Automatic Speech Recognition
Transformers
ONNX
Safetensors
ExecuTorch
English
asr4all
feature-extraction
speech
ctc
streaming
on-device
edge
custom_code
Eval Results (legacy)
Instructions to use futo-org/asr4all-m-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use futo-org/asr4all-m-plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="futo-org/asr4all-m-plus", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("futo-org/asr4all-m-plus", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from futo-org/asr4all-m-plus: direct link, hf CLI and curl.
- Browser
- Download file 248 Bytes
-
https://huggingface.co/futo-org/asr4all-m-plus/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://futo-org/asr4all-m-plus/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/futo-org/asr4all-m-plus/resolve/main/preprocessor_config.json
248 Bytes
| { | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "sampling_rate": 16000, | |
| "padding_value": 0.0, | |
| "padding_side": "right", | |
| "do_normalize": false, | |
| "return_attention_mask": true, | |
| "processor_class": "Wav2Vec2Processor" | |
| } |