Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
ONNX
Safetensors
whisper
audio
asr
hf-asr-leaderboard
Instructions to use NbAiLabBeta/nb-whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLabBeta/nb-whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("NbAiLabBeta/nb-whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("NbAiLabBeta/nb-whisper-tiny") - Notebooks
- Google Colab
- Kaggle
Update onnx/preprocessor_config.json
Browse files
onnx/preprocessor_config.json
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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": 80,
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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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"nb_max_frames": 3000,
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"padding_side": "right",
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"padding_value": 0.0,
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"processor_class": "WhisperProcessor",
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"return_attention_mask": false,
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"sampling_rate": 16000
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}
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