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
Update the pad token
#9
by Narsil - opened
- generation_config.json +2 -2
generation_config.json
CHANGED
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@@ -121,7 +121,7 @@
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| 121 |
"max_initial_timestamp_index": 1,
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| 122 |
"max_length": 448,
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"no_timestamps_token_id": 50363,
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| 124 |
-
"pad_token_id":
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| 125 |
"return_timestamps": false,
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| 126 |
"suppress_tokens": [
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| 127 |
1,
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@@ -216,4 +216,4 @@
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"translate": 50358
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},
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"transformers_version": "4.27.0.dev0"
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-
}
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| 121 |
"max_initial_timestamp_index": 1,
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| 122 |
"max_length": 448,
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| 123 |
"no_timestamps_token_id": 50363,
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| 124 |
+
"pad_token_id": 50257,
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| 125 |
"return_timestamps": false,
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| 126 |
"suppress_tokens": [
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| 127 |
1,
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| 216 |
"translate": 50358
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| 217 |
},
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| 218 |
"transformers_version": "4.27.0.dev0"
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| 219 |
+
}
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