Automatic Speech Recognition
Transformers
PyTorch
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-small")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-small") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update export_models.sh
Browse files- export_models.sh +1 -1
export_models.sh
CHANGED
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@@ -12,7 +12,7 @@ shutil.copyfile('./generation_config.json', './generation_config_backup.json')
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print("Saving model to PyTorch...", end=" ")
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model = WhisperForConditionalGeneration.from_pretrained("./", from_flax=True)
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model.save_pretrained("./", safe_serialization=True)
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model.save_pretrained("./")
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print("Done.")
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print("Saving model to TensorFlow...", end=" ")
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print("Saving model to PyTorch...", end=" ")
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model = WhisperForConditionalGeneration.from_pretrained("./", from_flax=True)
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model.save_pretrained("./", safe_serialization=True)
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+
model.save_pretrained("./", safe_serialization=False)
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print("Done.")
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print("Saving model to TensorFlow...", end=" ")
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