Wer is very low.
State: closed | Pull request: no
Labels: name: bug

name: stale

conf:

wandb_version: 1

_wandb:
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    python_version: 3.11.8
    cli_version: 0.16.6
    framework: huggingface
    huggingface_version: 4.39.3
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    is_kaggle_kernel: false
    start_time: 1712733521.0
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cfg:
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    sample_rate: 16000
    log_prediction: true
    ctc_reduction: mean_batch
    skip_nan_grad: false
    train_ds:
      manifest_filepath: /media/dilip/LaCie/Nitin/content/drive/MyDrive/NEW/hi/train.json
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      type: bpe
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    optim:
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      lr: 0.5
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      sched:
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        d_model: 512
        warmup_steps: 10000
        warmup_ratio: null
        min_lr: 1.0e-06
    target: nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE
    nemo_version:

```[tasklist]
### Tasks
- [ ] Add a draft title or issue reference here
```




{
  "train_loss": 8.534393310546875,
  "learning_rate": 0.00012372159108053893,
  "global_step": 5610,
  "train_backward_timing in s": 0.15601778030395508,
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  "training_batch_wer": 0.18061673641204834,
  "epoch": 30,
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  "_timestamp": 1712744688.4421802,
  "_runtime": 11167.270045042038,
  "_step": 293353,
  "validation_step_timing in s": 0.02845907211303711,
  "val_loss": 6.631827354431152,
  "val_wer": 0.12062931060791016,
  "test_step_timing in s": 0.043920040130615234,
  "test_loss": 10.514272689819336,
  "test_wer": 0.17779342830181122,
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i used common voice13 hindi as dataset


my issue is that wer for the model i used i.e conformer_large_ctc is very low .what could be the reason for such a low wer.
@titu1994 sir

I'm confused, the metric is "word error rate" - it's good if it's low. Ideal wer is 0

actually i was worried about overfitting of data btw thanks sir 🙌

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