Sortformer Diarizer 4spk v1 model PR Part 1: models, modules and dataloaders

examples/speaker_tasks/diarization/conf/neural_diarizer/sortformer_diarizer_hybrid_loss_4spk-v1.yaml

@@ -0,0 +1,213 @@
+sortformer_diarizer_hybrid_loss_4spk-v1.yaml# Sortformer Diarizer is an end-to-end speaker diarization model that is solely based on Transformer-encoder type of architecture.
+# Model name convention for Sortformer Diarizer: sortformer_diarizer_<loss_type>_<speaker count limit>_<version>.yaml
+# (Example) `sortformer_diarizer_hybrid_loss_4spk-v1.yaml`.
+# Sortformer Diarizer model checkpoint (.ckpt) and NeMo file (.nemo) contain Fast Conformer Encoder model (NEST Encoder) and the pre-trained NEST model is loaded along with the Transformer Encoder layers.
+# Example: a manifest line for training 
+# {"audio_filepath": "/path/to/audio01.wav", "offset": 390.83, "duration": 90.00, "text": "-", "num_speakers": 2, "rttm_filepath": "/path/to/audio01.rttm"}
+name: "SortFormerDiarizer"
+sample_rate: 16000
+num_workers: 18
+batch_size: 8
+
+model: 
+  pil_weight: 0.5 # Weight for Permutation Invariant Loss (PIL) used in training the Sortformer diarizer model
+  ats_weight: 0.5 # Weight for Arrival Time Sort (ATS) loss in training the Sortformer diarizer model
+  num_workers: ${num_workers} # Number of workers for data loading
+  fc_d_model: 512 # Hidden dimension size of the Fast-conformer Encoder
+  tf_d_model: 192 # Hidden dimension size of the Transformer Encoder
+  max_num_of_spks: 4 # Maximum number of speakers per model; currently set to 4
+  session_len_sec: 90 # Maximum session length in seconds
+
+  train_ds:
+    manifest_filepath: ???
+    sample_rate: ${sample_rate}
+    num_spks: ${model.max_num_of_spks}
+    session_len_sec: ${model.session_len_sec}
+    soft_label_thres: 0.5 # Threshold for binarizing target values; higher values make the model more conservative in predicting speaker activity.
+    soft_targets: False # If True, use continuous values as target values when calculating cross-entropy loss
+    labels: null
+    batch_size: ${batch_size}
+    shuffle: True
+    num_workers: ${num_workers}
+    validation_mode: False
+    # lhotse config
+    use_lhotse: False
+    use_bucketing: True
+    num_buckets: 10
+    bucket_duration_bins: [10, 20, 30, 40, 50, 60, 70, 80, 90]
+    pin_memory: True
+    min_duration: 80
+    max_duration: 90
+    batch_duration: 400
+    quadratic_duration: 1200
+    bucket_buffer_size: 20000
+    shuffle_buffer_size: 10000
+    window_stride: ${model.preprocessor.window_stride}
+    subsampling_factor: ${model.encoder.subsampling_factor}
+
+  validation_ds:
+    manifest_filepath: ???
+    is_tarred: False
+    tarred_audio_filepaths: null
+    sample_rate: ${sample_rate}
+    num_spks: ${model.max_num_of_spks}
+    session_len_sec: ${model.session_len_sec}
+    soft_label_thres: 0.5 # A threshold value for setting up the binarized labels. The higher the more conservative the model becomes. 
+    soft_targets: False
+    labels: null
+    batch_size: ${batch_size}
+    shuffle: False
+    num_workers: ${num_workers}
+    validation_mode: True
+    # lhotse config
+    use_lhotse: False
+    use_bucketing: False
+    drop_last: False
+    pin_memory: True
+    window_stride: ${model.preprocessor.window_stride}
+    subsampling_factor: ${model.encoder.subsampling_factor}
+
+  test_ds:
+    manifest_filepath: null
+    is_tarred: False
+    tarred_audio_filepaths: null
+    sample_rate: 16000
+    num_spks: ${model.max_num_of_spks}
+    session_len_sec: ${model.session_len_sec}
+    soft_label_thres: 0.5
+    soft_targets: False
+    labels: null
+    batch_size: ${batch_size}
+    shuffle: False
+    seq_eval_mode: True
+    num_workers: ${num_workers}
+    validation_mode: True
+    # lhotse config
+    use_lhotse: False
+    use_bucketing: False
+    drop_last: False
+    pin_memory: True
+    window_stride: ${model.preprocessor.window_stride}
+    subsampling_factor: ${model.encoder.subsampling_factor}
+
+  preprocessor:
+    _target_: nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor
+    normalize: "per_feature"
+    window_size: 0.025
+    sample_rate: ${sample_rate}
+    window_stride: 0.01
+    window: "hann"
+    features: 80
+    n_fft: 512
+    frame_splicing: 1
+    dither: 0.00001
+
+  sortformer_modules:
+    _target_: nemo.collections.asr.modules.sortformer_modules.SortformerModules
+    num_spks: ${model.max_num_of_spks} # Number of speakers per model. This is currently fixed at 4.
+    dropout_rate: 0.5 # Dropout rate
+    fc_d_model: ${model.fc_d_model}
+    tf_d_model: ${model.tf_d_model} # Hidden layer size for linear layers in Sortformer Diarizer module
+
+  encoder:
+    _target_: nemo.collections.asr.modules.ConformerEncoder
+    feat_in: ${model.preprocessor.features}
+    feat_out: -1
+    n_layers: 18
+    d_model: ${model.fc_d_model}
+
+    # Sub-sampling parameters
+    subsampling: dw_striding # vggnet, striding, stacking or stacking_norm, dw_striding
+    subsampling_factor: 8 # must be power of 2 for striding and vggnet
+    subsampling_conv_channels: 256 # set to -1 to make it equal to the d_model
+    causal_downsampling: false
+    # Feed forward module's params
+    ff_expansion_factor: 4
+    # Multi-headed Attention Module's params
+    self_attention_model: rel_pos # rel_pos or abs_pos
+    n_heads: 8 # may need to be lower for smaller d_models
+    # [left, right] specifies the number of steps to be seen from left and right of each step in self-attention
+    att_context_size: [-1, -1] # -1 means unlimited context
+    att_context_style: regular # regular or chunked_limited
+    xscaling: true # scales up the input embeddings by sqrt(d_model)
+    untie_biases: true # unties the biases of the TransformerXL layers
+    pos_emb_max_len: 5000
+    # Convolution module's params
+    conv_kernel_size: 9
+    conv_norm_type: 'batch_norm' # batch_norm or layer_norm or groupnormN (N specifies the number of groups)
+    conv_context_size: null
+    # Regularization
+    dropout: 0.1 # The dropout used in most of the Conformer Modules
+    dropout_pre_encoder: 0.1 # The dropout used before the encoder
+    dropout_emb: 0.0 # The dropout used for embeddings
+    dropout_att: 0.1 # The dropout for multi-headed attention modules
+    # Set to non-zero to enable stochastic depth
+    stochastic_depth_drop_prob: 0.0
+    stochastic_depth_mode: linear  # linear or uniform
+    stochastic_depth_start_layer: 1
+
+  transformer_encoder:
+    _target_: nemo.collections.asr.modules.transformer.transformer_encoders.TransformerEncoder
+    num_layers: 18
+    hidden_size: ${model.tf_d_model} # Needs to be multiple of num_attention_heads
+    inner_size: 768
+    num_attention_heads: 8
+    attn_score_dropout: 0.5
+    attn_layer_dropout: 0.5
+    ffn_dropout: 0.5
+    hidden_act: relu
+    pre_ln: False
+    pre_ln_final_layer_norm: True
+
+  loss: 
+    _target_: nemo.collections.asr.losses.bce_loss.BCELoss
+    weight: null # Weight for binary cross-entropy loss. Either `null` or list type input. (e.g. [0.5,0.5])
+    reduction: mean
+
+  lr: 0.0001
+  optim:
+    name: adamw
+    lr: ${model.lr}
+    # optimizer arguments
+    betas: [0.9, 0.98]
+    weight_decay: 1e-3
+
+    sched:
+      name: InverseSquareRootAnnealing
+      warmup_steps: 2500
+      warmup_ratio: null
+      min_lr: 1e-06
+
+trainer:
+  devices: 1 # number of gpus (devices)
+  accelerator: gpu 
+  max_epochs: 800
+  max_steps: -1 # computed at runtime if not set
+  num_nodes: 1
+  strategy: ddp_find_unused_parameters_true # Could be "ddp"
+  accumulate_grad_batches: 1
+  deterministic: True
+  enable_checkpointing: False
+  logger: False
+  log_every_n_steps: 1  # Interval of logging.
+  val_check_interval: 1.0  # Set to 0.25 to check 4 times per epoch, or an int for number of iterations
+
+exp_manager:
+  use_datetime_version: False
+  exp_dir: null
+  name: ${name}
+  resume_if_exists: True
+  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.
+  resume_ignore_no_checkpoint: True
+  create_tensorboard_logger: True
+  create_checkpoint_callback: True
+  create_wandb_logger: False
+  checkpoint_callback_params:
+    monitor: "val_f1_acc"
+    mode: "max"
+    save_top_k: 9
+    every_n_epochs: 1
+  wandb_logger_kwargs:
+    resume: True
+    name: null
+    project: null

examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_callhome-part1.yaml

@@ -0,0 +1,13 @@
+# Postprocessing parameters for timestamp outputs from speaker diarization models.
+# This speaker diarization postprocessing scheme is inspired by the postprocessing procedure in the following paper:
+# Medennikov, Ivan, et al. "Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario." (2020).
+# These parameters were optimized with with hybrid-loss trained Sortformer model introduced in https://arxiv.org/pdf/2409.06656.
+# These parameters were optimized on the development split of DIHARD3 dataset. See https://arxiv.org/pdf/2012.01477.
+# Trial 24682 finished with value: 0.10257785779242055 and parameters: {'onset': 0.53, 'offset': 0.49, 'pad_onset': 0.23, 'pad_offset': 0.01, 'min_duration_on': 0.42, 'min_duration_off': 0.34}. Best is trial 24682 with value: 0.10257785779242055.
+parameters: 
+  onset: 0.53  # Onset threshold for detecting the beginning and end of a speech
+  offset: 0.49  # Offset threshold for detecting the end of a speech
+  pad_onset: 0.23  # Adding durations before each speech segment
+  pad_offset: 0.01  # Adding durations after each speech segment
+  min_duration_on: 0.42  # Threshold for small non-speech deletion
+  min_duration_off: 0.34  # Threshold for short speech segment deletion

examples/speaker_tasks/diarization/conf/post_processing/sortformer_diar_4spk-v1_dihard-dev.yaml

@@ -0,0 +1,13 @@
+# Postprocessing parameters for timestamp outputs from speaker diarization models.
+# This speaker diarization postprocessing scheme is inspired by the postprocessing procedure in the following paper:
+# Medennikov, Ivan, et al. "Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario." (2020).
+# These parameters were optimized with with hybrid-loss trained Sortformer model introduced in https://arxiv.org/pdf/2409.06656.
+# These parameters were optimized on CallHome Dataset from the NIST SRE 2000 Disc8, especially from the split2 specified in: Kaldi, “Kaldi x-vector recipe v2,” https://github.com/kaldi-asr/kaldi/tree/master/egs/callhome_diarization/v2.
+# Trial 732 finished with value: 0.12171946949255649 and parameters: {'onset': 0.64, 'offset': 0.74, 'pad_onset': 0.06, 'pad_offset': 0.0, 'min_duration_on': 0.1, 'min_duration_off': 0.15}. Best is trial 732 with value: 0.12171946949255649. 
+parameters: 
+  onset: 0.64  # Onset threshold for detecting the beginning and end of a speech
+  offset: 0.74  # Offset threshold for detecting the end of a speech
+  pad_onset: 0.06  # Adding durations before each speech segment
+  pad_offset: 0.0  # Adding durations after each speech segment
+  min_duration_on: 0.1  # Threshold for small non-speech deletion
+  min_duration_off: 0.15  # Threshold for short speech segment deletion