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W2V2-BERT-withLM-Malayalam
trysem/W2V2-BERT-withLM-Malayalam
W2V2-BERT-withLM-Malayalam is a automatic speech recognition model from trysem. Use it when you need speech turned into text. 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
- License
- mit
- Model type
- wav2vec2-bert
- Access
- Public
- Created
- Sep 25, 2024
- Updated
- Sep 25, 2024
- SHA
- cd1db66f
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. -->
W2V2-BERT-withLM-Malayalam
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the IMASC, MSC, OpenSLR Malayalam Train split, Festvox Malayalam, CV16 .
It achieves the following results on the validation set : OpenSLR-Test:
- Loss: 0.1722
- Wer: 0.1299
Trigram Language Model Trained using KENLM Library on kavyamanohar/ml-sentences dataset
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 |
|---|---|---|---|---|
| 1.1416 | 0.46 | 600 | 0.3393 | 0.4616 |
| 0.1734 | 0.92 | 1200 | 0.2414 | 0.3493 |
| 0.1254 | 1.38 | 1800 | 0.2205 | 0.2963 |
| 0.1097 | 1.84 | 2400 | 0.2157 | 0.3133 |
| 0.0923 | 2.3 | 3000 | 0.1854 | 0.2473 |
| 0.0792 | 2.76 | 3600 | 0.1939 | 0.2471 |
| 0.0696 | 3.22 | 4200 | 0.1720 | 0.2282 |
| 0.0589 | 3.68 | 4800 | 0.1768 | 0.2013 |
| 0.0552 | 4.14 | 5400 | 0.1635 | 0.1864 |
| 0.0437 | 4.6 | 6000 | 0.1501 | 0.1826 |
| 0.0408 | 5.06 | 6600 | 0.1500 | 0.1645 |
| 0.0314 | 5.52 | 7200 | 0.1559 | 0.1655 |
| 0.0317 | 5.98 | 7800 | 0.1448 | 0.1553 |
| 0.022 | 6.44 | 8400 | 0.1592 | 0.1590 |
| 0.0218 | 6.9 | 9000 | 0.1431 | 0.1458 |
| 0.0154 | 7.36 | 9600 | 0.1514 | 0.1366 |
| 0.0141 | 7.82 | 10200 | 0.1540 | 0.1383 |
| 0.0113 | 8.28 | 10800 | 0.1558 | 0.1391 |
| 0.0085 | 8.74 | 11400 | 0.1612 | 0.1356 |
| 0.0072 | 9.2 | 12000 | 0.1697 | 0.1289 |
| 0.0046 | 9.66 | 12600 | 0.1722 | 0.1299 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Task
- Automatic Speech Recognition
- Type
- wav2vec2-bert
- License
- mit
- Languages
- ml
- Created
- Sep 25, 2024
- Updated
- Sep 25, 2024