espnet/arecho_base_v0

ESPnet2 universa model

espnet/arecho_base_v0

This model was trained by ftshijt using universa_unite 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 0b68ffd26362f4b50e7c73942c5bbdbc0a220bd4
pip install -e .
cd egs2/universa_unite/uni_versa1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/arecho_base_v0

universa config

expand

accum_grad: 2
adapter: lora
adapter_conf: {}
allow_multi_rates: false
allow_variable_data_keys: false
batch_bins: 1000000
batch_size: 16
batch_type: sorted
best_model_criterion:
- - train
  - loss
  - min
- - valid
  - loss
  - min
- - train
  - acc
  - max
- - valid
  - acc
  - max
bpemodel: null
category_sample_size: 10
chunk_default_fs: null
chunk_discard_short_samples: true
chunk_excluded_key_prefixes: []
chunk_length: 500
chunk_max_abs_length: null
chunk_shift_ratio: 0.5
cleaner: null
collect_stats: false
config: conf/train_aruniversa_wavlm.yaml
create_graph_in_tensorboard: false
cudnn_benchmark: false
cudnn_deterministic: false
cudnn_enabled: true
ddp_comm_hook: null
deepspeed_config: null
detect_anomaly: false
dist_backend: nccl
dist_init_method: env://
dist_launcher: null
dist_master_addr: null
dist_master_port: null
dist_rank: null
dist_world_size: null
distributed: false
drop_last_iter: false
dry_run: false
early_stopping_criterion:
- valid
- loss
- min
exclude_weight_decay: false
exclude_weight_decay_conf: {}
fold_length:
- 256000
freeze_param:
- frontend.upstream
frontend: s3prl
frontend_conf:
  download_dir: ./hub
  frontend_conf:
    upstream: wavlm_large
  multilayer_feature: true
g2p: null
grad_clip: -1
grad_clip_type: 2.0
grad_noise: false
gradient_as_bucket_view: true
ignore_init_mismatch: false
init: null
init_param: []
iterator_type: sequence
keep_nbest_models: 1
local_rank: 0
log_interval: 50
log_level: INFO
max_cache_fd: 32
max_cache_size: 0.0
max_epoch: 100
metric2id: dump/raw/overall_base/metric2id
metric2type: dump/raw/overall_base/metric2type
metric_pad_value: -100
metric_token_info: data/token_list/metric_500_percentile_overall_base_w-numerical/tokens.json
metric_token_pad_value: 0
model_conf: {}
multi_task_dataset: false
multiple_iterator: false
multiprocessing_distributed: false
nbest_averaging_interval: 0
ngpu: 1
no_forward_run: false
non_linguistic_symbols: null
num_att_plot: 0
num_cache_chunks: 1024
num_iters_per_epoch: null
num_workers: 1
optim: adamw
optim_conf:
  lr: 0.001
output_dir: exp/universa_universa_ar_overall_base_token_wavlm
patience: null
pretrain_path: null
print_config: false
randomize_sequential_metric: true
required:
- output_dir
- metric2id
resume: true
save_strategy: all
scheduler: warmuplr
scheduler_conf:
  warmup_steps: 25000
seed: 777
sequential_metric: true
sharded_ddp: false
shuffle_within_batch: false
sort_batch: descending
sort_in_batch: descending
token_list: null
token_type: bpe
tokenize_numerical_metric: true
train_data_path_and_name_and_type:
- - dump/raw/overall_base/wav.scp
  - audio
  - kaldi_ark
- - dump/raw/overall_base/metric.scp
  - metrics
  - metric
- - dump/raw/overall_base/ref_wav.scp
  - ref_audio
  - kaldi_ark
train_dtype: float32
train_shape_file:
- exp/universa_stats_overall_base/train/audio_shape
- exp/universa_stats_overall_base/train/ref_audio_shape
universa: ar_universa
universa_conf:
  audio_encoder_params:
    attention_dropout_rate: 0.1
    attention_heads: 4
    concat_after: false
    dropout_rate: 0.1
    input_layer: conv2d
    layer_drop_rate: 0.1
    linear_units: 1024
    normalize_before: true
    num_blocks: 4
    positional_dropout_rate: 0.1
    positionwise_conv_kernel_size: 1
    positionwise_layer_type: linear
    qk_norm: false
    use_flash_attn: false
  audio_encoder_type: transformer
  cross_attention_params:
    dropout_rate: 0.1
    n_head: 2
  cross_attention_type: multihead
  embedding_dim: 256
  lsm_weight: 0.1
  metric_decoder_params:
    attention_heads: 4
    concat_after: false
    dropout_rate: 0.1
    input_layer: embed
    layer_drop_rate: 0.1
    linear_units: 1024
    normalize_before: true
    num_blocks: 4
    positional_dropout_rate: 0.1
    qk_norm: false
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    use_flash_attn: false
  sym_eos: <eos>
  sym_sos: <sos>
  use_rope: true
unused_parameters: false
use_adapter: false
use_amp: false
use_deepspeed: false
use_matplotlib: true
use_preprocessor: true
use_ref_audio: true
use_ref_text: false
use_tensorboard: true
use_tf32: false
use_wandb: false
val_scheduler_criterion:
- valid
- loss
valid_batch_bins: null
valid_batch_size: null
valid_batch_type: null
valid_data_path_and_name_and_type:
- - dump/raw/overall_dev/wav.scp
  - audio
  - kaldi_ark
- - dump/raw/overall_dev/metric.scp
  - metrics
  - metric
- - dump/raw/overall_dev/ref_wav.scp
  - ref_audio
  - kaldi_ark
valid_iterator_type: null
valid_max_cache_size: null
valid_shape_file:
- exp/universa_stats_overall_base/valid/audio_shape
- exp/universa_stats_overall_base/valid/ref_audio_shape
version: '202503'
wandb_entity: null
wandb_id: null
wandb_model_log_interval: -1
wandb_name: null
wandb_project: null
write_collected_feats: false

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}
}