Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-base")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-base") - Notebooks
- Google Colab
- Kaggle
Update export_models.sh
Browse files- export_models.sh +1 -1
export_models.sh
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@@ -16,7 +16,7 @@ model.save_pretrained("./")
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print("Done.")
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print("Saving model to TensorFlow...", end=" ")
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tf_model = TFWhisperForConditionalGeneration.from_pretrained("./"
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tf_model.save_pretrained("./")
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print("Done.")
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print("Done.")
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print("Saving model to TensorFlow...", end=" ")
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tf_model = TFWhisperForConditionalGeneration.from_pretrained("./")
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tf_model.save_pretrained("./")
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print("Done.")
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