
## ESPnet2 DIAR model 

### `YushiUeda/test`

This model was trained by Yushi Ueda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).

### Demo: How to use in ESPnet2

```bash
cd espnet
git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4
pip install -e .
cd egs2/mini_librispeech/diar1
./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test
```

<!-- Generated by scripts/utils/show_diar_result.sh -->
# 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|

## DIAR config

<details><summary>expand</summary>

```
config: 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
```

</details>



{"license": "cc-by-4.0", "tags": ["espnet", "audio", "diarization"], "datasets": ["mini_librispeech"]}
espnet audio diarization dataset:mini_librispeech license:cc-by-4.0 region:us
TAGS
#espnet #audio #diarization #dataset-mini_librispeech #license-cc-by-4.0 #region-us 

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


### 'YushiUeda/test'


This model was trained by Yushi Ueda using mini\_librispeech recipe in espnet.


### Demo: How to use in ESPnet2


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'



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


expand


### 'YushiUeda/test'


This model was trained by Yushi Ueda using mini\_librispeech recipe in espnet. ### Demo: How to use in ESPnet2


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'



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


expand
TAGS
#espnet #audio #diarization #dataset-mini_librispeech #license-cc-by-4.0 #region-us 
 ### 'YushiUeda/test'


This model was trained by Yushi Ueda using mini\_librispeech recipe in espnet. ### Demo: How to use in ESPnet2


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'



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


expand
passage: TAGS
#espnet #audio #diarization #dataset-mini_librispeech #license-cc-by-4.0 #region-us 
### 'YushiUeda/test'


This model was trained by Yushi Ueda using mini\_librispeech recipe in espnet.### Demo: How to use in ESPnet2


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'



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


expand