Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +1 -1
config.json
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"model_type": "whisper",
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"num_hidden_layers": 6,
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"num_mel_bins": 80,
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"scale_embedding": false,
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"model_type": "whisper",
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"num_hidden_layers": 6,
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"num_mel_bins": 80,
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"pad_token_id": 50257,
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"scale_embedding": false,
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"suppress_tokens": [
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