Model reference · open weights

wav2vec2-large-xlsr-53-th-cv8-newmm

Audio wannaphong · community Speech→text 1 build Open weights 2k dl/mo

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 bywannaphong
TypeAudio & music
TaskSpeech→text
Parameters (lead)316M
Runs withtransformers
Released2022-06-06
Popularity2k downloads / month
Weights631 MB (wav2vec2-large-xlsr-53-th-cv8-newmm (FP32), file size)
LicenceOpen weights

What it runs on

Memory and cards for wav2vec2-large-xlsr-53-th-cv8-newmm (FP32)

Weights 631 MB (file size) · overhead about 1.6 GB.

CardOne streamCounted
memory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 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

What wannaphong says about wav2vec2-large-xlsr-53-th-cv8-newmm

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.

Read the full model card

Model description

Datasets

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.

Models

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.

Training

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

Evaluation

Test with CommonVoice V8 Testset

ModelWER by newmm (%)WER by deepcut (%)CER
AIResearch.in.th and PyThaiNLP17.41450311.9230893.854153
wav2vec2 with deepcut16.35452111.4244763.684060
wav2vec2 with newmm16.69829911.4369413.737407
wav2vec2 with deepcut + language model12.6302609.6138863.292073
wav2vec2 with newmm + language model12.5837069.5983053.276610

Test with CommonVoice V7 Testset (same test by CV V7)

ModelWER by newmm (%)WER by deepcut (%)CER
AIResearch.in.th and PyThaiNLP13.9366989.3474622.804787
wav2vec2 with deepcut12.7763818.7730062.628882
wav2vec2 with newmm12.7505968.6726162.623341
wav2vec2 with deepcut + language model9.9400507.4233132.344940
wav2vec2 with newmm + language model9.5597247.3396542.277071

This is use same testset from https://huggingface.co/airesearch/wav2vec2-large-xlsr-53-th.

Links:

BibTeX entry and citation info

@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.

© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms