Papers › Vietnamese end-to-end speech recognition using wav2vec 2.0
Vietnamese end-to-end speech recognition using wav2vec 2.0
Thai Binh Nguyen
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. 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. On the Vivos dataset, we achieved a WER score of 6.15
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Recognition | Common Voice vi | Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI | Test WER | 11.52 | #3 of 3 | Archive leaderboard | report |
| Speech Recognition | VIVOS | Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI | Test WER | 6.15 | #2 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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