[Audio] SSL Pretraining framework for flow-matching model for audio processing

examples/audio/audio_to_audio_train.py

@@ -34,6 +34,7 @@
 
 from nemo.collections.audio.models.enhancement import (
     EncMaskDecAudioToAudioModel,
+    FlowMatchingAudioToAudioModel,
     PredictiveAudioToAudioModel,
     SchroedingerBridgeAudioToAudioModel,
     ScoreBasedGenerativeAudioToAudioModel,
@@ -50,6 +51,7 @@ class ModelType(str, Enum):
     Predictive = 'predictive'
     ScoreBased = 'score_based'
     SchroedingerBridge = 'schroedinger_bridge'
+    FlowMatching = 'flow_matching'
 
 
 def get_model_class(model_type: ModelType):
@@ -62,6 +64,8 @@ def get_model_class(model_type: ModelType):
         return ScoreBasedGenerativeAudioToAudioModel
     elif model_type == ModelType.SchroedingerBridge:
         return SchroedingerBridgeAudioToAudioModel
+    elif model_type == ModelType.FlowMatching:
+        return FlowMatchingAudioToAudioModel
     else:
         raise ValueError(f'Unknown model type: {model_type}')
 

examples/audio/conf/flow_matching_generative.yaml

@@ -0,0 +1,164 @@
+name: flow_matching_generative
+
+model:
+  type: flow_matching
+  sample_rate: 16000
+  skip_nan_grad: false
+  num_outputs: 1
+  p_cond: 0.9 # Proability of feeding the conditional input into the model.
+  normalize_input: true # normalize the input signal to 0dBFS
+  max_utts_evaluation_metrics: 500
+
+  train_ds:
+    manifest_filepath: ???
+    input_key: noisy_filepath
+    target_key: clean_filepath
+    audio_duration: 6.14 # Number of STFT time frames = 1 + audio_duration // encoder.hop_length = 768
+    random_offset: true
+    batch_size: 8 # batch size may be increased based on the available memory
+    shuffle: true
+    num_workers: 8
+    pin_memory: true
+
+  validation_ds:
+    manifest_filepath: ???
+    input_key: noisy_filepath
+    target_key: clean_filepath
+    batch_size: 8
+    shuffle: false
+    num_workers: 4
+    pin_memory: true
+
+  log_config:
+    log_tensorboard: true
+    log_wandb: false
+    max_utts: 8
+
+  encoder:
+    _target_: nemo.collections.audio.modules.transforms.AudioToSpectrogram
+    fft_length: 510 # Number of subbands in the STFT = fft_length // 2 + 1 = 256
+    hop_length: 128
+    magnitude_power: 0.5
+    scale: 0.33
+
+  decoder:
+    _target_: nemo.collections.audio.modules.transforms.SpectrogramToAudio
+    fft_length: ${model.encoder.fft_length} 
+    hop_length: ${model.encoder.hop_length}
+    magnitude_power: ${model.encoder.magnitude_power}
+    scale: ${model.encoder.scale}
+
+  estimator:
+    _target_: nemo.collections.audio.parts.submodules.transformerunet.SpectrogramTransformerUNet
+    in_channels: 2 # concatenation of single-channel perturbed and noisy
+    out_channels: 1 # single-channel score estimate
+    depth: 24
+    ff_dropout: 0.1
+    time_hidden_dim: 1024
+
+  flow:
+    _target_: nemo.collections.audio.parts.submodules.flow.OptimalTransportFlow
+    sigma_start: 1.0
+    sigma_end: 1e-4
+
+  sampler:
+    _target_: nemo.collections.audio.parts.submodules.flow.ConditionalFlowMatchingEulerSampler
+    num_steps: 20
+    time_min: 1e-8
+    time_max: 1.0
+
+  loss:
+    _target_: nemo.collections.audio.losses.MSELoss
+    ndim: 4 # loss is calculated on the score in the encoded domain (batch, channel, dimension, time)
+
+  metrics:
+    val:
+      sisdr: # output SI-SDR
+        _target_: torchmetrics.audio.ScaleInvariantSignalDistortionRatio
+      estoi: # output ESTOI
+        _target_: torchmetrics.audio.ShortTimeObjectiveIntelligibility
+        fs: ${model.sample_rate}
+        extended: true
+      pesq: # output PESQ
+        _target_: torchmetrics.audio.PerceptualEvaluationSpeechQuality
+        fs: ${model.sample_rate}
+        mode: wb
+
+  optim:
+    name: adam
+    lr: 1e-4
+    # optimizer arguments
+    betas: [0.9, 0.999]
+    weight_decay: 0.0
+
+    # scheduler setup
+    sched:
+      name: CosineAnnealing
+      # scheduler config override
+      warmup_steps: 5000
+      warmup_ratio: null
+      min_lr: 0
+
+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: 0.2
+  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}
+
+  # use exponential moving average for model parameters
+  ema:
+      enable: true
+      decay: 0.999  # decay rate
+      cpu_offload: false  # offload EMA parameters to CPU to save GPU memory
+      every_n_steps: 1  # how often to update EMA weights
+      validate_original_weights: false  # use original weights for validation calculation?
+
+  # logging
+  create_tensorboard_logger: true
+
+  # checkpointing
+  create_checkpoint_callback: true
+  checkpoint_callback_params:
+    # in case of multiple validation sets, first one is used
+    monitor: val_pesq
+    mode: max
+    save_top_k: 3
+    always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints
+
+  # early stopping
+  create_early_stopping_callback: true
+  early_stopping_callback_params:
+    monitor: val_sisdr
+    mode: max
+    min_delta: 0.0
+    patience: 20 # patience in terms of check_val_every_n_epoch
+    verbose: true
+    strict: false # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training.
+
+  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: test
+    project: gense

examples/audio/conf/flow_matching_generative_finetuning.yaml

@@ -0,0 +1,167 @@
+name: flow_matching_generative_finetuning
+
+init_from_nemo_model: null
+init_strict: false
+
+model:
+  type: flow_matching
+  sample_rate: 16000
+  skip_nan_grad: false
+  num_outputs: 1
+  p_cond: 0.9 # Proability of feeding the conditional input into the model.
+  normalize_input: true # normalize the input signal to 0dBFS
+  max_utts_evaluation_metrics: 500
+
+  train_ds:
+    manifest_filepath: ???
+    input_key: noisy_filepath
+    target_key: clean_filepath
+    audio_duration: 6.14 # Number of STFT time frames = 1 + audio_duration // encoder.hop_length = 768
+    random_offset: true
+    batch_size: 8 # batch size may be increased based on the available memory
+    shuffle: true
+    num_workers: 8
+    pin_memory: true
+
+  validation_ds:
+    manifest_filepath: ???
+    input_key: noisy_filepath
+    target_key: clean_filepath
+    batch_size: 8
+    shuffle: false
+    num_workers: 4
+    pin_memory: true
+
+  log_config:
+    log_tensorboard: true
+    log_wandb: false
+    max_utts: 8
+
+  encoder:
+    _target_: nemo.collections.audio.modules.transforms.AudioToSpectrogram
+    fft_length: 510 # Number of subbands in the STFT = fft_length // 2 + 1 = 256
+    hop_length: 128
+    magnitude_power: 0.5
+    scale: 0.33
+
+  decoder:
+    _target_: nemo.collections.audio.modules.transforms.SpectrogramToAudio
+    fft_length: ${model.encoder.fft_length} 
+    hop_length: ${model.encoder.hop_length}
+    magnitude_power: ${model.encoder.magnitude_power}
+    scale: ${model.encoder.scale}
+
+  estimator:
+    _target_: nemo.collections.audio.parts.submodules.transformerunet.SpectrogramTransformerUNet
+    in_channels: 2 # concatenation of single-channel perturbed and noisy
+    out_channels: 1 # single-channel score estimate
+    depth: 24
+    ff_dropout: 0.1
+    time_hidden_dim: 1024
+
+  flow:
+    _target_: nemo.collections.audio.parts.submodules.flow.OptimalTransportFlow
+    sigma_start: 1.0
+    sigma_end: 1e-4
+
+  sampler:
+    _target_: nemo.collections.audio.parts.submodules.flow.ConditionalFlowMatchingEulerSampler
+    num_steps: 20
+    time_min: 1e-8
+    time_max: 1.0
+
+  loss:
+    _target_: nemo.collections.audio.losses.MSELoss
+    ndim: 4 # loss is calculated on the score in the encoded domain (batch, channel, dimension, time)
+
+  metrics:
+    val:
+      sisdr: # output SI-SDR
+        _target_: torchmetrics.audio.ScaleInvariantSignalDistortionRatio
+      estoi: # output ESTOI
+        _target_: torchmetrics.audio.ShortTimeObjectiveIntelligibility
+        fs: ${model.sample_rate}
+        extended: true
+      pesq: # output PESQ
+        _target_: torchmetrics.audio.PerceptualEvaluationSpeechQuality
+        fs: ${model.sample_rate}
+        mode: wb
+
+  optim:
+    name: adam
+    lr: 1e-4
+    # optimizer arguments
+    betas: [0.9, 0.999]
+    weight_decay: 0.0
+
+    # scheduler setup
+    sched:
+      name: CosineAnnealing
+      # scheduler config override
+      warmup_steps: 5000
+      warmup_ratio: null
+      min_lr: 0
+
+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: 0.2
+  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}
+
+  # use exponential moving average for model parameters
+  ema:
+      enable: true
+      decay: 0.999  # decay rate
+      cpu_offload: false  # offload EMA parameters to CPU to save GPU memory
+      every_n_steps: 1  # how often to update EMA weights
+      validate_original_weights: false  # use original weights for validation calculation?
+
+  # logging
+  create_tensorboard_logger: true
+
+  # checkpointing
+  create_checkpoint_callback: true
+  checkpoint_callback_params:
+    # in case of multiple validation sets, first one is used
+    monitor: val_pesq
+    mode: max
+    save_top_k: 3
+    always_save_nemo: true # saves the checkpoints as nemo files instead of PTL checkpoints
+
+  # early stopping
+  create_early_stopping_callback: true
+  early_stopping_callback_params:
+    monitor: val_sisdr
+    mode: max
+    min_delta: 0.0
+    patience: 20 # patience in terms of check_val_every_n_epoch
+    verbose: true
+    strict: false # Should be False to avoid a runtime error where EarlyStopping says monitor is unavailable, which sometimes happens with resumed training.
+
+  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: test
+    project: gense