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wav2vec2-xlsr-300M-NPSC-OH
NbAiLab/wav2vec2-xlsr-300M-NPSC-OH
wav2vec2-xlsr-300M-NPSC-OH is a automatic speech recognition model from NbAiLab. 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
- Mar 2, 2022
- Updated
- Feb 2, 2022
- SHA
- df6db091
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-xlsr-300M-NPSC-OH
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1692
- Wer: 0.1663
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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 15.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.1638 | 0.66 | 500 | 3.0686 | 1.0 |
| 2.9311 | 1.31 | 1000 | 2.9208 | 1.0 |
| 2.4175 | 1.97 | 1500 | 1.5009 | 0.9049 |
| 1.4442 | 2.63 | 2000 | 0.4426 | 0.3783 |
| 1.2624 | 3.28 | 2500 | 0.3193 | 0.2998 |
| 1.1889 | 3.94 | 3000 | 0.2867 | 0.2630 |
| 1.1315 | 4.6 | 3500 | 0.2566 | 0.2444 |
| 1.0864 | 5.26 | 4000 | 0.2368 | 0.2294 |
| 1.093 | 5.91 | 4500 | 0.2240 | 0.2151 |
| 1.0368 | 6.57 | 5000 | 0.2117 | 0.2056 |
| 1.0178 | 7.23 | 5500 | 0.2020 | 0.1954 |
| 1.0035 | 7.88 | 6000 | 0.2005 | 0.1924 |
| 0.9759 | 8.54 | 6500 | 0.1971 | 0.1863 |
| 0.9795 | 9.2 | 7000 | 0.1892 | 0.1812 |
| 0.9601 | 9.85 | 7500 | 0.1863 | 0.1795 |
| 0.9673 | 10.51 | 8000 | 0.1809 | 0.1761 |
| 0.9233 | 11.17 | 8500 | 0.1818 | 0.1755 |
| 0.9382 | 11.83 | 9000 | 0.1767 | 0.1741 |
| 0.9242 | 12.48 | 9500 | 0.1743 | 0.1703 |
| 0.9703 | 13.14 | 10000 | 0.1711 | 0.1711 |
| 0.9139 | 13.8 | 10500 | 0.1718 | 0.1672 |
| 0.9073 | 14.45 | 11000 | 0.1700 | 0.1665 |
Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Feb 2, 2022