added Lhotse online augmentation tutorial for SE

examples/audio/conf/masking_with_online_augmentation.yaml

@@ -0,0 +1,119 @@
+name: "masking_with_online_augmenatation"
+
+model:
+  sample_rate: 16000
+  skip_nan_grad: false
+  num_outputs: 1
+
+  train_ds:
+    use_lhotse: true # enable Lhotse data loader
+    cuts_path: ??? # path to Lhotse cuts manifest with speech signals for augmentation (including custom "target_recording" field with the same signals)
+    truncate_duration: 4.0 # Number of STFT time frames = 1 + truncate_duration // encoder.hop_length = 256
+    truncate_offset_type: random # if the file is longer than truncate_duration, use random offset to select a subsegment
+    batch_size: 64 # batch size may be increased based on the available memory
+    shuffle: true
+    num_workers: 8
+    pin_memory: true
+    rir_enabled: true # enable room impulse response augmentation
+    rir_path: ??? # path to Lhotse recordings manifest with room impulse response signals
+    noise_path: ??? # path to Lhotse cuts manifest with noise signals
+
+  validation_ds:
+    use_lhotse: true # enable Lhotse data loader
+    cuts_path: ??? # path to Lhotse cuts manifest with noisy speech signals (including custom "target_recording" field with the clean signals)
+    batch_size: 64 # batch size may be increased based on the available memory
+    shuffle: false
+    num_workers: 4
+    pin_memory: true
+
+  test_ds:
+    use_lhotse: true # enable Lhotse data loader
+    cuts_path: ??? # path to Lhotse cuts manifest with noisy speech signals (including custom "target_recording" field with the clean signals)
+    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.MaskReferenceChannel # Apply mask on the reference channel
+    ref_channel: 0 # Reference channel for the output
+
+  loss:
+    _target_: nemo.collections.audio.losses.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

nemo/collections/audio/data/audio_to_audio_lhotse.py

@@ -55,6 +55,13 @@ def __getitem__(self, cuts: CutSet) -> dict[str, torch.Tensor]:
         retained_cuts = [
             cut._first_non_padding_cut if isinstance(cut, MixedCut) else cut for cut in retained_padded_cuts
         ]
+
+        # if online augmentation is applied, some retained cuts still may be MixedCuts (including the original speech, noise, and augmentation)
+        # get the first non-padding cut from there, which is supposed to be the clean speech signal
+        for n, cut in enumerate(retained_cuts):
+            if isinstance(cut, MixedCut):
+                retained_cuts[n] = cut._first_non_padding_cut
+        # create cutset
         retained_cuts = CutSet.from_cuts(retained_cuts)
 
         if _key_available(retained_cuts, self.TARGET_KEY):