tgrhn
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wav2vec-turkish-300m

@@ -1,201 +1,180 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-300m
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: wav2vec-turkish
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_16_1
+      type: common_voice_16_1
+      config: tr
+      split: test
+      args: tr
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.27954553626002226
 ---
+<!-- 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. -->
+# wav2vec-turkish
+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_16_1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3312
+- Wer: 0.2795
+## 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: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 5.433         | 0.29  | 400   | 1.1787          | 0.9025 |
+| 0.7193        | 0.58  | 800   | 0.6239          | 0.6505 |
+| 0.5243        | 0.88  | 1200  | 0.5098          | 0.5901 |
+| 0.4514        | 1.17  | 1600  | 0.4618          | 0.5131 |
+| 0.419         | 1.46  | 2000  | 0.4341          | 0.4990 |
+| 0.3975        | 1.75  | 2400  | 0.4016          | 0.4809 |
+| 0.3756        | 2.05  | 2800  | 0.3926          | 0.4684 |
+| 0.3421        | 2.34  | 3200  | 0.3841          | 0.4639 |
+| 0.3418        | 2.63  | 3600  | 0.3889          | 0.4551 |
+| 0.3409        | 2.92  | 4000  | 0.3615          | 0.4295 |
+| 0.3039        | 3.21  | 4400  | 0.3939          | 0.4562 |
+| 0.2934        | 3.51  | 4800  | 0.3866          | 0.4531 |
+| 0.2971        | 3.8   | 5200  | 0.3891          | 0.4497 |
+| 0.2953        | 4.09  | 5600  | 0.3694          | 0.4405 |
+| 0.2836        | 4.38  | 6000  | 0.3583          | 0.4252 |
+| 0.2721        | 4.67  | 6400  | 0.3562          | 0.4164 |
+| 0.2685        | 4.97  | 6800  | 0.3574          | 0.4215 |
+| 0.251         | 5.26  | 7200  | 0.3660          | 0.4239 |
+| 0.2537        | 5.55  | 7600  | 0.3723          | 0.4308 |
+| 0.2629        | 5.84  | 8000  | 0.3758          | 0.4359 |
+| 0.2469        | 6.14  | 8400  | 0.3799          | 0.4295 |
+| 0.2342        | 6.43  | 8800  | 0.3453          | 0.3947 |
+| 0.2306        | 6.72  | 9200  | 0.3361          | 0.3977 |
+| 0.2284        | 7.01  | 9600  | 0.3592          | 0.3970 |
+| 0.213         | 7.3   | 10000 | 0.3451          | 0.3904 |
+| 0.2188        | 7.6   | 10400 | 0.3426          | 0.3828 |
+| 0.2239        | 7.89  | 10800 | 0.3392          | 0.3878 |
+| 0.205         | 8.18  | 11200 | 0.3729          | 0.4021 |
+| 0.2049        | 8.47  | 11600 | 0.3511          | 0.3981 |
+| 0.2082        | 8.77  | 12000 | 0.3719          | 0.4143 |
+| 0.2047        | 9.06  | 12400 | 0.3569          | 0.3984 |
+| 0.1895        | 9.35  | 12800 | 0.3416          | 0.3798 |
+| 0.1935        | 9.64  | 13200 | 0.3378          | 0.3793 |
+| 0.1963        | 9.93  | 13600 | 0.3301          | 0.3883 |
+| 0.1889        | 10.23 | 14000 | 0.3577          | 0.3881 |
+| 0.182         | 10.52 | 14400 | 0.3281          | 0.3776 |
+| 0.1794        | 10.81 | 14800 | 0.3368          | 0.3780 |
+| 0.1736        | 11.1  | 15200 | 0.3275          | 0.3664 |
+| 0.1659        | 11.4  | 15600 | 0.3504          | 0.3753 |
+| 0.1651        | 11.69 | 16000 | 0.3343          | 0.3733 |
+| 0.1735        | 11.98 | 16400 | 0.3510          | 0.3750 |
+| 0.1569        | 12.27 | 16800 | 0.3243          | 0.3558 |
+| 0.1535        | 12.56 | 17200 | 0.3239          | 0.3603 |
+| 0.1588        | 12.86 | 17600 | 0.3372          | 0.3655 |
+| 0.1524        | 13.15 | 18000 | 0.3453          | 0.3709 |
+| 0.1453        | 13.44 | 18400 | 0.3301          | 0.3590 |
+| 0.1483        | 13.73 | 18800 | 0.3443          | 0.3597 |
+| 0.1432        | 14.02 | 19200 | 0.3401          | 0.3584 |
+| 0.1374        | 14.32 | 19600 | 0.3357          | 0.3618 |
+| 0.1399        | 14.61 | 20000 | 0.3386          | 0.3621 |
+| 0.142         | 14.9  | 20400 | 0.3136          | 0.3547 |
+| 0.1307        | 15.19 | 20800 | 0.3328          | 0.3501 |
+| 0.1299        | 15.49 | 21200 | 0.3346          | 0.3458 |
+| 0.1301        | 15.78 | 21600 | 0.3188          | 0.3473 |
+| 0.1285        | 16.07 | 22000 | 0.3323          | 0.3522 |
+| 0.1197        | 16.36 | 22400 | 0.3333          | 0.3392 |
+| 0.1225        | 16.65 | 22800 | 0.3545          | 0.3590 |
+| 0.1263        | 16.95 | 23200 | 0.3360          | 0.3410 |
+| 0.1134        | 17.24 | 23600 | 0.3204          | 0.3332 |
+| 0.114         | 17.53 | 24000 | 0.3264          | 0.3349 |
+| 0.1165        | 17.82 | 24400 | 0.3160          | 0.3323 |
+| 0.1134        | 18.12 | 24800 | 0.3479          | 0.3377 |
+| 0.1066        | 18.41 | 25200 | 0.3306          | 0.3378 |
+| 0.1027        | 18.7  | 25600 | 0.3286          | 0.3286 |
+| 0.1083        | 18.99 | 26000 | 0.3285          | 0.3227 |
+| 0.0937        | 19.28 | 26400 | 0.3240          | 0.3259 |
+| 0.1007        | 19.58 | 26800 | 0.3286          | 0.3283 |
+| 0.0996        | 19.87 | 27200 | 0.3278          | 0.3277 |
+| 0.0972        | 20.16 | 27600 | 0.3171          | 0.3212 |
+| 0.0927        | 20.45 | 28000 | 0.3426          | 0.3283 |
+| 0.0932        | 20.75 | 28400 | 0.3418          | 0.3215 |
+| 0.0932        | 21.04 | 28800 | 0.3246          | 0.3192 |
+| 0.086         | 21.33 | 29200 | 0.3385          | 0.3201 |
+| 0.0868        | 21.62 | 29600 | 0.3441          | 0.3164 |
+| 0.0875        | 21.91 | 30000 | 0.3246          | 0.3161 |
+| 0.0815        | 22.21 | 30400 | 0.3303          | 0.3105 |
+| 0.0832        | 22.5  | 30800 | 0.3288          | 0.3062 |
+| 0.0781        | 22.79 | 31200 | 0.3411          | 0.3098 |
+| 0.077         | 23.08 | 31600 | 0.3343          | 0.3146 |
+| 0.0755        | 23.37 | 32000 | 0.3211          | 0.3093 |
+| 0.0742        | 23.67 | 32400 | 0.3268          | 0.3044 |
+| 0.0721        | 23.96 | 32800 | 0.3222          | 0.3045 |
+| 0.0699        | 24.25 | 33200 | 0.3266          | 0.2993 |
+| 0.0663        | 24.54 | 33600 | 0.3410          | 0.3008 |
+| 0.0719        | 24.84 | 34000 | 0.3221          | 0.3014 |
+| 0.0682        | 25.13 | 34400 | 0.3290          | 0.2976 |
+| 0.0674        | 25.42 | 34800 | 0.3356          | 0.2967 |
+| 0.0661        | 25.71 | 35200 | 0.3181          | 0.2964 |
+| 0.0681        | 26.0  | 35600 | 0.3318          | 0.2964 |
+| 0.0619        | 26.3  | 36000 | 0.3220          | 0.2945 |
+| 0.0617        | 26.59 | 36400 | 0.3270          | 0.2913 |
+| 0.0592        | 26.88 | 36800 | 0.3391          | 0.2909 |
+| 0.0569        | 27.17 | 37200 | 0.3394          | 0.2900 |
+| 0.0557        | 27.47 | 37600 | 0.3359          | 0.2877 |
+| 0.0555        | 27.76 | 38000 | 0.3306          | 0.2847 |
+| 0.055         | 28.05 | 38400 | 0.3344          | 0.2827 |
+| 0.0516        | 28.34 | 38800 | 0.3389          | 0.2845 |
+| 0.0544        | 28.63 | 39200 | 0.3360          | 0.2840 |
+| 0.0542        | 28.93 | 39600 | 0.3366          | 0.2828 |
+| 0.0524        | 29.22 | 40000 | 0.3343          | 0.2819 |
+| 0.0527        | 29.51 | 40400 | 0.3319          | 0.2803 |
+| 0.0503        | 29.8  | 40800 | 0.3312          | 0.2795 |
+### Framework versions
+- Transformers 4.38.1
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

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