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wav2vec2-xls-r-300m-ar-9
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:
- Loss: 86.4276
- Wer: 0.1947
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: 64
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 120
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 6312.2087 | 4.71 | 400 | 616.6482 | 1.0 |
| 1928.3641 | 9.41 | 800 | 135.8992 | 0.6373 |
| 502.0017 | 14.12 | 1200 | 84.4729 | 0.3781 |
| 299.4288 | 18.82 | 1600 | 76.2488 | 0.3132 |
| 224.0057 | 23.53 | 2000 | 77.6899 | 0.2868 |
| 183.0379 | 28.24 | 2400 | 77.7943 | 0.2725 |
| 160.6119 | 32.94 | 2800 | 79.4487 | 0.2643 |
| 142.7342 | 37.65 | 3200 | 81.3426 | 0.2523 |
| 127.1061 | 42.35 | 3600 | 83.4995 | 0.2489 |
| 114.0666 | 47.06 | 4000 | 82.9293 | 0.2416 |
| 108.4024 | 51.76 | 4400 | 78.6118 | 0.2330 |
| 99.6215 | 56.47 | 4800 | 87.1001 | 0.2328 |
| 95.5135 | 61.18 | 5200 | 84.0371 | 0.2260 |
| 88.2917 | 65.88 | 5600 | 85.9637 | 0.2278 |
| 82.5884 | 70.59 | 6000 | 81.7456 | 0.2237 |
| 77.6827 | 75.29 | 6400 | 88.2686 | 0.2184 |
| 73.313 | 80.0 | 6800 | 85.1965 | 0.2183 |
| 69.61 | 84.71 | 7200 | 86.1655 | 0.2100 |
| 65.6991 | 89.41 | 7600 | 84.0606 | 0.2106 |
| 62.6059 | 94.12 | 8000 | 83.8724 | 0.2036 |
| 57.8635 | 98.82 | 8400 | 85.2078 | 0.2012 |
| 55.2126 | 103.53 | 8800 | 86.6009 | 0.2021 |
| 53.1746 | 108.24 | 9200 | 88.4284 | 0.1975 |
| 52.3969 | 112.94 | 9600 | 85.2846 | 0.1972 |
| 49.8386 | 117.65 | 10000 | 86.4276 | 0.1947 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.11.6
