
## ESPnet2 DIAR model 

### `espnet/YushiUeda_librimix_diar_enh_2_3_spk`

This model was trained by YushiUeda using librimix recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

```bash
cd espnet
git checkout 4f0f9a2435549211ef670354d09eb45883441b2d
pip install -e .
cd egs2/librimix/diar_enh1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/YushiUeda_librimix_diar_enh_2_3_spk
```

<!-- Generated by local/show_enh_score.sh -->
# RESULTS
## Environments
- date: `Fri Mar 25 17:40:43 EDT 2022`
- python version: `3.7.11 (default, Jul 27 2021, 14:32:16)  [GCC 7.5.0]`
- espnet version: `espnet 0.10.7a1`
- pytorch version: `pytorch 1.10.1+cu102`
- Git hash: `4f0f9a2435549211ef670354d09eb45883441b2d`
  - Commit date: `Tue Mar 15 10:52:24 2022 -0400`


## ..

config: conf/tuning/train_diar_enh_convtasnet_adapt.yaml

|dataset|STOI|SAR|SDR|SIR|SI_SNR|DER|
|---|---|---|---|---|---|---|
|diarized_enhanced_test|0.7602|7.3687|5.9088|15.0722|4.3856|6.27|

## DIAR config

<details><summary>expand</summary>

```
config: conf/tuning/train_diar_enh_convtasnet_adapt.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: chunk
output_dir: exp/diar_enh_train_diar_enh_convtasnet_adapt
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 47601
dist_launcher: null
multiprocessing_distributed: true
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: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - si_snr_loss
    - min
keep_nbest_models: 1
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 4
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_enh_train_diar_enh_convtasnet_2_raw/valid.si_snr_loss.best.pth
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 4
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/diar_enh_stats_8k/train/speech_mix_shape
- exp/diar_enh_stats_8k/train/spk_labels_shape
- exp/diar_enh_stats_8k/train/speech_ref1_shape
- exp/diar_enh_stats_8k/train/speech_ref2_shape
- exp/diar_enh_stats_8k/train/speech_ref3_shape
- exp/diar_enh_stats_8k/train/noise_ref1_shape
valid_shape_file:
- exp/diar_enh_stats_8k/valid/speech_mix_shape
- exp/diar_enh_stats_8k/valid/spk_labels_shape
- exp/diar_enh_stats_8k/valid/speech_ref1_shape
- exp/diar_enh_stats_8k/valid/speech_ref2_shape
- exp/diar_enh_stats_8k/valid/speech_ref3_shape
- exp/diar_enh_stats_8k/valid/noise_ref1_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 800
- 80000
- 80000
- 80000
- 80000
- 80000
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 24000
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/train/wav.scp
    - speech_mix
    - sound
-   - dump/raw/train/espnet_rttm
    - spk_labels
    - rttm
-   - dump/raw/train/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/train/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/train/spk3.scp
    - speech_ref3
    - sound
-   - dump/raw/train/noise1.scp
    - noise_ref1
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech_mix
    - sound
-   - dump/raw/dev/espnet_rttm
    - spk_labels
    - rttm
-   - dump/raw/dev/spk1.scp
    - speech_ref1
    - sound
-   - dump/raw/dev/spk2.scp
    - speech_ref2
    - sound
-   - dump/raw/dev/spk3.scp
    - speech_ref3
    - sound
-   - dump/raw/dev/noise1.scp
    - noise_ref1
    - sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.0003
    weight_decay: 0
scheduler: reducelronplateau
scheduler_conf:
    mode: min
    factor: 0.5
    patience: 1
num_spk: 3
init: xavier_uniform
model_conf:
    loss_type: si_snr
    diar_weight: 0.2
    attractor_weight: 0.2
use_preprocessor: true
criterions:
-   name: si_snr
    conf:
        eps: 1.0e-07
    wrapper: pit2
    wrapper_conf:
        weight: 1.0
        independent_perm: true
frontend: null
frontend_conf:
    fs: 8k
    hop_length: 64
specaug: null
specaug_conf: {}
normalize: null
normalize_conf: {}
diar_encoder: transformer
diar_encoder_conf:
    input_size: 128
    input_layer: conv2d8
    num_blocks: 4
    linear_units: 512
    dropout_rate: 0.1
    output_size: 256
    attention_heads: 4
    attention_dropout_rate: 0.1
diar_decoder: linear
diar_decoder_conf: {}
label_aggregator: label_aggregator
label_aggregator_conf:
    win_length: 256
    hop_length: 64
attractor: rnn
attractor_conf:
    unit: 256
    layer: 1
    dropout: 0.1
    attractor_grad: true
enh_encoder: conv
enh_encoder_conf:
    channel: 512
    kernel_size: 16
    stride: 8
separator: tcn
separator_conf:
    layer: 8
    stack: 3
    bottleneck_dim: 128
    hidden_dim: 512
    kernel: 3
    causal: false
    norm_type: gLN
mask_module: mask
mask_module_conf:
    max_num_spk: 3
    mask_nonlinear: relu
    input_dim: 512
    bottleneck_dim: 128
enh_decoder: conv
enh_decoder_conf:
    channel: 512
    kernel_size: 16
    stride: 8
required:
- output_dir
version: 0.10.7a1
distributed: true
```

</details>



### 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}
}
```

{"language": "noinfo", "license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["librimix"]}
espnet audio diarization dataset:librimix arxiv:1804.00015 license:cc-by-4.0 region:us
TAGS
#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us 

ESPnet2 DIAR model
------------------


### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk'


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


### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Fri Mar 25 17:40:43 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1+cu102'
* Git hash: '4f0f9a2435549211ef670354d09eb45883441b2d'
	+ Commit date: 'Tue Mar 15 10:52:24 2022 -0400'


..
--


config: conf/tuning/train\_diar\_enh\_convtasnet\_adapt.yaml



DIAR config
-----------


expand

### Citing ESPnet


or arXiv:



### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk'


This model was trained by YushiUeda using librimix recipe in espnet. ### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Fri Mar 25 17:40:43 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1+cu102'
* Git hash: '4f0f9a2435549211ef670354d09eb45883441b2d'
	+ Commit date: 'Tue Mar 15 10:52:24 2022 -0400'


..
--


config: conf/tuning/train\_diar\_enh\_convtasnet\_adapt.yaml



DIAR config
-----------


expand ### Citing ESPnet


or arXiv:
TAGS
#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us 
 ### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk'


This model was trained by YushiUeda using librimix recipe in espnet. ### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Fri Mar 25 17:40:43 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1+cu102'
* Git hash: '4f0f9a2435549211ef670354d09eb45883441b2d'
	+ Commit date: 'Tue Mar 15 10:52:24 2022 -0400'


..
--


config: conf/tuning/train\_diar\_enh\_convtasnet\_adapt.yaml



DIAR config
-----------


expand ### Citing ESPnet


or arXiv:
passage: TAGS
#espnet #audio #diarization #dataset-librimix #arxiv-1804.00015 #license-cc-by-4.0 #region-us 
### 'espnet/YushiUeda\_librimix\_diar\_enh\_2\_3\_spk'


This model was trained by YushiUeda using librimix recipe in espnet.### Demo: How to use in ESPnet2


RESULTS
=======


Environments
------------


* date: 'Fri Mar 25 17:40:43 EDT 2022'
* python version: '3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]'
* espnet version: 'espnet 0.10.7a1'
* pytorch version: 'pytorch 1.10.1+cu102'
* Git hash: '4f0f9a2435549211ef670354d09eb45883441b2d'
	+ Commit date: 'Tue Mar 15 10:52:24 2022 -0400'


..
--


config: conf/tuning/train\_diar\_enh\_convtasnet\_adapt.yaml



DIAR config
-----------


expand### Citing ESPnet


or arXiv: