Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Oriya
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use auro/whisper-cli-small-or with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use auro/whisper-cli-small-or with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="auro/whisper-cli-small-or")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("auro/whisper-cli-small-or") model = AutoModelForSpeechSeq2Seq.from_pretrained("auro/whisper-cli-small-or", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from auro/whisper-cli-small-or: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/auro/whisper-cli-small-or/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://auro/whisper-cli-small-or/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/auro/whisper-cli-small-or/resolve/main/pytorch_model.bin
967 MB
- Xet hash:
- f973d7b683bfce20fbbc525137ff01e6607b39dfc7976fae46dcc461cc781817
- Size of remote file:
- 967 MB
- SHA256:
- 1a2eb4724de426eaf7bc855f1124b12942766b738568596189c0bebb29118c0c
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