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config.yaml

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+model:
+  _target_: src.model.ConformerModel
+  input_dim: 128
+writer:
+  _target_: src.logger.CometMLWriter
+  project_name: pytorch_template_asr_example
+  workspace: null
+  run_name: conformer_30m
+  mode: online
+  loss_names:
+  - loss
+  log_checkpoints: false
+  id_length: 32
+  run_id: m2guzao93o9ytjxogwt78mftkyiqalsf
+metrics:
+  train: []
+  inference:
+  - _target_: src.metrics.ArgmaxCERMetric
+    name: CER_(Argmax)
+  - _target_: src.metrics.ArgmaxWERMetric
+    name: WER_(Argmax)
+  - _target_: src.metrics.WER
+    name: WER
+  - _target_: src.metrics.CER
+    name: CER
+datasets:
+  train:
+    _target_: src.datasets.LibrispeechDataset
+    part: train-other-500
+    instance_transforms: ${transforms.instance_transforms.train}
+  val:
+    _target_: src.datasets.LibrispeechDataset
+    part: test-clean
+    instance_transforms: ${transforms.instance_transforms.inference}
+  test:
+    _target_: src.datasets.LibrispeechDataset
+    part: test-other
+    instance_transforms: ${transforms.instance_transforms.inference}
+dataloader:
+  _target_: torch.utils.data.DataLoader
+  batch_size: 30
+  num_workers: 2
+  pin_memory: true
+transforms:
+  instance_transforms:
+    train:
+      get_spectrogram:
+        _target_: torchaudio.transforms.MelSpectrogram
+        sample_rate: 16000
+      audio:
+        _target_: torchvision.transforms.v2.Compose
+        transforms:
+        - _target_: src.transforms.wav_augs.Gain
+          sample_rate: 16000
+          min_gain_in_db: -6
+          max_gain_in_db: 6
+          p: 0.2
+        - _target_: src.transforms.wav_augs.Shift
+          p: 0.2
+        - _target_: src.transforms.wav_augs.PitchShift
+          min_semitones: -2
+          max_semitones: 2
+          p: 0.2
+        - _target_: src.transforms.wav_augs.Noise
+          p: 0.3
+    inference:
+      get_spectrogram:
+        _target_: torchaudio.transforms.MelSpectrogram
+        sample_rate: 16000
+  batch_transforms:
+    train: null
+    inference: null
+optimizer:
+  _target_: torch.optim.AdamW
+  lr: 5.0e-05
+lr_scheduler:
+  _target_: torch.optim.lr_scheduler.OneCycleLR
+  max_lr: 0.0001
+  pct_start: 0.1
+  steps_per_epoch: ${trainer.epoch_len}
+  epochs: ${trainer.n_epochs}
+  anneal_strategy: cos
+loss_function:
+  _target_: src.loss.CTCLossWrapper
+text_encoder:
+  _target_: src.text_encoder.CTCTextEncoder
+trainer:
+  log_step: 200
+  n_epochs: 150
+  epoch_len: 1300
+  device_tensors:
+  - spectrogram
+  - text_encoded
+  resume_from: checkpoint-epoch62.pth
+  device: auto
+  override: false
+  monitor: min val_WER_(Argmax)
+  save_period: 5
+  early_stop: ${trainer.n_epochs}
+  save_dir: saved
+  seed: 1