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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload config
Browse files- config.json +10 -0
config.json
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 50257,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_source_positions": 1500,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 50257,
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"max_source_positions": 1500,
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