Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small") - Notebooks
- Google Colab
- Kaggle
update config of preprocessor
#34
by Pranjal12345 - opened
- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
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@@ -1,5 +1,5 @@
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{
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-
"chunk_length":
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 80,
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"hop_length": 160,
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{
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+
"chunk_length": 60,
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 80,
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"hop_length": 160,
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