Automatic Speech Recognition
Transformers
PyTorch
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results
Instructions to use openai/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Add max length to config
#38
by sanchit-gandhi - opened
- config.json +1 -0
config.json
CHANGED
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@@ -33,6 +33,7 @@
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"mask_time_length": 10,
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"mask_time_min_masks": 2,
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"mask_time_prob": 0.05,
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| 36 |
"max_source_positions": 1500,
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| 37 |
"max_target_positions": 448,
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| 38 |
"median_filter_width": 7,
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| 33 |
"mask_time_length": 10,
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| 34 |
"mask_time_min_masks": 2,
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| 35 |
"mask_time_prob": 0.05,
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| 36 |
+
"max_length": 448,
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| 37 |
"max_source_positions": 1500,
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| 38 |
"max_target_positions": 448,
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| 39 |
"median_filter_width": 7,
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