Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/whisper-medium: direct link, hf CLI and curl.
- Browser
- Download file 3.06 GB
-
https://huggingface.co/openai/whisper-medium/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openai/whisper-medium/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/whisper-medium/resolve/main/pytorch_model.bin
3.06 GB
- Xet hash:
- 33b368e3de2fd9504f151f26b6b8a44a6288d3bdbdda1498e1924fa510855c6b
- Size of remote file:
- 3.06 GB
- SHA256:
- 96d734d68ad5d63c8f41d525f5769788432f6963f32dbe36feefaa33d736a962
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.