ESPnet2 ASR model
espnet/sluevoxceleb_wavlm_finetune_asr
This model was trained by “siddhu001” using slue-voxceleb 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 e23ef85f0b3116ad5c60d0833f186da0deec0734
pip install -e .
cd egs2/slue-voxceleb/slu1_asr
./run.sh --skip_data_prep false --skip_train true --download_model espnet/sluevoxceleb_wavlm_finetune_asr
RESULTS
Environments
- date:
Tue Feb 6 13:49:10 CST 2024
- python version:
3.9.13 (main, Aug 25 2022, 23:26:10) [GCC 11.2.0]
- espnet version:
espnet 202310
- pytorch version:
pytorch 2.1.0+cu121
- Git hash:
21d2105784e4da98397bf487b2550d4c6e16d40d
- Commit date:
Wed Jan 31 13:40:37 2024 -0600
exp/slu_train_asr_wavlm_superb_finetune_raw_en_word_sp
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_ctc_slu_model_valid.cer_ctc.ave/test |
3426 |
135368 |
91.5 |
5.8 |
2.7 |
3.5 |
12.0 |
86.5 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| decode_asr_ctc_slu_model_valid.cer_ctc.ave/test |
3426 |
591261 |
96.5 |
1.2 |
2.3 |
3.2 |
6.7 |
86.5 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
exp/slu_train_asr_wavlm_superb_finetune_raw_en_word_sp/decode_asr_ctc_slu_model_valid.cer_ctc.ave
WER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/devel |
1437 |
56031 |
92.9 |
4.8 |
2.3 |
3.0 |
10.1 |
84.4 |
CER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
| org/devel |
1437 |
241556 |
97.2 |
0.9 |
1.9 |
2.7 |
5.5 |
84.4 |
TER
| dataset |
Snt |
Wrd |
Corr |
Sub |
Del |
Ins |
Err |
S.Err |
ASR config
expand
config: conf/tuning/train_asr_wavlm_superb_finetune.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/slu_train_asr_wavlm_superb_finetune_raw_en_word_sp
ngpu: 1
seed: 2022
num_workers: 2
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 35569
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: 60
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- cer_ctc
- min
- - valid
- loss
- min
keep_nbest_models: 10
nbest_averaging_interval: 0
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: false
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_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream.upstream.model.feature_extractor
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 6000000
valid_batch_bins: null
train_shape_file:
- exp/slu_stats_raw_en_word_sp/train/speech_shape
- exp/slu_stats_raw_en_word_sp/train/text_shape.word
valid_shape_file:
- exp/slu_stats_raw_en_word_sp/valid/speech_shape
- exp/slu_stats_raw_en_word_sp/valid/text_shape.word
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_sp/wav.scp
- speech
- sound
- - dump/raw/train_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/devel/wav.scp
- speech
- sound
- - dump/raw/devel/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: 0.0004
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 2500
token_list:
- <blank>
- <unk>
- ▁i
- ▁and
- ''''
- s
- ▁the
- ▁a
- ▁it
- ▁to
- ▁you
- ▁that
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- ▁was
- ▁uh
- ▁know
- t
- ▁so
- ▁we
- ▁he
- ing
- m
- ▁um
- ▁like
- ed
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- ▁but
- ▁just
- ▁they
- re
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- ▁this
- ▁for
- ▁be
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- er
- ▁with
- ▁on
- ▁think
- ▁have
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- ▁going
- ke
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- ▁mo
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- ▁every
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- ▁ro
- ▁also
- ▁read
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- ▁character
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- ▁will
- ag
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- ▁over
- ▁many
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- ▁than
- ▁pre
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- ▁person
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- ▁sp
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- ▁before
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- ▁find
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- ter
- me
- ▁such
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- ▁kinda
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- ▁bo
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- ▁most
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- ▁watch
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- ▁again
- ▁here
- are
- ght
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- ▁done
- ▁only
- ▁live
- ▁wasn
- ▁ho
- ▁u
- ▁maybe
- ▁need
- ▁everybody
- ust
- ▁three
- ▁having
- ▁music
- ack
- ld
- ▁trying
- ▁guys
- rou
- ach
- ving
- ▁tell
- ▁should
- ff
- ide
- ▁four
- ▁started
- ass
- ▁long
- ▁fe
- ans
- ▁course
- ▁called
- ▁own
- ress
- ▁moment
- ▁pl
- ▁still
- ▁anything
- ▁family
- ▁fin
- ▁dan
- ▁bro
- 'no'
- ▁com
- ther
- ▁amazing
- ▁stuff
- os
- ▁per
- ▁jo
- ▁certain
- ▁talk
- ater
- per
- ▁help
- ▁too
- ▁year
- ight
- ▁fa
- self
- ces
- ▁br
- ▁bet
- ▁someone
- ▁di
- ▁sing
- nt
- ick
- ▁ph
- row
- ▁script
- ▁remember
- ▁try
- qu
- ite
- ▁young
- ▁wh
- ▁ser
- ▁ask
- um
- ▁book
- ▁each
- ▁wr
- ▁best
- ▁ag
- ▁women
- ose
- ions
- ved
- j
- ue
- ▁does
- ty
- ▁five
- ▁both
- ▁friends
- ▁act
- iz
- ind
- cess
- ▁somebody
- ft
- ▁nice
- ▁tur
- ▁myself
- mb
- fe
- ict
- ▁child
- ud
- ▁hope
- ▁fact
- ▁saying
- les
- ave
- icul
- au
- ris
- ▁twenty
- ▁school
- ▁doesn
- ▁able
- pect
- ▁last
- ▁song
- od
- ▁str
- ▁interesting
- lf
- ▁wor
- sp
- ap
- og
- ▁ra
- ▁dis
- ▁coming
- ▁ab
- ▁house
- ▁next
- ▁tra
- ▁okay
- ere
- ib
- ary
- ▁incredib
- ▁car
- ▁job
- ▁used
- ▁give
- ▁god
- ▁americ
- ▁characters
- ▁app
- ▁walk
- ▁yes
- rew
- ▁getting
- ▁six
- ▁chan
- ▁ne
- ale
- ▁pretty
- mp
- ang
- ▁creat
- ▁another
- ▁ter
- ▁kids
- ▁felt
- ▁sometimes
- ▁place
- ▁int
- ically
- out
- ▁funny
- ase
- ich
- act
- ▁days
- ▁bring
- ▁making
- ▁become
- ute
- ▁wonderful
- ron
- ▁saw
- ▁point
- ia
- ▁realiz
- ▁away
- ays
- ▁home
- ace
- ▁relationship
- day
- ▁woman
- ▁everyone
- ▁comes
- ▁high
- ▁wee
- dd
- ▁night
- ath
- ts
- ▁else
- vent
- ▁shoot
- vers
- ▁sure
- ried
- ned
- ▁obviously
- ▁dra
- co
- iew
- man
- ▁playing
- ▁important
- ort
- uck
- ision
- pport
- ▁nor
- ▁seen
- ▁fl
- est
- ▁inter
- ks
- ▁actor
- ▁lear
- ▁worked
- ▁believe
- ▁gen
- ▁keep
- ull
- ▁friend
- ▁sw
- ▁des
- ▁times
- ▁sur
- ms
- ▁sit
- ▁probably
- ok
- ▁took
- ep
- ough
- ip
- ood
- ▁sa
- ▁season
- vel
- wn
- ▁dec
- ▁excited
- ame
- ian
- ire
- ▁name
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- ▁month
- ner
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- ▁rel
- ating
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- ▁aw
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- ph
- ▁cool
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- ord
- pl
- ble
- our
- ▁game
- ub
- ▁might
- ▁kid
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- ical
- ▁bad
- ▁scene
- iv
- ▁enough
- ▁sm
- ▁fift
- ▁eight
- ▁experience
- ▁actors
- ▁understand
- ▁few
- gin
- ting
- ▁director
- ▁almost
- ▁open
- ren
- ▁star
- ▁room
- ▁call
- oy
- ▁goes
- ▁told
- ▁once
- ▁found
- arly
- ations
- ward
- ▁audience
- ird
- ▁qu
- ▁ar
- ▁definitely
- ious
- iting
- ▁pol
- ▁huge
- ▁makes
- aking
- ▁la
- ▁ac
- iter
- ▁run
- ▁gotta
- ▁gr
- ▁cam
- sh
- ▁gets
- ▁wa
- ully
- ▁says
- ▁cont
- side
- ▁bus
- ▁shows
- ▁dr
- ▁inv
- ▁idea
- ▁talking
- way
- ▁art
- ▁whatever
- ▁write
- ash
- itt
- ▁met
- ▁wants
- ▁role
- if
- ▁mu
- ▁boy
- ▁wrote
- ger
- ately
- ▁exc
- ▁gu
- ▁mother
- ▁produ
- ▁cra
- ates
- ▁though
- av
- ▁episode
- ▁sl
- ▁change
- be
- ▁voice
- ▁played
- ily
- ▁guess
- ves
- ▁hand
- ady
- ▁happy
- ith
- ny
- ▁gi
- med
- ▁looking
- lev
- ream
- ▁acting
- aught
- iss
- ount
- rom
- ▁tw
- ▁john
- ▁far
- ▁res
- ▁sense
- ake
- ▁meet
- ▁bre
- ens
- ety
- ▁girl
- ▁york
- ▁count
- ▁shot
- ise
- ject
- ▁tot
- ▁stud
- ▁feels
- ▁thinking
- ma
- ▁head
- ▁cast
- ▁writing
- ▁imp
- ▁rehe
- ▁written
- ▁perfor
- ▁fan
- der
- ect
- ▁sk
- ▁hour
- ▁father
- ered
- ▁hundred
- ▁ind
- ▁che
- ▁acc
- up
- ▁while
- fort
- ▁true
- itch
- ▁inst
- ▁second
- ▁pick
- ▁record
- ross
- ▁quest
- ged
- ▁career
- ▁reason
- ▁since
- ▁bu
- ▁bra
- ▁char
- ree
- ▁girls
- ▁dad
- ▁fant
- ▁extra
- ▁laugh
- ▁stand
- ▁honest
- na
- als
- ▁yet
- ▁human
- ▁couple
- dy
- ▁mind
- ▁sound
- ▁ke
- ▁pop
- ▁ent
- ory
- ▁war
- ▁ten
- ink
- ▁bec
- ▁direct
- reat
- round
- ien
- ▁under
- ile
- ▁diff
- ually
- thing
- sic
- ▁gon
- ather
- ▁aud
- ert
- for
- ▁scen
- mber
- atch
- ▁sho
- ever
- tra
- ▁pe
- ▁hu
- ild
- int
- ▁ob
- ▁care
- ▁fam
- ▁ide
- ade
- right
- ▁may
- he
- mo
- ody
- ense
- ▁interest
- ah
- ork
- ▁episod
- ▁prob
- ▁rec
- ▁hop
- ited
- ▁exper
- gh
- ▁bel
- ▁el
- ▁stu
- enty
- ound
- ▁gott
- ▁id
- ime
- rie
- ▁inc
- ertain
- ▁wo
- ▁mon
- az
- xt
- riend
- now
- ▁y
- ple
- ome
- so
- ause
- ▁cou
- iously
- ▁sch
- ▁vo
- ▁fil
- ▁op
- ason
- ▁mov
- ▁hi
- ▁pers
- ▁ye
- ▁def
- ▁belie
- fore
- ix
- very
- ▁differe
- ▁wonder
- nder
- ▁obv
- ▁ep
- ship
- ▁lau
- ience
- ool
- ▁sin
- rect
- ▁happ
- ▁gir
- ▁hel
- du
- ng
- ▁underst
- most
- eric
- ouse
- time
- ▁cour
- ▁relation
- rough
- q
- ▁defin
- ▁reme
- redib
- ▁fir
- anna
- ways
- itten
- elt
- ▁sometime
- ':'
- alk
- ▁ok
- ably
- rote
- gether
- ▁definite
- ▁import
- '&'
- new
- fter
- onest
- erest
- ▁amaz
- ▁ano
- <sos/eos>
transcript_token_list: null
two_pass: false
pre_postencoder_norm: false
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
token_type: word
bpemodel: null
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
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
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: 2
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 1.0
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 80
encoder: conformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 1024
num_blocks: 2
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d2
normalize_before: true
macaron_style: true
rel_pos_type: latest
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
prepostencoder: null
prepostencoder_conf: {}
postencoder: null
postencoder_conf: {}
deliberationencoder: null
deliberationencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
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
postdecoder: null
postdecoder_conf: {}
required:
- output_dir
- token_list
version: '202310'
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}
}
