metadata
tags:
- espnet
- audio
- speech-translation
language:
- en
- es
datasets:
- cvss
license: cc-by-4.0
ESPnet2 S2ST model
Vocoder is located here, trained by realzza
espnet/jiyang_tang_cvss-c_es-en_discrete_unit
This model was trained by Jiyang Tang using cvss 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 c002f05ab3ef82938b6a980806cd7f97baba2299
pip install -e .
cd egs2/cvss/s2st1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/jiyang_tang_cvss-c_es-en_discrete_unit
RESULTS
Environments
- date:
Wed Oct 4 22:20:55 EDT 2023 - python version:
3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] - espnet version:
espnet 202308 - pytorch version:
pytorch 1.13.1 - Git hash:
79a3b3e2d9c9105f0f3f6d92d282e17f9ca91ed0- Commit date:
Mon Sep 25 16:39:40 2023 -0400
- Commit date:
S2ST config
expand
config: conf/train_s2st_discrete_unit.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/s2st_train_s2st_discrete_unit_raw_fbank_es_en
ngpu: 1
seed: 0
num_workers: 2
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: 56635
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: 500
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
- - train
- loss
- min
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 1.0
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
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
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 110
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/s2st_stats_raw_es_en/train/tgt_speech_shape
- exp/s2st_stats_raw_es_en/train/src_speech_shape
- exp/s2st_stats_raw_es_en/train/src_text_shape.char
- exp/s2st_stats_raw_es_en/train/tgt_text_shape.char
valid_shape_file:
- exp/s2st_stats_raw_es_en/valid/src_speech_shape
- exp/s2st_stats_raw_es_en/valid/tgt_speech_shape
- exp/s2st_stats_raw_es_en/valid/tgt_text_shape.char
- exp/s2st_stats_raw_es_en/valid/src_text_shape.char
batch_type: sorted
valid_batch_type: null
fold_length:
- 800
- 150
- 150
- 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: []
train_data_path_and_name_and_type:
- - dump/raw/train_es/text.km.hubert_layer6_500.en.unique
- tgt_speech
- text
- - dump/raw/train_es/wav.scp.es
- src_speech
- sound
- - dump/raw/train_es/text.es
- src_text
- text
- - dump/raw/train_es/text.en
- tgt_text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_es/wav.scp.es
- src_speech
- sound
- - dump/raw/dev_es/text.km.hubert_layer6_500.en.unique
- tgt_speech
- text
- - dump/raw/dev_es/text.en
- tgt_text
- text
- - dump/raw/dev_es/text.es
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adamw
optim_conf:
lr: 0.0005
eps: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 10000
s2st_type: discrete_unit
tgt_token_list:
- <blank>
- <unk>
- <space>
- e
- a
- t
- i
- o
- s
- n
- r
- h
- l
- d
- c
- u
- m
- f
- p
- g
- w
- y
- b
- v
- k
- ''''
- x
- j
- z
- q
- ñ
- '-'
- í
- ó
- á
- é
- ú
- â
- .
- ʻ
- ð
- º
- ə
- ā
- ̇
- '!'
- þ
- <sos/eos>
src_token_list:
- <blank>
- <unk>
- <space>
- E
- A
- O
- S
- N
- R
- I
- L
- D
- T
- C
- U
- M
- P
- .
- B
- G
- V
- F
- H
- Y
- ','
- ó
- '"'
- Q
- í
- J
- á
- Z
- ñ
- X
- ú
- K
- '!'
- '?'
- W
- é
- ':'
- '-'
- ¿
- ¡
- Á
- ''''
- ;
- ü
- ’
- —
- É
- ö
- ã
- Ó
- ‘
- ō
- “
- â
- –
- Ú
- ë
- ä
- _
- ā
- ´
- ū
- ―
- ¨
- ø
- ô
- ê
- æ
- Í
- ì
- ć
- Ñ
- č
- е
- À
- à
- '`'
- ゴ
- ː
- '|'
- Ş
- ‹
- ›
- Š
- Č
- ё
- š
- ï
- …
- ß
- ř
- ă
- ʻ
- ý
- °
- ė
- ò
- ミ
- 箱
- 消
- し
- ム
- ś
- „
- Ś
- ə
- 鮨
- 鮓
- ł
- Ö
- û
- ·
- Ä
- ń
- «
- »
- ذ
- ه
- ب
- ي
- ة
- ṃ
- ě
- ‧
- İ
- ı
- Ø
- î
- ī
- ț
- Æ
- Þ
- Ϙ
- ª
- の
- Е
- ð
- '='
- Ð
- '&'
- ž
- ”
- œ
- <sos/eos>
unit_token_list:
- '2'
- '179'
- '408'
- '66'
- '135'
- '442'
- '7'
- '130'
- '106'
- '112'
- '195'
- '363'
- '278'
- '249'
- '280'
- '243'
- '279'
- '197'
- '16'
- '270'
- '483'
- '212'
- '437'
- '313'
- '429'
- '110'
- '19'
- '142'
- '152'
- '175'
- '84'
- '34'
- '359'
- '14'
- '269'
- '267'
- '41'
- '60'
- '190'
- '450'
- '180'
- '171'
- '209'
- '348'
- '55'
- '383'
- '56'
- '158'
- '17'
- '200'
- '53'
- '35'
- '390'
- '122'
- '255'
- '491'
- '452'
- '471'
- '420'
- '306'
- '11'
- '54'
- '9'
- '26'
- '29'
- '454'
- '104'
- '107'
- '30'
- '147'
- '257'
- '448'
- '51'
- '232'
- '74'
- '43'
- '294'
- '151'
- '146'
- '226'
- '45'
- '461'
- '63'
- '369'
- '244'
- '4'
- '76'
- '131'
- '27'
- '327'
- '177'
- '204'
- '139'
- '358'
- '6'
- '284'
- '310'
- '415'
- '182'
- '407'
- '326'
- '319'
- '231'
- '88'
- '476'
- '109'
- '166'
- '417'
- '456'
- '105'
- '354'
- '0'
- '318'
- '336'
- '314'
- '159'
- '281'
- '413'
- '95'
- '73'
- '296'
- '422'
- '432'
- '39'
- '431'
- '36'
- '447'
- '468'
- '427'
- '378'
- '248'
- '322'
- '47'
- '220'
- '82'
- '181'
- '391'
- '494'
- '344'
- '435'
- '178'
- '61'
- '129'
- '114'
- '302'
- '392'
- '150'
- '223'
- '79'
- '438'
- '262'
- '371'
- '203'
- '239'
- '488'
- '247'
- '283'
- '416'
- '68'
- '395'
- '184'
- '474'
- '141'
- '89'
- '342'
- '13'
- '298'
- '125'
- '191'
- '165'
- '24'
- '441'
- '227'
- '196'
- '258'
- '133'
- '168'
- '64'
- '123'
- '400'
- '81'
- '217'
- '253'
- '132'
- '285'
- '28'
- '188'
- '375'
- '213'
- '242'
- '236'
- '453'
- '225'
- '164'
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- '272'
- '325'
- '495'
- '460'
- '330'
- '48'
- '451'
- '323'
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- '263'
- '144'
- '160'
- '149'
- '406'
- '77'
- '33'
- '368'
- '332'
- '205'
- '50'
- '290'
- '401'
- '490'
- '331'
- '436'
- '5'
- '186'
- '288'
- '148'
- '219'
- '215'
- '93'
- '434'
- '103'
- '489'
- '21'
- '92'
- '386'
- '97'
- '328'
- '121'
- '301'
- '46'
- '224'
- '154'
- '80'
- '352'
- '96'
- '124'
- '69'
- '457'
- '83'
- '52'
- '85'
- '62'
- '380'
- '410'
- '167'
- '333'
- '31'
- '315'
- '78'
- '271'
- '10'
- '492'
- '49'
- '208'
- '295'
- '86'
- '199'
- '445'
- '140'
- '357'
- '187'
- '161'
- '238'
- '351'
- '155'
- '193'
- '345'
- '486'
- '37'
- '266'
- '185'
- '143'
- '361'
- '174'
- '430'
- '349'
- '23'
- '423'
- '388'
- '309'
- '470'
- '169'
- '370'
- '463'
- '245'
- '320'
- '237'
- '316'
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- '218'
- '198'
- '117'
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- '90'
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- '137'
- '366'
- '353'
- '341'
- '241'
- '496'
- '228'
- '287'
- '214'
- '264'
- '108'
- '44'
- '350'
- '3'
- '286'
- '303'
- '12'
- '372'
- '156'
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- '116'
- '385'
- '194'
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- '22'
- '414'
- '462'
- '70'
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- '251'
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- '440'
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- '15'
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- '384'
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- '173'
- '59'
- '485'
- '75'
- '102'
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- '115'
- '210'
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- '18'
- '498'
- '99'
- '398'
- '91'
- '221'
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- '479'
- '300'
- '339'
- '367'
- '459'
- '20'
- '216'
- '426'
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- '382'
- '424'
- '446'
- '1'
- '265'
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- '346'
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- '25'
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- '111'
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- '8'
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- '138'
- '72'
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- '120'
- '425'
- '480'
- '32'
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- '412'
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- '230'
- '67'
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- '94'
- '128'
- '376'
- '433'
- '192'
- '235'
- '38'
- '312'
- '347'
- '499'
- '274'
- '389'
- '127'
- '439'
- '207'
- '478'
- '403'
- '467'
- '411'
- '455'
- '337'
- '469'
- '206'
- '497'
- '136'
- '481'
- '487'
- '40'
- '477'
- '472'
- '189'
- '308'
- '377'
- '305'
- '211'
- '126'
- <unk>
- <sos/eos>
odim: null
init: null
input_size: null
output_size: 500
asr_ctc: true
st_ctc: true
asr_ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
st_ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
model_conf:
ignore_id: -1
report_cer: true
report_wer: true
report_bleu: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
use_preprocessor: true
tgt_token_type: char
src_token_type: char
tgt_bpemodel: null
src_bpemodel: null
non_linguistic_symbols: null
cleaner: null
tgt_g2p: null
src_g2p: null
losses:
- name: asr_ctc
type: ctc
conf:
weight: 1.6
- name: src_attn
type: attention
conf:
weight: 8.0
smoothing: 0.2
padding_idx: -1
- name: tgt_attn
type: attention
conf:
weight: 8.0
smoothing: 0.2
padding_idx: -1
- name: st_ctc
type: ctc
conf:
weight: 1.6
- name: synthesis
type: attention
conf:
weight: 1.6
smoothing: 0.2
padding_idx: -1
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
frontend: default
frontend_conf:
n_fft: 512
win_length: 400
hop_length: 160
fs: 16k
tgt_feats_extract: null
tgt_feats_extract_conf: {}
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
src_normalize: global_mvn
src_normalize_conf:
stats_file: exp/s2st_stats_raw_es_en/train/src_feats_stats.npz
tgt_normalize: utterance_mvn
tgt_normalize_conf: {}
preencoder: null
preencoder_conf: {}
encoder: transformer
encoder_conf:
input_layer: conv2d
num_blocks: 12
linear_units: 2048
dropout_rate: 0.1
output_size: 256
attention_heads: 4
attention_dropout_rate: 0.0
normalize_before: true
postencoder: null
postencoder_conf: {}
asr_decoder: transformer
asr_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
attention_heads: 4
st_decoder: transformer
st_decoder_conf:
input_layer: embed
num_blocks: 2
linear_units: 2048
attention_heads: 4
aux_attention: null
aux_attention_conf: {}
unit_encoder: null
unit_encoder_conf: {}
synthesizer: discrete_unit
synthesizer_conf:
input_layer: embed
num_blocks: 6
linear_units: 2048
attention_heads: 8
loss: tacotron
loss_conf: {}
required:
- output_dir
version: '202308'
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}
}