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--- |
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base_model: facebook/w2v-bert-2.0 |
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license: mit |
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datasets: |
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- thennal/IMaSC |
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- vrclc/festvox-iiith-ml |
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- vrclc/openslr63 |
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- smcproject/msc |
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- mozilla-foundation/common_voice_16_1 |
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metrics: |
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- wer |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: w2v2bert-Malayalam |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: OpenSLR Malayalam -Test |
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type: vrclc/openslr63 |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- type: wer |
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value: 20.37 |
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name: WER |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Goole Fleurs |
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type: google/fleurs |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- type: wer |
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value: 39.27 |
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name: WER |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Common Voice 16 Malayalam |
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type: mozilla-foundation/common_voice_16_1 |
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config: ml |
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split: test |
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args: ml |
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metrics: |
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- type: wer |
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value: 53.14 |
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name: WER |
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--- |
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# W2V2-BERT-Malayalam |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an these datasets: [IMASC](https://huggingface.co/datasets/thennal/IMaSC), [MSC](https://huggingface.co/datasets/smcproject/MSC), [OpenSLR Malayalam Train split](https://huggingface.co/datasets/vrclc/openslr63), [Festvox Malayalam](https://huggingface.co/datasets/vrclc/festvox-iiith-ml), [common_voice_16_1](https://huggingface.co/datasets/mozilla-foundation/common_voice_16_1) |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1722 |
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- Wer: 0.1299 |
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## Training procedure |
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Trained on NVIDIA A100 GPU |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 1.1416 | 0.46 | 600 | 0.3393 | 0.4616 | |
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| 0.1734 | 0.92 | 1200 | 0.2414 | 0.3493 | |
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| 0.1254 | 1.38 | 1800 | 0.2205 | 0.2963 | |
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| 0.1097 | 1.84 | 2400 | 0.2157 | 0.3133 | |
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| 0.0923 | 2.3 | 3000 | 0.1854 | 0.2473 | |
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| 0.0792 | 2.76 | 3600 | 0.1939 | 0.2471 | |
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| 0.0696 | 3.22 | 4200 | 0.1720 | 0.2282 | |
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| 0.0589 | 3.68 | 4800 | 0.1768 | 0.2013 | |
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| 0.0552 | 4.14 | 5400 | 0.1635 | 0.1864 | |
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| 0.0437 | 4.6 | 6000 | 0.1501 | 0.1826 | |
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| 0.0408 | 5.06 | 6600 | 0.1500 | 0.1645 | |
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| 0.0314 | 5.52 | 7200 | 0.1559 | 0.1655 | |
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| 0.0317 | 5.98 | 7800 | 0.1448 | 0.1553 | |
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| 0.022 | 6.44 | 8400 | 0.1592 | 0.1590 | |
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| 0.0218 | 6.9 | 9000 | 0.1431 | 0.1458 | |
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| 0.0154 | 7.36 | 9600 | 0.1514 | 0.1366 | |
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| 0.0141 | 7.82 | 10200 | 0.1540 | 0.1383 | |
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| 0.0113 | 8.28 | 10800 | 0.1558 | 0.1391 | |
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| 0.0085 | 8.74 | 11400 | 0.1612 | 0.1356 | |
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| 0.0072 | 9.2 | 12000 | 0.1697 | 0.1289 | |
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| 0.0046 | 9.66 | 12600 | 0.1722 | 0.1299 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
