whisperspeech

Maintainer: collabora

Total Score

125

Last updated 5/28/2024

📉

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

whisperspeech is an open-source text-to-speech system built by inversing the Whisper model. The goal is to create a powerful and customizable speech generation model similar to Stable Diffusion. The model is trained on properly licensed speech recordings and the code is open-source, making it safe to use for commercial applications.

Currently, the models are trained on the English LibreLight dataset, but the team plans to target multiple languages in the future by leveraging the multilingual capabilities of Whisper and EnCodec. The model can also seamlessly mix languages in a single sentence, as demonstrated in the progress updates.

Model inputs and outputs

The whisperspeech model takes text as input and generates corresponding speech audio as output. It utilizes the Whisper model's architecture to invert the speech recognition task and produce speech from text.

Inputs

  • Text prompts for the model to generate speech from

Outputs

  • Audio files containing the generated speech

Capabilities

The whisperspeech model demonstrates the ability to generate high-quality speech in multiple languages, including the seamless mixing of languages within a single sentence. It has been optimized for inference performance, achieving over 12x real-time processing speed on a consumer GPU.

The model also showcases voice cloning capabilities, allowing users to generate speech that mimics the voice of a reference audio clip, such as a famous speech by Winston Churchill.

What can I use it for?

The whisperspeech model can be used to create various speech-based applications, such as:

  • Accessibility tools: The model's capabilities can be leveraged to improve accessibility by providing text-to-speech functionality.
  • Conversational AI: The model's ability to generate natural-sounding speech can be used to enhance conversational AI agents.
  • Audiobook creation: The model can be used to generate speech from text, enabling the creation of audiobooks and other spoken content.
  • Language learning: The model's multilingual capabilities can be utilized to create language learning resources with realistic speech output.

Things to try

One key feature of the whisperspeech model is its ability to seamlessly mix languages within a single sentence. This can be a useful technique for creating multilingual content or for training language models on code-switched data.

Additionally, the model's voice cloning capabilities open up possibilities for personalized speech synthesis, where users can generate speech that mimics the voice of a particular individual. This could be useful for audiobook narration, virtual assistants, or other applications where a specific voice is desired.



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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