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 training_args.bin from auro/whisper-cli-small-or: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/auro/whisper-cli-small-or/resolve/main/training_args.bin
- Command line
-
hf download hf://auro/whisper-cli-small-or/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/auro/whisper-cli-small-or/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- d3f176ef4cf97bbbe4e173992b542cca1b465b80e654daa83f45e69e4a7f901e
- Size of remote file:
- 3.52 kB
- SHA256:
- 2e5dde40aa544d7549334092b32db9a7f6ddb3eaa4bea5a6063d389c0b96811a
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