Automatic Speech Recognition
Transformers
Safetensors
English
femto_asr
ctc
e-branchformer
linear-attention
on-device
edge
microcontroller
custom_code
Eval Results (legacy)
Instructions to use igorktech/femto-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igorktech/femto-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="igorktech/femto-asr", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCTC model = AutoModelForCTC.from_pretrained("igorktech/femto-asr", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from igorktech/femto-asr: direct link, hf CLI and curl.
- Browser
- Download file 4.23 kB
-
https://huggingface.co/igorktech/femto-asr/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://igorktech/femto-asr/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/igorktech/femto-asr/resolve/main/preprocessor_config.json
4.23 kB
| { | |
| "auto_map": { | |
| "AutoFeatureExtractor": "feature_extraction_femto_asr.FemtoASRFeatureExtractor" | |
| }, | |
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| "cmvn_std": [ | |
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| ], | |
| "feature_extractor_type": "FemtoASRFeatureExtractor", | |
| "feature_size": 80, | |
| "hop_length": 160, | |
| "log_eps": 1e-06, | |
| "n_fft": 512, | |
| "pad_seconds": 0.3, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000, | |
| "win_length": 400 | |
| } | |