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W2V2-BERT-Malayalam

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W2V2-BERT-Malayalam / README.md
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metadata
base_model: facebook/w2v-bert-2.0
license: mit
datasets:
  - thennal/IMaSC
  - vrclc/festvox-iiith-ml
  - vrclc/openslr63
  - thennal/msc
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: w2v-bert-2.0-nonstudio_and_studioRecords
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 13 Malayalam
          type: mozilla-foundation/common_voice_16_1
          config: ml
          split: test
          args: ml
        metrics:
          - type: wer
            value: 52.1
            name: WER

w2v-bert-2.0-nonstudio_and_studioRecords

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: 0.1722
  • Wer: 0.1299

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