Model reference · open weights

xls-r-et

Audio TalTechNLP Speech→text 1 build Open weights 1k dl/mo

xls-r-et is an open-weight audio or speech model from TalTechNLP. xls-r-300m-et (BF16) weighs 1.3 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • xls-r-et is an automatic speech recognition model developed by TalTechNLP for Estonian language processing.
  • It is a finetuned version of the XLS-R-300M architecture trained on approximately 761 hours of diverse Estonian data, including broadcast speech and talks.
  • The model uses a CTC-based end-to-end approach without a separate language model and is licensed under cc-by-4.0.

Summary of the TalTechNLP/xls-r-300m-et model card, 2026-10-01

What it is

Released byTalTechNLP
Released2022-03-02
VRAM1.3 GB for the weights

What it runs on

Memory and cards for xls-r-300m-et (BF16)

1.3 GBweights, file size
1.6 GBruntime overhead
CardOne streamMemory
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

From the model card

What TalTechNLP says about xls-r-et

Read the model card

This is a XLS-R-300M model facebook/wav2vec2-xls-r-300m finetuned on around 800 hours of diverse Estonian data.

Model description

This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. It consists of only the CTC-based end-to-end model, no language model is currently provided.

Intended uses & limitations

This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.

How to use

TODO

Limitations and bias

Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:

  • Speech containing technical and other domain-specific terms
  • Children's speech
  • Non-native speech
  • Speech recorded under very noisy conditions or with a microphone far from the speaker
  • Very spontaneous and overlapping speech

Training data

Acoustic training data:

TypeAmount (h)
Broadcast speech591
Spontaneous speech53
Elderly speech corpus53
Talks, lectures49
Parliament speeches31
Total761

Training procedure

Finetuned using Fairseq.

Evaluation results

WER

DatasetWER
jutusaated.devset7.9
jutusaated.testset6.1
Common Voice 6.112.5
Common Voice 8.013.4

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Benchmarks

Reported results

As published on the model card: the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
Automatic Speech RecognitionCommon VoiceTest WER12.520
Automatic Speech RecognitionCommon VoiceTest CER2.709
Automatic Speech RecognitionCommon Voice 8Test WER13.384
Automatic Speech RecognitionCommon Voice 8Test CER2.982
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