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YushiUeda/
callhome_adapt_real

Name: callhome_adapt_real
Availability: InStock

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ESPnet2 DIAR model

`YushiUeda/callhome_adapt_real`

This model was trained by YushiUeda using callhome recipe in espnet.

Demo: How to use in ESPnet2

bash

cd espnet
git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8
pip install -e .
cd egs2/callhome/diar1
./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/callhome_adapt_real

RESULTS

Environments

date: Mon Jun 20 10:30:23 EDT 2022
python version: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
espnet version: espnet 202205
pytorch version: pytorch 1.9.1+cu102
Git hash: fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca
- Commit date: Thu Jun 9 16:29:52 2022 +0900

diar_train_diar_eda_adapt_real_lr0001

DER

diarized_callhome2_spkall

threshold_median_collar	DER
result_th0.3_med11_collar0.25	22.29
result_th0.3_med1_collar0.25	23.27
result_th0.4_med11_collar0.25	19.85
result_th0.4_med1_collar0.25	20.80
result_th0.5_med11_collar0.25	19.26
result_th0.5_med1_collar0.25	20.18
result_th0.6_med11_collar0.25	20.24
result_th0.6_med1_collar0.25	21.08
result_th0.7_med11_collar0.25	22.38
result_th0.7_med1_collar0.25	23.17

DIAR config

expand

config: conf/tuning/train_diar_eda_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/diar_train_diar_eda_adapt_real_lr0001
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: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
-   - train
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 16
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
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:
- exp/diar_train_diar_eda_adapt_simu/latest.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 1
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: sorted
valid_batch_type: null
fold_length:
- 80000
- 800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/callhome1_spkall/wav.scp
    - speech
    - sound
-   - dump/raw/callhome1_spkall/espnet_rttm
    - spk_labels
    - rttm
valid_data_path_and_name_and_type:
-   - dump/raw/callhome2_spkall/wav.scp
    - speech
    - sound
-   - dump/raw/callhome2_spkall/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: 0.001
scheduler: null
scheduler_conf: {}
num_spk: 7
init: null
input_size: null
model_conf:
    attractor_weight: 1.0
use_preprocessor: true
frontend: default
frontend_conf:
    fs: 8k
    hop_length: 128
specaug: specaug
specaug_conf:
    apply_time_warp: false
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 30
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_range:
    - 0
    - 40
    num_time_mask: 2
normalize: global_mvn
normalize_conf:
    stats_file: exp/diar_stats_8k/train/feats_stats.npz
encoder: transformer
encoder_conf:
    input_layer: conv2d
    num_blocks: 4
    linear_units: 512
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
    attention_dropout_rate: 0.1
decoder: linear
decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
    win_length: 1024
    hop_length: 512
attractor: rnn
attractor_conf:
    unit: 256
    layer: 1
    dropout: 0.0
    attractor_grad: false
required:
- output_dir
version: '202204'
distributed: false

Citing ESPnet

BibTex

@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

or arXiv:

bibtex

@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit}, 
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}