Model reference · open weights
sew-d-tiny-100k is an open-weight embedding model from asapp. 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 | asapp |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 24M |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 1k downloads / month |
| Licence | Open weights |
About
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Emotion Recognition, etc...
Paper: Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition
Authors: Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi
Abstract This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition (ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference time, SEW reduces word error rate by 25-50% across different model sizes.
The original model can be found under https://github.com/asappresearch/sew#model-checkpoints .
See this blog for more information on how to fine-tune the model. Note that the class Wav2Vec2ForCTC has to be replaced by SEWDForCTC.
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys sew-d-tiny-100k for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (sew-d-tiny-100k 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":"sew-d-tiny-100k","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.