hannabdul/etf4asr
Official repo for the paper "An Effective Training Framework for Light-Weight Automatic Speech Recognition Models" accepted at InterSpeech 2025.
This framework helps machine learning engineers and researchers optimize automatic speech recognition (ASR) models to be more efficient without sacrificing accuracy. It provides a structured approach for training ASR models using audio data and corresponding transcripts, resulting in 'light-weight' models that are faster and require fewer computational resources. It is designed for those who develop or deploy speech-to-text technologies.
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Use this if you are a machine learning engineer working on speech recognition and need to train highly efficient, smaller ASR models for deployment on resource-constrained devices or for faster inference.
Not ideal if you are a business user looking for an off-the-shelf speech-to-text transcription service, as this tool is for developing the underlying ASR models.
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Aug 15, 2025
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