Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mcamara/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mcamara/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mcamara/whisper-tiny-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mcamara/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("mcamara/whisper-tiny-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16e161feb9a9b05285fb86effd3955d762bc902dd6b40ac7f86df78e3fe98081
- Size of remote file:
- 151 MB
- SHA256:
- 3b32c9f54de4c371882028506dbede022cc140ea0e453af823741a636579a254
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