Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/collabora/WhisperSpeech

An Open Source text-to-speech system built by inverting Whisper.
https://github.com/collabora/WhisperSpeech

pytorch speech-synthesis tts

Last synced: about 2 months ago
JSON representation

An Open Source text-to-speech system built by inverting Whisper.

Awesome Lists containing this project

README 

        
# WhisperSpeech



[![Test it out yourself in

Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xxGlTbwBmaY6GKA24strRixTXGBOlyiw)

[![](https://dcbadge.vercel.app/api/server/FANw4rHD5E)](https://discord.gg/FANw4rHD5E)  

*If you have questions or you want to help you can find us in the

\#audio-generation channel on the LAION Discord server.*



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).



Sample of the synthesized voice:



https://github.com/collabora/WhisperSpeech/assets/107984/aa5a1e7e-dc94-481f-8863-b022c7fd7434



## Progress update \[2024-01-29\]



We successfully trained a `tiny` S2A model on an en+pl+fr dataset and it

can do voice cloning in French:



https://github.com/collabora/WhisperSpeech/assets/107984/267f2602-7eec-4646-a43b-059ff91b574e



https://github.com/collabora/WhisperSpeech/assets/107984/fbf08e8e-0f9a-4b0d-ab5e-747ffba2ccb9



We were able to do this with frozen semantic tokens that were only

trained on English and Polish. This supports the idea that we will be

able to train a single semantic token model to support all the languages

in the world. Quite likely even ones that are not currently well

supported by the Whisper model. Stay tuned for more updates on this

front. :)



## Progress update \[2024-01-18\]



We spend the last week optimizing inference performance. We integrated

`torch.compile`, added kv-caching and tuned some of the layers – we are

now working over 12x faster than real-time on a consumer 4090!



We can mix languages in a single sentence (here the highlighted English

project names are seamlessly mixed into Polish speech):



> To jest pierwszy test wielojęzycznego `Whisper Speech` modelu

> zamieniającego tekst na mowę, który `Collabora` i `Laion` nauczyli na

> superkomputerze `Jewels`.



https://github.com/collabora/WhisperSpeech/assets/107984/d7092ef1-9df7-40e3-a07e-fdc7a090ae9e



We also added an easy way to test voice-cloning. Here is a sample voice

cloned from [a famous speech by Winston

Churchill](https://en.wikipedia.org/wiki/File:Winston_Churchill_-_Be_Ye_Men_of_Valour.ogg)

(the radio static is a feature, not a bug ;) – it is part of the

reference recording):



https://github.com/collabora/WhisperSpeech/assets/107984/bd28110b-31fb-4d61-83f6-c997f560bc26



You can [test all of these on

Colab](https://colab.research.google.com/drive/1xxGlTbwBmaY6GKA24strRixTXGBOlyiw)

(we optimized the dependencies so now it takes less than 30 seconds to

install). A Huggingface Space is coming soon.



## Progress update \[2024-01-10\]



We’ve pushed a new SD S2A model that is a lot faster while still

generating high-quality speech. We’ve also added an example of voice

cloning based on a reference audio file.



As always, you can [check out our

Colab](https://colab.research.google.com/drive/1xxGlTbwBmaY6GKA24strRixTXGBOlyiw)

to try it yourself!



## Progress update \[2023-12-10\]



Another trio of models, this time they support multiple languages

(English and Polish). Here are two new samples for a sneak peek. You can

[check out our

Colab](https://colab.research.google.com/drive/1xxGlTbwBmaY6GKA24strRixTXGBOlyiw)

to try it yourself!



English speech, female voice (transferred from a Polish language

dataset):



https://github.com/collabora/WhisperSpeech/assets/107984/aa5a1e7e-dc94-481f-8863-b022c7fd7434



A Polish sample, male voice:



https://github.com/collabora/WhisperSpeech/assets/107984/4da14b03-33f9-4e2d-be42-f0fcf1d4a6ec



[Older progress updates are archived

here](https://github.com/collabora/WhisperSpeech/issues/23)



## Downloads



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](https://huggingface.co/collabora/whisperspeech) as well as [the

converted

datasets](https://huggingface.co/datasets/collabora/whisperspeech) are

available on HuggingFace.



## Roadmap



- [ ] [Gather a bigger emotive speech

  dataset](https://github.com/collabora/spear-tts-pytorch/issues/11)

- [ ] Figure out a way to condition the generation on emotions and

  prosody

- [ ] Create a community effort to gather freely licensed speech in

  multiple languages

- [ ] [Train final multi-language

  models](https://github.com/collabora/spear-tts-pytorch/issues/12)



## Architecture



The general architecture is similar to

[AudioLM](https://google-research.github.io/seanet/audiolm/examples/),

[SPEAR TTS](https://google-research.github.io/seanet/speartts/examples/)

from Google and [MusicGen](https://ai.honu.io/papers/musicgen/) from

Meta. We avoided the NIH syndrome and built it on top of powerful Open

Source models: [Whisper](https://github.com/openai/whisper) from OpenAI

to generate semantic tokens and perform transcription,

[EnCodec](https://github.com/facebookresearch/encodec) from Meta for

acoustic modeling and

[Vocos](https://github.com/charactr-platform/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:






[![](https://img.youtube.com/vi/6Fr-rq-yjXo/0.jpg)](https://www.youtube.com/watch?v=6Fr-rq-yjXo)



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:






[![](https://img.youtube.com/vi/1OBvf33S77Y/0.jpg)](https://www.youtube.com/watch?v=1OBvf33S77Y)



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](whisper-block.png)



## 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](https://github.com/facebookresearch/encodec/raw/main/architecture.png)



## Appreciation



[Collabora logo](https://www.collabora.com)      [LAION logo](https://laion.ai)



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



- [Collabora](https://www.collabora.com) – code development and model

  training

- [LAION](https://laion.ai) – community building and datasets (special

  thanks to

- [Jülich Supercomputing Centre](https://www.fz-juelich.de/en) - JUWELS

  Booster supercomputer



We gratefully acknowledge the Gauss Centre for Supercomputing e.V.

(www.gauss-centre.eu) 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:



- [inevitable-2031](https://github.com/inevitable-2031) (`qwerty_qwer`

  on Discord) for dataset curation



## Consulting



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

([![](https://dcbadge.vercel.app/api/shield/270267134960074762?style=flat)](https://discordapp.com/users/270267134960074762)

and

[![](https://dcbadge.vercel.app/api/shield/1088938086400016475?style=flat)](https://discordapp.com/users/1088938086400016475))



## Citations



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



``` bibtex

@article{SpearTTS,

  title = {Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision},

  url = {https://arxiv.org/abs/2302.03540},

  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},

}

```



``` bibtex

@article{MusicGen,

  title={Simple and Controllable Music Generation}, 

  url = {https://arxiv.org/abs/2306.05284},

  author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},

  publisher={arXiv},

  year={2023},

}

```



``` bibtex

@article{Whisper

  title = {Robust Speech Recognition via Large-Scale Weak Supervision},

  url = {https://arxiv.org/abs/2212.04356},

  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},

  publisher = {arXiv},

  year = {2022},

}

```



``` bibtex

@article{EnCodec

  title = {High Fidelity Neural Audio Compression},

  url = {https://arxiv.org/abs/2210.13438},

  author = {Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi},

  publisher = {arXiv},

  year = {2022},

}

```



``` bibtex

@article{Vocos

  title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis}, 

  url = {https://arxiv.org/abs/2306.00814},

  author={Hubert Siuzdak},

  publisher={arXiv},

  year={2023},

}

```