Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
French
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-fr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-fr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-fr") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-fr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 443055faeb0caf83c336fcaa95a81ebf132b0c046870d20ea5c1ae5111772279
- Size of remote file:
- 967 MB
- SHA256:
- cf02f4115b8fbf10f8a0405c5a3706258938386a89fc017f9b8e209c301f0c90
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