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wav2vec2-base-vios-commonvoice
tclong/wav2vec2-base-vios-commonvoice
wav2vec2-base-vios-commonvoice is a automatic speech recognition model from tclong. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- apache-2.0
- Model type
- wav2vec2
- Access
- Public
- Created
- Jun 8, 2022
- Updated
- Jun 9, 2022
- SHA
- fe5be39c
Model card
From the Hugging Face model README
<!-- 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-base-vios-commonvoice
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3823
- Wer: 0.2401
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.2268 | 0.66 | 500 | 0.8746 | 0.5939 |
| 0.8728 | 1.32 | 1000 | 0.6435 | 0.4554 |
| 0.6899 | 1.99 | 1500 | 0.5655 | 0.3995 |
| 0.5842 | 2.65 | 2000 | 0.5267 | 0.3694 |
| 0.5371 | 3.31 | 2500 | 0.4980 | 0.3431 |
| 0.4921 | 3.97 | 3000 | 0.4781 | 0.3276 |
| 0.4508 | 4.64 | 3500 | 0.4434 | 0.3134 |
| 0.433 | 5.3 | 4000 | 0.4348 | 0.2963 |
| 0.404 | 5.96 | 4500 | 0.4248 | 0.2874 |
| 0.3834 | 6.62 | 5000 | 0.4163 | 0.2775 |
| 0.3784 | 7.28 | 5500 | 0.4104 | 0.2751 |
| 0.3669 | 7.95 | 6000 | 0.4143 | 0.2724 |
| 0.3462 | 8.61 | 6500 | 0.4131 | 0.2699 |
| 0.3364 | 9.27 | 7000 | 0.4070 | 0.2617 |
| 0.3249 | 9.93 | 7500 | 0.4076 | 0.2603 |
| 0.3154 | 10.6 | 8000 | 0.3998 | 0.2577 |
| 0.3117 | 11.26 | 8500 | 0.3930 | 0.2505 |
| 0.3101 | 11.92 | 9000 | 0.4003 | 0.2492 |
| 0.298 | 12.58 | 9500 | 0.3960 | 0.2496 |
| 0.2968 | 13.24 | 10000 | 0.3877 | 0.2469 |
| 0.29 | 13.91 | 10500 | 0.3870 | 0.2456 |
| 0.2921 | 14.57 | 11000 | 0.3823 | 0.2401 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Jun 8, 2022
- Updated
- Jun 9, 2022