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w2v-bert-2.0-malayalam_mixeddataset_two.0
Bajiyo/w2v-bert-2.0-malayalam_mixeddataset_two.0
w2v-bert-2.0-malayalam_mixeddataset_two.0 is a automatic speech recognition model from Bajiyo. 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
- Apr 16, 2024
- Updated
- Apr 16, 2024
- SHA
- 48ff3a0f
Base models
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-malayalam_mixeddataset_two.0
This model is a fine-tuned version of facebook/w2v-bert-2.0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1425
- Wer: 0.1451
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.9341 | 0.24 | 300 | 0.4363 | 0.5138 |
| 0.228 | 0.47 | 600 | 0.3644 | 0.4847 |
| 0.1828 | 0.71 | 900 | 0.2752 | 0.3807 |
| 0.1479 | 0.95 | 1200 | 0.2671 | 0.3583 |
| 0.1213 | 1.19 | 1500 | 0.2291 | 0.2861 |
| 0.1114 | 1.42 | 1800 | 0.2098 | 0.2754 |
| 0.1049 | 1.66 | 2100 | 0.2088 | 0.2832 |
| 0.0962 | 1.9 | 2400 | 0.1789 | 0.2501 |
| 0.0777 | 2.14 | 2700 | 0.1945 | 0.2371 |
| 0.0685 | 2.37 | 3000 | 0.1788 | 0.2433 |
| 0.0663 | 2.61 | 3300 | 0.1707 | 0.2264 |
| 0.0652 | 2.85 | 3600 | 0.1834 | 0.2227 |
| 0.0573 | 3.08 | 3900 | 0.1663 | 0.2065 |
| 0.0445 | 3.32 | 4200 | 0.1479 | 0.1981 |
| 0.0417 | 3.56 | 4500 | 0.1477 | 0.1779 |
| 0.0415 | 3.8 | 4800 | 0.1504 | 0.1774 |
| 0.0368 | 4.03 | 5100 | 0.1407 | 0.1655 |
| 0.0248 | 4.27 | 5400 | 0.1568 | 0.1672 |
| 0.0258 | 4.51 | 5700 | 0.1495 | 0.1582 |
| 0.0227 | 4.74 | 6000 | 0.1460 | 0.1510 |
| 0.0225 | 4.98 | 6300 | 0.1425 | 0.1451 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Automatic Speech Recognition
- Library
- transformers
- Type
- wav2vec2-bert
- License
- mit
- Created
- Apr 16, 2024
- Updated
- Apr 16, 2024