ESPnet2 SPK model

`espnet/rats_ska_wavlm_frozen`

This model was trained by shimhz using TEMPLATE recipe in espnet.

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already.

cd espnet
git checkout 19787b1793eda2b4007aa5b2c4d03adf6c18abfb
pip install -e .
cd egs2/TEMPLATE/spk1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/rats_ska_wavlm_frozen

RESULTS

Environments

date: 2024-10-04 17:09:51.822703

python version: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0]
espnet version: 202402
pytorch version: 1.13.1

	Mean	Std
Target	-1.0478	0.2006
Non-target	0.1717	0.1717

Model name	EER(%)	minDCF
conf/train_SKA_wavlm_frozen	19.728	0.92968

SPK config

expand

config: conf/train_SKA_wavlm_frozen.yaml
print_config: false
log_level: INFO
drop_last_iter: true
dry_run: false
iterator_type: category
valid_iterator_type: sequence
output_dir: exp/spk_train_SKA_wavlm_frozen_raw_copy
ngpu: 1
seed: 0
num_workers: 6
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 57255
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: true
cudnn_deterministic: false
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - eer
    - min
keep_nbest_models: 3
nbest_averaging_interval: 0
grad_clip: 9999
grad_clip_type: 2.0
grad_noise: false
accum_grad: 8
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 100
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 64
valid_batch_size: 5
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/spk_stats_16k/train/speech_shape
valid_shape_file:
- exp/spk_stats_16k/valid/speech_shape
batch_type: folded
valid_batch_type: null
fold_length:
- 120000
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
-   - dump/raw_copy/rats_train/wav.scp
    - speech
    - sound
-   - dump/raw_copy/rats_train/utt2spk
    - spk_labels
    - text
valid_data_path_and_name_and_type:
-   - dump/raw_copy/rats_test/trial.scp
    - speech
    - sound
-   - dump/raw_copy/rats_test/trial2.scp
    - speech2
    - sound
-   - dump/raw_copy/rats_test/trial_label
    - spk_labels
    - text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
    lr: 0.001
    weight_decay: 5.0e-05
    amsgrad: false
scheduler: cosineannealingwarmuprestarts
scheduler_conf:
    first_cycle_steps: 71280
    cycle_mult: 1.0
    max_lr: 0.001
    min_lr: 5.0e-06
    warmup_steps: 1000
    gamma: 0.75
init: null
use_preprocessor: true
input_size: null
target_duration: 3.0
spk2utt: dump/raw_copy/rats_train/spk2utt
spk_num: 5903
sample_rate: 16000
num_eval: 10
rir_scp: ''
model_conf:
    extract_feats_in_collect_stats: false
frontend: s3prl
frontend_conf:
    frontend_conf:
        upstream: wavlm_large
    download_dir: ./hub
    multilayer_feature: true
specaug: null
specaug_conf: {}
normalize: utterance_mvn
normalize_conf:
    norm_vars: false
encoder: ska_tdnn
encoder_conf:
    model_scale: 8
    ndim: 1024
    ska_dim: 128
    output_size: 1536
pooling: chn_attn_stat
pooling_conf: {}
projector: ska_tdnn
projector_conf:
    output_size: 192
preprocessor: spk
preprocessor_conf:
    target_duration: 3.0
    sample_rate: 16000
    num_eval: 5
    noise_apply_prob: 0.0
    noise_info: null
    rir_apply_prob: 0.0
loss: aamsoftmax_sc_topk
loss_conf:
    margin: 0.3
    scale: 30
    K: 3
    mp: 0.06
    k_top: 5
required:
- output_dir
version: '202402'
distributed: true

Citing ESPnet

@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:

@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}
}