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
Commit History
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Changes for fast tokenizer (#7)
- Update tokens (7fa2c369b6be7766bb56a0446db6acd0946f5e6d) - Add tokenizer.json (40290b8a58806e23cab99957faef69f015e9dc19) Co-authored-by: Jonatan Kłosko <jonatanklosko@users.noreply.huggingface.co>bca8750
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Update `return_mask` param
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Upload processor
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