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wav2vec2-base-finetuned-spgispeech-dev
nickmuchi/wav2vec2-base-finetuned-spgispeech-dev
wav2vec2-base-finetuned-spgispeech-dev is a automatic speech recognition model from nickmuchi. 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
- Sep 1, 2022
- Updated
- Sep 2, 2022
- SHA
- 7efe7138
Datasets
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-finetuned-spgispeech-dev
This model is a fine-tuned version of facebook/wav2vec2-base on the kensho/spgispeech dev dataset. It achieves the following results on the evaluation set:
- Loss: 0.2897
- Wer: 0.1508
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.8285 | 2.22 | 1500 | 0.3361 | 0.2754 |
| 0.2582 | 4.44 | 3000 | 0.2643 | 0.2205 |
| 0.1697 | 6.66 | 4500 | 0.2467 | 0.2006 |
| 0.1314 | 8.88 | 6000 | 0.2711 | 0.1927 |
| 0.1084 | 11.09 | 7500 | 0.2521 | 0.1872 |
| 0.0922 | 13.31 | 9000 | 0.2588 | 0.1827 |
| 0.0818 | 15.53 | 10500 | 0.2572 | 0.1783 |
| 0.0712 | 17.75 | 12000 | 0.2720 | 0.1766 |
| 0.067 | 19.97 | 13500 | 0.2873 | 0.1751 |
| 0.0594 | 22.19 | 15000 | 0.2753 | 0.1704 |
| 0.0546 | 24.41 | 16500 | 0.2794 | 0.1694 |
| 0.0505 | 26.63 | 18000 | 0.2811 | 0.1665 |
| 0.0467 | 28.85 | 19500 | 0.2906 | 0.1657 |
| 0.0417 | 31.07 | 21000 | 0.3043 | 0.1661 |
| 0.0395 | 33.28 | 22500 | 0.3068 | 0.1627 |
| 0.0368 | 35.5 | 24000 | 0.3096 | 0.1617 |
| 0.0334 | 37.72 | 25500 | 0.3036 | 0.1581 |
| 0.0322 | 39.94 | 27000 | 0.2819 | 0.1564 |
| 0.0286 | 42.16 | 28500 | 0.2936 | 0.1544 |
| 0.0279 | 44.38 | 30000 | 0.2914 | 0.1534 |
| 0.0264 | 46.6 | 31500 | 0.2957 | 0.1519 |
| 0.0241 | 48.82 | 33000 | 0.2897 | 0.1508 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2
- License
- apache-2.0
- Created
- Sep 1, 2022
- Updated
- Sep 2, 2022