Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
dutch
whisper-event
Instructions to use qmeeus/whisper-small-nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qmeeus/whisper-small-nl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="qmeeus/whisper-small-nl")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("qmeeus/whisper-small-nl") model = AutoModelForSpeechSeq2Seq.from_pretrained("qmeeus/whisper-small-nl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -44,7 +44,7 @@ def iter_chunks(waveform, sampling_rate=16_000, chunk_length=30.):
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end = min(len(waveform), start + n_frames)
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yield waveform[start:end]
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for sentence in whisper_asr(iter_chunks(waveform, sr)):
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print(sentence["text"])
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```
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end = min(len(waveform), start + n_frames)
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yield waveform[start:end]
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for sentence in whisper_asr(iter_chunks(waveform, sr), max_new_tokens=448):
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print(sentence["text"])
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```
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