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
Correct long-form generation config parameters 'max_initial_timestamp_index' and 'prev_sot_token_id'.
#29
by patrickvonplaten - opened
- generation_config.json +2 -1
generation_config.json
CHANGED
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@@ -152,10 +152,11 @@
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| 152 |
"<|yo|>": 50325,
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| 153 |
"<|zh|>": 50260
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| 154 |
},
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| 155 |
-
"max_initial_timestamp_index":
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| 156 |
"max_length": 448,
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| 157 |
"no_timestamps_token_id": 50363,
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| 158 |
"pad_token_id": 50257,
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| 159 |
"return_timestamps": false,
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| 160 |
"suppress_tokens": [
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| 161 |
1,
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| 152 |
"<|yo|>": 50325,
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| 153 |
"<|zh|>": 50260
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| 154 |
},
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| 155 |
+
"max_initial_timestamp_index": 50,
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| 156 |
"max_length": 448,
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| 157 |
"no_timestamps_token_id": 50363,
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| 158 |
"pad_token_id": 50257,
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| 159 |
+
"prev_sot_token_id": 50361,
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| 160 |
"return_timestamps": false,
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| 161 |
"suppress_tokens": [
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| 162 |
1,
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