Model reference · open weights
wav2vec2-FR-7K is an open-weight embedding model from LeBenchmark. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | LeBenchmark |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 94M |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 1k downloads / month |
| Licence | Open weights |
About
LeBenchmark provides an ensemble of pretrained wav2vec2 models on different French datasets containing spontaneous, read, and broadcasted speech. It comes with 2 versions, in which, the later version (LeBenchmark 2.0) is an extended version of the first version in terms of both numbers of pre-trained SSL models, and numbers of downstream tasks. For more information on the different benchmarks that can be used to evaluate the wav2vec2 models, please refer to our paper at: LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
We release four different models that can be found under our HuggingFace organization. Four different wav2vec2 architectures Light, Base, Large and xLarge are coupled with our small (1K), medium (3K), large (7K), and extra large (14K) corpus. In short:
Pretrained wav2vec2 models are distributed under the Apache-2.0 license. Hence, they can be reused extensively without strict limitations. However, benchmarks and data may be linked to corpora that are not completely open-sourced.
As our wav2vec2 models were trained with Fairseq, then can be used in the different tools that they provide to fine-tune the model for ASR with CTC. The full procedure has been nicely summarized in this blogpost.
Please note that due to the nature of CTC, speech-to-text results aren't expected to be state-of-the-art. Moreover, future features might appear depending on the involvement of Fairseq and HuggingFace on this part.
Pretrained wav2vec models recently gained in popularity. At the same time, SpeechBrain toolkit came out, proposing a new and simpler way of dealing with state-of-the-art speech & deep-learning technologies.
While it currently is in beta, SpeechBrain offers two different ways of nicely integrating wav2vec2 models that were trained with Fairseq i.e our LeBenchmark models!
@misc{parcollet2023lebenchmark,
title={LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech},
author={Titouan Parcollet and Ha Nguyen and Solene Evain and Marcely Zanon Boito and Adrien Pupier and Salima Mdhaffar and Hang Le and Sina Alisamir and Natalia Tomashenko and Marco Dinarelli and Shucong Zhang and Alexandre Allauzen and Maximin Coavoux and Yannick Esteve and Mickael Rouvier and Jerome Goulian and Benjamin Lecouteux and Francois Portet and Solange Rossato and Fabien Ringeval and Didier Schwab and Laurent Besacier},
year={2023},
eprint={2309.05472},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys wav2vec2-fr-7k for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (wav2vec2-fr-7k below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"wav2vec2-fr-7k","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.