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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: xls-r-300m-hbs-ar-unfrozen-batch16 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: hsb |
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split: test |
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args: hsb |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.46954076850984067 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning/runs/tkr20gft) |
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# xls-r-300m-hbs-ar-unfrozen-batch16 |
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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_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7763 |
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- Wer: 0.4695 |
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- Cer: 0.1093 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:------:|:------:| |
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| 3.3679 | 3.2258 | 100 | 3.2752 | 1.0 | 1.0 | |
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| 3.0469 | 6.4516 | 200 | 2.9638 | 1.0 | 0.9902 | |
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| 0.5512 | 9.6774 | 300 | 0.7542 | 0.7664 | 0.1947 | |
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| 0.3029 | 12.9032 | 400 | 0.6819 | 0.6432 | 0.1584 | |
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| 0.1903 | 16.1290 | 500 | 0.7312 | 0.6361 | 0.1572 | |
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| 0.1464 | 19.3548 | 600 | 0.7223 | 0.5916 | 0.1456 | |
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| 0.1205 | 22.5806 | 700 | 0.7566 | 0.5738 | 0.1416 | |
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| 0.091 | 25.8065 | 800 | 0.7472 | 0.5527 | 0.1308 | |
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| 0.0686 | 29.0323 | 900 | 0.7029 | 0.5452 | 0.1337 | |
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| 0.0598 | 32.2581 | 1000 | 0.7889 | 0.5464 | 0.1309 | |
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| 0.0607 | 35.4839 | 1100 | 0.8012 | 0.5672 | 0.1412 | |
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| 0.0557 | 38.7097 | 1200 | 0.7628 | 0.5302 | 0.1333 | |
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| 0.0421 | 41.9355 | 1300 | 0.7861 | 0.5258 | 0.1265 | |
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| 0.0532 | 45.1613 | 1400 | 0.7843 | 0.5314 | 0.1272 | |
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| 0.0298 | 48.3871 | 1500 | 0.7888 | 0.5279 | 0.1253 | |
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| 0.0543 | 51.6129 | 1600 | 0.7847 | 0.5295 | 0.1290 | |
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| 0.0404 | 54.8387 | 1700 | 0.7314 | 0.5246 | 0.1249 | |
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| 0.0522 | 58.0645 | 1800 | 0.7505 | 0.5134 | 0.1222 | |
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| 0.0275 | 61.2903 | 1900 | 0.7588 | 0.5082 | 0.1202 | |
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| 0.0786 | 64.5161 | 2000 | 0.7733 | 0.4930 | 0.1171 | |
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| 0.0439 | 67.7419 | 2100 | 0.7953 | 0.4977 | 0.1133 | |
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| 0.0418 | 70.9677 | 2200 | 0.7664 | 0.4897 | 0.1126 | |
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| 0.0399 | 74.1935 | 2300 | 0.7599 | 0.4845 | 0.1100 | |
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| 0.0211 | 77.4194 | 2400 | 0.7747 | 0.4763 | 0.1115 | |
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| 0.0225 | 80.6452 | 2500 | 0.7607 | 0.4702 | 0.1094 | |
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| 0.0446 | 83.8710 | 2600 | 0.7583 | 0.4768 | 0.1103 | |
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| 0.0236 | 87.0968 | 2700 | 0.7824 | 0.4754 | 0.1102 | |
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| 0.0267 | 90.3226 | 2800 | 0.7861 | 0.4726 | 0.1110 | |
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| 0.0255 | 93.5484 | 2900 | 0.7928 | 0.4712 | 0.1106 | |
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| 0.0254 | 96.7742 | 3000 | 0.7834 | 0.4684 | 0.1102 | |
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| 0.0137 | 100.0 | 3100 | 0.7763 | 0.4695 | 0.1093 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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