Integrated Hydra into the training script

config.yaml

@@ -0,0 +1,55 @@
+training:
+ no_cuda: false # Enable CPU only training
+ finetune: false # Fine-tune the model from checkpoint "continue_from"
+ seed: 123456 # Seed for generators
+ dist_backend: nccl # If using distribution, the backend to be used
+ epochs: 70 # Number of Training Epochs
+data:
+ train_manifest: data/train_manifest.csv
+ val_manifest: data/val_manifest.csv
+ sample_rate: 16000 # The sample rate for the data/model features
+ batch_size: 20 # Batch size for training
+ num_workers: 4 # Number of workers used in data-loading
+ labels_path: labels.json # Contains tokens for model output
+ window_size: .02 # Window size for spectrogram generation (seconds)
+ window_stride: .01 # Window stride for spectrogram generation (seconds)
+ window: hamming # Window type for spectrogram generation
+model:
+ rnn_type: lstm # Type of RNN to use in modeel, rnn/gru/lstm are supported
+ hidden_size: 1024 # Hidden size of RNN Layer
+ hidden_layers: 5 # Number of RNN layers
+ bidirectional: true # Use BiRNNs. If False, uses lookahead conv
+optimizer:
+ learning_rate: 3e-4 # Initial Learning Rate
+ weight_decay: 1e-5 # Initial Weight Decay
+ momentum: 0.9
+ adam: false # Replace SGD with AdamW
+ eps: 1e-8 # Adam eps
+ beta: (0.9, 0.999) # Adam betas
+ max_norm: 400 # Norm cutoff to prevent explosion of gradients
+ learning_anneal: 1.1 # Annealing applied to learning rate after each epoch
+checkpointing:
+ continue_from: '' # Continue training from checkpoint model
+ checkpoint: True # Enables epoch checkpoint saving of model
+ checkpoint_per_iteration: 0 # Save checkpoint per N number of iterations. Default is disabled
+ save_n_recent_models: 10 # Maximum number of checkpoints to save. If the max is reached, we delete older checkpoints
+ save_folder: models/ # Location to save epoch models
+ best_val_model_name: deepspeech_final.pth # Name to save best validated model within the save folder
+ load_auto_checkpoint: false # Enable when handling interruptions. Automatically load the latest checkpoint from the save folder
+visualization:
+ visdom: false # Turn on visdom graphing
+ tensorboard: false # Turn on Tensorboard graphing
+ log_dir: visualize/deepspeech_final # Location of Tensorboard log
+ log_params: false # Log parameter values and gradients
+ id: DeepSpeech training # Identifier for visdom/tensorboard run
+augmentation:
+ speed_volume_perturb: false # Use random tempo and gain perturbations.
+ spec_augment: false # Use simple spectral augmentation on mel spectograms.
+ noise_dir: '' # Directory to inject noise into audio. If default, noise Inject not added
+ noise_prob: 0.4 # Probability of noise being added per sample
+ noise_min: 0.0 # Minimum noise level to sample from. (1.0 means all noise, not original signal)
+ noise_max: 0.5 # Maximum noise levels to sample from. Maximum 1.0
+apex:
+ opt_level: O1 # Apex optimization level, check https://nvidia.github.io/apex/amp.html for more information
+ loss_scale: 1 # Loss scaling used by Apex. Default is 1 due to warp-ctc not supporting scaling of gradients
+

requirements.txt

@@ -12,4 +12,5 @@ matplotlib
 flask
 sox
 sklearn
-soundfile
+soundfile
+hydra-core