Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use jarguello76/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jarguello76/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jarguello76/whisper-tiny-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("jarguello76/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("jarguello76/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f54e87507b7c7358c4bebe269e174e422c75c95405eeec332919b1f5ac1bbdd
- Size of remote file:
- 151 MB
- SHA256:
- 802f8d10a290ab6b80988863a56896e3803ccf8a5dc48006ce79f16fdecddc01
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