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---
language: tr
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
base_model: facebook/wav2vec2-xls-r-300m
model-index:
- name: wav2vec2-large-xls-r-300m-tr
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: Common Voice tr
type: common_voice_8_0
args: tr
metrics:
- type: wer
value: 28.69
name: Test WER
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-tr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2224
- Wer: 0.2869
## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 6.8222 | 0.64 | 500 | 3.5026 | 1.0 |
| 3.2136 | 1.28 | 1000 | 3.0593 | 1.0000 |
| 2.8882 | 1.91 | 1500 | 2.4670 | 0.9939 |
| 2.3743 | 2.55 | 2000 | 1.1844 | 0.8657 |
| 1.9456 | 3.19 | 2500 | 0.8228 | 0.7397 |
| 1.7781 | 3.83 | 3000 | 0.6826 | 0.6753 |
| 1.6848 | 4.46 | 3500 | 0.5885 | 0.6140 |
| 1.6228 | 5.1 | 4000 | 0.5274 | 0.5789 |
| 1.5768 | 5.74 | 4500 | 0.4900 | 0.5519 |
| 1.5431 | 6.38 | 5000 | 0.4508 | 0.5238 |
| 1.5019 | 7.02 | 5500 | 0.4248 | 0.5021 |
| 1.4684 | 7.65 | 6000 | 0.4009 | 0.4827 |
| 1.4635 | 8.29 | 6500 | 0.3830 | 0.4700 |
| 1.4291 | 8.93 | 7000 | 0.3707 | 0.4595 |
| 1.4271 | 9.57 | 7500 | 0.3570 | 0.4514 |
| 1.3938 | 10.2 | 8000 | 0.3479 | 0.4378 |
| 1.3914 | 10.84 | 8500 | 0.3396 | 0.4368 |
| 1.3767 | 11.48 | 9000 | 0.3253 | 0.4262 |
| 1.3641 | 12.12 | 9500 | 0.3251 | 0.4178 |
| 1.355 | 12.76 | 10000 | 0.3138 | 0.4136 |
| 1.336 | 13.39 | 10500 | 0.3121 | 0.4069 |
| 1.3292 | 14.03 | 11000 | 0.3041 | 0.4014 |
| 1.3249 | 14.67 | 11500 | 0.3014 | 0.3931 |
| 1.3156 | 15.31 | 12000 | 0.3014 | 0.3929 |
| 1.313 | 15.94 | 12500 | 0.2969 | 0.3968 |
| 1.3068 | 16.58 | 13000 | 0.2965 | 0.3966 |
| 1.2785 | 17.22 | 13500 | 0.2943 | 0.3850 |
| 1.2867 | 17.86 | 14000 | 0.2912 | 0.3782 |
| 1.2714 | 18.49 | 14500 | 0.2819 | 0.3747 |
| 1.2844 | 19.13 | 15000 | 0.2840 | 0.3740 |
| 1.2684 | 19.77 | 15500 | 0.2913 | 0.3828 |
| 1.26 | 20.41 | 16000 | 0.2739 | 0.3674 |
| 1.2543 | 21.05 | 16500 | 0.2740 | 0.3691 |
| 1.2532 | 21.68 | 17000 | 0.2709 | 0.3756 |
| 1.2409 | 22.32 | 17500 | 0.2669 | 0.3593 |
| 1.2404 | 22.96 | 18000 | 0.2673 | 0.3576 |
| 1.2347 | 23.6 | 18500 | 0.2678 | 0.3643 |
| 1.2351 | 24.23 | 19000 | 0.2715 | 0.3650 |
| 1.2409 | 24.87 | 19500 | 0.2637 | 0.3571 |
| 1.2152 | 25.51 | 20000 | 0.2785 | 0.3609 |
| 1.2046 | 26.15 | 20500 | 0.2610 | 0.3508 |
| 1.2082 | 26.79 | 21000 | 0.2619 | 0.3461 |
| 1.2109 | 27.42 | 21500 | 0.2597 | 0.3502 |
| 1.2014 | 28.06 | 22000 | 0.2608 | 0.3468 |
| 1.1948 | 28.7 | 22500 | 0.2573 | 0.3457 |
| 1.205 | 29.34 | 23000 | 0.2619 | 0.3464 |
| 1.2019 | 29.97 | 23500 | 0.2559 | 0.3474 |
| 1.1917 | 30.61 | 24000 | 0.2601 | 0.3462 |
| 1.1939 | 31.25 | 24500 | 0.2575 | 0.3387 |
| 1.1882 | 31.89 | 25000 | 0.2535 | 0.3368 |
| 1.191 | 32.53 | 25500 | 0.2489 | 0.3365 |
| 1.1767 | 33.16 | 26000 | 0.2501 | 0.3347 |
| 1.167 | 33.8 | 26500 | 0.2504 | 0.3347 |
| 1.1678 | 34.44 | 27000 | 0.2480 | 0.3378 |
| 1.1803 | 35.08 | 27500 | 0.2487 | 0.3345 |
| 1.167 | 35.71 | 28000 | 0.2442 | 0.3319 |
| 1.1661 | 36.35 | 28500 | 0.2495 | 0.3334 |
| 1.164 | 36.99 | 29000 | 0.2472 | 0.3292 |
| 1.1578 | 37.63 | 29500 | 0.2442 | 0.3242 |
| 1.1584 | 38.27 | 30000 | 0.2431 | 0.3314 |
| 1.1526 | 38.9 | 30500 | 0.2441 | 0.3347 |
| 1.1542 | 39.54 | 31000 | 0.2437 | 0.3330 |
| 1.1508 | 40.18 | 31500 | 0.2433 | 0.3294 |
| 1.1406 | 40.82 | 32000 | 0.2434 | 0.3271 |
| 1.1514 | 41.45 | 32500 | 0.2426 | 0.3255 |
| 1.1418 | 42.09 | 33000 | 0.2432 | 0.3233 |
| 1.1365 | 42.73 | 33500 | 0.2436 | 0.3240 |
| 1.1348 | 43.37 | 34000 | 0.2483 | 0.3257 |
| 1.1301 | 44.01 | 34500 | 0.2420 | 0.3271 |
| 1.1268 | 44.64 | 35000 | 0.2472 | 0.3225 |
| 1.1224 | 45.28 | 35500 | 0.2382 | 0.3205 |
| 1.1224 | 45.92 | 36000 | 0.2388 | 0.3184 |
| 1.1198 | 46.56 | 36500 | 0.2382 | 0.3202 |
| 1.1274 | 47.19 | 37000 | 0.2404 | 0.3172 |
| 1.1147 | 47.83 | 37500 | 0.2394 | 0.3164 |
| 1.121 | 48.47 | 38000 | 0.2406 | 0.3202 |
| 1.1109 | 49.11 | 38500 | 0.2384 | 0.3154 |
| 1.1164 | 49.74 | 39000 | 0.2375 | 0.3169 |
| 1.1105 | 50.38 | 39500 | 0.2387 | 0.3173 |
| 1.1054 | 51.02 | 40000 | 0.2362 | 0.3120 |
| 1.0893 | 51.66 | 40500 | 0.2399 | 0.3130 |
| 1.0913 | 52.3 | 41000 | 0.2357 | 0.3088 |
| 1.1017 | 52.93 | 41500 | 0.2345 | 0.3084 |
| 1.0937 | 53.57 | 42000 | 0.2330 | 0.3140 |
| 1.0945 | 54.21 | 42500 | 0.2399 | 0.3107 |
| 1.0933 | 54.85 | 43000 | 0.2383 | 0.3134 |
| 1.0912 | 55.48 | 43500 | 0.2372 | 0.3077 |
| 1.0898 | 56.12 | 44000 | 0.2339 | 0.3083 |
| 1.0903 | 56.76 | 44500 | 0.2367 | 0.3065 |
| 1.0947 | 57.4 | 45000 | 0.2352 | 0.3104 |
| 1.0751 | 58.04 | 45500 | 0.2334 | 0.3084 |
| 1.09 | 58.67 | 46000 | 0.2328 | 0.3100 |
| 1.0876 | 59.31 | 46500 | 0.2276 | 0.3050 |
| 1.076 | 59.95 | 47000 | 0.2309 | 0.3047 |
| 1.086 | 60.59 | 47500 | 0.2293 | 0.3047 |
| 1.082 | 61.22 | 48000 | 0.2328 | 0.3027 |
| 1.0714 | 61.86 | 48500 | 0.2290 | 0.3020 |
| 1.0746 | 62.5 | 49000 | 0.2313 | 0.3059 |
| 1.076 | 63.14 | 49500 | 0.2342 | 0.3050 |
| 1.0648 | 63.78 | 50000 | 0.2286 | 0.3025 |
| 1.0586 | 64.41 | 50500 | 0.2338 | 0.3044 |
| 1.0753 | 65.05 | 51000 | 0.2308 | 0.3045 |
| 1.0664 | 65.69 | 51500 | 0.2273 | 0.3009 |
| 1.0739 | 66.33 | 52000 | 0.2298 | 0.3027 |
| 1.0695 | 66.96 | 52500 | 0.2247 | 0.2996 |
| 1.06 | 67.6 | 53000 | 0.2276 | 0.3015 |
| 1.0742 | 68.24 | 53500 | 0.2280 | 0.2974 |
| 1.0618 | 68.88 | 54000 | 0.2291 | 0.2989 |
| 1.062 | 69.52 | 54500 | 0.2302 | 0.2971 |
| 1.0572 | 70.15 | 55000 | 0.2280 | 0.2990 |
| 1.055 | 70.79 | 55500 | 0.2278 | 0.2983 |
| 1.0553 | 71.43 | 56000 | 0.2282 | 0.2991 |
| 1.0509 | 72.07 | 56500 | 0.2261 | 0.2959 |
| 1.0469 | 72.7 | 57000 | 0.2216 | 0.2919 |
| 1.0476 | 73.34 | 57500 | 0.2267 | 0.2989 |
| 1.0494 | 73.98 | 58000 | 0.2260 | 0.2960 |
| 1.0517 | 74.62 | 58500 | 0.2297 | 0.2989 |
| 1.0458 | 75.26 | 59000 | 0.2246 | 0.2923 |
| 1.0382 | 75.89 | 59500 | 0.2255 | 0.2922 |
| 1.0462 | 76.53 | 60000 | 0.2258 | 0.2954 |
| 1.0375 | 77.17 | 60500 | 0.2251 | 0.2929 |
| 1.0332 | 77.81 | 61000 | 0.2277 | 0.2940 |
| 1.0423 | 78.44 | 61500 | 0.2243 | 0.2896 |
| 1.0379 | 79.08 | 62000 | 0.2274 | 0.2928 |
| 1.0398 | 79.72 | 62500 | 0.2237 | 0.2928 |
| 1.0395 | 80.36 | 63000 | 0.2265 | 0.2956 |
| 1.0397 | 80.99 | 63500 | 0.2240 | 0.2920 |
| 1.0262 | 81.63 | 64000 | 0.2244 | 0.2934 |
| 1.0335 | 82.27 | 64500 | 0.2265 | 0.2936 |
| 1.0385 | 82.91 | 65000 | 0.2238 | 0.2928 |
| 1.0289 | 83.55 | 65500 | 0.2219 | 0.2912 |
| 1.0372 | 84.18 | 66000 | 0.2236 | 0.2898 |
| 1.0279 | 84.82 | 66500 | 0.2219 | 0.2902 |
| 1.0325 | 85.46 | 67000 | 0.2240 | 0.2908 |
| 1.0202 | 86.1 | 67500 | 0.2206 | 0.2886 |
| 1.0166 | 86.73 | 68000 | 0.2219 | 0.2886 |
| 1.0259 | 87.37 | 68500 | 0.2235 | 0.2897 |
| 1.0337 | 88.01 | 69000 | 0.2210 | 0.2873 |
| 1.0264 | 88.65 | 69500 | 0.2216 | 0.2882 |
| 1.0231 | 89.29 | 70000 | 0.2223 | 0.2899 |
| 1.0281 | 89.92 | 70500 | 0.2214 | 0.2872 |
| 1.0135 | 90.56 | 71000 | 0.2218 | 0.2868 |
| 1.0291 | 91.2 | 71500 | 0.2209 | 0.2863 |
| 1.0321 | 91.84 | 72000 | 0.2199 | 0.2876 |
| 1.028 | 92.47 | 72500 | 0.2214 | 0.2858 |
| 1.0213 | 93.11 | 73000 | 0.2219 | 0.2875 |
| 1.0261 | 93.75 | 73500 | 0.2232 | 0.2869 |
| 1.0197 | 94.39 | 74000 | 0.2227 | 0.2866 |
| 1.0298 | 95.03 | 74500 | 0.2228 | 0.2868 |
| 1.0192 | 95.66 | 75000 | 0.2230 | 0.2865 |
| 1.0156 | 96.3 | 75500 | 0.2220 | 0.2869 |
| 1.0075 | 96.94 | 76000 | 0.2223 | 0.2866 |
| 1.0201 | 97.58 | 76500 | 0.2219 | 0.2866 |
| 1.0159 | 98.21 | 77000 | 0.2219 | 0.2876 |
| 1.0087 | 98.85 | 77500 | 0.2219 | 0.2873 |
| 1.0159 | 99.49 | 78000 | 0.2223 | 0.2867 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
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