docs/source/asr/speaker_diarization/configs.rst

Speaker Diarization Configuration Files
========================================

Hydra Configurations for Sortformer Diarizer Training 
-----------------------------------------------------

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 `<NeMo_root>/examples/speaker_tasks/diarization/neural_diarizer/conf/sortformer_diarizer_hybrid_loss_4spk-v1.yaml`.

.. code-block:: yaml

  name: "SortformerDiarizer"
  num_workers: 18
  batch_size: 8

  model: 
    sample_rate: 16000
    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
    max_num_of_spks: 4 # Maximum number of speakers per model; currently set to 4

    model_defaults:
      fc_d_model: 512 # Hidden dimension size of the Fast-conformer Encoder
      tf_d_model: 192 # Hidden dimension size of the Transformer Encoder

    train_ds:
      manifest_filepath: ???
      sample_rate: ${model.sample_rate}
      num_spks: ${model.max_num_of_spks}
      session_len_sec: 90 # Maximum session length in seconds
      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: 10
      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: ${model.sample_rate}
      num_spks: ${model.max_num_of_spks}
      session_len_sec: 90 # Maximum session length in seconds
      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: 90 # Maximum session length in seconds
      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: ${model.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.model_defaults.fc_d_model}
      tf_d_model: ${model.model_defaults.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.model_defaults.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.model_defaults.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

Hydra Configurations for Streaming Sortformer Diarizer Training
----------------------------------------------------------------

Model name convention for Streaming Sortformer Diarizer: streaming_sortformer_diarizer_<speaker count limit>-<version>.yaml

* Example `<NeMo_root>/examples/speaker_tasks/diarization/neural_diarizer/conf/streaming_sortformer_diarizer_4spk-v2.yaml`.

.. code-block:: yaml

  name: "StreamingSortformerDiarizer"
  num_workers: 18
  batch_size: 4

  model: 
    sample_rate: 16000
    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
    max_num_of_spks: 4 # Maximum number of speakers per model; currently set to 4
    streaming_mode: True

    model_defaults:
      fc_d_model: 512 # Hidden dimension size of the Fast-conformer Encoder
      tf_d_model: 192 # Hidden dimension size of the Transformer Encoder

    train_ds:
      manifest_filepath: ???
      sample_rate: ${model.sample_rate}
      num_spks: ${model.max_num_of_spks}
      session_len_sec: 90 # Maximum session length in seconds
      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: 10
      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: ${model.sample_rate}
      num_spks: ${model.max_num_of_spks}
      session_len_sec: 90 # Maximum session length in seconds
      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: 90 # Maximum session length in seconds
      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: "NA"
      window_size: 0.025
      sample_rate: ${model.sample_rate}
      window_stride: 0.01
      window: "hann"
      features: 128
      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} # Maximum number of speakers the model can handle
      dropout_rate: 0.5 # Dropout rate
      fc_d_model: ${model.model_defaults.fc_d_model} # Hidden dimension size for Fast Conformer encoder
      tf_d_model: ${model.model_defaults.tf_d_model} # Hidden dimension size for Transformer encoder
      # Streaming mode parameters
      spkcache_len: 188 # Length of speaker cache buffer (total number of frames for all speakers)
      fifo_len: 0 # Length of FIFO buffer for streaming processing (0 = disabled)
      chunk_len: 188 # Number of frames processed in each streaming chunk
      spkcache_update_period: 188 # Speaker cache update period in frames
      chunk_left_context: 1 # Number of previous frames for each streaming chunk
      chunk_right_context: 1 # Number of future frames for each streaming chunk
      # Speaker cache update parameters
      spkcache_sil_frames_per_spk: 3 # Number of silence frames allocated per speaker in the speaker cache
      scores_add_rnd: 0 # Standard deviation of random noise added to scores in speaker cache update (training only)
      pred_score_threshold: 0.25 # Probability threshold for internal scores processing in speaker cache update
      max_index: 99999 # Maximum allowed index value for internal processing in speaker cache update
      scores_boost_latest: 0.05 # Gain for scores for recently added frames in speaker cache update
      sil_threshold: 0.2 # Threshold for determining silence frames to calculate average silence embedding
      strong_boost_rate: 0.75 # Rate determining number of frames per speaker that receive strong score boosting
      weak_boost_rate: 1.5 # Rate determining number of frames per speaker that receive weak score boosting
      min_pos_scores_rate: 0.5 # Rate threshold for dropping overlapping frames when enough non-overlapping exist
      # Self-attention parameters (training only)
      causal_attn_rate: 0.5 # Proportion of batches that use self-attention with limited right context
      causal_attn_rc: 7 # Right context size for self-attention with limited right context

    encoder:
      _target_: nemo.collections.asr.modules.ConformerEncoder
      feat_in: ${model.preprocessor.features}
      feat_out: -1
      n_layers: 17
      d_model: ${model.model_defaults.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.model_defaults.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: 500
        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


Hydra Configurations for (Streaming) Sortformer Diarization Post-processing
-----------------------------------------------------------------------------

Post-processing converts the floating point number based Tensor output to time stamp output. While generating the speaker-homogeneous segments, onset and offset threshold, 
paddings can be considered to render the time stamps that can lead to the lowest diarization error rate (DER). This post-processing can be applied to both offline and streaming Sortformer diarizer.


By default, post-processing is bypassed, and only binarization is performed. If you want to reproduce DER scores reported on NeMo model cards, you need to apply post-processing steps. Use batch_size = 1 to have the longest inference window and the highest possible accuracy.

.. code-block:: yaml

  parameters: 
    onset: 0.64  # Onset threshold for detecting the beginning of a speech segment
    offset: 0.74  # Offset threshold for detecting the end of a speech segment
    pad_onset: 0.06  # Adds the specified duration at the beginning of each speech segment
    pad_offset: 0.0  # Adds the specified duration at the end of each speech segment
    min_duration_on: 0.1  # Removes short speech segments if the duration is less than the specified minimum duration
    min_duration_off: 0.15  # Removes short silences if the duration is less than the specified minimum duration


Hydra Configurations for Diarization Inference
==============================================

Example configuration files for speaker diarization inference can be found in ``<NeMo_root>/examples/speaker_tasks/diarization/conf/inference/``. Choose a yaml file that fits your targeted domain. For example, if you want to diarize audio recordings of telephonic speech, choose ``diar_infer_telephonic.yaml``.

The configurations for all the components of diarization inference are included in a single file named ``diar_infer_<domain>.yaml``. Each ``.yaml`` file has a few different sections for the following modules: VAD, Speaker Embedding, Clustering and ASR.

In speaker diarization inference, the datasets provided in manifest format denote the data that you would like to perform speaker diarization on. 

Diarizer Configurations
-----------------------

An example ``diarizer``  Hydra configuration could look like:

.. code-block:: yaml

  diarizer:
    manifest_filepath: ???
    out_dir: ???
    oracle_vad: False # If True, uses RTTM files provided in manifest file to get speech activity (VAD) timestamps
    collar: 0.25 # Collar value for scoring
    ignore_overlap: True # Consider or ignore overlap segments while scoring

Under ``diarizer`` key, there are ``vad``, ``speaker_embeddings``, ``clustering`` and ``asr`` keys containing configurations for the inference of the corresponding modules.

Configurations for Voice Activity Detector
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Parameters for VAD model are provided as in the following Hydra config example.

.. code-block:: yaml

  vad:
    model_path: null # .nemo local model path or pretrained model name or none
    external_vad_manifest: null # This option is provided to use external vad and provide its speech activity labels for speaker embeddings extraction. Only one of model_path or external_vad_manifest should be set

    parameters: # Tuned parameters for CH109 (using the 11 multi-speaker sessions as dev set) 
      window_length_in_sec: 0.15  # Window length in sec for VAD context input 
      shift_length_in_sec: 0.01 # Shift length in sec for generate frame level VAD prediction
      smoothing: "median" # False or type of smoothing method (eg: median)
      overlap: 0.875 # Overlap ratio for overlapped mean/median smoothing filter
      onset: 0.4 # Onset threshold for detecting the beginning and end of a speech 
      offset: 0.7 # Offset threshold for detecting the end of a speech
      pad_onset: 0.05 # Adding durations before each speech segment 
      pad_offset: -0.1 # Adding durations after each speech segment 
      min_duration_on: 0.2 # Threshold for short speech segment deletion
      min_duration_off: 0.2 # Threshold for small non_speech deletion
      filter_speech_first: True 

Configurations for Speaker Embedding in Diarization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Parameters for speaker embedding model are provided in the following Hydra config example. Note that multiscale parameters either accept list or single floating point number.

.. code-block:: yaml

  speaker_embeddings:
    model_path: ??? # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
    parameters:
      window_length_in_sec: 1.5 # Window length(s) in sec (floating-point number). Either a number or a list. Ex) 1.5 or [1.5,1.25,1.0,0.75,0.5]
      shift_length_in_sec: 0.75 # Shift length(s) in sec (floating-point number). Either a number or a list. Ex) 0.75 or [0.75,0.625,0.5,0.375,0.25]
      multiscale_weights: null # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. Ex) [1,1,1,1,1]
      save_embeddings: False # Save embeddings as pickle file for each audio input.

Configurations for Clustering in Diarization
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Parameters for clustering algorithm are provided in the following Hydra config example.

.. code-block:: yaml
  
  clustering:
    parameters:
      oracle_num_speakers: False # If True, use num of speakers value provided in the manifest file.
      max_num_speakers: 20 # Max number of speakers for each recording. If oracle_num_speakers is passed, this value is ignored.
      enhanced_count_thres: 80 # If the number of segments is lower than this number, enhanced speaker counting is activated.
      max_rp_threshold: 0.25 # Determines the range of p-value search: 0 < p <= max_rp_threshold. 
      sparse_search_volume: 30 # The higher the number, the more values will be examined with more time. 

Configurations for Diarization with ASR
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The following configuration needs to be appended under ``diarizer`` to run ASR with diarization to get a transcription with speaker labels. 

.. code-block:: yaml

  asr:
    model_path: ??? # Provide NGC cloud ASR model name. stt_en_conformer_ctc_* models are recommended for diarization purposes.
    parameters:
      asr_based_vad: False # if True, speech segmentation for diarization is based on word-timestamps from ASR inference.
      asr_based_vad_threshold: 50 # threshold (multiple of 10ms) for ignoring the gap between two words when generating VAD timestamps using ASR based VAD.
      asr_batch_size: null # Batch size can be dependent on each ASR model. Default batch sizes are applied if set to null.
      lenient_overlap_WDER: True # If true, when a word falls into speaker-overlapped regions, consider the word as a correctly diarized word.
      decoder_delay_in_sec: null # Native decoder delay. null is recommended to use the default values for each ASR model.
      word_ts_anchor_offset: null # Offset to set a reference point from the start of the word. Recommended range of values is [-0.05  0.2]. 
      word_ts_anchor_pos: "start" # Select which part of the word timestamp we want to use. The options are: 'start', 'end', 'mid'.
      fix_word_ts_with_VAD: False # Fix the word timestamp using VAD output. You must provide a VAD model to use this feature.
      colored_text: False # If True, use colored text to distinguish speakers in the output transcript.
      print_time: True # If True, the start of the end time of each speaker turn is printed in the output transcript.
      break_lines: False # If True, the output transcript breaks the line to fix the line width (default is 90 chars)
    
    ctc_decoder_parameters: # Optional beam search decoder (pyctcdecode)
      pretrained_language_model: null # KenLM model file: .arpa model file or .bin binary file.
      beam_width: 32
      alpha: 0.5
      beta: 2.5

    realigning_lm_parameters: # Experimental feature
      arpa_language_model: null # Provide a KenLM language model in .arpa format.
      min_number_of_words: 3 # Min number of words for the left context.
      max_number_of_words: 10 # Max number of words for the right context.
      logprob_diff_threshold: 1.2  # The threshold for the difference between two log probability values from two hypotheses.