Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: - Clean audio - Noise audio - Conversation - Multi-gender and dialects We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8. Since our model has the same architecture as the English wav2vec2 version, you can use this notebook for more information on how to fine-tune the model.

Category pending

Input Not verified→ Output Not verified

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wav2vec2-base-vi-vlsp2020

nguyenvulebinh

Our models use wav2vec2 architecture, 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. You can find more description here The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

Text generationOpen weights

Input Audio→ Output Text

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wav2vec2-base-vietnamese-250h

nguyenvulebinh

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.

Speech recognition

Input Audio→ Output Text

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