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