Model reference · open weights
wav2vec2-large-xlsr-53-th-cv8-newmm is an open-weight audio or speech model from wannaphong. wav2vec2-large-xlsr-53-th-cv8-newmm (FP32) weighs 631 MB; the smallest configuration that runs it is RTX 3060 12 GB.
What it is
| Released by | wannaphong |
|---|---|
| Type | Audio & music |
| Task | Speech→text |
| Parameters (lead) | 316M |
| Runs with | transformers |
| Released | 2022-06-06 |
| Popularity | 2k downloads / month |
| Weights | 631 MB (wav2vec2-large-xlsr-53-th-cv8-newmm (FP32), file size) |
| Licence | Open weights |
What it runs on
Weights 631 MB (file size) · overhead about 1.6 GB.
| Card | One stream | Counted memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
Estimates, not measurements: the weights are the build's file size. A speech model's decoder keeps a small cache for every stream it transcribes, so memory grows with the streams and beams at once. Counted memory is 92 % of what CUDA reports for the card.
From the model card
This model trained with CommonVoice V8 dataset by increase data from CommonVoice V7 dataset that It was use in airesearch/wav2vec2-large-xlsr-53-th. It was finetune wav2vec2-large-xlsr-53.
It is increase new data from The Common Voice V8 dataset to Common Voice V7 dataset or remove all data in Common Voice V7 dataset before split Common Voice V8 then add CommonVoice V7 dataset back to dataset.
It use ekapolc/Thai_commonvoice_split script for split Common Voice dataset.
This model was finetune wav2vec2-large-xlsr-53 model with Thai Common Voice V8 dataset and It use pre-tokenize with pythainlp.tokenize.word_tokenize.
I used many code from vistec-AI/wav2vec2-large-xlsr-53-th and I fixed bug training code in vistec-AI/wav2vec2-large-xlsr-53-th#2
Test with CommonVoice V8 Testset
| Model | WER by newmm (%) | WER by deepcut (%) | CER |
|---|---|---|---|
| AIResearch.in.th and PyThaiNLP | 17.414503 | 11.923089 | 3.854153 |
| wav2vec2 with deepcut | 16.354521 | 11.424476 | 3.684060 |
| wav2vec2 with newmm | 16.698299 | 11.436941 | 3.737407 |
| wav2vec2 with deepcut + language model | 12.630260 | 9.613886 | 3.292073 |
| wav2vec2 with newmm + language model | 12.583706 | 9.598305 | 3.276610 |
Test with CommonVoice V7 Testset (same test by CV V7)
| Model | WER by newmm (%) | WER by deepcut (%) | CER |
|---|---|---|---|
| AIResearch.in.th and PyThaiNLP | 13.936698 | 9.347462 | 2.804787 |
| wav2vec2 with deepcut | 12.776381 | 8.773006 | 2.628882 |
| wav2vec2 with newmm | 12.750596 | 8.672616 | 2.623341 |
| wav2vec2 with deepcut + language model | 9.940050 | 7.423313 | 2.344940 |
| wav2vec2 with newmm + language model | 9.559724 | 7.339654 | 2.277071 |
This is use same testset from https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th.
Links:
@misc{phatthiyaphaibun2022thai,
title={Thai Wav2Vec2.0 with CommonVoice V8},
author={Wannaphong Phatthiyaphaibun and Chompakorn Chaksangchaichot and Peerat Limkonchotiwat and Ekapol Chuangsuwanich and Sarana Nutanong},
year={2022},
eprint={2208.04799},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.