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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from veluchs/whisper-tiny-us: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/veluchs/whisper-tiny-us/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://veluchs/whisper-tiny-us@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/veluchs/whisper-tiny-us/resolve/refs%2Fpr%2F1/pytorch_model.bin
151 MB
- Xet hash:
- 498fdad3c7b1ace379d1a854fe8dd522a0303500b2d1a3a62b37fcf1145cd646
- Size of remote file:
- 151 MB
- SHA256:
- f069f1072cbd4869ab00a76b02a8133d9011b8e3d7e7bf111e778955f073161e
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