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| # 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 3~5 speakers participating in a meeting and may not show the best performance on other types of 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: 0.63 # 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: False # False or type of smoothing method (eg: median) | |
| overlap: 0.5 # Overlap ratio for overlapped mean/median smoothing filter | |
| onset: 0.9 # Onset threshold for detecting the beginning and end of a speech | |
| offset: 0.5 # Offset threshold for detecting the end of a speech | |
| pad_onset: 0 # 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: 0.6 # 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: [3.0,2.5,2.0,1.5,1.0,0.5] # 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: [1.5,1.25,1.0,0.75,0.5,0.25] # 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: [1,1,1,1,1,1] # 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: null # .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. | |