Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-tiny.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny.en") - Notebooks
- Google Colab
- Kaggle
Update tokenizer_config.json
#8
by ArthurZ HF Staff - opened
- tokenizer_config.json +1 -1
tokenizer_config.json
CHANGED
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@@ -27,7 +27,7 @@
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"tokenizer_class": "WhisperTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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-
"content": "
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"tokenizer_class": "WhisperTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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+
"content": "",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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