An Open Source text-to-speech system built by inverting Whisper. Previously known as spear-tts-pytorch.

We want this model to be like Stable Diffusion but for speech – both powerful and easily customizable.

We are working only with properly licensed speech recordings and all the code is Open Source so the model will be always safe to use for commercial applications.

Currently the models are trained on the English LibreLight dataset. In the next release we want to target multiple languages (Whisper and EnCodec are both multilanguage).


We encourage you to start with the Google Colab link above or run the provided notebook locally. If you want to download manually or train the models from scratch then both the WhisperSpeech pre-trained models as well as the converted datasets are available on HuggingFace.



The general architecture is similar to AudioLMSPEAR TTS from Google and MusicGen from Meta. We avoided the NIH syndrome and built it on top of powerful Open Source models: Whisper from OpenAI to generate semantic tokens and perform transcription, EnCodec from Meta for acoustic modeling and Vocos from Charactr Inc as the high-quality vocoder.

We gave two presentation diving deeper into WhisperSpeech. The first one talks about the challenges of large scale training:

Tricks Learned from Scaling WhisperSpeech Models to 80k+ Hours of Speech – video recording by Jakub Cłapa, Collabora

The other one goes a bit more into the architectural choices we made:

Open Source Text-To-Speech Projects: WhisperSpeech – In Depth Discussion

Whisper for modeling semantic tokens

We utilize the OpenAI Whisper encoder block to generate embeddings which we then quantize to get semantic tokens.

If the language is already supported by Whisper then this process requires only audio files (without ground truth transcriptions).

Using Whisper for semantic token extraction diagram

EnCodec for modeling acoustic tokens

We use EnCodec to model the audio waveform. Out of the box it delivers reasonable quality at 1.5kbps and we can bring this to high-quality by using Vocos – a vocoder pretrained on EnCodec tokens.

EnCodec block diagram


Collabora logo      LAION logo

This work would not be possible without the generous sponsorships from:

We gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( for funding part of this work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC), with access to compute provided via LAION cooperation on foundation models research.

We’d like to also thank individual contributors for their great help in building this model:


We are available to help you with both Open Source and proprietary AI projects. You can reach us via the Collabora website or on Discord ( and )


We rely on many amazing Open Source projects and research papers:

  title = {Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision},
  url = {},
  author = {Kharitonov, Eugene and Vincent, Damien and Borsos, Zalán and Marinier, Raphaël and Girgin, Sertan and Pietquin, Olivier and Sharifi, Matt and Tagliasacchi, Marco and Zeghidour, Neil},
  publisher = {arXiv},
  year = {2023},
  title={Simple and Controllable Music Generation}, 
  url = {},
  author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
  url = {},
  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
  publisher = {arXiv},
  year = {2022},
  title = {High Fidelity Neural Audio Compression},
  url = {},
  author = {Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi},
  publisher = {arXiv},
  year = {2022},
  title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis}, 
  url = {},
  author={Hubert Siuzdak},