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__-.yaml * Example `/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_-.yaml * Example `/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 ``/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_.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.