pytorch wav2vec2 automatic-speech-recognition tr dataset:mozilla-foundation/common_voice_8_0 transformers generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_8_0 robust-speech-event license:apache-2.0 model-index
---
license: apache-2.0
language: tr
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
model-index:
- name: wav2vec2-large-xls-r-300m-tr
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice tr
      type: common_voice_8_0
      args: tr
    metrics:
    - name: Test WER
      type: wer
      value: 28.69
---

<!-- 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
