This model was trained by Yushi Ueda using mini_librispeech recipe in espnet. Python APISee https://github.com/espnet/espnet_model_zoo Evaluate in the recipegit clone https://github.com/espnet/espnet cd espnet git checkout 19bcd34f9395e01e54a97c4db5ecbcedb429dd92 pip install -e . cd egs2/mini_librispeech/diar1 ./run.sh --skip_data_prep false --skip_train true --download_model Yushi Ueda/mini_librispeech_diar_train_diar_raw_max_epoch20_valid.acc.best Results # RESULTS ## Environments - date: `Wed Aug 25 23:29:07 EDT 2021` - python version: `3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]` - espnet version: `espnet 0.10.2a1` - pytorch version: `pytorch 1.9.0+cu102` - Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92` - Commit date: `Tue Aug 24 19:50:44 2021 -0400` ## `diar_train_diar_raw_max_epoch20` ### DER `dev_clean_2_ns2_beta2_500` |threshold_median_collar|DER| |---|---| |result_th0.3_med1_collar0.0|32.42| |result_th0.3_med11_collar0.0|32.03| |result_th0.4_med1_collar0.0|30.96| |result_th0.4_med11_collar0.0|30.26| |result_th0.5_med1_collar0.0|30.35| |result_th0.5_med11_collar0.0|29.37| |result_th0.6_med1_collar0.0|30.77| |result_th0.6_med11_collar0.0|29.52| |result_th0.7_med1_collar0.0|32.60| |result_th0.7_med11_collar0.0|31.03| Diarization configconfig: conf/train_diar.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_train_diar_raw_max_epoch20 ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: 0 dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 20 patience: 3 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 3 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_stats_8k/train/speech_shape - exp/diar_stats_8k/train/spk_labels_shape valid_shape_file: - exp/diar_stats_8k/valid/speech_shape - exp/diar_stats_8k/valid/spk_labels_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 800 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 200000 chunk_shift_ratio: 0.5 num_cache_chunks: 64 train_data_path_and_name_and_type: - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm - spk_labels - rttm valid_data_path_and_name_and_type: - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp - speech - sound - - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/espnet_rttm - spk_labels - rttm allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.01 scheduler: noamlr scheduler_conf: warmup_steps: 1000 num_spk: 2 init: xavier_uniform input_size: null model_conf: loss_type: pit use_preprocessor: true frontend: default frontend_conf: fs: 8k hop_length: 128 normalize: global_mvn normalize_conf: stats_file: exp/diar_stats_8k/train/feats_stats.npz encoder: transformer encoder_conf: input_layer: linear num_blocks: 2 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.0 decoder: linear decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: {} required: - output_dir version: 0.10.2a1 distributed: false