Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-medium.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-medium.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium.en", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update generation_config.json
Browse files- generation_config.json +2 -1
generation_config.json
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{
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"_from_model_config":
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"begin_suppress_tokens": [
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"bos_token_id": 50257,
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"decoder_start_token_id": 50257,
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"eos_token_id": 50256,
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"_from_model_config": false,
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"begin_suppress_tokens": [
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"return_timestamps":false,
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"bos_token_id": 50257,
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"decoder_start_token_id": 50257,
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"eos_token_id": 50256,
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