Model reference · open weights
w2v-bert-2.0 is an open-weight embedding model from facebook, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
W2v-BERT 2.0 speech encoder We are open-sourcing our Conformer-based W2v-BERT 2.0 speech encoder as described in Section 3.2.1 of the paper, which is at the core of our Seamless models. This model was pre-trained on 4.5M hours of unlabeled audio data covering more than 143 languages. It requires finetuning to be used for downstream tasks such as Automatic Speech Recognition (ASR), or Audio Classification. This model and its training are supported by 🤗 Transformers, more on it in the docs. 🤗 Transformers usage This is a bare checkpoint without any modeling head, and thus requires finetuning to be used for downstream tasks such as ASR. You can however use it to extract audio embeddings from the top layer with this code snippet: To learn more about the model use, refer to the following resources: - its docs - a blog post showing how to fine-tune it on Mongolian ASR - a training script example Seamless Communication usage This model can be used in Seamless Communication, where it was released. Here's how to make a forward pass through the voice encoder, after having completed the installation steps:
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 580M |
| Variants | 1 |
| Runs with | transformers |
| Released | 2023-12-19 |
| Popularity | 2.2M downloads / month |
| Likes | 227 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| w2v-bert-2.0 | 580M | BF16 | ~1.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys w2v-bert-2-0 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (w2v-bert-2-0 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":"w2v-bert-2-0","input":"text to embed"}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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