```
config: conf/train_asr_conformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_conformer_win400_hop160_ctc0.3_lr2e-3_warmup15k_timemask5_amp_no-deterministic
ngpu: 1
seed: 2022
num_workers: 4
num_att_plot: 0
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: false
collect_stats: false
write_collected_feats: false
max_epoch: 70
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: 4
no_forward_run: false
resume: true
train_dtype: float32
use_amp: true
log_interval: 400
use_matplotlib: true
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: 16000000
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_clean_100_sp/wav.scp
    - speech
    - kaldi_ark
-   - dump/raw/train_clean_100_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: 0.002
    weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 15000
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- ▁INFLUENCE
- ▁DINNER
- ▁FOOD
- ▁CHAPTER
- ▁YE
- ▁REACH
- ▁GETT
- ▁PAPER
- ▁GIVING
- ▁BEGINNING
- ▁SEND
- ▁FIGHT
- ▁SCENE
- ▁RUSH
- ▁PI
- ▁MARK
- ▁NA
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- ▁EASY
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- ▁STEPS
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- ▁BEGIN
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- ACK
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- ▁PLEASED
- ▁ENEMY
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- ▁DECLARED
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- ▁MAIN
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- ▁IDEAS
- IZED
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- ▁EUROPE
- ▁KNOCK
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- ▁TONGUE
- ▁SPACE
- ▁PATSY
- ▁MISTRESS
- ▁HENRY
- ▁JERRY
- ▁LIKED
- ▁PLAYED
- ▁BOOKS
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- NER
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- ▁EXCITEMENT
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- ▁EXTEND
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- ▁SUGGESTED
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- ▁SELDOM
- ▁BIRTH
- ▁GLOW
- ▁INCLUD
- ▁REQUEST
- ▁TYPE
- ▁SLEPT
- ▁CRIME
- ▁MOTIVE
- ▁ELSIE
- ▁BEGUN
- ▁CONSENT
- ▁ADMITTED
- ▁AVOID
- ▁ADDRESS
- ▁HATE
- ▁DEMANDED
- ▁APPARENTLY
- ▁SUGGESTION
- ▁CONSIDERATION
- ▁BLESS
- ▁PROCEEDED
- NCY
- ▁PRISON
- ▁CONT
- ▁SHOUTED
- ▁FACES
- ▁SPIRITS
- ▁DEVELOP
- ▁ACCIDENT
- ▁ADVICE
- ▁INNOCENT
- ▁INSTINCT
- ▁UNCONSCIOUS
- ▁MYSTERIOUS
- ▁PRETEND
- ▁PEEP
- ▁ANYONE
- ▁DUKE
- ▁PLUM
- VILLE
- ▁SEVERE
- ▁ALAS
- ▁DELIGHTED
- ▁ISSUE
- ▁ASKING
- ▁CROW
- ▁ACCEPTED
- ▁RIDE
- ▁DOORS
- ▁TAR
- ▁PREPAR
- ▁SUGGEST
- WOOD
- ▁CITIZEN
- ▁ENTRANCE
- ▁LINCOLN
- ▁POLITICAL
- ▁PRACTICAL
- ▁STIFF
- ▁WIDOW
- ▁CAPITAL
- ▁CLEVER
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- ▁CREDIT
- ▁OBEY
- ▁STRING
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- ▁ARGUMENT
- ▁HEAP
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- ▁PUR
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- ▁GAZE
- ▁NATIONAL
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- DDING
- ISTS
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- CAL
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- AFF
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- UNG
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- GUE
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- ▁INVENT
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- ▁CAPABLE
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- ETH
- ▁ELEANOR
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- ▁PRINCIPAL
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- ▁UNUSUAL
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- ▁INCOME
- ▁FATE
- ▁SUPER
- ▁LAUGHTER
- ▁EASE
- ▁LOAD
- ▁JEW
- ▁ZE
- ▁FEVER
- ▁WEDDING
- ▁JOINED
- ▁TRACE
- ▁LEADER
- ▁CLEARLY
- ▁FLOWER
- ▁TERMS
- ▁EMPLOYED
- OCK
- ▁PARTICULARLY
- ▁MEMBERS
- ▁CONFESS
- ▁GRO
- ▁ADDRESSED
- ▁CHRIST
- ▁ACCOMPANI
- ▁AFFORD
- ▁AMOUNT
- ▁BRILLIANT
- ▁COMMUNICAT
- ▁FIERCE
- ▁RECORD
- ▁SACRIFICE
- ▁TEMPT
- ▁CORDIAL
- ▁COLOUR
- ▁PROOF
- ▁ESTATE
- ▁PARDON
- ▁ADVIS
- ▁ATTITUDE
- ▁IMPORTANCE
- ▁BOOT
- ▁SHOCK
- ▁FIR
- ▁PLENT
- ▁HIT
- ▁MEMBER
- ▁SUR
- ▁SEATED
- ▁MAG
- AVING
- ▁FAVOUR
- ▁REMARK
- ▁DIM
- ▁FAITHFUL
- ▁SAVED
- CHI
- ▁SIN
- THE
- ▁CONFIDENCE
- ▁EXTRAORDINARY
- ▁FORTUNATE
- ▁MISFORTUNE
- ▁PATIENCE
- ▁RELIGIOUS
- ▁SATISFACTION
- ▁POSITIVE
- ▁SIMILAR
- ▁EXCHANG
- ▁RETREAT
- ▁FLESH
- ▁ADMIRE
- ▁SPIRITUAL
- ▁DAWN
- ▁BURIED
- ▁URGE
- ▁SUNDAY
- ▁FOX
- ▁EMMA
- ▁NURSE
- ▁SNAPP
- ▁PARK
- ▁OBTAIN
- ▁RECOGNIZED
- ▁SPEED
- ▁MAGIC
- ▁LAWS
- ▁REMOVED
- ▁HAM
- ▁PRESERV
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- HOUSE
- ▁MENTIONED
- ▁CONSCIENCE
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- ▁DETAIL
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- ▁PIRATE
- ▁WICKED
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- ▁DESIGN
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- ▁GIFT
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- ▁BRIDE
- ▁ANGER
- ▁BASE
- ITUDE
- ▁CONCLUDED
- ▁ALTER
- ▁FRI
- ▁PANT
- ▁BID
- ▁HIGHEST
- ▁SAILOR
- MPLE
- ▁OBSERV
- ▁CHEERFUL
- IFICATION
- RID
- ▁DESCRIBED
- ▁BIN
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- ▁ARTIST
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- ▁DISTRESS
- ▁UGLY
- ▁WARRIOR
- ▁MOURN
- ▁RELIEV
- ▁DESK
- ▁FOOLISH
- ▁STARTLED
- ▁SKILL
- SHONE
- ▁LONE
- ▁OBSERVATION
- ▁DENI
- ▁NEST
- ▁SOLDIER
- ▁RELATION
- ▁TRULY
- ▁VISITOR
- ▁OFFICERS
- ERSON
- ▁YA
- ▁EVIDENT
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- ▁KEEPING
- ▁PLAINLY
- ▁DRUNK
- ▁EMBRAC
- ▁INTELLIGENCE
- ▁LIEUTENANT
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- ▁UNIVERSAL
- ▁GLEAM
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- ▁INQUIRED
- ▁DEFI
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- ▁WRETCHED
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- ▁ENTIRE
- ▁NET
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- ▁PRODUCE
- ▁GAINED
- ▁ARRIVAL
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- ▁MEANWHILE
- ▁SUSPICION
- ▁CHOICE
- ▁IMPULSE
- ▁THRUST
- ▁PROCESS
- ▁SUMMON
- ▁SHEPHERD
- ▁HASTILY
- ▁GRASP
- ▁COUNTESS
- ▁STYLE
- ▁DWELL
- ▁MERIT
- ▁PITCH
- ▁HUNGRY
- ▁SPORT
- ▁LOUISE
- ▁STERN
- ▁PROVIDED
- ▁ASSUME
- ▁EARLIE
- ▁RAGE
- ▁U
- ▁RAPIDLY
- PORT
- ▁SUCCESSFUL
- ▁FLED
- ▁AGREE
- ▁CONDITIONS
- ▁RELATIONS
- ▁DREAD
- ▁NATURALLY
- ▁EARL
- ▁GAY
- ▁HYPNOTI
- ▁PUTT
- ▁GAZ
- ▁JIM
- ▁PAUS
- ▁PROPOS
- ▁ADMINISTRATION
- ▁ELEVEN
- ▁HOSPITAL
- ▁MAGISTRATE
- ▁STRIKE
- ▁DIGNITY
- ▁GLORY
- ▁BOTTLE
- ▁THRONE
- ▁RECKON
- ▁COSETTE
- ▁MOREOVER
- ▁APPLI
- ▁HIND
- ▁PRODUCT
- ▁POOL
- ▁TRIAL
- HAN
- ▁ERIC
- ▁CUB
- ▁PIECES
- ▁EXCEPTION
- ▁ENJOYED
- ▁DARED
- ▁TRU
- ▁CLOSELY
- ▁RAPID
- ▁AFFECTED
- ▁REQUIRE
- ▁SOFTLY
- ▁BROW
- UCK
- ▁MARKED
- ▁SEVENT
- ▁ELECT
- ▁FORGOT
- ▁CORRECT
- ▁FRANCS
- ▁MARGUERITE
- ▁SCIENCE
- ▁UNEXPECTED
- ▁FOUGHT
- ▁MILITA
- ▁THUNDER
- ▁VOYAGE
- ▁GANEM
- ▁FREEDOM
- ▁NODDED
- ▁CAPTURE
- ▁MORTAL
- ▁OWNER
- ▁POLITE
- ▁VISION
- ▁EDUCATION
- ▁GOVERNOR
- ▁RAV
- ▁REWARD
- ▁HASTE
- ▁REPEAT
- ▁DETERMIN
- ▁PITI
- ▁KNEE
- LINE
- ▁DEVOTED
- ▁INTERRUPTED
- ▁FOLKS
- ▁EXTREME
- ▁APPROACH
- ▁CONTINUE
- ▁BEARING
- ▁CHAP
- ▁ACQUAINTED
- ▁GLIMPSE
- ▁GRADUALLY
- ▁SUNSHINE
- ▁PRACTICE
- ▁SUPPLI
- ▁DAVID
- ▁DRIFT
- ▁SHOWING
- ▁LEVEL
- ▁PROMPT
- ▁QUARREL
- ▁REPRESENTATIVE
- ▁PLUNG
- ▁GIANT
- FALL
- ▁STOUT
- CHA
- WEPT
- ▁GLANC
- ▁SALT
- ▁CHOSEN
- ▁BUCK
- ▁REALIZED
- ▁REALITY
- ▁TUR
- ▁DRIVEN
- ▁CARD
- ▁PRAYER
- ▁TERM
- AID
- ▁HOLY
- ▁ENDURE
- ▁RANGE
- ▁HANG
- ▁SAM
- LAN
- ▁CAVE
- INA
- ▁GRI
- ▁SIGH
- ▁NEIGHBOUR
- ▁COUNCIL
- ▁EXERCISE
- ▁NAUTILUS
- ▁SOMEWHERE
- ▁SYLVIA
- ▁THOROUGH
- ▁VICTIM
- ▁BRIDGE
- ▁COMPELLED
- ▁INCLINED
- ▁OVERCOME
- ▁RESERVE
- ▁ARREST
- ▁PRECIOUS
- ▁DUTCH
- ▁OCEAN
- ▁ACQUIR
- ▁RECALL
- ▁DESTIN
- ▁ATTACH
- ▁SLIM
- ▁WEEP
- ▁CONSCIOUSNESS
- ▁TIGHT
- ▁WAKE
- ▁COMFORTABLE
- ▁ACTIVE
- ▁WINGS
- ▁GRIN
- ▁AFFECT
- ▁WHIT
- ▁IDEAL
- ▁EASTER
- ▁APPROACHING
- ▁CREATED
- ▁PLANS
- ▁INCREASE
- ▁FLYING
- ▁SHOUT
- OES
- MISSION
- ▁ARMED
- ABILITY
- ▁BLUSH
- ▁CONNECTION
- ▁MATTHEW
- ▁MEDICINE
- ▁REMIND
- ▁EXHIBIT
- ▁BLOCK
- ▁DESERVE
- ▁LISTENING
- ▁TITLE
- ▁FLOUR
- ▁FLAME
- ▁AGENT
- ▁USEFUL
- ▁BRIG
- ▁BOIL
- ▁ASSURED
- ▁REFLECTION
- ▁PINE
- ▁WAG
- ▁YOUNGER
- ▁BEARD
- ▁KINDNESS
- CTUALLY
- ▁ACTUAL
- ▁WEIGHT
- ▁LILY
- ▁IMPRESS
- ▁DESCRIBE
- ▁BEHELD
- ▁COMMUNITY
- ▁DESPERATE
- ▁DISPLAY
- ▁ENEMIES
- ▁MELANCHOLY
- ▁MIRROR
- ▁RECOMMEND
- ▁SPANISH
- ▁BLAME
- ▁VOLUME
- ▁SHOOT
- ▁COMBIN
- ▁SHAKING
- ▁SOUTHERN
- ▁MYSTERY
- ▁EVERYONE
- ▁COMMISSION
- ▁COMPOSED
- ▁UDO
- ▁IMAGE
- ▁DECEIV
- ▁FAILURE
- ▁PATTY
- ▁ALICE
- ▁FRAME
- ▁MODEST
- ▁MAGNIFICENT
- ▁BRANCHES
- ▁REIGN
- ▁RAG
- ▁PARISH
- ▁KATE
- ▁AMID
- ▁SLEEPING
- ▁ANNOUNCED
- ▁EAGERLY
- ▁WIRE
- ▁LAP
- ▁ARAB
- ▁EATING
- ▁RUM
- ▁CAREFUL
- ▁DISCUSS
- WORTH
- ▁DISTRICT
- ▁FOREHEAD
- ▁FRANCIS
- ▁INCIDENT
- ▁APPEAL
- ▁EMBARRASS
- ▁MAINTAIN
- ▁PRONOUNC
- ▁FURNISH
- ▁STRAIN
- ▁ELEMENT
- ▁SILK
- ▁FEAST
- ▁RECENT
- ▁DANCING
- ▁LODGE
- ▁ASHAMED
- ▁TRICK
- ▁BOBO
- ▁STUFF
- ▁ET
- ▁ASSERT
- ▁SANK
- ▁TREATMENT
- ECI
- ▁SWIM
- ▁BECOMING
- ▁SINGING
- ▁PLATE
- ▁SCATTERED
- ▁EXTREMELY
- ▁GRIM
- ▁SANG
- ▁FIGHTING
- ▁FACTOR
- ▁PAINFUL
- ▁HIDE
- ▁FUNN
- ▁AFTERWARD
- ▁FROG
- ▁VENTURE
- ▁DISAPPOINT
- ▁COMRADE
- ▁MONSIEUR
- ▁OBVIOUS
- ▁PASSENGER
- ▁PROFOUND
- ▁PUBLISH
- ▁ACCUSTOM
- ▁BLOOM
- ▁SMITH
- ▁RELATIVE
- ▁ACCUSE
- ▁MANIFEST
- ▁SOLID
- ▁MONSTER
- ▁MARIUS
- ▁CANDLE
- ▁PROCUR
- ▁INTERFERE
- ▁HOUSEHOLD
- ▁DEVELOPMENT
- ▁AGREEABLE
- ▁HALT
- ▁NECESSITY
- FOLD
- ▁CITIES
- ▁REGI
- ▁GLOOMY
- BBL
- ▁SEPARATED
- ▁CHEST
- ▁STRIP
- ▁SPAR
- ▁DUN
- ▁SETTLE
- ▁STARED
- ▁HANGING
- ▁FEATURES
- ▁PILE
- ▁ORIGIN
- ARIES
- ▁LION
- ▁ALI
- ▁ASTONISHMENT
- ▁COMPLIMENT
- ▁DELICATE
- ▁COUNSEL
- ▁FIFTH
- ▁SUPPRESS
- ▁BURDEN
- ▁COMPLEX
- ▁ADDITION
- ▁CRUSH
- ▁TWIST
- ▁PIANO
- ▁BRUSH
- ▁CHECK
- ▁ANNIE
- ▁SHELTER
- ▁IMPROV
- ▁WESTERN
- ▁LOCAL
- ▁APPLE
- ▁GREET
- ▁MASK
- ▁RUSSIAN
- ▁TOWER
- ▁CREW
- ▁TIP
- ▁WANDERING
- ▁READER
- ▁WANDERED
- ▁DESTROY
- ▁OBSERVE
- MORE
- ▁ESCAPED
- ▁PET
- ▁BUILD
- ▁REAR
- ▁DESTROYED
- HIN
- ▁OWE
- ▁RANG
- ▁TEAR
- ▁NED
- ▁OFFICER
- ▁TRAP
- ▁OCCUR
- ▁APPOINTED
- ▁ATMOSPHERE
- ▁CHOOSE
- ▁CONCLUSION
- ▁CULTIVAT
- ▁DESCRIPTION
- ▁ENORMOUS
- ▁EXHAUSTED
- ▁LANDSCAPE
- ▁NATASHA
- ▁PROSPECT
- ▁REFRESH
- ▁SPECIES
- ▁SURROUNDED
- ▁WEAPON
- ▁BLANK
- ▁DEFEND
- ▁EDITH
- ▁HORRIBL
- ▁BETRAY
- ▁FERKO
- ▁LABOUR
- ▁NEGRO
- ▁RESUMED
- ▁LEAF
- ▁MUSKET
- ▁INTENSE
- ▁MERCY
- ▁ADOPT
- ▁SCORE
- ▁DASH
- ▁LAWYER
- ▁SLOPE
- ▁CHUCK
- ▁ASSISTANCE
- ▁BROOK
- ▁BREAKING
- ▁ASSIST
- ▁GROAN
- ▁HELEN
- ▁BEHAV
- ▁MAIDEN
- ▁CRIS
- ▁SHOUTING
- ▁NAY
- ▁PIG
- ▁ACCORDINGLY
- ETTE
- ▁DESIR
- ▁RUB
- ▁GRU
- ▁PIT
- ▁HEAVI
- ▁OBTAINED
- ▁SPARE
- ▁BRANCH
- ▁COUNTER
- ▁APART
- ▁AMBITION
- ▁ASTONISHED
- ▁CORRESPOND
- ▁DRIVING
- ▁ENERGY
- ▁HISTORIAN
- ▁REVOLUTION
- ▁SWEEP
- ▁TREMBLING
- ▁CRAFT
- ▁FAMILIES
- ▁LITERATURE
- SBURG
- ▁FEMALE
- ▁TILNEY
- ▁GENEROUS
- ▁SUBMIT
- ▁INTELLECTUAL
- ▁ORCHARD
- ▁STORIES
- ▁DIANA
- ▁VEIN
- ▁TRIFL
- ▁TWIN
- ▁WORSHIP
- ▁MARBLE
- ▁GALLANT
- ▁SENSIBLE
- ▁NEAT
- ▁BROWNIE
- ▁JUNE
- ▁SHAW
- ▁WORST
- ▁USELESS
- ▁FISHING
- ▁CRYING
- ▁MAYBE
- ▁VARI
- ▁PRESERVE
- ▁VOL
- ▁EMPLOY
- ▁INTERRUPT
- ▁SLIGHTLY
- ▁ACCOMPLISHED
- NEY
- ▁STEAM
- ▁BALANC
- ▁LEANING
- ▁SIGHED
- ▁REFUSE
- ▁IMAGINED
- ▁DATE
- GROUND
- ▁ENTERTAIN
- ▁PERCEIVE
- ▁ABROAD
- ▁CHEESE
- ▁DESTRUCTION
- ▁ESSENTIAL
- ▁EXPEDITION
- ▁GRANDFATHER
- ▁INFINITE
- ▁LIBRARY
- ▁MULTITUDE
- ▁NEGLECT
- ▁SWALLOW
- ▁VILLEFORT
- ▁BELOVED
- ▁COMMITTEE
- ▁CONFIDENT
- ▁PURPLE
- ▁PURCHAS
- ▁SCRAP
- ▁SPOIL
- ▁LIKEWISE
- ▁EXTRA
- ▁STRAW
- ▁SALUT
- ▁SOURCE
- ▁HASTENED
- ▁RESENT
- ▁FLOCK
- ▁LOFT
- ▁FLO
- ▁CLO
- ▁CONVINCED
- ▁GOODNESS
- ▁HYPNOTIZ
- ▁SETTING
- ▁HAIL
- ▁PHI
- ▁GROVE
- ▁DISCOVERY
- ▁DAMP
- ▁WHISPER
- ▁LIFT
- ▁HOP
- ▁SUSPECTED
- ▁SCR
- OLI
- ▁FAC
- ▁BUSH
- ▁FOREVER
- ▁BARRICADE
- ▁CONSTITUTION
- ▁ENDEAVOR
- ▁ENTHUSIASM
- ▁EXECUTION
- ▁HYACINTH
- ▁PERCEVAL
- ▁PSYCHE
- ▁REPROACH
- ▁THIRTEEN
- ▁ABSORB
- ▁GRATITUDE
- ▁MERCER
- ▁REPUTATION
- ▁SCREAM
- ▁PUPIL
- ▁RETIRED
- ▁STEEP
- ▁SUMMIT
- ▁MISERABLE
- ▁STRICT
- ▁MINGLED
- ▁DEFEAT
- ▁REVEAL
- ▁LOVING
- ▁GOOSE
- ▁ECHO
- ▁AWAIT
- ▁MOOD
- ▁CRAWLEY
- ▁CELL
- ▁ENGAGEMENT
- ▁PRECED
- ▁SOMEONE
- ▁ARRANGEMENT
- ▁PICKET
- ▁GASP
- ▁HUMOR
- ▁INVITATION
- ▁JOB
- WITHSTAND
- ▁LAMENT
- ▁CLASSES
- ▁HUNGER
- ▁DISPOSED
- ▁STEAMER
- ▁FEARFUL
- ▁GER
- ▁FINAL
- ▁FLAG
- ▁JULY
- ▁DIG
- WORK
- ▁OPPOS
- ▁ANXIETY
- ▁AUDIENCE
- ▁BACHELOR
- ▁COLUMN
- ▁HANDKERCHIEF
- ▁IMPATIENT
- ▁JUDGMENT
- ▁KNIFE
- ▁SOVEREIGN
- ▁STRIKING
- ▁THOMPSON
- ▁EMPIRE
- ▁FULFIL
- ▁CONSULT
- ▁JENNY
- ▁THENARDIER
- ▁POYSER
- ▁FOURTEEN
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- ▁ROBBER
- ▁CROOK
- ▁DEPARTURE
- ▁MABEL
- ▁PREACH
- ESCENT
- ▁WHIP
- ▁NAIL
- ▁DELIGHTFUL
- ▁DISCUSSION
- ▁SENTENCE
- ▁LANE
- ▁ENGINEER
- ▁ARRANGED
- MMY
- ▁LEST
- ▁RENT
- MMED
- ▁LIST
- ▁ROBE
- ▁MISSION
- ▁GRACEFUL
- ▁LIGHTN
- STONE
- COURT
- ▁CONCEPTION
- ▁CONTRACT
- ▁DROWN
- ▁EXPERIMENT
- ▁HITHERTO
- ▁PLAGUE
- ▁PORTHOS
- ▁SHRIEK
- ▁DETECT
- ▁ACCENT
- ▁ERECT
- ▁SAZEN
- ▁PROFIT
- ▁VIVID
- ▁SQUIRE
- ▁OPERATION
- ▁SMELL
- ▁SIMON
- ▁EXTENT
- ▁KEEN
- ▁EMERG
- ▁REVIV
- ▁REGIMENT
- ▁DISAPPOINTMENT
- ▁STOLE
- ▁DIVINE
- ▁GUILTY
- ▁COWARD
- ▁EXPECTATION
- ▁SIGNOR
- ▁MODE
- ▁CENTRE
- ▁FIL
- HOW
- ▁WEARI
- ▁TOTAL
- ▁VICTOR
- ▁GOVERN
- ▁RAISE
- ▁ABANDON
- ▁ABSURD
- ▁ASPECT
- ▁CRIMINAL
- ▁DEFINITE
- ▁DELIBERAT
- ▁FEATHER
- ▁FLORINA
- ▁MIDNIGHT
- ▁RICHMOND
- ▁SATISFY
- ▁SINGULAR
- ▁STEADILY
- ▁SUPREME
- ▁TIMBER
- ▁PSYCHOLOG
- ▁GESTURE
- ▁VALUABLE
- ▁INTERVAL
- ▁CONFUSION
- ▁FLUTTER
- ▁SACRED
- ▁DISEASE
- ▁UNDERTAKE
- ▁PENETRAT
- ▁MARVEL
- ▁NORTHERN
- ▁GRIEV
- ▁GENIUS
- ▁SADDLE
- ▁NOVEL
- ▁MISERY
- ▁CONVICTION
- ▁SINK
- ▁WAGON
- ▁ARISE
- ▁COMMENT
- ▁BARN
- UPON
- ▁FENCE
- ▁ASSOCIATION
- ▁BONES
- ▁IDLE
- ▁DOUBTFUL
- ▁PREPARATION
- IZZ
- ▁RAIS
- ▁BITTERLY
- ▁JOE
- ▁RELI
- ADI
- ▁METAL
- ▁EXACT
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- FIELD
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- GGY
- ▁NECESSIT
- ▁PROFESS
- WASH
- ▁ADMIRING
- ▁AMBITIOUS
- ▁ANTHONY
- ▁CEREMONY
- ▁CONTRIBUTE
- ▁CRAGGS
- ▁DETAIN
- ▁DISCLOS
- ▁DWELT
- ▁EGYPT
- ▁FELIX
- ▁JOURNAL
- ▁KWAIRYO
- ▁LIBERAL
- ▁LUMBER
- ▁OCTOBER
- ▁ORGANIZATION
- ▁POPULACE
- ▁PRECAUTION
- ▁PREJUDICE
- ▁PROCLAIM
- ▁PROPRIETOR
- ▁RESPONSIBLE
- ▁RHYTHM
- ▁RIDICULOUS
- ▁SCHOLAR
- ▁SQUEEZ
- ▁SUBSTITUTE
- ▁SURPASS
- ▁THRESHOLD
- ▁WHARTON
- ▁FLICKER
- ▁AMAZED
- ▁BRONZE
- ▁COSSACK
- ▁SPILETT
- ▁CASUAL
- ▁DARCY
- ▁PARLOUR
- ▁SEXUAL
- ▁INSECT
- ▁NATHAN
- ▁EMINENT
- ▁PENCIL
- ▁PETITION
- ▁ROTTEN
- ▁VIGIL
- ▁CAESAR
- ▁EAGLE
- ▁TREAD
- ▁REACTION
- ▁TACIT
- ▁PARLOR
- ▁SPAIN
- ▁WILDERNESS
- ▁DICTAT
- ▁GRATIFY
- ▁STOVE
- ▁SKIRT
- ▁UTILI
- ▁CONCERT
- ▁GORGE
- ▁DECORAT
- ▁LATIN
- ▁ANCHOR
- ▁KNOT
- ▁MONDAY
- ▁GABLES
- ▁TOLERABL
- ▁ROGER
- BERRIES
- ▁INVAD
- IMMER
- OMETER
- ▁PRODUC
- OBIL
- ▁PERMISSI
- FICIENCY
- ▁WANDER
- RREL
- PIECE
- HORN
- ▁COMMIT
- ▁ACCUMULAT
- ▁JAPAN
- ▁ABUNDANT
- ▁ACADEMY
- ▁ALBERT
- ▁BANQUET
- ▁DELICIOUS
- ▁DOCUMENT
- ▁EXCLAMATION
- ▁FEBRUARY
- ▁GROTESQUE
- ▁HEATHERSTONE
- ▁HUMPHREY
- ▁HURSTWOOD
- ▁MOHAMMED
- ▁MOSCOW
- ▁NICHOLAS
- ▁OBSTINATE
- ▁PHANTOM
- ▁PHILOSOPHER
- ▁RECEPTION
- ▁SPANIARD
- ▁SWOLLEN
- ▁TELEPHONE
- ▁TRIBUTE
- ▁TUNNEL
- ▁UNREASONABL
- ▁WIGWAM
- ▁BUTTERFLY
- ▁COLLINS
- ▁DISPATCH
- ▁EDITOR
- ▁CONTINENT
- ▁DIMINISH
- ▁HORRID
- ▁KEATS
- ▁PROVIDENCE
- ▁BEHALF
- ▁CHARLEY
- ▁DRAKE
- ▁LAUNCH
- ▁SALOON
- ▁GIGANT
- ▁DISPUTE
- ▁HYSTERI
- ▁DEFENCE
- ▁SCREEN
- ▁VAULT
- ▁NINTH
- ▁HARBOR
- ▁FLANK
- ▁SPECK
- ▁UPRIGHT
- ▁KEMP
- ▁CANADA
- ▁STALK
- ▁OWL
- ▁BRUTE
- ▁FERRIS
- ▁DECREE
- ▁HABITUAL
- ▁BRISK
- ▁INSPIRE
- ▁HUSH
- ▁CROUCH
- ▁FRIDAY
- ▁MOUNTAINEER
- ▁HISTORIC
- ▁BATES
- ▁RUSK
- ▁SEMI
- DICTION
- ▁BUSI
- ▁REMOV
- MMI
- ▁SUFFIC
- ▁FLEE
- ▁LOUIS
- NLEA
- ▁IMPORT
- OLOGY
- ▁CLERGY
- ▁ADVERTISEMENT
- ▁BENEVOLEN
- ▁BORODINO
- ▁CATHOLIC
- ▁COMMERCIAL
- ▁CONJECTURE
- ▁CURTAIN
- ▁CUTHBERT
- ▁DEMOCRACY
- ▁GUARANTEE
- ▁HYPNOSIS
- ▁INDEFINITE
- ▁INVESTIGATION
- ▁IRREGULAR
- ▁KOYO
- ▁MERRIWIG
- ▁MIRANDA
- ▁NICHOLL
- ▁ONLOOKER
- ▁PERSECUT
- ▁RECOGNITION
- ▁REJOICE
- ▁REMEMBRANCE
- ▁REVELATION
- ▁SCOLD
- ▁SENIOR
- ▁SQUIRREL
- ▁SYMPATHETIC
- ▁TEMPEST
- ▁TREACHER
- ▁UNDERNEATH
- ▁UNEASINESS
- ▁UNNECESSARY
- ▁UPSTAIRS
- ▁VEXATION
- ▁ACCESS
- ▁CHEAP
- ▁ESTIMATE
- ▁HAZARD
- ▁HORSEBACK
- ▁PLUNDER
- ▁RASCAL
- ▁ROSTOV
- ▁ACCUR
- ▁GRAVITY
- ▁SITUATED
- ▁INVARIABL
- ▁PLENTIFUL
- ▁SPENCER
- ▁WALLACE
- ▁POLICY
- ▁WARRANT
- ▁ENVY
- ▁LAMB
- ▁EXTRACT
- ▁CORRAL
- ▁PANEL
- ▁LINK
- ▁LILIES
- ▁BECKON
- ▁SENOR
- ▁BORG
- ▁DEBATE
- ▁STEER
- COGNI
- COMB
- ▁SETTL
- ▁VENERA
- ▁FEATURE
- ▁TERRIBL
- CAPABLE
- OLOGICAL
- ▁INCESSANT
- ▁RESOLUTE
- SHAUGHNESSY
- ▁ABOLITION
- ▁ASSASSIN
- ▁BEHAVIOUR
- ▁BLUNT
- ▁COMMERCE
- ▁CONSTANTINOPLE
- ▁CRICKET
- ▁DISCIPLINE
- ▁DROUET
- ▁DWARF
- ▁INJUSTICE
- ▁LUXURY
- ▁MANUSCRIPT
- ▁MISUNDERSTAND
- ▁POLITICIAN
- ▁REDOUBT
- ▁SALVATION
- ▁SERMON
- ▁STRUGGLING
- ▁SURPRISING
- ▁TRIGGER
- ▁TUESDAY
- ▁TWILIGHT
- ▁UNDOUBTEDLY
- ▁VEGETABLE
- ▁VULGAR
- ▁WAISTCOAT
- ▁WRINKLE
- ▁ALEXANDER
- ▁CEILING
- ▁ECONOMIC
- ▁EVERLASTING
- ▁INFLICT
- ▁LEVISON
- ▁LOBSTER
- ▁OVERFLOW
- ▁SNATCH
- ▁TRAGEDY
- ▁DEASEY
- ▁ENLIGHTEN
- ▁FRIGATE
- ▁INSPECT
- ▁MARVELLOUS
- ▁ATLANTIC
- ▁LUFTON
- ▁BLADE
- ▁CRASH
- ▁SLAUGHTER
- ▁ANNUAL
- ▁CONFERENCE
- ▁TWIG
- ▁REASSUR
- ▁UNIQUE
- ▁WRATH
- ▁CRADLE
- ▁HULLO
- ▁LIQUID
- ▁MIRTH
- ▁EXPERT
- ▁HARVEY
- ▁RESTORATION
- ▁PRETTI
- ▁APOLOGY
- ▁SLAIN
- ▁BARBER
- ▁UPROAR
- ▁SCANT
- ▁BADGER
- ▁GROCER
- ▁ACRES
- ▁BRIDLE
- ▁SPECIFI
- ▁TANGLE
- ▁FERTIL
- ▁PATRON
- WIXT
- LAMOUR
- ▁DARN
- ▁POPE
- ▁PERCEIV
- ▁CONCLUDE
- ▁SIMPL
- ▁GUILT
- ▁CARRIE
- EFFICIENT
- SGIVING
- ▁APPOINTMENT
- ▁APPRECIATION
- ▁CARTRIDGE
- ▁CHALLENGE
- ▁CRAYFISH
- ▁CRIMSON
- ▁CUCUMETTO
- ▁ENERGETIC
- ▁EPOCH
- ▁EXAMINING
- ▁EXTENSIVE
- ▁EXTINGUISH
- ▁GLOODY
- ▁INSIGNIFICANT
- ▁LANDLORD
- ▁LANGUID
- ▁LEGISLATURE
- ▁MAJESTIC
- ▁PACIFIC
- ▁PASTRINI
- ▁PHRONSIE
- ▁RECONCIL
- ▁SIMULTANEOUS
- ▁SKELETON
- ▁SKETCH
- ▁TRANSFORM
- ▁UNJUST
- ▁VEXED
- ▁ASYLUM
- ▁CLUSTER
- ▁ERRAND
- ▁EXPEND
- ▁NEGATIVE
- ▁NORHALA
- ▁SCANDAL
- ▁STIMULAT
- ▁SWEAT
- ▁COMPOUND
- ▁DECEMBER
- ▁EXPAND
- ▁PROLONG
- ▁PURITAN
- ▁CONQUEST
- ▁MAGUA
- ▁SANCHO
- ▁TRENCH
- ▁ENTITLE
- ▁PEPPER
- ▁DISASTER
- ▁REGAIN
- ▁SHREWD
- ▁SULLEN
- ▁CLAVIER
- ▁COLOSS
- ▁SHILLING
- ▁ETHEL
- ▁MYSTERIES
- ▁BULK
- ▁GRANDEUR
- ▁AGNES
- ▁CONVERT
- ▁WRIST
- ▁GLID
- ▁TERRACE
- ▁SONYA
- ▁DANTES
- ▁MOULD
- ▁MAGNET
- ▁PLOT
- RANK
- ▁CAVIT
- ▁SUBSID
- ▁SLAP
- TURNED
- ▁THREAT
- BREAK
- ▁ANCESTORS
- ▁ANTICIPATED
- ▁APPLAUSE
- ▁ASSAULT
- ▁ATTORNEY
- ▁AUTOMATIC
- ▁CARAVAN
- ▁CATASTROPHE
- ▁CAVALCANTI
- ▁CROMWELL
- ▁ENVOY
- ▁EXHAUSTION
- ▁FIEND
- ▁GENEROSITY
- ▁GIMBLET
- ▁HARDQUANONNE
- ▁HOUARN
- ▁INJURY
- ▁MACKINSON
- ▁OGLETHORPE
- ▁PETTICOAT
- ▁RASPBERR
- ▁REHNHJELM
- ▁REJOICING
- ▁REMNANT
- ▁SCOTLAND
- ▁SHRINK
- ▁STANDPOINT
- ▁TESTIMONY
- ▁THEREAFTER
- ▁THIRTIETH
- ▁TWENTIETH
- ▁TYRANT
- ▁VENTNOR
- ▁VETERAN
- ▁WHITTAKER
- ▁ZVERKOV
- ▁ARCHITECTUR
- ▁BLUNDER
- ▁DENSHER
- ▁FORTNIGHT
- ▁JUDITH
- ▁MARIANNE
- ▁MEMORABLE
- ▁REFINED
- ▁REVOLV
- ▁UNDERTAKING
- ▁CLUMP
- ▁GRUMBLE
- ▁SYMPATHI
- ▁TICKET
- ▁TWITCH
- ▁EDITION
- ▁FALANDER
- ▁CARTHAGE
- ▁ORLEANS
- ▁POSSUM
- ▁SWITCH
- ▁CLUNG
- ▁CARDINAL
- ▁GNAW
- ▁LOCATED
- ▁HARROW
- ▁RASH
- ▁SIEGE
- ▁LOAF
- ▁BRUISE
- ▁REGULAT
- ▁RESORT
- ▁SARAH
- ▁LEVIN
- ▁NAVY
- ▁MOOSE
- ▁STOOL
- ▁CHANCELLOR
- ▁INGENIOUS
- ▁CHALK
- ▁PRETENCE
- ▁REPAY
- ▁ROAST
- ▁PLUTO
- ▁BAFFL
- ▁STUMBL
- ▁SPHERE
- ▁PLEDGE
- ▁SPRAWL
- ▁WRAP
- ▁FRINGE
- ▁DREAR
- ARRINGTON
- ▁FEDERA
- KEEPER
- ▁PHYSIC
- ▁ADVENT
- HUMAN
- OLOGIST
- ▁ALEXANDR
- ▁APPARITION
- ▁BARTHOLEMY
- ▁CITOYEN
- ▁CLIMATE
- ▁CONTEMPORAR
- ▁DESOLATE
- ▁DISCONTENT
- ▁ELEPHANT
- ▁FERNANDO
- ▁FERRALTI
- ▁FOLIAGE
- ▁FUGITIVE
- ▁GAMBLING
- ▁INVOLUNTARILY
- ▁LABYRINTH
- ▁LEGITIMATE
- ▁MILLIONAIRE
- ▁PERCEPTION
- ▁PROPRIETY
- ▁REBELLION
- ▁REFRAIN
- ▁RUGGLES
- ▁SCRIPTURE
- ▁SPLENDOR
- ▁SQUADRON
- ▁STRICKEN
- ▁SWARM
- ▁THEODORA
- ▁TOMORROW
- ▁VELVET
- ▁WOLVES
- ▁DISREGARD
- ▁GLIMMER
- ▁SHROUD
- ▁TWINKLING
- ▁UNEQUAL
- ▁CHANNING
- ▁CLUMS
- ▁ENIGMA
- ▁NAVIGAT
- ▁TARKAS
- ▁TEMPERATURE
- ▁DIVISION
- ▁GRATIFICATION
- ▁MONUMENT
- ▁SQUEAK
- ▁KAVIN
- ▁INTERPOSE
- ▁THORNTON
- ▁SOLUTION
- ▁STREAK
- ▁SHRILL
- ▁APRON
- ▁PITEOUS
- ▁HAUGHTY
- ▁RECKLESS
- ▁EMPTI
- ▁WADMAN
- ▁BONNET
- ▁MARTHA
- ▁DUMB
- ▁SHATTER
- ▁ACUTE
- ▁BRINK
- ▁CAPRICE
- ▁HURON
- ▁INFERN
- ▁FOWL
- ▁ENRAGE
- ▁ADORN
- ▁CRUIS
- ▁PROBABILIT
- ▁EXPIR
- ▁IMPETU
- ▁OVERHEAR
- BURTON
- ▁TRANSLAT
- ▁ENGAGE
- ▁CONVINCE
- ▁ABNORMAL
- ▁GESTICULAT
- ▁ABOMINABL
- ▁ADVERSARY
- ▁ADVERTISER
- ▁ADVERTISING
- ▁ANNIHILAT
- ▁ARTILLERY
- ▁CATHEDRAL
- ▁COMPETITOR
- ▁COULSON
- ▁CREVICE
- ▁CUSHION
- ▁DEBRAY
- ▁DEJECT
- ▁DIETRICH
- ▁DISADVANTAGE
- ▁ELLISON
- ▁EMPHASIS
- ▁EXCURSION
- ▁FANTASTIC
- ▁HYPOTHES
- ▁INCONVENIENCE
- ▁INDESCRIBABLE
- ▁INDUSTRI
- ▁INVALID
- ▁MERCILESS
- ▁MESOPOTAMIA
- ▁MOSQUITO
- ▁NARRATIVE
- ▁NOWADAYS
- ▁OPPORTUNITIES
- ▁PROMISING
- ▁RECTANGLE
- ▁REMONSTRANCE
- ▁RESTAURANT
- ▁RIBBON
- ▁SCIENTIST
- ▁SHALMANESER
- ▁SKULL
- ▁SPRUCE
- ▁SUBSTANTIAL
- ▁SYMBOL
- ▁TEAPOT
- ▁TERRITORY
- ▁TRAFFIC
- ▁TREASON
- ▁TRUMPET
- ▁TYRANN
- ▁UNANIMOUS
- ▁UNAWARE
- ▁VICINITY
- ▁WREATH
- ▁ZADIG
- ▁CHATEAU
- ▁CONFRONT
- ▁DUCHESS
- ▁EMBODI
- ▁FEMININ
- ▁FURNACE
- ▁MONTONI
- ▁RENOWN
- ▁SMASH
- ▁HARVARD
- ▁NEWBERRY
- ▁PERFUME
- ▁SIGNATURE
- ▁SPLASH
- ▁SUPPOSITION
- ▁HARBOUR
- ▁ASSURANCE
- ▁BRISTOL
- ▁BUCKINGHAM
- ▁DUDLEY
- ▁INTENSITY
- ▁CHOPIN
- ▁ENLIST
- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: true
joint_net_conf: null
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram5000/bpe.model
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:
    n_fft: 512
    win_length: 400
    hop_length: 160
    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: 5
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_en_bpe5000_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: conformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 1024
    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
    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
postencoder: null
postencoder_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
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```


</details>


### Citing ESPnet


```BibTex
@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:


```bibtex
@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}
}
```


model name: librispeech_100h_conformer


model author: pyf98


task: automatic-speech-recognition


tags: en, dataset:librispeech_100, arxiv:1804.00015, espnet, audio, automatic-speech-recognition, license:cc-by-4.0


tags:


- conversational


#Rick Dialogpt model


model name: DialoGPT-small-rick


model author: crystalgate


task: conversational


tags: pytorch, gpt2, text-generation, transformers, conversational


## language: cs
license: cc-by-4.0


# Small-E-Czech


Small-E-Czech is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a Czech web corpus created at [Seznam.cz](https://www.seznam.cz/) and introduced in an [IAAI 2022 paper](https://arxiv.org/abs/2112.01810). Like other pretrained models, it should be finetuned on a downstream task of interest before use. At Seznam.cz, it has helped improve [web search ranking](https://blog.seznam.cz/2021/02/vyhledavani-pomoci-vyznamovych-vektoru/), query typo correction or clickbait titles detection. We release it under [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/) (i.e. allowing commercial use). To raise an issue, please visit our [github](https://github.com/seznam/small-e-czech).


### How to use the discriminator in transformers


```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch

discriminator = ElectraForPreTraining.from_pretrained("Seznam/small-e-czech")
tokenizer = ElectraTokenizerFast.from_pretrained("Seznam/small-e-czech")

sentence = "Za hory, za doly, mé zlaté parohy"
fake_sentence = "Za hory, za doly, kočka zlaté parohy"

fake_sentence_tokens = ["[CLS]"] + tokenizer.tokenize(fake_sentence) + ["[SEP]"]
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
outputs = discriminator(fake_inputs)
predictions = torch.nn.Sigmoid()(outputs[0]).cpu().detach().numpy()

for token in fake_sentence_tokens:
    print("{:>7s}".format(token), end="")
print()

for prediction in predictions.squeeze():
    print("{:7.1f}".format(prediction), end="")
print()
```


In the output we can see the probabilities of particular tokens not belonging in the sentence (i.e. having been faked by the generator) according to the discriminator:


```
  [CLS]     za   hory      ,     za    dol    ##y      ,  kočka  zlaté   paro   ##hy  [SEP]
    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.8    0.3    0.2    0.1    0.0
```


### Finetuning


For instructions on how to finetune the model on a new task, see the official HuggingFace transformers [tutorial](https://huggingface.co/transformers/training.html).


model name: small-e-czech


model author: Seznam


tags: pytorch, electra, cs, arxiv:2003.10555, arxiv:2112.01810, transformers, license:cc-by-4.0, infinity_compatible


Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN
MT-DNN: github.com/namisan/mt-dnn


model name: mt-dnn-ev-mrpc


model author: HungVo


tags:


language: vi
tags:


- exbert
license: cc-by-nc-4.0


# RoBERTa for Vietnamese and English (envibert)


This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named ***envibert***. The model architecture is custom for production so it only contains 70M parameters.


## Usages


```python
from transformers import RobertaModel
from transformers.file_utils import cached_path, hf_bucket_url
from importlib.machinery import SourceFileLoader
import os

cache_dir='./cache'
model_name='nguyenvulebinh/envibert'

def download_tokenizer_files():
  resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
  for item in resources:
    if not os.path.exists(os.path.join(cache_dir, item)):
      tmp_file = hf_bucket_url(model_name, filename=item)
      tmp_file = cached_path(tmp_file,cache_dir=cache_dir)
      os.rename(tmp_file, os.path.join(cache_dir, item))

download_tokenizer_files()
tokenizer = SourceFileLoader("envibert.tokenizer", os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
model = RobertaModel.from_pretrained(model_name,cache_dir=cache_dir)

# Encode text
text_input = 'Đại học Bách Khoa Hà Nội .'
text_ids = tokenizer(text_input, return_tensors='pt').input_ids
# tensor([[   0,  705,  131, 8751, 2878,  347,  477,    5,    2]])

# Extract features
text_features = model(text_ids)
text_features['last_hidden_state'].shape
# torch.Size([1, 9, 768])
len(text_features['hidden_states'])
# 7
```


### Citation


```text
@inproceedings{nguyen20d_interspeech,
  author={Thai Binh Nguyen and Quang Minh Nguyen and Thi Thu Hien Nguyen and Quoc Truong Do and Chi Mai Luong},
  title={{Improving Vietnamese Named Entity Recognition from Speech Using Word Capitalization and Punctuation Recovery Models}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={4263--4267},
  doi={10.21437/Interspeech.2020-1896}
}
```


**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.


# Contact


nguyenvulebinh@gmail.com


model name: envibert


model author: nguyenvulebinh


task: fill-mask


tags: pytorch, roberta, fill-mask, vi, transformers, exbert, license:cc-by-nc-4.0, autonlp_compatible, infinity_compatible


language:


- vi
- vn
- en
tags:
- question-answering
- pytorch
datasets:
- squad
license: cc-by-nc-4.0
pipeline_tag: question-answering
metrics:
- squad
widget:
- text: "Bình là chuyên gia về gì ?"
context: "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"
- text: "Bình được công nhận với danh hiệu gì ?"
context: "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"


## Model Description


- Language model: [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)
- Fine-tune: [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc)
- Language: Vietnamese, Englsih
- Downstream-task: Extractive QA
- Dataset (combine English and Vietnamese):
  - [Squad 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
  - [mailong25](https://github.com/mailong25/bert-vietnamese-question-answering/tree/master/dataset)
  - [UIT-ViQuAD](https://www.aclweb.org/anthology/2020.coling-main.233/)
  - [MultiLingual Question Answering](https://github.com/facebookresearch/MLQA)


This model is intended to be used for QA in the Vietnamese language so the valid set is Vietnamese only (but English works fine). The evaluation result below using 10% of the Vietnamese dataset.


| Model  | EM | F1 |
| ------------- | ------------- | ------------- |
| [base](https://huggingface.co/nguyenvulebinh/vi-mrc-base)  | 76.43  | 84.16  |
| [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large)  | 77.32  | 85.46  |


[MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc) using [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html) as a pre-trained language model. By default, XLM-RoBERTa will split word in to sub-words. But in my implementation, I re-combine sub-words representation (after encoded by BERT layer) into word representation using sum strategy.


## Using pre-trained model


- Hugging Face pipeline style (**NOT using sum features strategy**).


```python
from transformers import pipeline
# model_checkpoint = "nguyenvulebinh/vi-mrc-large"
model_checkpoint = "nguyenvulebinh/vi-mrc-base"
nlp = pipeline('question-answering', model=model_checkpoint,
                   tokenizer=model_checkpoint)
QA_input = {
  'question': "Bình là chuyên gia về gì ?",
  'context': "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"
}
res = nlp(QA_input)
print('pipeline: {}'.format(res))
#{'score': 0.5782045125961304, 'start': 45, 'end': 68, 'answer': 'xử lý ngôn ngữ tự nhiên'}
```


- More accurate infer process ([**Using sum features strategy**](https://github.com/nguyenvulebinh/extractive-qa-mrc))


```python
from infer import tokenize_function, data_collator, extract_answer
from model.mrc_model import MRCQuestionAnswering
from transformers import AutoTokenizer

# model_checkpoint = "nguyenvulebinh/vi-mrc-large"
model_checkpoint = "nguyenvulebinh/vi-mrc-base"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = MRCQuestionAnswering.from_pretrained(model_checkpoint)

QA_input = {
  'question': "Bình được công nhận với danh hiệu gì ?",
  'context': "Bình Nguyễn là một người đam mê với lĩnh vực xử lý ngôn ngữ tự nhiên . Anh nhận chứng chỉ Google Developer Expert năm 2020"
}

inputs = [tokenize_function(*QA_input)]
inputs_ids = data_collator(inputs)
outputs = model(**inputs_ids)
answer = extract_answer(inputs, outputs, tokenizer)

print(answer)
# answer: Google Developer Expert. Score start: 0.9926977753639221, Score end: 0.9909810423851013
```


## About


*Built by Binh Nguyen*
[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
For more details, visit the project repository.
[![GitHub stars](https://img.shields.io/github/stars/nguyenvulebinh/extractive-qa-mrc?style=social)](https://github.com/nguyenvulebinh/extractive-qa-mrc)


model name: vi-mrc-base


model author: nguyenvulebinh


task: question-answering


tags: pytorch, roberta, question-answering, vi, vn, en, dataset:squad, transformers, license:cc-by-nc-4.0, autonlp_compatible, infinity_compatible


language:


## Model Description


- Language model: [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)
- Fine-tune: [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc)
- Language: Vietnamese, Englsih
- Downstream-task: Extractive QA
- Dataset (combine English and Vietnamese):
  - [Squad 2.0](https://rajpurkar.github.io/SQuAD-explorer/)
  - [mailong25](https://github.com/mailong25/bert-vietnamese-question-answering/tree/master/dataset)
  - [VLSP MRC 2021](https://vlsp.org.vn/vlsp2021/eval/mrc)
  - [MultiLingual Question Answering](https://github.com/facebookresearch/MLQA)


This model is intended to be used for QA in the Vietnamese language so the valid set is Vietnamese only (but English works fine). The evaluation result below uses the VLSP MRC 2021 test set. This experiment achieves TOP 1 on the leaderboard.


| Model  | EM | F1 |
| ------------- | ------------- | ------------- |
| [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large)  public_test_set  | 85.847  | 83.826  |
| [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large)  private_test_set  | 82.072  | 78.071  |


Public leaderboard             |  Private leaderboard
:-------------------------:|:-------------------------:
![](https://i.ibb.co/tJX6V6T/public-leaderboard.jpg)  |  ![](https://i.ibb.co/nmsX2pG/private-leaderboard.jpg)


## Using pre-trained model


model name: vi-mrc-large


# Transformation spoken text to written text


This model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting "out of vocab" by using external vocabulary.


Some of examples:


```text
input  : tám giờ chín phút ngày mười tám tháng năm năm hai nghìn không trăm hai mươi hai
output : 8h9 18/5/2022

input  : mã số quy đê tê tê đê hai tám chéo hai không không ba
output : mã số qdttd28/2003

input  : thể tích tám mét khối trọng lượng năm mươi ki lô gam
output : thể tích 8 m3 trọng lượng 50 kg

input    : ngày hai tám tháng tư cô vít bùng phát ở sờ cốt lờn chiếm tám mươi phần trăm là biến chủng đen ta và bê ta
ex_vocab : ['scotland', 'covid', 'delta', 'beta']
output   : 28/4 covid bùng phát ở scotland chiếm 80 % là biến chủng delta và beta

```


## Model architecture


# Infer model


- Play around at [Huggingface Space](https://huggingface.co/spaces/nguyenvulebinh/spoken-norm)


```python
import torch
import model_handling
from data_handling import DataCollatorForNormSeq2Seq
from model_handling import EncoderDecoderSpokenNorm
import os
os.environ["CUDA_VISIBLE_DEVICES"] = ""
```


## Init tokenizer and model


```python
tokenizer = model_handling.init_tokenizer()
model = EncoderDecoderSpokenNorm.from_pretrained('nguyenvulebinh/spoken-norm', cache_dir=model_handling.cache_dir)
data_collator = DataCollatorForNormSeq2Seq(tokenizer)
```


## Infer sample


```python
bias_list = ['scotland', 'covid', 'delta', 'beta']
input_str = 'ngày hai tám tháng tư cô vít bùng phát ở sờ cốt lờn chiếm tám mươi phần trăm là biến chủng đen ta và bê ta'
```


```python
inputs = tokenizer([input_str])
input_ids = inputs['input_ids']
attention_mask = inputs['attention_mask']
if len(bias_list) > 0:
    bias = data_collator.encode_list_string(bias_list)
    bias_input_ids = bias['input_ids']
    bias_attention_mask = bias['attention_mask']
else:
    bias_input_ids = None
    bias_attention_mask = None

inputs = {
    "input_ids": torch.tensor(input_ids),
    "attention_mask": torch.tensor(attention_mask),
    "bias_input_ids": bias_input_ids,
    "bias_attention_mask": bias_attention_mask,
}
```


## Format input text **with** bias phrases


```python
outputs = model.generate(**inputs, output_attentions=True, num_beams=1, num_return_sequences=1)

for output in outputs.cpu().detach().numpy().tolist():
    # print('\n', tokenizer.decode(output, skip_special_tokens=True).split(), '\n')
    print(tokenizer.sp_model.DecodePieces(tokenizer.decode(output, skip_special_tokens=True).split()))
```


28/4 covid bùng phát ở scotland chiếm 80 % là biến chủng delta và beta


## Format input text **without** bias phrases


```python
outputs = model.generate(**{
    "input_ids": torch.tensor(input_ids),
    "attention_mask": torch.tensor(attention_mask),
    "bias_input_ids": None,
    "bias_attention_mask": None,
}, output_attentions=True, num_beams=1, num_return_sequences=1)

for output in outputs.cpu().detach().numpy().tolist():
    # print('\n', tokenizer.decode(output, skip_special_tokens=True).split(), '\n')
    print(tokenizer.sp_model.DecodePieces(tokenizer.decode(output, skip_special_tokens=True).split()))
```


28/4 cô vít bùng phát ở sờ cốt lờn chiếm 80 % là biến chủng đen ta và bê ta


## Contact


nguyenvulebinh@gmail.com


model name: spoken-norm


model author: nguyenvulebinh


tags: pytorch, transformers


language: vi
datasets:


- vlsp
- vivos
tags:
- audio
- automatic-speech-recognition
license: cc-by-nc-4.0
widget:
- example_title: VLSP ASR 2020 test T1
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
- example_title: VLSP ASR 2020 test T1
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_utt000000042.wav
- example_title: VLSP ASR 2020 test T2
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t2_0000006682.wav
model-index:
- name: Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
    - name: Test WER
type: wer
value: 11.52
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: VIVOS
type: vivos
args: vi
metrics:
    - name: Test WER
type: wer
value: 6.15


# Vietnamese end-to-end speech recognition using wav2vec 2.0


[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)


### Model description


[Our models](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of [VLSP ASR dataset](https://vlsp.org.vn/vlsp2020/eval/asr) on 16kHz sampled speech audio.


We use [wav2vec2 architecture](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) for the pre-trained model. Follow wav2vec2 paper:


> For the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.


For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition.


| Model | #params | Pre-training data | Fine-tune data |
|---|---|---|---|
| [base]((https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h)) | 95M | 13k hours | 250 hours |


In a formal ASR system, two components are required: acoustic model and language model. Here ctc-wav2vec fine-tuned model works as an acoustic model. For the language model, we provide a [4-grams model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/blob/main/vi_lm_4grams.bin.zip) trained on 2GB of spoken text.


Detail of training and fine-tuning process, the audience can follow [fairseq github](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec) and [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english).


### Benchmark WER result:


| | [VIVOS](https://ailab.hcmus.edu.vn/vivos) | [COMMON VOICE VI](https://paperswithcode.com/dataset/common-voice) | [VLSP-T1](https://vlsp.org.vn/vlsp2020/eval/asr) | [VLSP-T2](https://vlsp.org.vn/vlsp2020/eval/asr) |
|---|---|---|---|---|
|without LM| 10.77 | 18.34 | 13.33 | 51.45 |
|with 4-grams LM| 6.15 | 11.52 | 9.11 | 40.81 |


### Example usage


When using the model make sure that your speech input is sampled at 16Khz. Audio length should be shorter than 10s. Following the Colab link below to use a combination of CTC-wav2vec and 4-grams LM.


[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pVBY46gSoWer2vDf0XmZ6uNV3d8lrMxx?usp=sharing)


```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import soundfile as sf
import torch

# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")

# define function to read in sound file
def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

# load dummy dataset and read soundfiles
ds = map_to_array({
    "file": 'audio-test/t1_0001-00010.wav'
})

# tokenize
input_values = processor(ds["speech"], return_tensors="pt", padding="longest").input_values  # Batch size 1

# retrieve logits
logits = model(input_values).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
```


### Model Parameters License


The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode


### Citation


```text
@misc{Thai_Binh_Nguyen_wav2vec2_vi_2021,
  author = {Thai Binh Nguyen},
  doi = {10.5281/zenodo.5356039},
  month = {09},
  title = {{Vietnamese end-to-end speech recognition using wav2vec 2.0}},
  url = {https://github.com/vietai/ASR},
  year = {2021}
}
```


**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.


# Contact


nguyenvulebinh@gmail.com / binh@vietai.org


model name: wav2vec2-base-vietnamese-250h


model author: nguyenvulebinh


task: automatic-speech-recognition


tags: pytorch, wav2vec2, automatic-speech-recognition, vi, dataset:vlsp, dataset:vivos, transformers, audio, license:cc-by-nc-4.0, model-index


```python
from transformers import EncoderDecoderModel
from importlib.machinery import SourceFileLoader
from transformers.file_utils import cached_path, hf_bucket_url
import torch
import os

## Load model & tokenizer
cache_dir='./cache'
model_name='nguyenvulebinh/spelling-oov'

def download_tokenizer_files():
    resources = ['envibert_tokenizer.py', 'dict.txt', 'sentencepiece.bpe.model']
    for item in resources:
        if not os.path.exists(os.path.join(cache_dir, item)):
            tmp_file = hf_bucket_url(model_name, filename=item)
            tmp_file = cached_path(tmp_file,cache_dir=cache_dir)
            os.rename(tmp_file, os.path.join(cache_dir, item))

download_tokenizer_files()
spell_tokenizer = SourceFileLoader("envibert.tokenizer",os.path.join(cache_dir,'envibert_tokenizer.py')).load_module().RobertaTokenizer(cache_dir)
spell_model = EncoderDecoderModel.from_pretrained(model_name)

def oov_spelling(word, num_candidate=1):
    result = []
    inputs = spell_tokenizer([word.lower()])
    input_ids = inputs['input_ids']
    attention_mask = inputs['attention_mask']
    inputs = {
        "input_ids": torch.tensor(input_ids),
        "attention_mask": torch.tensor(attention_mask)
    }
    outputs = spell_model.generate(**inputs, num_return_sequences=num_candidate)
    for output in outputs.cpu().detach().numpy().tolist():
        result.append(spell_tokenizer.sp_model.DecodePieces(spell_tokenizer.decode(output, skip_special_tokens=True).split()))
    return result

oov_spelling('spacespeaker')
# output: ['x pây x pếch cơ']

```


model name: spelling-oov


task: text2text-generation


tags: pytorch, encoder-decoder, text2text-generation, transformers, autonlp_compatible


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 15.037146%


# Wav2Vec2-Large-XLSR-53-Portuguese


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Portuguese test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 15.037146%


## Training


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py


model name: wav2vec2-large-xlsr-portuguese-a


model author: joorock12


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, pt, dataset:common_voice, transformers, audio, speech, apache-2.0, portuguese-speech-corpus, xlsr-fine-tuning-week, PyTorch, license:apache-2.0, model-index


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
- voxpopuli
license: apache-2.0
model-index:
- name: JoaoAlvarenga Wav2Vec2 Large 100k VoxPopuli Portuguese
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 19.735723%


# Wav2Vec2-Large-100k-VoxPopuli-Portuguese


Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


## Usage


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


You need to install Enelvo, an open-source spell correction trained with Twitter user posts
`pip install enelvo`


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from enelvo import normaliser
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-100k-voxpopuli-pt")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\”\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
norm = normaliser.Normaliser()

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = [norm.normalise(i) for i in processor.batch_decode(pred_ids)]
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 19.735723%


## Training


model name: wav2vec2-large-100k-voxpopuli-pt


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, pt, dataset:common_voice, transformers, audio, speech, apache-2.0, portuguese-speech-corpus, PyTorch, voxpopuli, license:apache-2.0, model-index


language: it
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- it
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Italian
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice it
type: common_voice
args: it
metrics:
    - name: Test WER
type: wer
value: 13.914924%


# Wav2Vec2-Large-XLSR-53-Italian


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Italian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "it", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


The model can be evaluated as follows on the Italian test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "it", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-italian")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 13.914924%


The Common Voice `train`, `validation` datasets were used for training.


The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-italian/blob/main/fine_tuning.py


model name: wav2vec2-large-xlsr-italian


model author: joorock12


task: automatic-speech-recognition


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, it, dataset:common_voice, transformers, audio, speech, apache-2.0, portuguese-speech-corpus, xlsr-fine-tuning-week, PyTorch, license:apache-2.0, model-index


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese A
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 15.037146%


# Wav2Vec2-Large-XLSR-53-Portuguese


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


## Usage


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


The model can be evaluated as follows on the Portuguese test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\â€œ\'\ï¿½]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 15.037146%


## Training


The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-portuguese/blob/main/fine-tuning.py


model name: wav2vec2-cv-coral-30ep


tags: pytorch, jax, wav2vec2, automatic-speech-recognition, pt, dataset:common_voice, transformers, audio, speech, apache-2.0, portuguese-speech-corpus, xlsr-fine-tuning-week, PyTorch, license:apache-2.0, model-index


language: es
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- es
- apache-2.0
- spanish-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Spanish
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ES
type: common_voice
args: es
metrics:
    - name: Test WER
type: wer
value: Training


# Wav2Vec2-Large-XLSR-53-Spanish


Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Spanish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "es", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re

test_dataset = load_dataset("common_voice", "es", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-53-spanish")
model.to("cuda")

chars_to_ignore_regex = '[\\,\\?\\.\\!\\-\\;\\:\\"\\“]'  # TODO: adapt this list to include all special characters you removed from the data
resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
\treturn batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

\twith torch.no_grad():
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

  pred_ids = torch.argmax(logits, dim=-1)
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
\treturn batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer) **: Training


The script used for training can be found at: https://github.com/joaoalvarenga/wav2vec2-large-xlsr-53-spanish/blob/main/fine-tuning.py


model name: wav2vec2-large-xlsr-53-spanish


model author: joorock12


task: automatic-speech-recognition


tags: es, dataset:common_voice, audio, speech, wav2vec2, apache-2.0, spanish-speech-corpus, automatic-speech-recognition, xlsr-fine-tuning-week, PyTorch, license:apache-2.0, model-index


language: pt
datasets:


- common_voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- apache-2.0
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
- PyTorch
license: apache-2.0
model-index:
- name: JoaoAlvarenga XLSR Wav2Vec2 Large 53 Portuguese
results:
  - task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice pt
type: common_voice
args: pt
metrics:
    - name: Test WER
type: wer
value: 13.766801%


# Wav2Vec2-Large-XLSR-53-Portuguese


## Usage


The model can be used directly (without a language model) as follows:


```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese")

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1)

print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation


You need to install Enelvo, an open-source spell correction trained with Twitter user posts
`pip install enelvo`


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from enelvo import normaliser
import re

test_dataset = load_dataset("common_voice", "pt", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model = Wav2Vec2ForCTC.from_pretrained("joorock12/wav2vec2-large-xlsr-portuguese-a")
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
norm = normaliser.Normaliser()

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits

    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = [norm.normalise(i) for i in processor.batch_decode(pred_ids)]
    return batch

result = test_dataset.map(evaluate, batched=True, batch_size=8)

print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```


**Test Result (wer)**: 13.766801%


## Training


The Common Voice `train`, `validation` datasets were used for training.


model name: wav2vec2-large-xlsr-portuguese