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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium") - Notebooks
- Google Colab
- Kaggle
Upload processor
Browse files- preprocessor_config.json +1 -2
- tokenizer_config.json +2 -1
preprocessor_config.json
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],
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"n_fft": 400,
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"n_samples": 480000,
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"
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"num_mel_bins": 80,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "WhisperProcessor",
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],
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"n_fft": 400,
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"n_samples": 480000,
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"nb_max_frames": 3000,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "WhisperProcessor",
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tokenizer_config.json
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},
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"errors": "replace",
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"model_max_length": 1024,
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"name_or_path": "whisper-
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"pad_token": null,
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"special_tokens_map_file": null,
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"tokenizer_class": "WhisperTokenizer",
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"unk_token": {
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},
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"errors": "replace",
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"model_max_length": 1024,
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"name_or_path": "openai/whisper-large",
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"pad_token": null,
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"processor_class": "WhisperProcessor",
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"special_tokens_map_file": null,
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"tokenizer_class": "WhisperTokenizer",
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"unk_token": {
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