Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-tiny") - Notebooks
- Google Colab
- Kaggle
Commit ·
8852c40
1
Parent(s): dcca072
Update unknown token
Browse files- special_tokens_map.json +1 -1
- tokenizer_config.json +1 -1
special_tokens_map.json
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@@ -124,7 +124,7 @@
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},
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"pad_token": "<|endoftext|>",
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"unk_token": {
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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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},
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"pad_token": "<|endoftext|>",
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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tokenizer_config.json
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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": "<|endoftext|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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