librispeech_960_multiconvformer_ctcatt_conv_fusion

ESPnet2 ASR model

Darshan7575/librispeech_960_multiconvformer_ctcatt_conv_fusion

This model was trained by Darshan Prabhu using librispeech 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 b8b31c06da081ee1690c8dc1bf102a7811124529
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model Darshan7575/librispeech_960_multiconvformer_ctcatt_conv_fusion

<!-- Generated by scripts/utils/show_asr_result.sh -->

RESULTS

Environments

• date: Fri Mar 1 15:40:42 UTC 2024
• python version: 3.9.16 (main, May 15 2023, 23:46:34) [GCC 11.2.0]
• espnet version: espnet 202402
• pytorch version: pytorch 2.1.2+cu118
• Git hash: a50d6a0c8c31b4ef775473a657de031a40be30c1
  • Commit date: Mon Feb 19 07:37:52 2024 -0500

exp/asr_train_asr_multiconvformer_conv_fusion_raw_en_bpe5000_sp

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ASR config

<details><summary>expand</summary>

config: conf/tuning/train_asr_multiconvformer_conv_fusion.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_asr_multiconvformer_conv_fusion_raw_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 45655
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 80
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10
nbest_averaging_interval: 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 2
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: null
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_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 70000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe5000_sp/valid/text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 80000
- 150
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
train_data_path_and_name_and_type:
-   - dump/raw/train_960_sp/wav.scp
    - speech
    - sound
-   - dump/raw/train_960_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech
    - sound
-   - dump/raw/dev/text
    - text
    - 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: 0.002
    weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 40000
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- ▁FAVOR
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- ▁PIT
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- ▁OTHERWISE
- ▁CABIN
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- ▁DWELL
- ▁GAIN
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- ▁YIELD
- ▁TREAT
- RIGHT
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- ▁ROPE
- WIN
- ▁ORDERS
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- AB
- ▁ELEVEN
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- DDING
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- INA
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- ▁CHEER
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- LUS
- LOW
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- ILITY
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- ITUDE
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- ▁PROOF
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- ▁SOFTLY
- ▁UNFORTUNATE
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- ▁SINGING
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- ▁PREVIOUS
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- ▁INTENDED
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- ▁OFFICERS
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- ▁SHAKE
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- ▁GENTLY
- ▁SUCCEEDED
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- ▁SUPERIOR
- ▁BAY
- ▁PAL
- EMENT
- UE
- ▁SEVENTY
- ▁HOTEL
- ▁SHEEP
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- ▁AFFORD
- ▁FAIRY
- ▁WAKE
- ▁SIDES
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- ▁ARTICLE
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- ▁MIST
- ▁JOINED
- ▁PRACTICAL
- ▁CLEARLY
- ▁TRACE
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- ▁OBSERVE
- ▁BASKET
- ▁LACK
- VILLE
- ▁SPIRITS
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- ▁SHINING
- ▁FULLY
- ▁CALLING
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- ▁SPRANG
- ▁MILE
- ▁DOZEN
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- ▁WIT
- ▁JEW
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- NNY
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- KY
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- ▁HAVEN
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- ▁CITIZEN
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- ▁INTERESTED
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- ETTE
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- IED
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- ▁PRESENTED
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- ▁DYING
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- ▁DAILY
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- ▁BODIES
- ▁MODERN
- ▁COMPLETELY
- ▁SOONER
- TAN
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- ▁SPEND
- WELL
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- ▁EMPLOY
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- ▁NINETEEN
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- ▁NAY
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- ▁DIG
- HIN
- ▁CONDITIONS
- JA
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- ▁MOREOVER
- ▁OPERATION
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- ▁KEEN
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- ▁DEPTH
- ▁PREFER
- ▁CROSSED
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- IUM
- ▁RAPID
- ▁APPEAL
- ▁FAMOUS
- ▁USEFUL
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- ▁NEWSPAPER
- ▁PLENTY
- ▁BEARING
- ▁NERVOUS
- ▁PARA
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- ▁WOUNDED
- ▁CHAIN
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- ▁QUARREL
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- CUS
- ▁QUEER
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- ▁WHISPER
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- ▁UNION
- HY
- ▁COMPANIONS
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- ▁APPROACHED
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- ▁EVIDENT
- ▁SCAR
- ▁DETERMIN
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- ▁STEAL
- ▁OWE
- ▁KNIFE
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- ▁LEADER
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- OON
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- ▁THRUST
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- ▁LIFT
- ▁ADMIRE
- ▁ISSUE
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- ▁LESSON
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- ▁ACCEPTED
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- ▁CAPABLE
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- ▁SANG
- ▁REMOVED
- ▁HOPED
- ▁HORN
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- ▁SOMEBODY
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- ▁READER
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- ▁EVERYWHERE
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- ▁RECALL
- ▁BULL
- ▁HIT
- ▁COUNCIL
- ▁POPULAR
- ▁CHAP
- ▁TRIAL
- ▁DUN
- ▁WISHES
- ▁BRILLIANT
- ▁ASSURED
- ▁FORGOT
- ▁CONTINUE
- ▁ACKNOWLEDG
- ▁RETREAT
- ▁INCREASED
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- ▁GRANDFATHER
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- ▁HIND
- ▁PLAYING
- ▁MISERABLE
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- ▁HIGHEST
- ▁PRIM
- ▁KISSED
- ▁DESERVE
- ▁HUT
- ▁BEGGED
- ▁EIGHTY
- ▁CLOSELY
- ▁WONDERED
- ▁MILITARY
- ▁REMIND
- ▁ACCORDINGLY
- ▁LARGER
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- ▁ENGINE
- ▁MOTIVE
- ▁DESTROY
- ▁STRIP
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- ▁INFINITE
- ▁PROMPT
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- TTLE
- ▁PEER
- ▁PRESSED
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- ▁BOUGHT
- ▁VISIBLE
- ▁ASHAMED
- ▁TEAR
- ▁NEIGHBOUR
- ▁CONSTITUTION
- ▁INTELLIGENCE
- ▁PROFESSION
- ▁HUNGRY
- RIDGE
- ▁SMELL
- ▁STORIES
- ▁LISTENING
- ▁APPROACH
- ▁STRING
- ▁EXPLANATION
- ▁IMMENSE
- ▁RELIGIOUS
- ▁THROUGHOUT
- ▁HOLLOW
- ▁AWAIT
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- ▁ACTIVE
- ▁RUM
- ▁PRODUCT
- ▁UNHAPPY
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- PORT
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- ▁HINT
- ▁NEST
- ▁TRICK
- ▁THOROUGHLY
- ▁HOSPITAL
- ▁WEAPON
- ▁ROME
- ▁STYLE
- ▁ADMITTED
- ▁SAFETY
- FIELD
- ▁UNDERSTANDING
- ▁TREMBLE
- ▁PRINT
- ▁SLAVES
- ▁WEARY
- ▁ARTIST
- ▁CREDIT
- BURG
- ▁CONCLUSION
- ▁SELDOM
- ▁UNUSUAL
- ▁CLOUDS
- ▁UNABLE
- ▁GAY
- ▁HANGING
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- ▁DAVID
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- ▁PUSHED
- ▁ESCAPED
- MOND
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- ▁PHILIP
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- ▁EAGERLY
- ▁BILLY
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- ▁CONSCIENCE
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- ▁PROVIDED
- ▁OBTAIN
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- ▁RAGE
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- ▁UNCONSCIOUS
- ▁ADVISE
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- ▁TINY
- ▁REFUSE
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- ▁LAWYER
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- ▁PLUM
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- ▁EU
- ▁CONSTANTLY
- CUM
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- ▁FAIRLY
- HOUSE
- ▁KIT
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- ▁FEATURES
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- ▁JOE
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- ▁LAUGHTER
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- ▁CHEEK
- ▁HUMBLE
- ▁WRITER
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- ▁JUMPED
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- ▁REFER
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- J
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- ▁TRU
- ▁LUCY
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- ▁POWDER
- ▁RESUMED
- ▁INTENSE
- ▁BRITISH
- ▁STRAW
- ▁AGREEABLE
- ▁EVERYONE
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- ▁VOYAGE
- ▁SOUTHERN
- ▁BOSOM
- ▁UTTERLY
- ▁FEED
- ▁ESSENTIAL
- ▁CONFINE
- ▁HOUSEHOLD
- ▁EXTREMELY
- ▁WONDERING
- ▁LIST
- ▁PINE
- PHA
- ▁EXPERIMENT
- ▁JOSEPH
- ▁MYSTERY
- ▁RESTORE
- ▁BLUSH
- FOLD
- ▁CHOSEN
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- OLOGY
- ▁MOUNTED
- ▁LAP
- ▁EPI
- ▁PUNISH
- ▁WEDDING
- ▁RECOGNIZED
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- ▁RESOLUTION
- ▁OPPRESS
- ▁FIX
- ▁VICTIM
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- ▁PRIZE
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- ▁VEIL
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- ▁REIGN
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- ▁ENORMOUS
- ▁RESTRAIN
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- ▁COUNTESS
- ▁TRIBE
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- ▁EXECUT
- ▁FINAL
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- ▁PARTIES
- ▁PLUNGE
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- ▁ASSEMBLED
- ▁IMPRESSED
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- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
    brctc_risk_strategy: exp
    brctc_group_strategy: end
    brctc_risk_factor: 0.0
joint_net_conf: null
use_preprocessor: true
use_lang_prompt: false
use_nlp_prompt: false
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
aux_ctc_tasks: []
frontend: default
frontend_conf:
    n_fft: 512
    hop_length: 160
    fs: 16k
specaug: specaug
specaug_conf:
    apply_time_warp: true
    time_warp_window: 5
    time_warp_mode: bicubic
    apply_freq_mask: true
    freq_mask_width_range:
    - 0
    - 27
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_ratio_range:
    - 0.0
    - 0.05
    num_time_mask: 10
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
model: espnet
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: multiconv_conformer
encoder_conf:
    output_size: 512
    attention_heads: 8
    selfattention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    cgmlp_linear_units: 3072
    multicgmlp_type: concat_fusion
    multicgmlp_kernel_sizes: 7,15,23,31
    use_linear_after_conv: false
    gate_activation: identity
    num_blocks: 18
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    layer_drop_rate: 0.1
    linear_units: 1024
    positionwise_layer_type: linear
    macaron_style: true
    use_cnn_module: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202402'
distributed: true

</details>

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}
}