| # This configuration contains the exemplary values for training a multichannel speech enhancement model with a mask-based beamformer. | |
| # | |
| name: "beamforming" | |
| model: | |
| sample_rate: 16000 | |
| skip_nan_grad: false | |
| num_outputs: 1 | |
| train_ds: | |
| manifest_filepath: ??? | |
| input_key: audio_filepath # key of the input signal path in the manifest | |
| target_key: target_filepath # key of the target signal path in the manifest | |
| target_channel_selector: 0 # target signal is the first channel from files in target_key | |
| audio_duration: 4.0 # in seconds, audio segment duration for training | |
| random_offset: true # if the file is longer than audio_duration, use random offset to select a subsegment | |
| min_duration: ${model.train_ds.audio_duration} | |
| batch_size: 64 # batch size may be increased based on the available memory | |
| shuffle: true | |
| num_workers: 8 | |
| pin_memory: true | |
| validation_ds: | |
| manifest_filepath: ??? | |
| input_key: audio_filepath # key of the input signal path in the manifest | |
| target_key: target_filepath | |
| target_channel_selector: 0 # target signal is the first channel from files in target_key | |
| batch_size: 1 # batch size may be increased based on the available memory | |
| shuffle: false | |
| num_workers: 4 | |
| pin_memory: true | |
| test_ds: | |
| manifest_filepath: ??? | |
| input_key: audio_filepath # key of the input signal path in the manifest | |
| target_key: target_filepath # key of the target signal path in the manifest | |
| target_channel_selector: 0 # target signal is the first channel from files in target_key | |
| batch_size: 1 # batch size may be increased based on the available memory | |
| shuffle: false | |
| num_workers: 4 | |
| pin_memory: true | |
| encoder: | |
| _target_: nemo.collections.audio.modules.transforms.AudioToSpectrogram | |
| fft_length: 512 # Length of the window and FFT for calculating spectrogram | |
| hop_length: 256 # Hop length for calculating spectrogram | |
| decoder: | |
| _target_: nemo.collections.audio.modules.transforms.SpectrogramToAudio | |
| fft_length: 512 # Length of the window and FFT for calculating spectrogram | |
| hop_length: 256 # Hop length for calculating spectrogram | |
| mask_estimator: | |
| _target_: nemo.collections.audio.modules.masking.MaskEstimatorRNN | |
| num_outputs: ${model.num_outputs} | |
| num_subbands: 257 # Number of subbands of the input spectrogram | |
| num_features: 256 # Number of features at RNN input | |
| num_layers: 5 # Number of RNN layers | |
| bidirectional: true # Use bi-directional RNN | |
| mask_processor: | |
| _target_: nemo.collections.audio.modules.masking.MaskBasedBeamformer # Mask-based multi-channel processing | |
| ref_channel: 0 # Reference channel for the output | |
| loss: | |
| _target_: nemo.collections.audio.losses.audio.SDRLoss | |
| scale_invariant: true # Use scale-invariant SDR | |
| metrics: | |
| val: | |
| sdr: # output SDR | |
| _target_: torchmetrics.audio.SignalDistortionRatio | |
| test: | |
| sdr_ch0: # SDR on output channel 0 | |
| _target_: torchmetrics.audio.SignalDistortionRatio | |
| channel: 0 | |
| optim: | |
| name: adamw | |
| lr: 1e-4 | |
| # optimizer arguments | |
| betas: [0.9, 0.98] | |
| weight_decay: 1e-3 | |
| trainer: | |
| devices: -1 # number of GPUs, -1 would use all available GPUs | |
| num_nodes: 1 | |
| max_epochs: -1 | |
| max_steps: -1 # computed at runtime if not set | |
| val_check_interval: 1.0 # Set to 0.25 to check 4 times per epoch, or an int for number of iterations | |
| accelerator: auto | |
| strategy: ddp | |
| accumulate_grad_batches: 1 | |
| gradient_clip_val: null | |
| precision: 32 # Should be set to 16 for O1 and O2 to enable the AMP. | |
| log_every_n_steps: 25 # Interval of logging. | |
| enable_progress_bar: true | |
| num_sanity_val_steps: 0 # number of steps to perform validation steps for sanity check the validation process before starting the training, setting to 0 disables it | |
| check_val_every_n_epoch: 1 # number of evaluations on validation every n epochs | |
| sync_batchnorm: true | |
| enable_checkpointing: False # Provided by exp_manager | |
| logger: false # Provided by exp_manager | |
| exp_manager: | |
| exp_dir: null | |
| name: ${name} | |
| create_tensorboard_logger: true | |
| create_checkpoint_callback: true | |
| checkpoint_callback_params: | |
| # in case of multiple validation sets, first one is used | |
| monitor: "val_loss" | |
| mode: "min" | |
| save_top_k: 5 | |
| always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints | |
| resume_from_checkpoint: null # The path to a checkpoint file to continue the training, restores the whole state including the epoch, step, LR schedulers, apex, etc. | |
| # you need to set these two to true to continue the training | |
| resume_if_exists: false | |
| resume_ignore_no_checkpoint: false | |
| # You may use this section to create a W&B logger | |
| create_wandb_logger: false | |
| wandb_logger_kwargs: | |
| name: null | |
| project: null | |