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Simpleoier Librispeech Asr Train Asr Conformer7 Wavlm Large Raw En Bpe5000 Sp

espnetによって開発

ESPnetフレームワークでトレーニングされた自動音声認識(ASR)モデルで、ConformerアーキテクチャとWavLM大型事前学習モデルを使用し、LibriSpeechデータセットでトレーニングされました。

音声認識英語#高精度音声認識 #WavLM事前学習 #LibriSpeech適応

ダウンロード数 66

リリース時間 : 3/2/2022

モデル概要

このモデルは高性能な英語自動音声認識システムで、生の音声入力を処理しテキストに変換するように設計されています。

モデル特徴

高性能アーキテクチャ

Conformer7とWavLM大型事前学習モデルを組み合わせ、優れた音声認識能力を提供します

LibriSpeechトレーニング

広く使用されているLibriSpeechデータセットでトレーニングされており、様々な音声条件下でのロバスト性を確保しています

低エラー率

テストセットで優れた性能を示し、クリーンな音声では単語誤り率(WER)が1.8%、ノイズのある音声では3.7%と低い

モデル能力

英語音声認識

生音声処理

大規模音声からテキストへ

使用事例

音声文字起こし

会議議事録

会議録音を自動的に文字起こし

テストセットのクリーンデータで98.4%の精度

音声字幕生成

ポッドキャストや動画コンテンツの字幕を生成

ノイズのある音声環境でも96.7%の精度を維持

音声アシスタント

音声コマンド認識

音声コマンドを認識し実行

🚀 ESPnet2 ASRモデル

このモデルは、espnet のlibrispeechレシピを使用して、simpleoierによってトレーニングされました。

デモ: ESPnet2での使用方法

cd espnet
git checkout b0ff60946ada6753af79423a2e6063984bec2926
pip install -e .
cd egs2/librispeech/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_librispeech_asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp

結果

環境

日付: Tue Jan 4 20:52:48 EST 2022
Pythonバージョン: 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0]
espnetバージョン: espnet 0.10.5a1
PyTorchバージョン: pytorch 1.8.1
Gitハッシュ: ``
- コミット日付: ``

asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp

単語誤り率 (WER)

データセット	文数	単語数	正解率	置換誤り率	削除誤り率	挿入誤り率	誤り率	文誤り率
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean	2703	54402	98.4	1.4	0.1	0.2	1.7	23.1
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other	2864	50948	96.7	3.0	0.3	0.3	3.6	35.5
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean	2620	52576	98.4	1.5	0.1	0.2	1.8	23.7
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other	2939	52343	96.7	3.0	0.3	0.4	3.7	37.9

文字誤り率 (CER)

データセット	文数	文字数	正解率	置換誤り率	削除誤り率	挿入誤り率	誤り率	文誤り率
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean	2703	288456	99.7	0.2	0.2	0.2	0.5	23.1
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other	2864	265951	98.9	0.6	0.4	0.4	1.5	35.5
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean	2620	281530	99.6	0.2	0.2	0.2	0.6	23.7
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other	2939	272758	99.1	0.5	0.4	0.4	1.3	37.9

トークン誤り率 (TER)

データセット	文数	トークン数	正解率	置換誤り率	削除誤り率	挿入誤り率	誤り率	文誤り率
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_clean	2703	68010	98.2	1.4	0.4	0.3	2.1	23.1
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/dev_other	2864	63110	96.0	3.1	0.9	0.9	4.9	35.5
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_clean	2620	65818	98.1	1.4	0.5	0.4	2.3	23.7
decode_asr_lm_lm_train_lm_transformer2_en_bpe5000_valid.loss.ave_asr_model_valid.acc.ave/test_other	2939	65101	96.1	2.9	1.0	0.8	4.7	37.9

ASR設定

展開する

config: conf/tuning/train_asr_conformer7_wavlm_large.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_conformer7_wavlm_large_raw_en_bpe5000_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
num_targets: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 45342
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: 35
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: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 3
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:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 40000000
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
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_960_sp/wav.scp
    - speech
    - kaldi_ark
-   - dump/raw/train_960_sp/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dev/wav.scp
    - speech
    - kaldi_ark
-   - dump/raw/dev/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: 0.0025
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 40000
token_list:
- <blank>
- <unk>
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- ''''
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- ▁DEAR
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- ▁WHOM
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- ▁HOWEVER
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- ▁STOOD
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- ▁KIND
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- ▁INDEED
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- ▁DA
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- PP
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- CI
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- IM
- IOUS
- ▁THOUSAND
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- ▁W
- ▁BEHIND
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- AC
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- TI
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- ▁OTHERS
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- ▁CA
- ▁MANNER
- MO
- NI
- ▁BRING
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- AGE
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- NG
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- ▁LU
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- ▁LETTER
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- ▁STA
- BE
- ▁TRUTH
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- BO
- ▁BUSINESS
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- ▁JOHN
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- ▁RIVER
- ATING
- ▁FAMILY
- HE
- ▁DIDN
- ▁GLAD
- ▁SEVERAL
- IAL
- ▁UNDERSTAND
- ▁SC
- ▁POSSIBLE
- ▁DIFFERENT
- ▁RETURN
- ▁ARMS
- ▁LOW
- ▁HOLD
- ▁TALK
- ▁RU
- ▁WINDOW
- ▁INTEREST
- ▁SISTER
- SON
- ▁SH
- ▁BLOOD
- ▁SAYS
- ▁CAP
- ▁DI
- ▁HUMAN
- ▁CAUSE
- NCE
- ▁THANK
- ▁LATE
- GO
- ▁CUT
- ▁ACROSS
- ▁STORY
- NT
- ▁COUNT
- ▁ABLE
- DY
- LEY
- ▁NUMBER
- ▁STAND
- ▁CHURCH
- ▁THY
- ▁SUPPOSE
- LES
- BLE
- OP
- ▁EFFECT
- BY
- ▁K
- ▁NA
- ▁SPOKE
- ▁MET
- ▁GREEN
- ▁HUSBAND
- ▁RESPECT
- ▁PA
- ▁FOLLOWED
- ▁REMEMBER
- ▁LONGER
- ▁AGE
- ▁TAKING
- ▁LINE
- ▁SEEM
- ▁HAPPY
- LAND
- EM
- ▁STAY
- ▁PLAY
- ▁COMMON
- ▁GA
- ▁BOOK
- ▁TIMES
- ▁OBJECT
- ▁SEVEN
- QUI
- DO
- UND
- ▁FL
- ▁PRETTY
- ▁FAIR
- WAY
- ▁WOOD
- ▁REACHED
- ▁APPEARED
- ▁SWEET
- ▁FALL
- BA
- ▁PASS
- ▁SIGN
- ▁TREE
- IONS
- ▁GARDEN
- ▁ILL
- ▁ART
- ▁REMAIN
- ▁OPENED
- ▁BRIGHT
- ▁STREET
- ▁TROUBLE
- ▁PAIN
- ▁CONTINUED
- ▁SCHOOL
- OUR
- ▁CARRIED
- ▁SAYING
- HA
- ▁CHANGE
- ▁FOLLOW
- ▁GOLD
- ▁SW
- ▁FEELING
- ▁COMMAND
- ▁BEAR
- ▁CERTAINLY
- ▁BLUE
- ▁NE
- CA
- ▁WILD
- ▁ACCOUNT
- ▁OUGHT
- UD
- ▁T
- ▁BREATH
- ▁WANTED
- ▁RI
- ▁HEAVEN
- ▁PURPOSE
- ▁CHARACTER
- ▁RICH
- ▁PE
- ▁DRESS
- OS
- FA
- ▁TH
- ▁ENGLISH
- ▁CHANCE
- ▁SHIP
- ▁VIEW
- ▁TOWARD
- AK
- ▁JOY
- ▁JA
- ▁HAR
- ▁NEITHER
- ▁FORCE
- ▁UNCLE
- DER
- ▁PLAN
- ▁PRINCESS
- DI
- ▁CHIEF
- ▁HAT
- ▁LIVED
- ▁AB
- ▁VISIT
- ▁MOR
- TEN
- ▁WALL
- UC
- ▁MINE
- ▁PLEASURE
- ▁SMILE
- ▁FRONT
- ▁HU
- ▁DEAL
- OW
- ▁FURTHER
- GED
- ▁TRIED
- DA
- VA
- ▁NONE
- ▁ENTERED
- ▁QUEEN
- ▁PAY
- ▁EL
- ▁EXCEPT
- ▁SHA
- ▁FORWARD
- ▁EIGHT
- ▁ADDED
- ▁PUBLIC
- ▁EIGHTEEN
- ▁STAR
- ▁HAPPENED
- ▁LED
- ▁WALKED
- ▁ALTHOUGH
- ▁LATER
- ▁SPIRIT
- ▁WALK
- ▁BIT
- ▁MEET
- LIN
- ▁FI
- LT
- ▁MOUTH
- ▁WAIT
- ▁HOURS
- ▁LIVING
- ▁YOURSELF
- ▁FAST
- ▁CHA
- ▁HALL
- ▁BEYOND
- ▁BOAT
- ▁SECRET
- ENS
- ▁CHAIR
- RN
- ▁RECEIVED
- ▁CAT
- RESS
- ▁DESIRE
- ▁GENTLEMAN
- UGH
- ▁LAID
- EVER
- ▁OCCASION
- ▁WONDER
- ▁GU
- ▁PARTY
- DEN
- ▁FISH
- ▁SEND
- ▁NEARLY
- ▁TRY
- CON
- ▁SEEMS
- RS
- ▁BELL
- ▁BRA
- ▁SILENCE
- IG
- ▁GUARD
- ▁DIE
- ▁DOING
- ▁TU
- ▁COR
- ▁EARLY
- ▁BANK
- ▁FIGURE
- IF
- ▁ENGLAND
- ▁MARY
- ▁AFRAID
- LER
- ▁FO
- ▁WATCH
- ▁FA
- ▁VA
- ▁GRE
- ▁AUNT
- PED
- ▁SERVICE
- ▁JE
- ▁PEN
- ▁MINUTES
- ▁PAN
- ▁TREES
- NED
- ▁GLASS
- ▁TONE
- ▁PLEASE
- ▁FORTH
- ▁CROSS
- ▁EXCLAIMED
- ▁DREW
- ▁EAT
- ▁AH
- ▁GRAVE
- ▁CUR
- PA
- URE
- CENT
- ▁MILES
- ▁SOFT
- ▁AGO
- ▁POSITION
- ▁WARM
- ▁LENGTH
- ▁NECESSARY
- ▁THINKING
- ▁PICTURE
- ▁PI
- SHIP
- IBLE
- ▁HEAVY
- ▁ATTENTION
- ▁DOG
- ABLY
- ▁STANDING
- ▁NATURAL
- ▁APPEAR
- OV
- ▁CAUGHT
- VO
- ISM
- ▁SPRING
- ▁EXPERIENCE
- ▁PAT
- OT
- ▁STOPPED
- ▁REGARD
- ▁HARDLY
- ▁SELF
- ▁STRENGTH
- ▁GREW
- ▁KNIGHT
- ▁OPINION
- ▁WIDE
- ▁INSTEAD
- ▁SOUTH
- ▁TRANS
- ▁CORNER
- ▁LEARN
- ▁ISLAND
- ▁MI
- ▁THIRD
- ▁STE
- ▁STRAIGHT
- ▁TEA
- ▁BOUND
- ▁SEEING
- ▁JU
- ▁DINNER
- ▁BEAUTY
- ▁PEACE
- AH
- ▁REP
- ▁SILENT
- ▁CRE
- ALLY
- RIC
- ▁STEP
- ▁VER
- ▁JO
- GER
- ▁SITTING
- ▁THIRTY
- ▁SAVE
- ENED
- ▁GLANCE
- ▁REACH
- ▁ACTION
- ▁SAL
- ▁SAD
- ▁STONE
- ITIES
- ▁FRENCH
- ▁STRUCK
- ▁PAPER
- ▁WHATEVER
- ▁SUB
- ▁DISTANCE
- ▁WRONG
- ▁KNOWLEDGE
- ▁SAFE
- ▁SNOW
- ▁MUSIC
- ▁FIFTY
- RON
- ▁ATTEMPT
- ▁GOVERNMENT
- TU
- ▁CROWD
- ▁BESIDES
- ▁LOVED
- ▁BOX
- ▁DIRECTION
- ▁TRAIN
- ▁NORTH
- ▁THICK
- ▁GETTING
- AV
- ▁FLOOR
- ▁COMPANY
- ▁BLOW
- ▁PLAIN
- TRO
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- ▁ROCK
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- FI
- ▁SHADOW
- ▁SIT
- ORS
- ILE
- ▁DRINK
- ▁SPOT
- ▁DANGER
- ▁AL
- ▁SAINT
- ▁SLOWLY
- ▁PALACE
- IER
- ▁RESULT
- ▁PETER
- ▁FOREST
- ▁BELONG
- ▁SU
- ▁PAR
- RIS
- ▁TEARS
- ▁APPEARANCE
- ▁GATE
- BU
- ITION
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- ▁QUIET
- ▁LONDON
- ▁START
- ▁BROWN
- TRA
- KIN
- ▁CONSIDER
- ▁BATTLE
- ▁ANNE
- ▁PIECE
- ▁DIED
- ▁SUCCESS
- ▁LIPS
- ▁FILLED
- ▁FORGET
- ▁POST
- IFIED
- ▁MARGARET
- ▁FOOD
- HAM
- ▁PLEASANT
- ▁FE
- ▁EXPRESSION
- ▁POCKET
- ▁FRESH
- ▁WEAR
- TRI
- ▁BROKEN
- ▁LAUGHED
- GING
- ▁FOLLOWING
- WN
- IP
- ▁TOUCH
- ▁YOUTH
- ATIVE
- ▁LEG
- ▁WEEK
- ▁REMAINED
- ▁EASY
- NER
- RK
- ▁ENTER
- ▁FIGHT
- ▁PLACED
- ▁TRAVEL
- ▁SIMPLE
- ▁GIRLS
- ▁WAITING
- ▁STOP
- ▁WAVE
- AU
- ▁WISE
- ▁CAMP
- TURE
- UB
- ▁VE
- ▁OFFICE
- ▁GRAND
- ▁FIT
- ▁JUDGE
- UP
- MENTS
- ▁QUICK
- HI
- ▁FLO
- RIES
- VAL
- ▁COMFORT
- ▁PARTICULAR
- ▁STARTED
- ▁SUIT
- ▁NI
- ▁PALE
- ▁IMPOSSIBLE
- ▁HOT
- ▁CONVERSATION
- ▁SCENE
- ▁BOYS
- ▁WIN
- ▁BRE
- ▁SOCIETY
- ▁OUTSIDE
- ▁WRITE
- ▁EFFORT
- ▁TALKING
- ▁FORTUNE
- ▁NINE
- ▁WA
- ▁SINGLE
- ▁RULE
- ▁PORT
- ▁WINTER
- ▁CAST
- ▁CRA
- ▁HAPPEN
- ▁CRO
- ▁SHUT
- NING
- ▁GUN
- ▁NOBLE
- ▁BEGIN
- ▁PATH
- ▁SKY
- ▁WONDERFUL
- ▁SUDDEN
- ▁ARMY
- ▁CHE
- ▁WORTH
- ▁MOUNTAIN
- ▁MIN
- AG
- ▁FLU
- ▁GRACE
- ▁CHAPTER
- ▁BELOW
- ▁RING
- ▁TURNING
- ▁IRON
- ▁TOP
- ▁AFTERNOON
- ORY
- ▁EVIL
- ▁TRUST
- ▁BOW
- ▁TRI
- ▁SAIL
- ▁CONTENT
- ▁HORS