{
  "name": "default_config",
  "n_gpu": 1,
  "text_encoder": {
    "type": "CTCCharTextEncoder",
    "args": {
        "kenlm_model_path": "hw_asr/text_encoder/lower_3-gram.arpa",
        "unigrams_path": "hw_asr/text_encoder/librispeech-fixed-vocab.txt"
    }
  },
  "preprocessing": {
    "sr": 16000,
    "spectrogram": {
      "type": "MelSpectrogram",
      "args": {
        "n_mels": 256
      }
    },
    "log_spec": true
  },
  "augmentations": {
    "random_apply_p": 0,
    "wave": [],
    "spectrogram": []
  },
  "arch": {
    "type": "DeepSpeech2Model",
    "args": {
      "n_feats": 256,
      "n_rnn_layers": 6,
      "rnn_hidden_size": 512,
      "rnn_dropout": 0.2
    }
  },
  "data": {
    "train": {
      "batch_size": 128,
      "num_workers": 4,
      "datasets": [
        {
          "type": "LibrispeechDataset",
          "args": {
            "part": "train-other-500",
            "max_audio_length": 40.0,
            "max_text_length": 400
          }
        }
      ]
    },
    "val": {
      "batch_size": 64,
      "num_workers": 4,
      "datasets": [
        {
          "type": "LibrispeechDataset",
          "args": {
            "part": "dev-clean"
          }
        }
      ]
    },
    "test-other": {
      "batch_size": 64,
      "num_workers": 4,
      "datasets": [
        {
          "type": "LibrispeechDataset",
          "args": {
            "part": "test-other"
          }
        }
      ]
    }
  },
  "optimizer": {
    "type": "AdamW",
    "args": {
      "lr": 6e-5,
      "weight_decay": 1e-5
    }
  },
  "loss": {
    "type": "CTCLoss",
    "args": {}
  },
  "metrics": [
    {
      "type": "ArgmaxWERMetric",
      "args": {
        "name": "WER (argmax)"
      }
    },
    {
      "type": "ArgmaxCERMetric",
      "args": {
        "name": "CER (argmax)"
      }
    }
  ],
  "lr_scheduler": {
    "type": "OneCycleLR",
    "args": {
      "steps_per_epoch": 1000,
      "epochs": 10,
      "anneal_strategy": "cos",
      "max_lr": 6e-5,
      "pct_start": 0.2
    }
  },
  "trainer": {
    "epochs": 10,
    "save_dir": "saved/",
    "save_period": 5,
    "verbosity": 2,
    "monitor": "min val_loss",
    "early_stop": 100,
    "visualize": "wandb",
    "wandb_project": "asr_project",
    "len_epoch": 1000,
    "grad_norm_clip": 10
  }
}