Model reference · open weights
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.
Summary of the TalTechNLP/xls-r-300m-et model card, 2026-10-01
What it is
| Released by | TalTechNLP |
|---|---|
| Released | 2022-03-02 |
| VRAM | 1.3 GB for the weights |
What it runs on
| Card | One stream | 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 |
From the model card
This is a XLS-R-300M model facebook/wav2vec2-xls-r-300m finetuned on around 800 hours of diverse Estonian data.
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.
This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.
TODO
Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:
Acoustic training data:
| Type | Amount (h) |
|---|---|
| Broadcast speech | 591 |
| Spontaneous speech | 53 |
| Elderly speech corpus | 53 |
| Talks, lectures | 49 |
| Parliament speeches | 31 |
| Total | 761 |
Finetuned using Fairseq.
| Dataset | WER |
|---|---|
| jutusaated.devset | 7.9 |
| jutusaated.testset | 6.1 |
| Common Voice 6.1 | 12.5 |
| Common Voice 8.0 | 13.4 |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.
Benchmarks
As published on the model card: the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Automatic Speech Recognition | Common Voice | Test WER | 12.520 |
| Automatic Speech Recognition | Common Voice | Test CER | 2.709 |
| Automatic Speech Recognition | Common Voice 8 | Test WER | 13.384 |
| Automatic Speech Recognition | Common Voice 8 | Test CER | 2.982 |