Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-medium")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-medium") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-medium") - Notebooks
- Google Colab
- Kaggle
Add `return_timestamps` attribute to generation config
#29
by sanchit-gandhi - opened
- generation_config.json +1 -0
generation_config.json
CHANGED
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@@ -148,6 +148,7 @@
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| 148 |
"max_length": 448,
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| 149 |
"no_timestamps_token_id": 50363,
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"pad_token_id": 50257,
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| 151 |
"suppress_tokens": [
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1,
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| 153 |
2,
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| 148 |
"max_length": 448,
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| 149 |
"no_timestamps_token_id": 50363,
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| 150 |
"pad_token_id": 50257,
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| 151 |
+
"return_timestamps": false,
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| 152 |
"suppress_tokens": [
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1,
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| 154 |
2,
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