mouseyy/result_data-1

1---2library_name: transformers3license: apache-2.04base_model: facebook/wav2vec2-xls-r-300m5tags:6- generated_from_trainer7datasets:8- common_voice_17_09metrics:10- wer11model-index:12- name: result_data-113  results:14  - task:15      name: Automatic Speech Recognition16      type: automatic-speech-recognition17    dataset:18      name: common_voice_17_019      type: common_voice_17_020      config: uk21      split: test22      args: uk23    metrics:24    - name: Wer25      type: wer26      value: 0.3651287857345032527---28 29<!-- This model card has been generated automatically according to the information the Trainer had access to. You30should probably proofread and complete it, then remove this comment. -->31 32# result_data-133 34This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.35It achieves the following results on the evaluation set:36- Loss: 0.222037- Wer: 0.365138- Cer: 0.169139 40## Model description41 42More information needed43 44## Intended uses & limitations45 46More information needed47 48## Training and evaluation data49 50More information needed51 52## Training procedure53 54### Training hyperparameters55 56The following hyperparameters were used during training:57- learning_rate: 6.532628754904162e-0558- train_batch_size: 1659- eval_batch_size: 1660- seed: 4261- distributed_type: multi-GPU62- num_devices: 263- total_train_batch_size: 3264- total_eval_batch_size: 3265- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments66- lr_scheduler_type: linear67- lr_scheduler_warmup_steps: 20668- num_epochs: 7.069- mixed_precision_training: Native AMP70 71### Training results72 73| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |74|:-------------:|:------:|:----:|:---------------:|:------:|:------:|75| 0.6324        | 0.9099 | 1000 | 0.5004          | 0.6083 | 0.2381 |76| 0.3497        | 1.8198 | 2000 | 0.3087          | 0.4650 | 0.1965 |77| 0.2642        | 2.7298 | 3000 | 0.2636          | 0.4249 | 0.1841 |78| 0.2328        | 3.6397 | 4000 | 0.2431          | 0.3960 | 0.1789 |79| 0.1933        | 4.5496 | 5000 | 0.2289          | 0.3773 | 0.1732 |80| 0.1783        | 5.4595 | 6000 | 0.2300          | 0.3728 | 0.1711 |81| 0.1617        | 6.3694 | 7000 | 0.2233          | 0.3637 | 0.1700 |82 83 84### Framework versions85 86- Transformers 4.49.087- Pytorch 2.6.0+cu12488- Datasets 3.3.289- Tokenizers 0.21.090