NeMo Speaker Diarization Configuration Files
NeMo Speaker Diarization Configuration Files#
Since speaker diarization model here is not a fully-trainable End-to-End model but an inference pipeline, we use diarizer instead of model which is used in other tasks.
The diarizer section will generally require information about the dataset(s) being used, models used in this pipeline, as well as inference related parameters such as post processing of each models. The sections on this page cover each of these in more detail.
Example configuration files for speaker diarization can be found in
For model details and deep understanding about configs, fine-tuning, tuning threshold, and evaluation,
please refer to
for other applications such as possible integration with ASR, have a look at
In contrast to other ASR related tasks or models in NeMo, speaker diarization supported in NeMo is a modular inference pipeline and training is only required for speaker embedding extractor model. Therefore, the datasets provided in manifest format denote the data that you would like to perform speaker diarization on.
An example Speaker Diarization dataset Hydra configuration could look like:
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
We expect audio and the corresponding RTTM to have the same base name and the name should be unique.
Diarizer Model Configurations#
Parameters for VAD model and speaker embedding model are provided in the following Hydra config example.
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 small non_speech deletion min_duration_off: 0.2 # Threshold for short speech segment deletion filter_speech_first: True 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 [1.5,1.0,0.5] shift_length_in_sec: 0.75 # Shift length(s) in sec (floating-point number). Either a number or a list. Ex) 0.75 or [0.75,0.5,0.25] multiscale_weights: null # Weight for each scale. should be null (for single scale) or a list matched with window/shift scale count. Ex) [0.33,0.33,0.33] save_embeddings: False # Save embeddings as pickle file for each audio input.
Configuration for Clustering in Diarization#
Parameters for clustering algorithm are provided in the following Hydra config example.
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.
Configuration 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. This configuration can be found in
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.