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License: CC BY
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ESPnet2 pretrained model, Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best, fs=16k, lang=en

Authors: Watanabe, Shinji;

ESPnet2 pretrained model, Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best, fs=16k, lang=en

Abstract

This model was trained by Shinji Watanabe using librispeech recipe in espnet. Python APISee https://github.com/espnet/espnet_model_zoo Evaluate in the recipegit clone https://github.com/espnet/espnet cd espnet git checkout fca1edc18f8235c1de13925147519e6ecd03ec96 pip install -e . cd egs2/librispeech/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best Results # RESULTS ## Environments - date: `Tue Jul 21 07:58:39 EDT 2020` - python version: `3.7.3 (default, Mar 27 2019, 22:11:17) [GCC 7.3.0]` - espnet version: `espnet 0.8.0` - pytorch version: `pytorch 1.4.0` - Git hash: `75db853dd26a40d3d4dd979b2ff2457fbbb0cd69` - Commit date: `Mon Jul 20 10:49:12 2020 -0400` ## asr_train_asr_transformer_e18_raw_bpe_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.8|0.2|0.2|2.3|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|54402|97.9|1.9|0.2|0.3|2.4|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.6|4.7|0.7|0.7|6.0|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|50948|94.4|5.0|0.5|0.8|6.3|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.3|0.3|2.6|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|52576|97.7|2.0|0.2|0.3|2.6|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.5|4.8|0.7|0.7|6.2|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|52343|94.3|5.1|0.6|0.8|6.5|50.3| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.3|0.3|0.2|0.9|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|288456|99.3|0.4|0.3|0.2|0.9|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.2|1.1|0.6|2.9|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|265951|97.7|1.3|1.0|0.8|3.0|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.3|0.3|0.4|0.3|1.0|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|281530|99.4|0.3|0.3|0.3|0.9|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.8|1.1|1.1|0.7|2.9|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|272758|97.9|1.2|0.9|0.8|2.9|50.3| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_dev_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.8|1.0|0.4|3.2|28.2| |decode_dev_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2703|69307|97.2|1.9|1.0|0.5|3.3|29.5| |decode_dev_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.3|4.4|2.2|1.2|7.9|46.6| |decode_dev_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2864|64239|93.2|4.9|1.9|1.5|8.3|47.5| |decode_test_clean_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.0|1.9|1.1|0.4|3.3|30.4| |decode_test_clean_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2620|66712|97.1|1.9|1.0|0.5|3.3|30.1| |decode_test_other_decode_asr_beam_size20_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.5|2.4|1.0|7.9|49.7| |decode_test_other_decode_asr_beam_size5_lm_train_lm_adam_bpe_valid.loss.best_asr_model_valid.acc.best|2939|66329|93.1|4.8|2.1|1.4|8.3|50.3| ASR configconfig: conf/tuning/train_asr_transformer_e18.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_transformer_e18_raw_bpe_sp ngpu: 1 seed: 0 num_workers: 1 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 3 local_rank: 3 dist_master_addr: localhost dist_master_port: 33643 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 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_noise: false accum_grad: 6 no_forward_run: false resume: true train_dtype: float32 log_interval: null pretrain_path: [] pretrain_key: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 15000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_sp/train/speech_shape - exp/asr_stats_raw_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_sp/valid/speech_shape - exp/asr_stats_raw_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 - sound - - dump/raw/train_960_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 valid_max_cache_size: null optim: adam optim_conf: lr: 0.002 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - - - "\u2581EXCLAIM" - "\u2581OPPORTUNITIES" - "\u2581REMEDY" - "\u2581DEFENSE" - "\u2581ETERNITY" - "\u2581SKULL" - "\u2581PLEADED" - "\u2581INSTINCTIVELY" - "\u2581SLAUGHTER" - "\u2581RATIONAL" - "\u2581PULSE" - "\u2581PARALLEL" - "\u2581SCOUNDREL" - "\u2581PRUDENCE" - "\u2581PROBABILITY" - "\u2581DERIVED" - "\u2581MONSTROUS" - "\u2581POTATOES" - "\u2581IMPRESSIVE" - "\u2581DAINTY" - "\u2581SULTAN" - "\u2581CARPENTER" - "\u2581INNUMERABLE" - "\u2581INVITE" - "\u2581CIRCULAT" - "\u2581ELOQUENCE" - "\u2581DISCIPLE" - "\u2581ATTIRE" - "\u2581OBSTINATE" - "\u2581STREAK" - "\u2581WOLVES" - "\u2581GRINNED" - "\u2581ORCHARD" - "\u2581JAPANESE" - "\u2581ANNUAL" - "\u2581SHAWL" - "\u2581PACIFIC" - "\u2581VEHICLE" - "\u2581APOSTLE" - "\u2581CONGREGATION" - "\u2581AMAZING" - "\u2581OCCURRENCE" - "\u2581CONFERENCE" - "\u2581MIXTURE" - "\u2581EXAMINING" - "\u2581SAUCE" - "\u2581ADMIRING" - "\u2581AMBASSADOR" - "\u2581DEVICE" - "\u2581INCAPABLE" - "\u2581WHEREUPON" - "\u2581IMPERFECT" - "\u2581PERCEPTION" - "\u2581LOUNG" - "\u2581VACANT" - "\u2581EXCURSION" - "\u2581DISCOURAGE" - "\u2581FANTASTIC" - "\u2581REBELLION" - "\u2581CONVINCE" - "\u2581DEFIANCE" - "\u2581CONNECT" - "\u2581EMPHASIS" - "\u2581MEXICO" - "\u2581OPPONENT" - "\u2581DETERMINE" - "\u2581MANUSCRIPT" - "\u2581INCESSANT" - "\u2581BRONZE" - "\u2581COURTEOUS" - "\u2581COFFIN" - "\u2581CONSTRUCTION" - "\u2581BLUNDER" - "\u2581SENATE" - "\u2581CIRCUM" - "\u2581DANIEL" - "\u2581ELOQUENT" - "\u2581QUIVERING" - "\u2581VIGIL" - "\u2581HAZARD" - "\u2581UNWORTHY" - "\u2581TAB" - "\u2581ILLUSION" - "\u2581AGITATED" - "\u2581CHAMPION" - "\u2581DIMINISH" - "\u2581STUMP" - "\u2581CONFIDE" - "\u2581PHILADELPHIA" - "\u2581DOUGLAS" - "\u2581BUMP" - "\u2581COMMERCE" - "\u2581FACULTY" - "\u2581CONFEDERATE" - "\u2581EMBARRASSMENT" - "\u2581EXPLORE" - "\u2581MAGGIE" - "\u2581PHILOSOPHIC" - "\u2581ADMINISTRATION" - "\u2581HEADQUARTERS" - "\u2581SOLUTION" - "\u2581REFRAIN" - "\u2581ELDEST" - "\u2581FORMIDABLE" - "\u2581VERANDA" - "\u2581DISMAL" - "\u2581ESTHER" - "\u2581PRUDENT" - "\u2581BLAZING" - "\u2581RESOLVE" - "\u2581ELSIE" - "\u2581TURKEY" - "\u2581DECREE" - "\u2581CONVERSE" - "\u2581GRAVITY" - "\u2581MIRTH" - "\u2581RESEMBLANCE" - "\u2581GULF" - "\u2581SHRUB" - "\u2581EXHIBITION" - "\u2581AUSTRALIA" - "\u2581ELEANOR" - "\u2581UNCOMMON" - "\u2581RACHEL" - "\u2581TOMORROW" - "\u2581INJUSTICE" - "\u2581WISTFUL" - "\u2581WREATH" - "\u2581DISDAIN" - "\u2581CRUMB" - "\u2581CLINGING" - "\u2581COMMEND" - "\u2581SUPERSTITION" - "\u2581CRISIS" - "\u2581MAXIM" - "\u2581DESIRABLE" - "\u2581GIGANTIC" - "\u2581JUNGLE" - "\u2581DIGNIFIED" - "\u2581INVALID" - "\u2581UNNECESSARY" - "\u2581SUBLIME" - "\u2581PLOUGH" - "\u2581SUFFICE" - "\u2581BUNK" - "\u2581LUNCHEON" - "\u2581DRAUGHT" - "\u2581COLONY" - "\u2581PARLOUR" - "\u2581TERRIFIED" - "\u2581LOATH" - "\u2581SIGNIFICANCE" - "\u2581EXTENSIVE" - "\u2581HORACE" - "\u2581SERENE" - "\u2581CHEESE" - "\u2581PRECEDING" - "\u2581LEVI" - "\u2581INVARIABLY" - "\u2581OBSERVING" - "\u2581EARLIEST" - "\u2581WHEAT" - "\u2581DEMOCRAT" - "\u2581YOURSELVES" - "\u2581FEMININE" - "\u2581ARTIFICIAL" - "\u2581IDIOT" - "\u2581TORRENT" - "\u2581CONVICT" - "\u2581CONSUME" - "\u2581EMBROIDER" - "\u2581CONQUEST" - "\u2581CALCULATED" - "\u2581HAPPIER" - "\u2581DECAY" - "\u2581LITERALLY" - "\u2581RADIANT" - ENNI - "\u2581AMAZED" - "\u2581SPLIT" - "\u2581SUPPOSING" - "\u2581CANADA" - "\u2581PAVEMENT" - "\u2581ANTHONY" - "\u2581BULK" - "\u2581MEDIUM" - "\u2581MAURICE" - "\u2581SALOON" - "\u2581BARRIER" - "\u2581SWORE" - GUARD - "\u2581TEMPORARY" - "\u2581STALK" - "\u2581IRREGULAR" - "\u2581FRANTIC" - "\u2581BLISS" - "\u2581CONSPICUOUS" - "\u2581GERALD" - "\u2581EXCITING" - "\u2581SMASH" - "\u2581EXTERNAL" - "\u2581HESITATE" - "\u2581PATHETIC" - "\u2581NINTH" - "\u2581HAMILTON" - "\u2581UNSEEN" - "\u2581DEFECT" - "\u2581ACCURATE" - "\u2581LIQUOR" - "\u2581ENLIGHTEN" - WICH - "\u2581CLARK" - "\u2581REVERSE" - "\u2581PRIMITIVE" - "\u2581BLUFF" - "\u2581PRECAUTION" - "\u2581AWHILE" - "\u2581SPOON" - "\u2581TEMPERAMENT" - "\u2581SCHOONER" - "\u2581FREDERICK" - "\u2581REMORSE" - "\u2581CUSHION" - "\u2581EXCLUSIVE" - "\u2581CONTEMPLATE" - "\u2581SYLVIA" - "\u2581SITUATED" - "\u2581SKIPPER" - "\u2581DESOLATE" - "\u2581WORRIED" - "\u2581DWELT" - "\u2581TROUSERS" - "\u2581MARTYR" - "\u2581RIV" - "\u2581MEXICAN" - "\u2581DIVIDE" - "\u2581AMIABLE" - "\u2581PRUSSIA" - "\u2581COMMERCIAL" - "\u2581CONFRONT" - MPTON - "\u2581PROSPERITY" - "\u2581FEBRUARY" - "\u2581ADJUST" - "\u2581FUGITIVE" - "\u2581ABUNDANT" - "\u2581CRUELTY" - "\u2581UNCOMFORTABLE" - "\u2581THUMB" - "\u2581LAWRENCE" - "\u2581INTERCOURSE" - "\u2581STUDIO" - "\u2581PROMINENT" - "\u2581CHARLOTTE" - SHAW - "\u2581BRETHREN" - "\u2581COMPLEXION" - "\u2581TRAITOR" - "\u2581UNJUST" - BOAT - "\u2581TICK" - "\u2581KNELT" - "\u2581PROPRIETOR" - "\u2581ELABORATE" - "\u2581WRIT" - "\u2581OBSTACLE" - "\u2581CONSOLATION" - "\u2581DETAIN" - "\u2581FLANK" - "\u2581SAXON" - "\u2581SCRIPTURE" - "\u2581PERFUME" - "\u2581TRAGIC" - "\u2581EGYPTIAN" - "\u2581FOWL" - Q - "\u2581NOWHERE" - ARIA - "\u2581ATTENTIVE" - "\u2581STAIRCASE" - "\u2581BRITAIN" - "\u2581NORMAL" - "\u2581INFLICT" - "\u2581ECONOMI" - "\u2581OXFORD" - OTTE - "\u2581TUMULT" - "\u2581CLIMATE" - "\u2581CONTRIBUTE" - "\u2581TEMPEST" - "\u2581DISASTER" - "\u2581HYMN" - "\u2581FIERY" - "\u2581SUPERINTEND" - "\u2581SHERIFF" - "\u2581REVOLT" - "\u2581SURPRISING" - "\u2581DOWNSTAIRS" - "\u2581STRAY" - "\u2581QUAINT" - "\u2581SHAKESPEARE" - "\u2581WITHDREW" - "\u2581INJURY" - "\u2581SUPPLIES" - "\u2581PUMP" - "\u2581COMPASSION" - "\u2581DEVOUR" - "\u2581TENDENCY" - "\u2581VEGETABLE" - "\u2581BEHALF" - "\u2581SCOTCH" - "\u2581ALFRED" - "\u2581SPHERE" - "\u2581BACKGROUND" - "\u2581BEWILDERED" - "\u2581CONCLUD" - "\u2581COMPOSITION" - "\u2581EXTRACT" - "\u2581DIPLOMA" - "\u2581INQUIRIES" - "\u2581DESTINED" - "\u2581CONFOUND" - "\u2581INHABIT" - "\u2581DISTRACT" - "\u2581PILLAR" - "\u2581TELEGRAM" - "\u2581HENRI" - "\u2581HARBOUR" - "\u2581BONNET" - "\u2581COMBINATION" - "\u2581PLEASING" - "\u2581CABINET" - "\u2581SWAMP" - "\u2581SYN" - "\u2581HARVEST" - "\u2581RIGHTEOUS" - "\u2581TRUMPET" - "\u2581ARTILLERY" - "\u2581RIGID" - "\u2581EDITH" - "\u2581RELUCTANT" - "\u2581COURTESY" - "\u2581CEILING" - "\u2581ATTRACTION" - "\u2581ASSAULT" - "\u2581CHICAGO" - "\u2581GLIDE" - "\u2581EXCEED" - "\u2581CONCERT" - "\u2581ORGANIZATION" - "\u2581INVENTION" - "\u2581RASCAL" - "\u2581PATTERN" - "\u2581RESOLUTE" - "\u2581INVESTIGATION" - ABOUT - "\u2581LUXURY" - "\u2581YACHT" - "\u2581SHRINK" - "\u2581ARDENT" - "\u2581RESORT" - "\u2581MUSKET" - "\u2581INCLINATION" - "\u2581ALEXANDER" - "\u2581SWARM" - "\u2581TRAVERS" - "\u2581CLARA" - "\u2581TESTIMONY" - "\u2581AGITATION" - "\u2581RECOGNITION" - "\u2581BELIEVING" - "\u2581AFFIRM" - "\u2581FRONTIER" - "\u2581TERRITORY" - "\u2581PARLOR" - "\u2581GRIEVE" - "\u2581REMEMBRANCE" - "\u2581SATISFACTORY" - "\u2581SIGNIFICANT" - "\u2581THRESHOLD" - "\u2581CATHEDRAL" - "\u2581HISTORICAL" - "\u2581MONUMENT" - "\u2581FORTNIGHT" - "\u2581ANCESTOR" - "\u2581SENATOR" - "\u2581LIMP" - "\u2581FROZEN" - "\u2581INNOCENCE" - "\u2581ADAPT" - "\u2581ENVY" - "\u2581SALVATION" - "\u2581CRYSTAL" - "\u2581SYMPATHETIC" - "\u2581TACT" - "\u2581CARDINAL" - "\u2581PROFESS" - "\u2581ADVERTISE" - "\u2581HORRID" - "\u2581EXERTION" - "\u2581CROOK" - "\u2581TERRIBLY" - "\u2581CALIFORNIA" - "\u2581IMPATIENCE" - "\u2581ISRAEL" - "\u2581WORM" - "\u2581DESTINY" - "\u2581EXAGGERAT" - "\u2581MOIST" - "\u2581SPLASH" - "\u2581MARVELLOUS" - "\u2581DISCOURSE" - "\u2581ENTITLED" - "\u2581LAUNCH" - "\u2581PUNCH" - "\u2581KITTY" - "\u2581RELAX" - "\u2581DROOP" - "\u2581FLOURISH" - "\u2581STRICKEN" - "\u2581ERRAND" - "\u2581CONVENT" - "\u2581JOHNNY" - "\u2581SHAFT" - "\u2581SLIM" - "\u2581PRAIRIE" - "\u2581INDEPENDENCE" - "\u2581TIGER" - LOP - "\u2581DEAF" - "\u2581EDUCATED" - "\u2581OBLIGATION" - "\u2581NECESSARILY" - "\u2581CASUAL" - "\u2581EMINENT" - "\u2581WARRANT" - RVA - "\u2581ADMIRABLE" - "\u2581FEATURE" - "\u2581VOLUNTEER" - "\u2581PLEDGE" - "\u2581MAGISTRATE" - "\u2581INSPIRATION" - "\u2581WRIST" - "\u2581SULLEN" - "\u2581MANUFACTURE" - "\u2581MONDAY" - "\u2581CERTAINTY" - "\u2581CELLAR" - "\u2581SERPENT" - "\u2581MONKEY" - "\u2581APPROPRIATE" - "\u2581PENCIL" - "\u2581ESSAY" - "\u2581ULTIMATE" - "\u2581HEROIC" - "\u2581DISCERN" - "\u2581MEDICAL" - "\u2581TOMMY" - "\u2581STUDIES" - "\u2581GORDON" - "\u2581DECEMBER" - "\u2581PARADISE" - BOROUGH - "\u2581OBEDIENCE" - "\u2581JACKET" - "\u2581GARRISON" - "\u2581GUILT" - "\u2581SUCCESSION" - "\u2581CONSTRUCT" - "\u2581PENETRATE" - "\u2581ANGRILY" - "\u2581TRANQUIL" - "\u2581CONSTITUTE" - "\u2581NANCY" - "\u2581ACCESS" - "\u2581ESTIMATE" - "\u2581TICKET" - "\u2581ADVANCING" - "\u2581BRUTAL" - "\u2581CORRUPT" - "\u2581COPPER" - "\u2581WHIM" - "\u2581BROOD" - "\u2581PERSECUT" - "\u2581JERRY" - "\u2581INFERIOR" - "\u2581PRODUCTION" - FEL - "\u2581SCANDAL" - "\u2581SMOT" - "\u2581APPOINTMENT" - "\u2581PROPOSAL" - "\u2581REVERENCE" - "\u2581HOARSE" - "\u2581SHREWD" - "\u2581SPECTATOR" - "\u2581SNEER" - "\u2581TERRACE" - "\u2581SECURITY" - "\u2581NEIGHBORHOOD" - "\u2581OUTRAGE" - "\u2581UNWILLING" - "\u2581CRAZY" - "\u2581COMPARISON" - "\u2581STRENGTHEN" - "\u2581PRESUME" - "\u2581ADORN" - "\u2581REJOINED" - "\u2581CHERISH" - "\u2581IMPORT" - "\u2581DISORDER" - MOUTH - "\u2581ELEGANT" - "\u2581HARMONY" - "\u2581SURGEON" - "\u2581DESPATCH" - "\u2581INDUSTRY" - "\u2581UNEASY" - "\u2581OVERLOOK" - "\u2581PIERRE" - "\u2581PRODUCT" - "\u2581THIEF" - "\u2581RECOGNISED" - "\u2581PIERCE" - FOOT - "\u2581AMUSING" - "\u2581SUBSEQUENT" - "\u2581APPLICATION" - "\u2581TREAD" - "\u2581EDITION" - "\u2581STRUCTURE" - "\u2581LANDLORD" - "\u2581BRISK" - "\u2581NAUGHT" - "\u2581SHRILL" - "\u2581CORRIDOR" - "\u2581DRAMATIC" - "\u2581ABSTRACT" - "\u2581SENOR" - "\u2581WRETCH" - "\u2581CONVENIENT" - OLU - "\u2581DISMAY" - "\u2581CONTEST" - "\u2581IDOL" - "\u2581ASSEMBLY" - "\u2581CLUNG" - "\u2581IGNOR" - "\u2581FRIGHTFUL" - "\u2581INQUIRE" - "\u2581CULTURE" - "\u2581LEGEND" - "\u2581DROPPING" - "\u2581ANGUISH" - "\u2581ESTABLISH" - "\u2581ENSU" - "\u2581PIRATE" - "\u2581GLANCING" - "\u2581AVERAGE" - "\u2581MEMORIES" - "\u2581NIECE" - "\u2581SLUMBER" - ETTA - "\u2581ELECTION" - "\u2581OBSCURE" - "\u2581UNDOUBTEDLY" - "\u2581PONY" - "\u2581PILOT" - "\u2581SARAH" - "\u2581RIBBON" - "\u2581IMPROVEMENT" - "\u2581PRECEDE" - "\u2581APPREHENSION" - "\u2581INEVITABLE" - "\u2581PRACTISE" - "\u2581ABUSE" - LIUS - "\u2581GOSSIP" - "\u2581DIFFER" - "\u2581CHATTER" - "\u2581INVISIBLE" - "\u2581CHORUS" - "\u2581PERMANENT" - "\u2581JANUARY" - "\u2581PROCLAIM" - "\u2581AUTHORITIES" - "\u2581CHALLENGE" - COTT - "\u2581SALUTE" - "\u2581POMP" - "\u2581SUPPLIED" - "\u2581THITHER" - "\u2581RETORTED" - DOLPH - "\u2581BARBARA" - "\u2581ATTRACTIVE" - GUI - "\u2581EXCLAMATION" - "\u2581OCCUPY" - "\u2581ELLEN" - "\u2581LANDSCAPE" - "\u2581CHANGING" - "\u2581BUFFALO" - "\u2581GALLERY" - "\u2581CLOSING" - "\u2581SOFTEN" - "\u2581CHINESE" - "\u2581NIGH" - "\u2581NAR" - "\u2581FLOATING" - "\u2581AUSTRIA" - "\u2581CHICKEN" - "\u2581CAUTION" - "\u2581CRITICISM" - "\u2581VENGEANCE" - "\u2581IMPERIAL" - "\u2581NOVEMBER" - "\u2581DOCUMENT" - "\u2581ARCHITECT" - "\u2581IMPART" - "\u2581RESPONSE" - "\u2581CONTRADICT" - "\u2581QUOTE" - "\u2581SIMPLICITY" - "\u2581AROUSED" - "\u2581BOMB" - TTIE - "\u2581TELEGRAPH" - "\u2581PILGRIM" - "\u2581MAGAZINE" - WYN - "\u2581RECKLESS" - "\u2581SCRATCH" - "\u2581CASH" - "\u2581RESISTANCE" - "\u2581MUTUAL" - "\u2581ATTRIBUTE" - "\u2581STOMACH" - "\u2581FUNCTION" - "\u2581SENSITIVE" - BOY - "\u2581SCREEN" - "\u2581PROPOSITION" - "\u2581PICTURESQUE" - "\u2581DELICIOUS" - "\u2581HESITATION" - "\u2581GLEN" - "\u2581REVIEW" - "\u2581SUMMIT" - "\u2581INTELLECT" - "\u2581GREETED" - "\u2581BLADE" - "\u2581SOFA" - "\u2581SUNLIGHT" - "\u2581HAPPILY" - "\u2581CULTIVATE" - "\u2581COMMIT" - "\u2581AWAKENED" - "\u2581TWILIGHT" - FFE - "\u2581PROVIDENCE" - "\u2581DENIED" - "\u2581BARGAIN" - "\u2581COMBAT" - "\u2581GREETING" - "\u2581SYMBOL" - "\u2581SHEPHERD" - "\u2581FOOTSTEPS" - "\u2581WROUGHT" - "\u2581MECHANICAL" - "\u2581LATIN" - "\u2581DEPOSIT" - "\u2581KU" - "\u2581PROMOT" - "\u2581SPANIARD" - "\u2581INTRODUCTION" - "\u2581PREPARING" - "\u2581MASSES" - "\u2581DIVERS" - "\u2581VELVET" - "\u2581NARRATIVE" - "\u2581AWOKE" - "\u2581CANDID" - "\u2581DISPUTE" - "\u2581APPETITE" - "\u2581PREFERRED" - "\u2581REDUCED" - "\u2581EASIER" - "\u2581ENCLOS" - "\u2581SCARLET" - "\u2581PERPETUAL" - "\u2581RESEMBLE" - "\u2581ELEPHANT" - "\u2581DISCIPLINE" - "\u2581SPECIMEN" - "\u2581VIRGIN" - "\u2581SHILLING" - "\u2581SOLICIT" - "\u2581RESPONSIBLE" - "\u2581COUCH" - "\u2581EGYPT" - "\u2581UNIVERSITY" - "\u2581ASCERTAIN" - "\u2581PEAK" - "\u2581RUSSIA" - "\u2581RESPONSIBILITY" - "\u2581CAPACITY" - "\u2581JACOB" - "\u2581KH" - "\u2581TUNE" - "\u2581STOPPING" - "\u2581BLAST" - "\u2581CRIMSON" - "\u2581DUMB" - "\u2581GOSPEL" - "\u2581SOLITUDE" - "\u2581HAMMER" - "\u2581ACCENT" - "\u2581CONTACT" - "\u2581DEPRIV" - "\u2581RETIRE" - "\u2581TRIUMPHANT" - "\u2581THRONG" - "\u2581ESCORT" - "\u2581NICK" - "\u2581NEIGHBOURHOOD" - "\u2581TWAS" - "\u2581OAR" - "\u2581HOUND" - "\u2581NEPHEW" - "\u2581FUNERAL" - "\u2581RECEIVING" - "\u2581PLUCK" - "\u2581TRENCH" - "\u2581REPOSE" - "\u2581PORCH" - "\u2581DESPITE" - "\u2581LINCOLN" - "\u2581ZI" - "\u2581REC

Keywords

python, speech-synthesis, pytorch, machine-translation, ESPnet, speech-translation, deep-learning, speech-recognition

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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OpenAIRE UsageCountsViews provided by UsageCounts
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79
11