Instructions to use bangla-speech-processing/BanglaASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bangla-speech-processing/BanglaASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bangla-speech-processing/BanglaASR")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("bangla-speech-processing/BanglaASR") model = AutoModelForSpeechSeq2Seq.from_pretrained("bangla-speech-processing/BanglaASR") - Notebooks
- Google Colab
- Kaggle
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README.md
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pipeline_tag: automatic-speech-recognition
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Bangla ASR
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For training Bangla ASR model here used 40k traning and 7k Validation around 400 hours data. We trained 12000 steps this model and get word
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error rate 4.58%.
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```py
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pipeline_tag: automatic-speech-recognition
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Bangla ASR model which was trained Bangla Mozilla Common Voice Dataset. This is Fine-tuning Whisper for Bangla mozilla common voice dataset.
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For training Bangla ASR model here used 40k traning and 7k Validation around 400 hours data. We trained 12000 steps this model and get word
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error rate 4.58%. This model was fine-tune whisper small[244 M] model.
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```py
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