Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Spanish
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-es with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-es with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-es")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-es") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-es", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48d2b7b21076563706d4a62d3f2fa743a809449b82d17bd9de95358edaeebf45
- Size of remote file:
- 967 MB
- SHA256:
- fffa00d5c5029fd3bfe866ace90c0b0335ec0c451519e30978cc149e1bd14741
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