ESPnet2 ASR model
popcornell/chime7_task1_asr1_baseline
This model was trained by popcornell using chime7_task1 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 89ebca463c544dfaa19e5f76ad5f615f473f6957
pip install -e .
cd egs2/chime7_task1/asr1
./run.sh --skip_data_prep false --skip_train true --download_model popcornell/chime7_task1_asr1_baseline
RESULTS
Environments
- date:
Mon Mar 13 13:43:21 UTC 2023
- python version:
3.9.2 (default, Mar 3 2021, 20:02:32) [GCC 7.3.0]
- espnet version:
espnet 202301
- pytorch version:
pytorch 1.13.1
- Git hash:
89ebca463c544dfaa19e5f76ad5f615f473f6957
- Commit date:
Tue Mar 7 04:02:43 2023 +0000
exp/kaldi/mixer6/gss
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
14804 |
148981 |
84.4 |
10.4 |
5.2 |
6.2 |
21.8 |
60.6 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
14804 |
731649 |
91.0 |
3.6 |
5.4 |
6.5 |
15.5 |
60.6 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
14804 |
251635 |
87.0 |
7.4 |
5.6 |
6.9 |
19.9 |
60.6 |
exp/kaldi/chime6/gss_inf
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss_inf |
6644 |
58881 |
73.0 |
18.2 |
8.8 |
6.7 |
33.6 |
71.2 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss_inf |
6644 |
281489 |
83.2 |
7.0 |
9.8 |
7.3 |
24.1 |
71.2 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss_inf |
6644 |
98596 |
75.0 |
14.2 |
10.9 |
7.1 |
32.2 |
71.2 |
exp/kaldi/dipco/gss
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
3673 |
29966 |
74.0 |
17.8 |
8.2 |
8.5 |
34.5 |
72.6 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
3673 |
146438 |
84.5 |
6.3 |
9.3 |
8.9 |
24.4 |
72.6 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| dev/gss |
3673 |
51347 |
77.1 |
13.5 |
9.4 |
9.2 |
32.1 |
72.6 |
ASR config
expand
config: conf/tuning/train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-4_specaugm_accum1_preenc128_warmup20k_raw_en_bpe500_batch_size640_scheduler_confwarmup_steps8000_max_epoch8_optim_conflr0.000500000000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 5
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 38257
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: 8
patience: 4
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
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:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 640
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe500_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
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/kaldi/train_all_mdm_ihm_rvb_gss_sp/wav.scp
- speech
- sound
- - dump/raw/kaldi/train_all_mdm_ihm_rvb_gss_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/kaldi/chime6/dev/gss/wav.scp
- speech
- sound
- - dump/raw/kaldi/chime6/dev/gss/text
- 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: adam
optim_conf:
lr: 0.0005
scheduler: warmuplr
scheduler_conf:
warmup_steps: 8000
token_list:
- <blank>
- <unk>
- '[inaudible]'
- '[laughs]'
- '[noise]'
- s
- ''''
- ▁i
- t
- ▁it
- ▁a
- e
- ▁you
- ▁the
- ▁like
- ▁yeah
- a
- d
- ▁and
- m
- ▁that
- ▁to
- n
- i
- y
- ing
- o
- u
- ▁so
- p
- ▁of
- ▁in
- re
- ▁was
- c
- r
- ▁just
- er
- ▁know
- ▁oh
- ed
- ▁but
- ▁ummm
- ▁we
- l
- ▁no
- ▁they
- ▁have
- ▁do
- g
- ▁he
- k
- ll
- ▁uhhh
- ▁don
- ▁for
- h
- ▁what
- ▁be
- ar
- ▁is
- ▁there
- '-'
- ▁s
- ▁this
- in
- b
- ▁
- en
- ▁on
- ▁p
- ▁can
- al
- ▁not
- w
- ▁my
- ▁one
- ic
- f
- ▁or
- ▁really
- ▁go
- ▁right
- ▁me
- an
- ▁w
- or
- le
- ▁f
- ▁think
- ▁okay
- ▁all
- ▁then
- ▁with
- ▁are
- ▁get
- it
- ▁t
- ▁st
- ve
- ▁hmmm
- ▁g
- ▁if
- ce
- 'on'
- ▁she
- ▁good
- ▁e
- es
- ▁well
- v
- ▁re
- th
- ter
- ch
- ▁out
- ▁up
- ly
- ▁b
- ▁ma
- il
- ▁would
- ▁at
- ▁want
- ▁mean
- ▁ch
- ▁your
- ▁people
- ur
- ▁how
- ▁k
- ▁co
- ▁about
- ▁tr
- ▁ba
- ▁kind
- ▁when
- ▁mi
- ▁because
- ro
- ▁had
- ▁ho
- ▁gonna
- ▁time
- ▁more
- ▁got
- ▁some
- ▁two
- ▁did
- ▁see
- ▁now
- ▁pa
- ra
- ▁de
- ▁lot
- ▁actually
- ▁o
- ▁too
- ate
- ▁here
- ▁cuz
- ▁sp
- ▁where
- ▁going
- ▁j
- ▁from
- ▁bo
- ▁them
- ▁bu
- ▁put
- ▁thing
- ng
- ▁were
- ▁n
- ▁sh
- ▁work
- el
- ▁something
- ▁se
- ▁say
- ke
- ow
- ▁ca
- ▁fa
- ▁need
- sh
- ▁di
- ▁po
- ▁make
- la
- ▁br
- ▁v
- ▁an
- ▁who
- ion
- ▁y
- ▁look
- ▁didn
- ▁could
- ▁little
- ver
- ▁c
- ▁mo
- ▁much
- ▁very
- ir
- ▁sa
- ▁play
- ▁pretty
- ▁been
- ▁d
- ▁other
- ▁year
- and
- ▁mm
- ▁stuff
- ▁dr
- ▁why
- ▁con
- ▁su
- ▁back
- ▁ex
- ting
- ▁take
- ▁li
- ▁even
- ▁should
- ▁her
- ally
- lo
- ation
- ▁way
- ▁guess
- ▁has
- z
- ▁three
- ry
- ▁ha
- ies
- is
- x
- ▁ro
- ▁yes
- ▁th
- ▁use
- ▁down
- ous
- ▁over
- ▁probably
- ▁guys
- ▁maybe
- ▁still
- ▁cr
- ▁which
- ▁nice
- und
- ▁sure
- ▁l
- ▁off
- ▁la
- ▁cu
- est
- ▁any
- ▁fi
- ▁these
- ▁ra
- ▁went
- ▁things
- ment
- ▁doing
- ▁day
- ▁un
- ▁lo
- ▁da
- ▁only
- igh
- ▁come
- ▁big
- ▁those
- ▁wanna
- ▁bit
- ▁never
- ▁us
- ol
- ▁though
- ▁first
- ive
- ▁their
- ▁let
- ▁start
- ▁his
- ▁four
- ▁le
- ▁eat
- ist
- ▁school
- us
- ▁into
- ▁yep
- uck
- ▁than
- ▁him
- ▁hi
- ▁also
- ▁five
- side
- ▁new
- ▁comp
- ▁cool
- ▁talk
- ▁said
- ▁pro
- ▁r
- ▁always
- ▁ri
- ▁cl
- ▁long
- able
- ▁sc
- ▁gra
- ▁by
- ▁friend
- age
- ▁different
- ▁live
- ▁doesn
- ▁place
- ▁sorry
- ▁will
- ▁feel
- ▁does
- ▁part
- ▁wait
- ▁six
- ▁watch
- ▁anything
- ▁man
- ▁our
- ▁car
- ▁huh
- ▁whatever
- ▁last
- ▁give
- ▁ten
- ▁before
- ▁thought
- ▁after
- ▁game
- ▁card
- ▁fl
- ▁every
- cause
- ▁same
- ▁around
- ▁cook
- ▁week
- ▁hu
- ▁everything
- ▁fine
- ▁many
- ▁qu
- ▁read
- ▁tea
- ough
- ance
- ▁turn
- ▁wow
- ▁fun
- ▁hard
- ▁great
- ▁love
- ▁remember
- ▁twenty
- ▁whole
- ▁happen
- ▁seven
- ▁keep
- ▁food
- ▁most
- j
- ▁might
- ▁thank
- ▁move
- ▁job
- ▁eight
- ▁mu
- ▁sort
- ▁better
- port
- ▁another
- ful
- ▁point
- ▁show
- ▁again
- ▁high
- ize
- ▁house
- ▁home
- ▁person
- ▁old
- ▁end
- ▁through
- ▁pick
- ▁else
- ▁guy
- ▁app
- ▁find
- ▁nine
- ▁hand
- ▁kid
- ▁interesting
- ▁city
- ▁called
- ▁tell
- ▁half
- ▁name
- ▁definitely
- ▁made
- ▁exactly
- ▁came
- ▁wood
- ▁funny
- ▁basically
- ▁count
- ▁usually
- ▁help
- ▁someone
- ▁already
- ▁dunno
- ▁enough
- ction
- ▁own
- ▁weird
- ▁next
- ▁hundred
- ▁small
- ▁money
- ▁couple
- ▁while
- ▁close
- ▁movie
- ▁sometimes
- ▁everyone
- ▁away
- ▁true
- ▁super
- ▁cheese
- ▁class
- ▁night
- ▁life
- ▁leave
- ▁plan
- ▁water
- ▁left
- ▁thirty
- ▁family
- ▁phone
- ▁build
- ▁room
- ▁month
- ▁open
- ▁idea
- ▁second
- ▁dude
- ▁music
- ▁each
- ▁learn
- ▁girl
- ▁together
- ▁under
- ▁run
- ▁chicken
- ▁having
- ▁either
- ▁almost
- ▁crazy
- ▁book
- ▁sauce
- ▁supposed
- ▁course
- ▁speak
- ▁awesome
- ▁anyway
- ▁throw
- ▁finish
- ▁world
- ▁reason
- ▁check
- ▁least
- '&'
- ä
- '#'
- ñ
- â
- é
- ü
- î
- ']'
- q
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram500/bpe.model
non_linguistic_symbols: data/nlsyms.txt
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: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: false
freq_mask_width_range:
- 0
- 150
num_freq_mask: 4
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.15
num_time_mask: 3
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 128
dropout: 0.2
encoder: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d2
normalize_before: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.0
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202301'
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}
}
