Instructions to use RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR") model = AutoModelForCTC.from_pretrained("RajGothi/Fine-tuning_Wav2Vec2_for_English_ASR") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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This repository contains a fine-tuned Wav2Vec2 model that has been trained on a small TIMIT dataset for speech recognition tasks. The model achieved a Word Error Rate (WER) of
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You can utilize this model for further research and experimentation in the field of speech recognition.
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In the near future, I plan to expand the collection of fine-tuned Wav2Vec2 models to include various Indian languages. Stay tuned for updates and additions to this repository.
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license: apache-2.0
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This repository contains a fine-tuned Wav2Vec2 model that has been trained on a small TIMIT dataset for speech recognition tasks. The model achieved a Word Error Rate (WER) of 30, indicating promising performance on the given small dataset.
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You can utilize this model for further research and experimentation in the field of speech recognition.
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In the near future, I plan to expand the collection of fine-tuned Wav2Vec2 models to include various Indian languages. Stay tuned for updates and additions to this repository.
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