Vietnamese end to end speech recognition using wav2vec 2.0 Facebook's Wav2Vec2 Model description Our models are pre trained on 13k hours of Vietnamese youtube audio (un label data) and fine tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. We use wav2vec2 architecture for the pre trained model. Follow wav2vec2 paper: For the first time that learning powerful representations from speech audio alone followed by fine tuning on transcribed speech can outperform the best semi supervised methods while being conceptually simpler. For fine tuning phase, wav2vec2 is fine tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence to sequence problems and mainly in Automatic Speech Recognition and handwriting recognition. Model params Pre training data Fine tune data base) 95M 13k hours 250 hours In a formal ASR system, two components are required: acoustic model and language model. Here ctc wav2vec fine tuned model works as an acoustic model. For the language model, we provide a 4 grams model trained on 2GB of spoken text. Detail of training and fine tuning process, the audience can follow fair…
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