---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300-cv17-russian
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: ru
      split: validation
      args: ru
    metrics:
    - name: Wer
      type: wer
      value: 0.188997883332954
---

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# xls-r-300-cv17-russian

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2217
- Wer: 0.1890
- Cer: 0.0400

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 7.4458        | 0.1213 | 100  | 5.2323          | 1.0    | 1.0    |
| 3.5842        | 0.2426 | 200  | 3.2968          | 1.0    | 1.0    |
| 3.3917        | 0.3639 | 300  | 3.1896          | 1.0    | 1.0    |
| 2.8337        | 0.4851 | 400  | 2.5859          | 0.9996 | 0.9331 |
| 1.7449        | 0.6064 | 500  | 1.0910          | 0.9241 | 0.3271 |
| 1.2373        | 0.7277 | 600  | 0.7784          | 0.8014 | 0.2249 |
| 1.2848        | 0.8490 | 700  | 0.6509          | 0.6905 | 0.1832 |
| 0.901         | 0.9703 | 800  | 0.4997          | 0.6067 | 0.1426 |
| 0.3194        | 1.0916 | 900  | 0.4008          | 0.4666 | 0.1054 |
| 0.4158        | 1.2129 | 1000 | 0.3633          | 0.4323 | 0.0960 |
| 0.3942        | 1.3341 | 1100 | 0.3397          | 0.4175 | 0.0915 |
| 0.3117        | 1.4554 | 1200 | 0.3323          | 0.3879 | 0.0853 |
| 0.443         | 1.5767 | 1300 | 0.3133          | 0.3749 | 0.0815 |
| 0.4101        | 1.6980 | 1400 | 0.2976          | 0.3475 | 0.0759 |
| 0.2915        | 1.8193 | 1500 | 0.2900          | 0.3452 | 0.0752 |
| 0.3708        | 1.9406 | 1600 | 0.2857          | 0.3385 | 0.0732 |
| 0.2434        | 2.0619 | 1700 | 0.3089          | 0.3785 | 0.0826 |
| 0.1939        | 2.1831 | 1800 | 0.2973          | 0.3488 | 0.0770 |
| 0.1718        | 2.3044 | 1900 | 0.2746          | 0.3223 | 0.0695 |
| 0.1663        | 2.4257 | 2000 | 0.2847          | 0.3493 | 0.0753 |
| 0.1692        | 2.5470 | 2100 | 0.2802          | 0.3231 | 0.0709 |
| 0.2616        | 2.6683 | 2200 | 0.2775          | 0.3305 | 0.0721 |
| 0.1747        | 2.7896 | 2300 | 0.2781          | 0.3186 | 0.0699 |
| 0.2515        | 2.9109 | 2400 | 0.2740          | 0.3117 | 0.0685 |
| 0.3123        | 3.0321 | 2500 | 0.2442          | 0.2777 | 0.0599 |
| 0.1759        | 3.1534 | 2600 | 0.2461          | 0.2738 | 0.0586 |
| 0.2088        | 3.2747 | 2700 | 0.2506          | 0.2737 | 0.0588 |
| 0.15          | 3.3960 | 2800 | 0.2404          | 0.2714 | 0.0583 |
| 0.2458        | 3.5173 | 2900 | 0.2384          | 0.2673 | 0.0574 |
| 0.1834        | 3.6386 | 3000 | 0.2343          | 0.2633 | 0.0562 |
| 0.1895        | 3.7599 | 3100 | 0.2326          | 0.2639 | 0.0568 |
| 0.2214        | 3.8811 | 3200 | 0.2322          | 0.2624 | 0.0564 |
| 0.1076        | 4.0024 | 3300 | 0.2395          | 0.2680 | 0.0580 |
| 0.1195        | 4.1237 | 3400 | 0.2445          | 0.2623 | 0.0565 |
| 0.1276        | 4.2450 | 3500 | 0.2485          | 0.2776 | 0.0600 |
| 0.1139        | 4.3663 | 3600 | 0.2436          | 0.2639 | 0.0573 |
| 0.1151        | 4.4876 | 3700 | 0.2617          | 0.2753 | 0.0601 |
| 0.1482        | 4.6089 | 3800 | 0.2378          | 0.2610 | 0.0565 |
| 0.1237        | 4.7301 | 3900 | 0.2335          | 0.2584 | 0.0555 |
| 0.1441        | 4.8514 | 4000 | 0.2332          | 0.2525 | 0.0544 |
| 0.1557        | 4.9727 | 4100 | 0.2376          | 0.2606 | 0.0564 |
| 0.1246        | 5.0940 | 4200 | 0.2297          | 0.2370 | 0.0507 |
| 0.1676        | 5.2153 | 4300 | 0.2209          | 0.2385 | 0.0507 |
| 0.1348        | 5.3366 | 4400 | 0.2202          | 0.2337 | 0.0501 |
| 0.1198        | 5.4579 | 4500 | 0.2197          | 0.2293 | 0.0491 |
| 0.1468        | 5.5791 | 4600 | 0.2239          | 0.2287 | 0.0489 |
| 0.1706        | 5.7004 | 4700 | 0.2157          | 0.2284 | 0.0487 |
| 0.0946        | 5.8217 | 4800 | 0.2097          | 0.2255 | 0.0480 |
| 0.138         | 5.9430 | 4900 | 0.2094          | 0.2230 | 0.0476 |
| 0.0447        | 6.0643 | 5000 | 0.2330          | 0.2271 | 0.0483 |
| 0.0863        | 6.1856 | 5100 | 0.2283          | 0.2295 | 0.0491 |
| 0.0813        | 6.3069 | 5200 | 0.2311          | 0.2287 | 0.0492 |
| 0.0768        | 6.4281 | 5300 | 0.2305          | 0.2307 | 0.0494 |
| 0.0688        | 6.5494 | 5400 | 0.2249          | 0.2271 | 0.0487 |
| 0.0919        | 6.6707 | 5500 | 0.2075          | 0.2204 | 0.0470 |
| 0.0813        | 6.7920 | 5600 | 0.2251          | 0.2249 | 0.0478 |
| 0.1033        | 6.9133 | 5700 | 0.2236          | 0.2183 | 0.0468 |
| 0.0857        | 7.0346 | 5800 | 0.2220          | 0.2142 | 0.0458 |
| 0.1046        | 7.1559 | 5900 | 0.2227          | 0.2122 | 0.0451 |
| 0.0851        | 7.2771 | 6000 | 0.2319          | 0.2087 | 0.0446 |
| 0.0679        | 7.3984 | 6100 | 0.2195          | 0.2102 | 0.0450 |
| 0.1116        | 7.5197 | 6200 | 0.2205          | 0.2077 | 0.0441 |
| 0.0749        | 7.6410 | 6300 | 0.2126          | 0.2061 | 0.0439 |
| 0.087         | 7.7623 | 6400 | 0.2161          | 0.2058 | 0.0437 |
| 0.0947        | 7.8836 | 6500 | 0.2150          | 0.2042 | 0.0433 |
| 0.0472        | 8.0049 | 6600 | 0.2194          | 0.2041 | 0.0431 |
| 0.0521        | 8.1261 | 6700 | 0.2317          | 0.2037 | 0.0433 |
| 0.0477        | 8.2474 | 6800 | 0.2277          | 0.2024 | 0.0428 |
| 0.042         | 8.3687 | 6900 | 0.2232          | 0.2020 | 0.0428 |
| 0.0327        | 8.4900 | 7000 | 0.2240          | 0.2012 | 0.0422 |
| 0.0427        | 8.6113 | 7100 | 0.2241          | 0.1984 | 0.0420 |
| 0.0438        | 8.7326 | 7200 | 0.2174          | 0.1986 | 0.0417 |
| 0.0277        | 8.8539 | 7300 | 0.2216          | 0.1949 | 0.0413 |
| 0.0415        | 8.9751 | 7400 | 0.2140          | 0.1958 | 0.0415 |
| 0.0803        | 9.0964 | 7500 | 0.2267          | 0.1937 | 0.0410 |
| 0.0388        | 9.2177 | 7600 | 0.2265          | 0.1925 | 0.0409 |
| 0.0716        | 9.3390 | 7700 | 0.2189          | 0.1916 | 0.0406 |
| 0.0498        | 9.4603 | 7800 | 0.2190          | 0.1913 | 0.0404 |
| 0.0692        | 9.5816 | 7900 | 0.2198          | 0.1911 | 0.0404 |
| 0.0691        | 9.7029 | 8000 | 0.2233          | 0.1898 | 0.0401 |
| 0.0743        | 9.8241 | 8100 | 0.2242          | 0.1897 | 0.0401 |
| 0.0496        | 9.9454 | 8200 | 0.2217          | 0.1890 | 0.0400 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1