Model save

README.md

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+---
+base_model: ylacombe/w2v-bert-2.0
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_16_0
+metrics:
+- wer
+model-index:
+- name: wav2vec2-common_voice-tr-demo
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_16_0
+      type: common_voice_16_0
+      config: tr
+      split: test
+      args: tr
+    metrics:
+    - name: Wer
+      type: wer
+      value: 1.0
+---
+
+<!-- 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-common_voice-tr-demo
+
+This model is a fine-tuned version of [ylacombe/w2v-bert-2.0](https://huggingface.co/ylacombe/w2v-bert-2.0) on the common_voice_16_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.0815
+- Wer: 1.0
+
+## 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: 0.00072018208512399
+- train_batch_size: 20
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 40
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 5000
+- num_epochs: 15.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| No log        | 0.27  | 300   | 7.2663          | 1.0    |
+| 10.5256       | 0.55  | 600   | 3.0893          | 1.0    |
+| 10.5256       | 0.82  | 900   | 3.0612          | 1.0    |
+| 2.9795        | 1.1   | 1200  | 2.9937          | 1.0    |
+| 2.9564        | 1.37  | 1500  | 3.2424          | 1.0    |
+| 2.9564        | 1.64  | 1800  | 3.2866          | 1.0    |
+| 3.1552        | 1.92  | 2100  | 3.6339          | 1.0    |
+| 3.1552        | 2.19  | 2400  | 3.1185          | 1.0    |
+| 3.2079        | 2.47  | 2700  | 3.1832          | 1.0    |
+| 3.1275        | 2.74  | 3000  | 3.3952          | 1.0    |
+| 3.1275        | 3.01  | 3300  | 3.2982          | 1.0    |
+| 3.0987        | 3.29  | 3600  | 3.1036          | 1.0    |
+| 3.0987        | 3.56  | 3900  | 3.1223          | 1.0    |
+| 2.9301        | 3.84  | 4200  | 3.1145          | 1.0    |
+| 2.9197        | 4.11  | 4500  | 3.0324          | 1.0    |
+| 2.9197        | 4.38  | 4800  | 2.9994          | 1.9599 |
+| 2.9023        | 4.66  | 5100  | 2.9917          | 1.8240 |
+| 2.9023        | 4.93  | 5400  | 2.9946          | 1.9589 |
+| 2.9007        | 5.21  | 5700  | 3.1955          | 1.0    |
+| 3.1887        | 5.48  | 6000  | 3.1902          | 1.0    |
+| 3.1887        | 5.75  | 6300  | 3.1672          | 1.0    |
+| 3.135         | 6.03  | 6600  | 3.2076          | 1.0    |
+| 3.135         | 6.3   | 6900  | 3.2120          | 1.0    |
+| 3.1482        | 6.58  | 7200  | 3.1832          | 1.0    |
+| 3.1546        | 6.85  | 7500  | 3.1799          | 1.0    |
+| 3.1546        | 7.12  | 7800  | 3.2452          | 1.0    |
+| 3.1567        | 7.4   | 8100  | 3.2319          | 1.0    |
+| 3.1567        | 7.67  | 8400  | 3.2228          | 1.0    |
+| 3.1719        | 7.95  | 8700  | 3.2055          | 1.0    |
+| 3.168         | 8.22  | 9000  | 3.2553          | 1.0    |
+| 3.168         | 8.49  | 9300  | 3.1975          | 1.0    |
+| 3.1643        | 8.77  | 9600  | 3.2446          | 1.0    |
+| 3.1643        | 9.04  | 9900  | 3.2781          | 1.0    |
+| 3.169         | 9.32  | 10200 | 3.2597          | 1.0    |
+| 3.1789        | 9.59  | 10500 | 3.2586          | 1.0    |
+| 3.1789        | 9.86  | 10800 | 3.2690          | 1.0    |
+| 3.1701        | 10.14 | 11100 | 3.2737          | 1.0    |
+| 3.1701        | 10.41 | 11400 | 3.2738          | 1.0    |
+| 3.1698        | 10.68 | 11700 | 3.2595          | 1.0    |
+| 3.1595        | 10.96 | 12000 | 3.2467          | 1.0    |
+| 3.1595        | 11.23 | 12300 | 3.2524          | 1.0    |
+| 3.15          | 11.51 | 12600 | 3.2327          | 1.0    |
+| 3.15          | 11.78 | 12900 | 3.2196          | 1.0    |
+| 3.1444        | 12.05 | 13200 | 3.1943          | 1.0    |
+| 3.132         | 12.33 | 13500 | 3.1911          | 1.0    |
+| 3.132         | 12.6  | 13800 | 3.2075          | 1.0    |
+| 3.1153        | 12.88 | 14100 | 3.1938          | 1.0    |
+| 3.1153        | 13.15 | 14400 | 3.1639          | 1.0    |
+| 3.1039        | 13.42 | 14700 | 3.1515          | 1.0    |
+| 3.0839        | 13.7  | 15000 | 3.1535          | 1.0    |
+| 3.0839        | 13.97 | 15300 | 3.1307          | 1.0    |
+| 3.0632        | 14.25 | 15600 | 3.1138          | 1.0    |
+| 3.0632        | 14.52 | 15900 | 3.1289          | 1.0    |
+| 3.0518        | 14.79 | 16200 | 3.0815          | 1.0    |
+
+
+### Framework versions
+
+- Transformers 4.37.0.dev0
+- Pytorch 2.1.0+cu121
+- Datasets 2.14.5
+- Tokenizers 0.15.0

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