ESPnet2 ASR model
espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
This model was trained by Siddhant using slue-voxceleb recipe in espnet.
Demo: How to use in ESPnet2
cd espnet
git checkout 17758ad804fd7c4b6f88ef5601f475a241dc4605
pip install -e .
cd egs2/slue-voxceleb/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/siddhana_slue_asr_train_asr_conformer_raw_en_word_valid.acc.ave_10best
RESULTS
Environments
- date:
Tue Dec 28 12:28:28 EST 2021 - python version:
3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0] - espnet version:
espnet 0.10.3a2 - pytorch version:
pytorch 1.8.1+cu102 - Git hash:
6bf3c2a4f138d35331634d2e879bbc5c32a5266e- Commit date:
Mon Dec 22 15:41:32 EST 2021
- Commit date:
Using Conformer based encoder and Transformer based decoder with spectral augmentation and predicting transcript along with intent
- ASR config: conf/train_asr.yaml
- token_type: word
| dataset | Snt | Intent Classification Accuracy (%) | Intent Classification Macro F1 (%) |
|---|---|---|---|
| inference_asr_model_valid.acc.ave_10best/devel | 955 | 80.2 | 29.7 |
Detailed Classification Report
| dataset | Label | Snt | Prec | Recall | F1 |
|---|---|---|---|---|---|
| inference_asr_model_valid.acc.ave_10best/devel | Neutral | 784 | 85 | 93 | 89 |
| inference_asr_model_valid.acc.ave_10best/devel | Positive | 167 | 40 | 24 | 30 |
| inference_asr_model_valid.acc.ave_10best/devel | Negative | 3 | 0 | 0 | 0 |
| inference_asr_model_valid.acc.ave_10best/devel | Mixed | 1 | 0 | 0 | 0 |
ASR config
expand
config: conf/tuning/train_asr_conformer.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer_raw_en_word
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
grad_clip: 5.0
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_tensorboard: true
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: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_word/train/speech_shape
- exp/asr_stats_raw_en_word/train/text_shape.word
valid_shape_file:
- exp/asr_stats_raw_en_word/valid/speech_shape
- exp/asr_stats_raw_en_word/valid/text_shape.word
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
train_data_path_and_name_and_type:
- - dump/raw/train/wav.scp
- speech
- sound
- - dump/raw/train/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
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.0002
scheduler: warmuplr
scheduler_conf:
warmup_steps: 25000
token_list:
- <blank>
- <unk>
- ▁i
- s
- ▁and
- ''''
- ▁the
- ▁a
- ▁to
- ▁it
- Neutral
- ▁you
- ▁that
- ▁of
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- ▁in
- ▁was
- ed
- ▁uh
- ▁know
- e
- m
- ▁he
- y
- er
- ▁so
- ▁we
- re
- a
- o
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- ▁um
- i
- ▁s
- c
- ▁like
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- ▁is
- ▁be
- ▁f
- ▁but
- ▁c
- Positive
- en
- l
- ve
- ▁just
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- st
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- le
- an
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- u
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- ar
- p
- ▁this
- ▁for
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- ▁think
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- or
- ▁h
- r
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- ▁e
- ▁have
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- it
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- ▁w
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- ▁do
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- ▁not
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- ▁when
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- ent
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- ▁get
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- ce
- ▁because
- ▁are
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- ▁some
- et
- ▁can
- ge
- ▁or
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- ▁up
- ▁n
- ▁if
- ▁no
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- ▁were
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- ▁mean
- ad
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- ▁then
- ro
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- ▁co
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- ▁se
- ol
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- ▁su
- ▁got
- ▁k
- th
- ▁love
- ▁from
- ld
- ation
- ▁him
- ▁said
- ▁how
- ▁well
- ▁lot
- ▁show
- ch
- ard
- ie
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- ▁de
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- ▁bo
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- one
- ▁his
- ther
- ▁been
- ur
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- ▁
- ▁yeah
- pe
- ▁which
- ▁come
- ▁them
- ot
- ▁play
- ab
- ite
- ▁way
- ally
- id
- gh
- ▁r
- ▁sc
- our
- x
- mp
- ers
- ong
- ate
- ▁your
- ss
- ast
- ▁did
- ▁sort
- ▁am
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- and
- ▁make
- ant
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- ▁ha
- ▁te
- ▁has
- ess
- ▁v
- ▁something
- ▁back
- ▁where
- ▁things
- red
- ▁al
- ut
- el
- ight
- ment
- un
- ive
- ▁th
- ▁le
- il
- ▁j
- op
- ▁more
- ▁ro
- ill
- ▁fi
- ies
- ▁much
- ck
- ▁ne
- ▁wh
- ▁always
- ▁act
- ine
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- z
- ▁now
- ▁con
- thing
- ▁us
- body
- ▁want
- ▁other
- ort
- ice
- ▁doing
- ▁sa
- ▁feel
- ow
- ▁int
- ne
- ▁these
- ▁could
- ▁good
- ▁cause
- Negative
- ▁actually
- ▁wr
- ▁little
- ain
- ▁being
- ▁look
- ▁into
- ere
- ul
- ▁our
- ▁guy
- ▁first
- ud
- ▁by
- ▁fun
- ▁qu
- ▁didn
- us
- ity
- ▁jo
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- ▁u
- ▁part
- ▁off
- ▁pre
- ▁right
- ▁film
- ▁start
- ok
- ▁two
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- pt
- um
- te
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- nder
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- ▁ag
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- au
- ▁po
- nt
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- om
- ▁real
- ▁y
- ame
- ▁went
- ry
- ber
- ▁even
- ci
- ▁ho
- ▁years
- ▁their
- ▁happen
- ure
- self
- per
- ▁pl
- ▁those
- ble
- 'no'
- ▁day
- ▁take
- ▁does
- ien
- ▁br
- be
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- ▁thought
- ▁fe
- ght
- ▁tr
- ▁story
- ty
- ▁down
- ous
- ish
- ▁wom
- ▁wanna
- ▁put
- ▁through
- ide
- ▁ab
- ▁new
- ▁also
- ▁big
- ▁call
- ▁around
- ▁character
- ▁read
- iz
- ▁came
- act
- ily
- ath
- ag
- ree
- ▁per
- ▁will
- ▁mu
- ▁talk
- ▁over
- ▁friend
- atch
- ▁bl
- ade
- ▁world
- ▁many
- ▁sp
- sic
- ▁cl
- ▁bit
- ▁man
- ace
- ▁person
- ft
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- ▁than
- ▁wanted
- ▁may
- ven
- ick
- ious
- ▁mar
- ▁before
- ▁rel
- j
- ting
- ▁set
- sh
- ep
- ▁un
- ue
- ▁aw
- ▁find
- ▁kid
- tain
- ▁such
- ter
- ▁end
- ▁tw
- ind
- aking
- ▁after
- ▁fam
- ars
- ig
- ore
- ▁bec
- ak
- art
- reat
- ust
- rou
- ack
- ▁ye
- ould
- ime
- itt
- ▁gu
- qu
- ose
- fe
- ▁wor
- lf
- alk
- ▁charact
- ▁mov
- out
- ich
- ▁happ
- ▁thou
- ith
- <mixed>
- rom
- ake
- ▁diff
- ▁char
- na
- round
- ory
- ink
- ually
- ▁gon
- ▁pe
- right
- ody
- ah
- rie
- riend
- now
- so
- ause
- ▁fil
- ▁pers
- fore
- very
- ▁differe
- rough
- q
- ▁fir
- anna
- ways
- ':'
- '&'
- fter
- <sos/eos>
transcript_token_list: null
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
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'
frontend: default
frontend_conf:
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
- 30
num_freq_mask: 2
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
output_size: 512
attention_heads: 8
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv2d
normalize_before: true
macaron_style: true
pos_enc_layer_type: rel_pos
selfattention_layer_type: rel_selfattn
activation_type: swish
use_cnn_module: true
cnn_module_kernel: 31
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
postdecoder: null
postdecoder_conf: {}
required:
- output_dir
- token_list
version: 0.10.3a2
distributed: 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}
}
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