<!-- 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. -->
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ID dataset. It achieves the following results on the evaluation set:
- Loss: 0.3975
- Wer: 0.2633
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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 0.78 | 100 | 4.5645 | 1.0 |
| No log | 1.55 | 200 | 2.9016 | 1.0 |
| No log | 2.33 | 300 | 2.2666 | 1.0982 |
| No log | 3.1 | 400 | 0.6079 | 0.6376 |
| 3.2188 | 3.88 | 500 | 0.4985 | 0.5008 |
| 3.2188 | 4.65 | 600 | 0.4477 | 0.4469 |
| 3.2188 | 5.43 | 700 | 0.3953 | 0.3915 |
| 3.2188 | 6.2 | 800 | 0.4319 | 0.3921 |
| 3.2188 | 6.98 | 900 | 0.4171 | 0.3698 |
| 0.2193 | 7.75 | 1000 | 0.3957 | 0.3600 |
| 0.2193 | 8.53 | 1100 | 0.3730 | 0.3493 |
| 0.2193 | 9.3 | 1200 | 0.3780 | 0.3348 |
| 0.2193 | 10.08 | 1300 | 0.4133 | 0.3568 |
| 0.2193 | 10.85 | 1400 | 0.3984 | 0.3193 |
| 0.1129 | 11.63 | 1500 | 0.3845 | 0.3174 |
| 0.1129 | 12.4 | 1600 | 0.3882 | 0.3162 |
| 0.1129 | 13.18 | 1700 | 0.3982 | 0.3008 |
| 0.1129 | 13.95 | 1800 | 0.3902 | 0.3198 |
| 0.1129 | 14.73 | 1900 | 0.4082 | 0.3237 |
| 0.0765 | 15.5 | 2000 | 0.3732 | 0.3126 |
| 0.0765 | 16.28 | 2100 | 0.3893 | 0.3001 |
| 0.0765 | 17.05 | 2200 | 0.4168 | 0.3083 |
| 0.0765 | 17.83 | 2300 | 0.4193 | 0.3044 |
| 0.0765 | 18.6 | 2400 | 0.4006 | 0.3013 |
| 0.0588 | 19.38 | 2500 | 0.3836 | 0.2892 |
| 0.0588 | 20.16 | 2600 | 0.3761 | 0.2903 |
| 0.0588 | 20.93 | 2700 | 0.3895 | 0.2930 |
| 0.0588 | 21.71 | 2800 | 0.3885 | 0.2791 |
| 0.0588 | 22.48 | 2900 | 0.3902 | 0.2891 |
| 0.0448 | 23.26 | 3000 | 0.4200 | 0.2849 |
| 0.0448 | 24.03 | 3100 | 0.4013 | 0.2799 |
| 0.0448 | 24.81 | 3200 | 0.4039 | 0.2731 |
| 0.0448 | 25.58 | 3300 | 0.3970 | 0.2647 |
| 0.0448 | 26.36 | 3400 | 0.4081 | 0.2690 |
| 0.0351 | 27.13 | 3500 | 0.4090 | 0.2674 |
| 0.0351 | 27.91 | 3600 | 0.3953 | 0.2663 |
| 0.0351 | 28.68 | 3700 | 0.4044 | 0.2650 |
| 0.0351 | 29.46 | 3800 | 0.3969 | 0.2646 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
