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
Commit ·
013fe3b
1
Parent(s): 894f251
add special tokens for fast (#25)
Browse files- add special tokens for fast (55f5fcdfb66210824046aeee3c5c1028bc364427)
- tokenizer.json +0 -0
- tokenizer_config.json +1 -1
- vocab.json +0 -0
tokenizer.json
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tokenizer_config.json
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@@ -19,7 +19,7 @@
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"single_word": false
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},
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"errors": "replace",
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"model_max_length":
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"pad_token": null,
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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"single_word": false
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},
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"errors": "replace",
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+
"model_max_length": 1024,
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"pad_token": null,
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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vocab.json
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