Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from openai/whisper-small: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/openai/whisper-small/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://openai/whisper-small/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/openai/whisper-small/resolve/main/flax_model.msgpack
967 MB
- Xet hash:
- ea8de9aa5c6595d18ef1dfc3d61ab7435710cc3c371e4aeeb7de395b7a1aa524
- Size of remote file:
- 967 MB
- SHA256:
- c375de483cc3fef3050ee369779c112f177e99ef010bb52cc9a2e72f9d7b0a9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.