler-TTS avoids using voice cloning technology that could infringe on privacy, instead controlling voice generation through text prompts, thus ensuring the ethical and compliant nature of the technology.

How to experience Parler-TTS

  1. accessHugging Face Demo by Parler-TTSThen enter the text you want to transcribe in the Input Text field.
  2. Enter a description of the sound prompt in the Description field.
  3. Finally, click "Generate Audio" to generate the sound.

Parler-TTS Technical Architecture

Parler-TTS is a highly flexible and customizable system, based on the MusicGen architecture with some key improvements and adjustments:

  1. Text Encoder:
    • The role of a text encoder is to map text descriptions to a series of hidden state representations.
    • Parler-TTS uses a frozen text encoder that is completely initialized from the Flan-T5 model. This means that the encoder's parameters do not change during training; it is only used to convert the input text into an internal representation that the model can understand.
  2. Parler-TTS decoder:
    • The decoder is a language model that generates audio tokens (or codes) autoregressively based on the encoder’s hidden state representation.
    • In this process, the decoder gradually generates an audio representation of the speech, taking into account the previous output and text description at each step, thereby generating coherent speech that matches the description.
  3. audio codec:
    • The role of an audio codec is to convert the audio tokens predicted by the decoder back into audible audio waveforms.
    • Parler-TTS uses the DAC model provided by Descript, but other codec models, such as EnCodec, can also be used.
  4. Architectural improvements:
    • Parler-TTS makes some minor adjustments to the MusicGen architecture to improve model performance and flexibility.
    • The text description is not only processed by the text encoder, but also used in the cross-attention layer of the decoder, which enables the decoder to better combine the text description and audio generation.
    • The text prompts are processed through an embedding layer and then concatenated with the hidden state of the decoder input. This allows the semantic information of the text prompts to be directly integrated into the speech generation process.
    • Audio encoders choose DACs instead of encoders because DACs perform better in terms of quality.
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