From ca85302fa01326ae6b1f7ab300b87f5901441fa6 Mon Sep 17 00:00:00 2001 From: dohe0342 Date: Fri, 9 Jun 2023 16:44:43 +0900 Subject: [PATCH] from local --- egs/tedlium3/ASR/.lora.sh.swp | Bin 12288 -> 12288 bytes egs/tedlium3/ASR/lora.sh | 1 - .../pruned_transducer_stateless/.train.py.swp | Bin 4096 -> 16384 bytes .../.train_tta.py.swp | Bin 81920 -> 86016 bytes .../train_tta.py | 41 ++++++------------ 5 files changed, 14 insertions(+), 28 deletions(-) diff --git a/egs/tedlium3/ASR/.lora.sh.swp b/egs/tedlium3/ASR/.lora.sh.swp index debe70d267cbb46f68c27ea439f6a0c04c87ef12..688dbd961cf0d5f1ff578b521c20e570772c18b8 100644 GIT binary patch delta 14 VcmZojXh_)bo|%zh^9N=D9RMu|1x5e> delta 14 VcmZojXh_)bo|!Rp^9N=D9RM!|1)Bf> diff --git a/egs/tedlium3/ASR/lora.sh b/egs/tedlium3/ASR/lora.sh index f27b74630..216ad1905 100755 --- a/egs/tedlium3/ASR/lora.sh +++ b/egs/tedlium3/ASR/lora.sh @@ -63,7 +63,6 @@ else --additional-block True \ --prune-range 10 \ --spk-id $2 \ - --prefix vox touch ./pruned_transducer_stateless_d2v_v2/$1/.train.done fi fi diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp b/egs/tedlium3/ASR/pruned_transducer_stateless/.train.py.swp index ed59853761b10563978e09defd9b0b7e6b0316d8..59b44523ec66a2631754ea80abc06702046ca66f 100644 GIT binary patch literal 16384 zcmeHOU5q6~6>bCsRKy?}eKEydgLjg9`_2!W$m#`W_s+_7cV{Ovvp=w4r*3!Mdz1M;K^CdTjpLNpM6UI3#phRBnVC_%yRRCnK>nQ?#K zjCNFGMBPE~z%s=DfQO>g#i_XxYmopo@%$8m1|`~AU{-~8oeAOE4_1ac^57jB#< z(YdY;^h6dmk8pp#lp8Bbx{>sGv^~##rNP#wR2w?wzG#KM=<_IQiGgkz5kz4YFSHid zR$Hmc5)pVt@k9riFO;VZHzE=mii(Gcn{I6{pn9$ts2Dg016Ml>^K*8n@2OqS-h1t_ zbLdv0ih+uOih+uOih+uOih+uOih+uO|1}0odWCa88uhlKVP}i`CEM`JcDR-zemtDdfkC3YYmWEz;fM{`@w1kzCy> z1}X+B1}X+B1}X+B1}X+B1}X+B1}X+B2L3M@;DO^@j+VcV_RIMGU+({3xyErG1vY>> za0Br2yB+66;9=k--~tWcjjJ8!b>Im=0v`rmdY9uo3H%uN5%4(hCE!7T1IxfZ;0E9y z?{u6$0xttE0WSj20Y3%43w#mi0|Gb&+ycx4*8s1-!*QMlz6X36(7;Kc1^nSE$9W3) z0U&`6a4qoKm5%c_;3?of;4ttJ;I~&e&ToKU13w484SX6n37i0K25tbZ11)X3 z26!5H4EP4{IUoUU0v3RI;DdkzyaJ+M03HIq0DK-ufa5?1m<28e-nz_j-UR*xJPZ5; zcoeuBxCwX*!H&NIuL6Goo(G-;z7L!M)`9l}zek_G06Ygg3p@dQ3wRJX1?&Y{fD2In zUkBKW;YzvELb*H$Jgx>=ED}?99Fok~-}Ki|L}|OW!p)Ew$;?o&{VPi>mVt z6CPP)+O>ci-imlK@Or86_Rh|>aCf(>o2^u|wNYaOwPq8F%}K=zsa>r$QHg$tlOqi# z+=1I*{!sWEsSFc?q(}~sZu|!0N-)h&i(pjsu3pGipG$&qBjO%~GjD6P^|gdfp5{OeA?Q!8PkE8ISUn z&qm!>TQECtb5+@33^BE5|AD7hB(^&3qA*namta!T)+?@1Yrlfl`9nTxLK5v~n5OQkYISEdC+Wm%`QGV0mgsx~*ju=jif z#@2L`OkZ@h#$EZzm1FwCsx;%^ z@-qEM@C0Tltt^86p3X6zmCJlI8G94ST-*71BFRGu(fQ2KJT#G~V8>2*)G_eeqMpl= z$GI^~@%}I;&Yc(+rQ%u^$6RflP$6}bB|FcNB-3pBQJ8uIh4K@W{Vu$H z$HafiKINGL;bQ?Y>XT0Xn9ESB=^i^!CggIil}D=#aFIn~M1}|s<6P;YR>bNND|3o2 zFT0I_<0P3*jj?J8QzdeIRN{0>rF_tMuZzSNX()Uw3gD(`T*@|Zem3`zt(2@3 z1s_dmBIi+qt!HT@auq$Kv0AG5GToweOD;>X%!X$G0fUmgpyvo^66(Lr_S z^>N@>XVE*}UF+^YyySJ3j&~0%t+i{jjk(5rW1;3a$21>^eYRzLSQN$~js=5B&9a2t zhE}16G4mzxP-hQY6Ife}lk6*io{MFj&uBfG!6F+-jE#hP2rIQXM3<9MxHPPs=&s*- z^w>H(vADXrxV+w7T4P67*@2_Wo$h+~=rW!Tvc=`w*rD!n2aCxNB`8|*DR>UUdyr1~W9!X{=aG!2ul?3+z2be%o0L)XxlbYH8rxw+|boX8#uSE@k^ zwunL9It==k*Or>|I3Z41Zj%{{`4*EsgEQESGjdW|D@~Hj4K%k3%|sfz9^|98O8fJcA_ z00U4w;6uPAz{}YGKL&gaSO%^Fp2t4_VITljflmOp05=2kzz2X=u%CYhcm((k@BlCX zR)9+Z+Uq|H`~r9ecpUgD@D;!S)P^BYT@?cr0~G@m0~G@m0~G@m1OEjK&;m8amZ3i5 zgnj0D36F*6v38r)JdXsPS3~3~-;W~-Y1SuJka&InQaOag8btI6@&jA zts@q((VlHEyBqZ=T-TnX2>-wa-*o;BLqr-oNyt zxfV|ORNix2N9rj6EU zV~rR?T_6%%GFWW6SF^<6Bh(VvqA=Bj_mieXOe)4uQ7)qSY2h@biEW|Gx{WuIk6|FA z=ip-vw%5gw!&ZE{&m*a|+e3gEfrnt5)oD#CD?~VwzP5mBJEq3*__8KFZv0`}{@y9n zX*;w`noey}>X~*;oa_=7^1JJ>3su+CZJO;ogZ>!qdk6U@Hw{ zwuN30xfHF0W1T{bRNv`P3lZQPJ2$>nLO?xga|-QZl*HgVH(4Y?Ydn%*y&oolmr~Rc VaV!)1UZGbX6{|S{FG9^Y{|4o-(zyTt delta 7 OcmZo@U~EuWAOHXgJOWYx diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/.train_tta.py.swp index f3d14bf04a984cc6246c7b01b650c52d84b0658b..1fa2e8fbca7db5f5fa5eb2d665dc0dbd08e65a7f 100644 GIT binary patch delta 1949 zcmb8ve{2(F9LMqJS%0jZ`$41t6otbzlDH+9h`~)IfDyt!nhiCMInh7-@yC4M+BsNo(Mw*v zyXT(g>2r7A&-L|L`i@v!EfpKrJ*iatDg@gCAr@VJC9tvbwCkEl3VqV#OqH3sX+giV zJB_8pe^flQnyC-$4vUmV#^^PEhvQw7qk z)q15%hBc2#W@+X^nLFK}PIlYp^r0!`?xx%nV;1EF<7aj*`i<&S4HhMBeUcELnd0l; zq&vE*WJ?MUW0-rsfHx6F2$gWbfg21lhP{fHa2Rjk8949_(_Y2_>_KpwcA zr4M!QlP+Ubz5e#LKy#p7sZ}DuKzmzHht^OcbEEgVHm9wAduLD>ZIO__t@Exqe2FH1 z#NWQ%9|(rR|8uOH`DO;++Hy2Ug=~x-I5=tTgHScR4F!h#7({uahDhylEdBWOjDB}#m$ z`>${uhY`ecSdAhS!h~_^U&k5j#%4T=CAdijZr~G~$5BM_G|Es4D}JCNjI7LTN^Hy{ z*V?6`Rg}oAqb*bDVla^G)HZ12u$boj#w79iKS=|%y)(Bx#@MFBSQ@mLt zT{B<#AyQ?et{?RtUonFBamuPov&W*1dSrR*>r7df P6e}u}Us$z?#j@^i@z9#+ delta 1076 zcma*lPe>F|90&0C_K)taF1mwh8Pt>{F1xLpOIm_P6oLnn1WPhl#@$he*4=S-Oe<<7 zs7oMi0}Wwh6a>~Gb)xMML=YWJ1tBUWY@sYqrKe6(-fShC-*qG2I2}zOXBfSChLyYI*;eElN@k^Dq-kQ(43lKM>?+d}Bnn$do zT^8*2HBxv9H=z$=5CA>30&u}cDFD3rB?gY zFHjfPmkjjq@hC5;qdpqgk`QGf5pNJV>CYFhlD<}=F8V019`Vy1l&Fo>bi2|jk5q3? z@@!%#m)PGilH!@L5cLwhLW-qruNE4Zf4Iw!`gNE!MJmQv#`7cS*S}@L%elrg_T53;GszJHZDA@}l9c>hM3b1VPX%!qM7p<45R%$Y(rD{{MydUOxOVqqia zdcsA~fY`T$%Ib#~S3N=6nHnu5go92v1%6n^A-{qba0!}$f%h&#-hl!#9EB3dgGDDH z<8T4aLkBp)0pA>ie1%zFh-{)kLc^OHDNEP=D6b1InBvX3)g B>i+-$ diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py index 143a23f09..0468e016e 100755 --- a/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py +++ b/egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/train_tta.py @@ -1345,23 +1345,19 @@ def run(rank, world_size, args, wb=None): register_inf_check_hooks(model) #librispeech = LibriSpeechAsrDataModule(args) - ted = TedLiumAsrDataModule(args) - - train_cuts = librispeech.train_clean_100_cuts() - if params.full_libri: - train_cuts += librispeech.train_clean_360_cuts() - train_cuts += librispeech.train_other_500_cuts() + tedlium = TedLiumAsrDataModule(args) + train_cuts = tedlium.train_cuts() def remove_short_and_long_utt(c: Cut): - # Keep only utterances with duration between 1 second and 20 seconds + # Keep only utterances with duration between 1 second and 17 seconds # - # Caution: There is a reason to select 20.0 here. Please see + # Caution: There is a reason to select 17.0 here. Please see # ../local/display_manifest_statistics.py # # You should use ../local/display_manifest_statistics.py to get # an utterance duration distribution for your dataset to select # the threshold - return 1.0 <= c.duration <= 20.0 + return 1.0 <= c.duration <= 17.0 train_cuts = train_cuts.filter(remove_short_and_long_utt) @@ -1372,15 +1368,12 @@ def run(rank, world_size, args, wb=None): else: sampler_state_dict = None - train_dl = librispeech.train_dataloaders( - train_cuts, sampler_state_dict=sampler_state_dict - ) + train_dl = tedlium.train_dataloaders(train_cuts) + valid_cuts = tedlium.dev_cuts() + valid_dl = tedlium.valid_dataloaders(valid_cuts) - valid_cuts = librispeech.dev_clean_cuts() - valid_cuts += librispeech.dev_other_cuts() - valid_dl = librispeech.valid_dataloaders(valid_cuts) - scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) + if checkpoints and "grad_scaler" in checkpoints: logging.info("Loading grad scaler state dict") scaler.load_state_dict(checkpoints["grad_scaler"]) @@ -1537,24 +1530,18 @@ def run_pea(rank, world_size, args, wb=None): scheduler_pea = Eden(optimizer_pea, 10000, 7) optimizer, scheduler = optimizer_pea, scheduler_pea - librispeech = LibriSpeechAsrDataModule(args) - train_cuts = librispeech.vox_cuts(option=params.spk_id) + tedlium = TedLiumAsrDataModule(args) + train_cuts = tedlium.user_test_cuts(spk_id=params.spk_id) def remove_short_and_long_utt(c: Cut): return 1.0 <= c.duration <= 20.0 train_cuts = train_cuts.filter(remove_short_and_long_utt) - sampler_state_dict = None + + train_dl = tedlium.train_dataloaders(train_cuts) + valid_dl = None - train_dl = librispeech.train_dataloaders( - train_cuts, sampler_state_dict=sampler_state_dict - ) - - valid_cuts = librispeech.dev_clean_cuts(option=params.gender) - valid_cuts += librispeech.dev_other_cuts(option=params.gender) - valid_dl = librispeech.valid_dataloaders(valid_cuts) - scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0) for epoch in range(params.start_epoch, params.num_epochs + 1):