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w2v-bert-2.0-pt_pt_v2
tiagomosantos/w2v-bert-2.0-pt_pt_v2
w2v-bert-2.0-pt_pt_v2 is a automatic speech recognition model from tiagomosantos. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Automatic Speech Recognition
- Library
- transformers
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Mar 17, 2024
- Updated
- Mar 19, 2024
- SHA
- 62a8b3b6
Base models
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. -->
w2v-bert-2.0-pt_pt_v2
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_16_1 Portuguese subset using 1XRTX 3090. It achieves the following results on the test set:
- Wer: 0.10491320595991134
- Cer: 0.032070871626631914
- Bert Score: 0.9619712047981167
- Sentence Similarity: 0.93867844
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bert Score |
|---|---|---|---|---|---|---|
| 1.2735 | 1.0 | 678 | 0.2292 | 0.1589 | 0.0415 | 0.9498 |
| 0.1715 | 2.0 | 1356 | 0.1762 | 0.1283 | 0.0344 | 0.9599 |
| 0.1158 | 3.0 | 2034 | 0.1539 | 0.1100 | 0.0298 | 0.9646 |
| 0.0821 | 4.0 | 2712 | 0.1362 | 0.0949 | 0.0258 | 0.9703 |
| 0.0605 | 5.0 | 3390 | 0.1349 | 0.0860 | 0.0236 | 0.9728 |
| 0.0475 | 6.0 | 4068 | 0.1395 | 0.0871 | 0.0239 | 0.9728 |
| 0.0355 | 7.0 | 4746 | 0.1487 | 0.0837 | 0.0230 | 0.9739 |
| 0.0309 | 8.0 | 5424 | 0.1452 | 0.0873 | 0.0240 | 0.9728 |
| 0.0308 | 9.0 | 6102 | 0.1390 | 0.0843 | 0.0228 | 0.9735 |
| 0.0239 | 10.0 | 6780 | 0.1282 | 0.0832 | 0.0224 | 0.9739 |
Evaluation results
| Test Wer | Test Cer | Test Bert Score | Runtime | Samples per second |
|---|---|---|---|---|
| 0.09146400542583083 | 0.02643665913309742 | 0.9702128323433327 | 266.8185 | 35.282 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Languages
- pt
- Created
- Mar 17, 2024
- Updated
- Mar 19, 2024