# This YAML file is created for all types of offline speaker diarization inference tasks in `<NeMo git root>/example/speaker_tasks/diarization` folder.
# The inference parameters for VAD, speaker embedding extractor, clustering module, MSDD module, ASR decoder are all included in this YAML file. 
# All the keys under `diarizer` key (`vad`, `speaker_embeddings`, `clustering`, `msdd_model`, `asr`) can be selectively used for its own purpose and also can be ignored if the module is not used.
# The configurations in this YAML file is suitable for telephone recordings involving 2~8 speakers in a session and may not show the best performance on the other types of acoustic conditions or dialogues.
# An example line in an input manifest file (`.json` format):
# {"audio_filepath": "/path/to/audio_file", "offset": 0, "duration": null, "label": "infer", "text": "-", "num_speakers": null, "rttm_filepath": "/path/to/rttm/file", "uem_filepath": "/path/to/uem/file"}
name: &name "ClusterDiarizer"

num_workers: 1
sample_rate: 16000
batch_size: 64
device: null # can specify a specific device, i.e: cuda:1 (default cuda if cuda available, else cpu)
verbose: True # enable additional logging

diarizer:
  manifest_filepath: ???
  out_dir: ???
  oracle_vad: False # If True, uses RTTM files provided in the 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

  vad:
    model_path: vad_multilingual_marblenet # .nemo local model path or pretrained VAD model name 
    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: 0.5 # Overlap ratio for overlapped mean/median smoothing filter
      onset: 0.1 # Onset threshold for detecting the beginning and end of a speech 
      offset: 0.1 # Offset threshold for detecting the end of a speech
      pad_onset: 0.1 # Adding durations before each speech segment 
      pad_offset: 0 # Adding durations after each speech segment 
      min_duration_on: 0 # Threshold for small non_speech deletion
      min_duration_off: 0.2 # Threshold for short speech segment deletion
      filter_speech_first: True 

  speaker_embeddings:
    model_path: titanet_large # .nemo local model path or pretrained model name (titanet_large, ecapa_tdnn or speakerverification_speakernet)
    parameters:
      window_length_in_sec: [1.5,1.25,1.0,0.75,0.5] # Window length(s) in sec (floating-point number). either a number or a list. ex) 1.5 or [1.5,1.0,0.5]
      shift_length_in_sec: [0.75,0.625,0.5,0.375,0.25] # Shift length(s) in sec (floating-point number). either a number or a list. ex) 0.75 or [0.75,0.5,0.25]
      multiscale_weights: [1,1,1,1,1] # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. ex) [0.33,0.33,0.33]
      save_embeddings: True # If True, save speaker embeddings in pickle format. This should be True if clustering result is used for other models, such as `msdd_model`.
  
  clustering:
    parameters:
      oracle_num_speakers: False # If True, use num of speakers value provided in manifest file.
      max_num_speakers: 8 # Max number of speakers for each recording. If an oracle number of 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. 
      maj_vote_spk_count: False  # If True, take a majority vote on multiple p-values to estimate the number of speakers.
  
  msdd_model:
    model_path: diar_msdd_telephonic # .nemo local model path or pretrained model name for multiscale diarization decoder (MSDD)
    parameters:
      use_speaker_model_from_ckpt: True # If True, use speaker embedding model in checkpoint. If False, the provided speaker embedding model in config will be used.
      infer_batch_size: 25 # Batch size for MSDD inference. 
      sigmoid_threshold: [0.7] # Sigmoid threshold for generating binarized speaker labels. The smaller the more generous on detecting overlaps.
      seq_eval_mode: False # If True, use oracle number of speaker and evaluate F1 score for the given speaker sequences. Default is False.
      split_infer: True # If True, break the input audio clip to short sequences and calculate cluster average embeddings for inference.
      diar_window_length: 50 # The length of split short sequence when split_infer is True.
      overlap_infer_spk_limit: 5 # If the estimated number of speakers are larger than this number, overlap speech is not estimated.
  
  asr:
    model_path: stt_en_conformer_ctc_large # 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: 1.0 # Threshold (in sec) that caps 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.
      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 and 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.