Automatic Speech Recognition
Transformers
PyTorch
Safetensors
whisper
feature-extraction
audio
hf-asr-leaderboard
Instructions to use versae/whisper-large-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use versae/whisper-large-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="versae/whisper-large-v3")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("versae/whisper-large-v3") model = AutoModel.from_pretrained("versae/whisper-large-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update preprocessor_config.json
Browse files- preprocessor_config.json +1 -1
preprocessor_config.json
CHANGED
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@@ -1,7 +1,7 @@
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{
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"chunk_length": 30,
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size":
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"hop_length": 160,
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"n_fft": 400,
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"n_samples": 480000,
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{
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"chunk_length": 30,
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"feature_extractor_type": "WhisperFeatureExtractor",
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"feature_size": 128,
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"hop_length": 160,
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"n_fft": 400,
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"n_samples": 480000,
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