tgrhn
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wav2vec-turkish-300m-xls

@@ -1,201 +1,178 @@
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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-1b
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: wav2vec-turkish-300m-xls
+  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: 0.4719196059396685
 ---
+<!-- 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-300m-xls
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_16_1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6040
+- Wer: 0.4719
+## 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: 0.0005
+- train_batch_size: 32
+- eval_batch_size: 8
+- 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: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 2.2653        | 0.29  | 400   | 1.1675          | 0.9024 |
+| 1.0108        | 0.58  | 800   | 1.2504          | 0.9083 |
+| 0.9497        | 0.88  | 1200  | 1.2197          | 0.9001 |
+| 0.8842        | 1.17  | 1600  | 0.8955          | 0.8195 |
+| 0.8603        | 1.46  | 2000  | 0.8623          | 0.8267 |
+| 0.8271        | 1.75  | 2400  | 0.8324          | 0.7950 |
+| 0.8037        | 2.05  | 2800  | 0.7933          | 0.7751 |
+| 0.7488        | 2.34  | 3200  | 0.8006          | 0.7841 |
+| 0.7683        | 2.63  | 3600  | 0.8042          | 0.7925 |
+| 0.7554        | 2.92  | 4000  | 0.7955          | 0.7728 |
+| 0.7078        | 3.21  | 4400  | 0.8038          | 0.7781 |
+| 0.7161        | 3.51  | 4800  | 0.8470          | 0.7967 |
+| 0.7114        | 3.8   | 5200  | 0.8424          | 0.8074 |
+| 0.696         | 4.09  | 5600  | 0.7482          | 0.7617 |
+| 0.6581        | 4.38  | 6000  | 0.7177          | 0.7427 |
+| 0.6448        | 4.67  | 6400  | 0.7201          | 0.7292 |
+| 0.6343        | 4.97  | 6800  | 0.6995          | 0.7265 |
+| 0.6072        | 5.26  | 7200  | 0.7234          | 0.7549 |
+| 0.598         | 5.55  | 7600  | 0.6919          | 0.7215 |
+| 0.5938        | 5.84  | 8000  | 0.7364          | 0.7310 |
+| 0.5641        | 6.14  | 8400  | 0.7075          | 0.7074 |
+| 0.5557        | 6.43  | 8800  | 0.6785          | 0.7069 |
+| 0.5537        | 6.72  | 9200  | 0.6434          | 0.7019 |
+| 0.5475        | 7.01  | 9600  | 0.6415          | 0.6797 |
+| 0.5053        | 7.3   | 10000 | 0.6402          | 0.6751 |
+| 0.518         | 7.6   | 10400 | 0.6214          | 0.6618 |
+| 0.5041        | 7.89  | 10800 | 0.6156          | 0.6607 |
+| 0.4853        | 8.18  | 11200 | 0.6600          | 0.6879 |
+| 0.4738        | 8.47  | 11600 | 0.6359          | 0.6659 |
+| 0.4766        | 8.77  | 12000 | 0.6674          | 0.6980 |
+| 0.4822        | 9.06  | 12400 | 0.6189          | 0.6545 |
+| 0.4421        | 9.35  | 12800 | 0.6090          | 0.6434 |
+| 0.4562        | 9.64  | 13200 | 0.6099          | 0.6383 |
+| 0.4559        | 9.93  | 13600 | 0.6009          | 0.6511 |
+| 0.4456        | 10.23 | 14000 | 0.6090          | 0.6409 |
+| 0.4277        | 10.52 | 14400 | 0.5940          | 0.6374 |
+| 0.4228        | 10.81 | 14800 | 0.5992          | 0.6402 |
+| 0.4139        | 11.1  | 15200 | 0.6287          | 0.6344 |
+| 0.3918        | 11.4  | 15600 | 0.6134          | 0.6326 |
+| 0.3957        | 11.69 | 16000 | 0.5952          | 0.6275 |
+| 0.4022        | 11.98 | 16400 | 0.5957          | 0.6350 |
+| 0.3733        | 12.27 | 16800 | 0.5466          | 0.6010 |
+| 0.3749        | 12.56 | 17200 | 0.5566          | 0.6044 |
+| 0.3736        | 12.86 | 17600 | 0.5453          | 0.5994 |
+| 0.3701        | 13.15 | 18000 | 0.5846          | 0.6203 |
+| 0.359         | 13.44 | 18400 | 0.5880          | 0.5997 |
+| 0.3501        | 13.73 | 18800 | 0.5738          | 0.6153 |
+| 0.3403        | 14.02 | 19200 | 0.5766          | 0.5855 |
+| 0.3302        | 14.32 | 19600 | 0.5507          | 0.5954 |
+| 0.3295        | 14.61 | 20000 | 0.5467          | 0.5899 |
+| 0.3371        | 14.9  | 20400 | 0.5571          | 0.5907 |
+| 0.3253        | 15.19 | 20800 | 0.5266          | 0.5745 |
+| 0.3054        | 15.49 | 21200 | 0.5211          | 0.5681 |
+| 0.3044        | 15.78 | 21600 | 0.5409          | 0.5698 |
+| 0.2982        | 16.07 | 22000 | 0.5467          | 0.5827 |
+| 0.293         | 16.36 | 22400 | 0.5426          | 0.5706 |
+| 0.2901        | 16.65 | 22800 | 0.5404          | 0.5793 |
+| 0.2913        | 16.95 | 23200 | 0.5342          | 0.5647 |
+| 0.269         | 17.24 | 23600 | 0.5309          | 0.5623 |
+| 0.2803        | 17.53 | 24000 | 0.5300          | 0.5637 |
+| 0.2697        | 17.82 | 24400 | 0.5103          | 0.5539 |
+| 0.2727        | 18.12 | 24800 | 0.5414          | 0.5607 |
+| 0.2505        | 18.41 | 25200 | 0.5472          | 0.5508 |
+| 0.2554        | 18.7  | 25600 | 0.5260          | 0.5511 |
+| 0.2552        | 18.99 | 26000 | 0.5246          | 0.5389 |
+| 0.2333        | 19.28 | 26400 | 0.5392          | 0.5497 |
+| 0.2315        | 19.58 | 26800 | 0.5230          | 0.5395 |
+| 0.2366        | 19.87 | 27200 | 0.5303          | 0.5344 |
+| 0.2317        | 20.16 | 27600 | 0.5348          | 0.5350 |
+| 0.2229        | 20.45 | 28000 | 0.5138          | 0.5328 |
+| 0.2243        | 20.75 | 28400 | 0.5147          | 0.5235 |
+| 0.2169        | 21.04 | 28800 | 0.5494          | 0.5266 |
+| 0.2068        | 21.33 | 29200 | 0.5361          | 0.5266 |
+| 0.2073        | 21.62 | 29600 | 0.5660          | 0.5346 |
+| 0.2035        | 21.91 | 30000 | 0.5048          | 0.5196 |
+| 0.1954        | 22.21 | 30400 | 0.5498          | 0.5137 |
+| 0.1974        | 22.5  | 30800 | 0.5338          | 0.5157 |
+| 0.1914        | 22.79 | 31200 | 0.5311          | 0.5080 |
+| 0.1874        | 23.08 | 31600 | 0.5600          | 0.5020 |
+| 0.1792        | 23.37 | 32000 | 0.5428          | 0.5012 |
+| 0.182         | 23.67 | 32400 | 0.5237          | 0.5013 |
+| 0.1825        | 23.96 | 32800 | 0.5383          | 0.4999 |
+| 0.1723        | 24.25 | 33200 | 0.5690          | 0.5063 |
+| 0.1673        | 24.54 | 33600 | 0.5525          | 0.5030 |
+| 0.165         | 24.84 | 34000 | 0.5519          | 0.5001 |
+| 0.162         | 25.13 | 34400 | 0.5553          | 0.4966 |
+| 0.1597        | 25.42 | 34800 | 0.5614          | 0.4938 |
+| 0.1505        | 25.71 | 35200 | 0.5569          | 0.4932 |
+| 0.157         | 26.0  | 35600 | 0.5629          | 0.4931 |
+| 0.1468        | 26.3  | 36000 | 0.5808          | 0.4879 |
+| 0.1438        | 26.59 | 36400 | 0.5675          | 0.4871 |
+| 0.1462        | 26.88 | 36800 | 0.5568          | 0.4852 |
+| 0.138         | 27.17 | 37200 | 0.5995          | 0.4821 |
+| 0.1394        | 27.47 | 37600 | 0.5810          | 0.4798 |
+| 0.1363        | 27.76 | 38000 | 0.5776          | 0.4771 |
+| 0.1318        | 28.05 | 38400 | 0.5909          | 0.4763 |
+| 0.128         | 28.34 | 38800 | 0.5967          | 0.4782 |
+| 0.1304        | 28.63 | 39200 | 0.5866          | 0.4758 |
+| 0.1284        | 28.93 | 39600 | 0.5904          | 0.4747 |
+| 0.1207        | 29.22 | 40000 | 0.6023          | 0.4739 |
+| 0.1275        | 29.51 | 40400 | 0.6038          | 0.4733 |
+| 0.1241        | 29.8  | 40800 | 0.6040          | 0.4719 |
+### Framework versions
+- Transformers 4.38.1
+- Pytorch 2.2.0+cu121
+- Datasets 2.17.0
+- Tokenizers 0.15.2

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