Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use veluchs/whisper-tiny-us with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veluchs/whisper-tiny-us with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="veluchs/whisper-tiny-us")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("veluchs/whisper-tiny-us") model = AutoModelForSpeechSeq2Seq.from_pretrained("veluchs/whisper-tiny-us", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57ff193c1aab6e153953e31277dcc008b7f9a9f165adff66172fb79a9391c18b
- Size of remote file:
- 4.09 kB
- SHA256:
- 497078286783c61443a96584005a6f336ec1d6690272dab65b0313fff42df659
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