def process_feat_ali(training=True):
if training:
Name = "train"
else:
Name = "dev"
feat = exkaldi.load_feat( f"{args.expDir}/train_lstm/data/{Name}/fmllr.ark" )
if args.useCMVN:
cmvn = exkaldi.load_cmvn(f"{args.expDir}/train_lstm/data/{Name}/cmvn_of_fmllr.ark")
feat = exkaldi.use_cmvn(feat,cmvn,f"{args.expDir}/train_lstm/data/{Name}/utt2spk")
del cmvn
if args.delta > 0:
feat = feat.add_delta(args.delta)
if args.splice > 0:
feat = feat.splice(args.splice)
feat = feat.to_numpy()
if args.normalizeFeat:
feat = feat.normalize(std=True)
pdfAli = exkaldi.load_ali( f"{args.expDir}/train_lstm/data/{Name}/pdfID.npy" )
phoneAli = exkaldi.load_ali( f"{args.expDir}/train_lstm/data/{Name}/phoneID.npy" )
feat.rename("feat")
pdfAli.rename("pdfID")
phoneAli.rename("phoneID")
return feat, pdfAli, phoneAli
def prepare_test_data(postProbDim):
feat = exkaldi.load_feat( f"{args.expDir}/train_lstm/data/test/fmllr.ark" )
if args.useCMVN:
cmvn = exkaldi.load_cmvn( f"{args.expDir}/train_lstm/data/test/cmvn_of_fmllr.ark" )
feat = exkaldi.use_cmvn(feat, cmvn, utt2spk=f"{args.expDir}/train_lstm/data/test/utt2spk")
del cmvn
if args.delta > 0:
feat = feat.add_delta(args.delta)
if args.splice > 0:
feat = feat.splice(args.splice)
feat = feat.to_numpy()
if args.normalizeFeat:
feat = feat.normalize(std=True)
# Normalize acoustic model output
if args.normalizeAMP:
ali = exkaldi.load_ali(f"{args.expDir}/train_lstm/data/train/pdfID.npy", aliType="pdfID")
normalizeBias = exkaldi.nn.compute_postprob_norm(ali,postProbDim)
else:
normalizeBias = 0
# ref transcription
trans = exkaldi.load_transcription(f"{args.expDir}/train_lstm/data/test/text")
convertTable = exkaldi.load_list_table(f"{args.expDir}/dict/phones.48_to_39.map")
trans = trans.convert(convertTable)
return feat, normalizeBias, trans
def prepare_data(training=True):
if training:
flag = "train_clean_5"
else:
flag = "dev_clean_2"
print(f"{flag}: Load feature...")
featsFile = f"{args.root}/{args.feat}/raw_{args.feat}_{flag}.*.ark"
feats = exkaldi.load_feat(featsFile)
if args.cmn:
print(f"{flag}: Use cmvn...")
cmvnFile = f"{args.root}/{args.feat}/cmvn_{flag}.ark"
cmvn = exkaldi.load_cmvn(cmvnFile)
feats = exkaldi.use_cmvn(feats,
cmvn,
utt2spk=f"{args.root}/data/{flag}/utt2spk")
del cmvn
if args.delta > 0:
print(f"{flag}: Add delta...")
feats = feats.add_delta(args.delta)
if args.splice > 0:
print(f"{flag}: Splice feature...")
feats = feats.splice(args.splice)
feats = feats.to_numpy()
featDim = feats.dim
print(f"{flag}: Load alignment...")
ali = exkaldi.load_ali(f"{args.feat}/exp/tri3b_ali_{flag}/ali.*.gz")
print(f"{flag}: Get pdf alignment...")
pdfAli = ali.to_numpy(aliType="pdfID",
hmm=f"{args.feat}/exp/tri3b_ali_{flag}/final.mdl")
del ali
feats.rename("feat")
pdfAli.rename("pdfID")
#phoneAli.rename("phoneID")
print(f"{flag}: Tuple dataset...")
dataset = exkaldi.tuple_dataset([feats, pdfAli], frameLevel=True)
random.shuffle(dataset)
return featDim, dataset
def train_model():
global args
print("\n############## Parameters Configure ##############")
# Show configure information and write them to file
def configLog(message, f):
print(message)
f.write(message + '\n')
f = open(args.outDir + '/configure', "w")
configLog(
'Start System Time:{}'.format(
datetime.datetime.now().strftime("%Y-%m-%d %X")), f)
configLog('Host Name:{}'.format(socket.gethostname()), f)
configLog('Fix Random Seed:{}'.format(args.randomSeed), f)
configLog('Mini Batch Size:{}'.format(args.batchSize), f)
configLog('GPU ID:{}'.format(args.gpu), f)
configLog('Train Epochs:{}'.format(args.epoch), f)
configLog('Output Folder:{}'.format(args.outDir), f)
configLog('GRU layers:{}'.format(args.layer), f)
configLog('GRU hidden nodes:{}'.format(args.hiddenNode), f)
configLog('GRU dropout:{}'.format(args.dropout), f)
configLog('Use CMVN:{}'.format(args.useCMVN), f)
configLog('Splice N Frames:{}'.format(args.splice), f)
configLog('Add N Deltas:{}'.format(args.delta), f)
configLog('Normalize Chunk:{}'.format(args.normalizeChunk), f)
configLog('Normalize AMP:{}'.format(args.normalizeAMP), f)
configLog('Decode Minimum Active:{}'.format(args.minActive), f)
configLog('Decode Maximum Active:{}'.format(args.maxActive), f)
configLog('Decode Maximum Memory:{}'.format(args.maxMemory), f)
configLog('Decode Beam:{}'.format(args.beam), f)
configLog('Decode Lattice Beam:{}'.format(args.latBeam), f)
configLog('Decode Acoustic Weight:{}'.format(args.acwt), f)
configLog('Decode minimum Language Weight:{}'.format(args.minLmwt), f)
configLog('Decode maximum Language Weight:{}'.format(args.maxLmwt), f)
f.close()
print("\n############## Train GRU Acoustic Model ##############")
#----------------- STEP 1: Prepare Train Data -----------------
print('Prepare data iterator...')
# Fmllr data file
trainScpFile = args.TIMITpath + '/data-fmllr-tri3/train/feats.scp'
devScpFile = args.TIMITpath + '/data-fmllr-tri3/dev/feats.scp'
# Alignment label file
trainAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali/ali.*.gz'
devAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_dev/ali.*.gz'
# Load label
trainLabelPdf = E.load_ali(trainAliFile)
trainLabelPho = E.load_ali(trainAliFile, returnPhone=True)
for i in trainLabelPho.keys():
trainLabelPho[i] = trainLabelPho[i] - 1
devLabelPdf = E.load_ali(devAliFile)
devLabelPho = E.load_ali(devAliFile, returnPhone=True)
for i in devLabelPho.keys():
devLabelPho[i] = devLabelPho[i] - 1
# CMVN file
trainUttSpk = args.TIMITpath + '/data-fmllr-tri3/train/utt2spk'
trainCmvnState = args.TIMITpath + '/data-fmllr-tri3/train/cmvn.ark'
devUttSpk = args.TIMITpath + '/data-fmllr-tri3/dev/utt2spk'
devCmvnState = args.TIMITpath + '/data-fmllr-tri3/dev/cmvn.ark'
# Design a process function
def loadChunkData(iterator, feat, otherArgs):
# <feat> is KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho, toDo = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict and sort them by frame length
feat = feat.array.sort(by='frame')
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate label
datas = feat.concat([labelPdf, labelPho], axis=1)
# cut frames
if toDo == 'train':
if iterator.epoch >= 4:
datas = datas.cut(1000)
elif iterator.epoch >= 3:
datas = datas.cut(800)
elif iterator.epoch >= 2:
datas = datas.cut(400)
elif iterator.epoch >= 1:
datas = datas.cut(200)
elif iterator.epoch >= 0:
datas = datas.cut(100)
# Transform trainable numpy data
datas, _ = datas.merge(keepDim=True, sortFrame=True)
return datas
# Make data iterator
train = E.DataIterator(trainScpFile,
loadChunkData,
args.batchSize,
chunks=5,
shuffle=False,
otherArgs=(trainUttSpk, trainCmvnState,
trainLabelPdf, trainLabelPho, 'train'))
print('Generate train dataset. Chunks:{} / Batch size:{}'.format(
train.chunks, train.batchSize))
dev = E.DataIterator(devScpFile,
loadChunkData,
args.batchSize,
chunks=1,
shuffle=False,
otherArgs=(devUttSpk, devCmvnState, devLabelPdf,
devLabelPho, 'dev'))
print('Generate validation dataset. Chunks:{} / Batch size:{}.'.format(
dev.chunks, dev.batchSize))
print("Done.")
print('Prepare model...')
featDim = 40
if args.delta > 0:
featDim *= (args.delta + 1)
if args.splice > 0:
featDim *= (2 * args.splice + 1)
model = GRU(featDim, trainLabelPdf.target, trainLabelPho.target)
lossfunc = nn.NLLLoss()
if args.gpu >= 0:
model = model.cuda(args.gpu)
lossfunc = lossfunc.cuda(args.gpu)
print('Generate model done.')
print('Prepare optimizer and supporter...')
#lr = [(0,0.5),(8,0.25),(13,0.125),(15,0.07),(17,0.035),(20,0.02),(23,0.01)]
lr = [(0, 0.0004)]
print('Learning Rate:', lr)
optimizer = torch.optim.RMSprop(model.parameters(),
lr=lr[0][1],
alpha=0.95,
eps=1e-8,
weight_decay=0,
momentum=0,
centered=False)
lr.pop(0)
supporter = E.Supporter(args.outDir)
print('Done.')
print('Prepare test data...')
# Fmllr file
testFilePath = args.TIMITpath + '/data-fmllr-tri3/test/feats.scp'
testFeat = E.load(testFilePath)
# Use CMVN
if args.useCMVN:
testUttSpk = args.TIMITpath + '/data-fmllr-tri3/test/utt2spk'
testCmvnState = args.TIMITpath + '/data-fmllr-tri3/test/cmvn.ark'
testFeat = E.use_cmvn(testFeat, testCmvnState, testUttSpk)
# Add delta
if args.delta > 0:
testFeat = E.add_delta(testFeat, args.delta)
# Splice frames
if args.splice > 0:
testFeat = testFeat.splice(args.splice)
# Transform to array
testFeat = testFeat.array
# Normalize
if args.normalizeChunk:
testFeat = testFeat.normalize()
# Normalize acoustic model output
if args.normalizeAMP:
# compute pdf counts in order to normalize acoustic model posterior probability.
countFile = args.outDir + '/pdfs_counts.txt'
if not os.path.isfile(countFile):
_ = E.analyze_counts(aliFile=trainAliFile, outFile=countFile)
with open(countFile) as f:
line = f.readline().strip().strip("[]").strip()
counts = np.array(list(map(float, line.split())), dtype=np.float32)
normalizeBias = np.log(counts / np.sum(counts))
else:
normalizeBias = 0
print('Done.')
print('Prepare test data decode and score function...')
# Design a function to compute WER of test data
def wer_fun(model, feat, normalizeBias):
global args
# Tranform the formate of KaldiDict feature data in order to forward network
temp = E.KaldiDict()
utts = feat.utts
with torch.no_grad():
for index, utt in enumerate(utts):
data = torch.Tensor(feat[utt][:, np.newaxis, :])
data = torch.autograd.Variable(data)
if args.gpu >= 0:
data = data.cuda(args.gpu)
out1, out2 = model(data, is_training=False, device=args.gpu)
out = out1.cpu().detach().numpy() - normalizeBias
temp[utt] = out
print("(testing) Forward network {}/{}".format(
index, len(utts)),
end=" " * 20 + '\r')
# Tansform KaldiDict to KaldiArk format
print('(testing) Transform to ark', end=" " * 20 + '\r')
amp = temp.ark
# Decode and obtain lattice
hmm = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_test/final.mdl'
hclg = args.TIMITpath + '/exp/tri3/graph/HCLG.fst'
lexicon = args.TIMITpath + '/exp/tri3/graph/words.txt'
print('(testing) Generate Lattice', end=" " * 20 + '\r')
lattice = E.decode_lattice(amp, hmm, hclg, lexicon, args.minActive,
args.maxActive, args.maxMemory, args.beam,
args.latBeam, args.acwt)
# Change language weight from 1 to 10, get the 1best words.
print('(testing) Get 1-best words', end=" " * 20 + '\r')
outs = lattice.get_1best(lmwt=args.minLmwt,
maxLmwt=args.maxLmwt,
outFile=args.outDir + '/outRaw')
# If reference file is not existed, make it.
phonemap = args.TIMITpath + '/conf/phones.60-48-39.map'
outFilter = args.TIMITpath + '/local/timit_norm_trans.pl -i - -m {} -from 48 -to 39'.format(
phonemap)
if not os.path.isfile(args.outDir + '/test_filt.txt'):
refText = args.TIMITpath + '/data/test/text'
cmd = 'cat {} | {} > {}/test_filt.txt'.format(
refText, outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
# Score WER and find the smallest one.
print('(testing) Score', end=" " * 20 + '\r')
minWER = None
for k in range(args.minLmwt, args.maxLmwt + 1, 1):
cmd = 'cat {} | {} > {}/test_prediction_filt.txt'.format(
outs[k], outFilter, args.outDir)
(_, _) = E.run_shell_cmd(cmd)
os.remove(outs[k])
score = E.wer('{}/test_filt.txt'.format(args.outDir),
"{}/test_prediction_filt.txt".format(args.outDir),
mode='all')
if minWER == None or score['WER'] < minWER:
minWER = score['WER']
os.remove("{}/test_prediction_filt.txt".format(args.outDir))
return minWER
print('Done.')
print('Now Start to Train')
for e in range(args.epoch):
print()
i = 0
usedTime = 0
supporter.send_report({'epoch': e})
# Train
model.train()
while True:
start = time.time()
# Get batch data and label
batch = train.next()
batch, lengths = E.pad_sequence(batch, shuffle=True, pad=0)
batch = torch.Tensor(batch)
data, label1, label2 = batch[:, :, 0:-2], batch[:, :,
-2], batch[:, :,
-1]
data = torch.autograd.Variable(data)
label1 = torch.autograd.Variable(label1).view(-1).long()
label2 = torch.autograd.Variable(label2).view(-1).long()
# Send to GPU if use
if args.gpu >= 0:
data = data.cuda(args.gpu)
label1 = label1.cuda(args.gpu)
label2 = label2.cuda(args.gpu)
# Clear grad
optimizer.zero_grad()
# Forward model
out1, out2 = model(data, is_training=True, device=args.gpu)
# Loss back propagation
loss1 = lossfunc(out1, label1)
loss2 = lossfunc(out2, label2)
loss = loss1 + loss2
loss.backward()
# Update parameter
optimizer.step()
# Compute accuracy
pred = torch.max(out1, dim=1)[1]
acc = 1 - torch.mean((pred != label1).float())
# Record train information
supporter.send_report({
'train_loss': float(loss),
'train_acc': float(acc)
})
ut = time.time() - start
usedTime += ut
batchLoss = float(loss.cpu().detach().numpy())
print(
"(training) Epoch:{}/{}% Chunk:{}/{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * train.epochProgress), train.chunk,
int(100 * train.chunkProgress), i, int(usedTime),
"%.4f" % (batchLoss), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
# If forwarded all data, break
if train.isNewEpoch:
break
# Evaluation
model.eval()
with torch.no_grad():
while True:
start = time.time()
# Get batch data and label
batch = dev.next()
batch, lengths = E.pad_sequence(batch, shuffle=True, pad=0)
maxLen, bSize, _ = batch.shape
batch = torch.Tensor(batch)
data, label1, label2 = batch[:, :,
0:-2], batch[:, :,
-2], batch[:, :, -1]
data = torch.autograd.Variable(data)
label1 = torch.autograd.Variable(label1).view(-1).long()
# Send to GPU if use
if args.gpu >= 0:
data = data.cuda(args.gpu)
label1 = label1.cuda(args.gpu)
# Forward model
out1, out2 = model(data, is_training=False, device=args.gpu)
# Compute accuracy of padded label
pred = torch.max(out1, dim=1)[1]
acc_pad = 1 - torch.mean((pred != label1).float())
# Compute accuracy of not padded label. This should be more correct.
label = label1.cpu().numpy().reshape([maxLen, bSize])
pred = pred.cpu().numpy().reshape([maxLen, bSize])
label = E.unpack_padded_sequence(label, lengths)
pred = E.unpack_padded_sequence(pred, lengths)
acc_nopad = E.accuracy(pred, label)
# Record evaluation information
supporter.send_report({
'dev_acc_pad': float(acc_pad),
'dev_acc_nopad': acc_nopad
})
ut = time.time() - start
usedTime += ut
batchLoss = float(loss.cpu().detach().numpy())
print(
"(Validating) Epoch:{}/{}% Chunk:{}/{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s"
.format(e, int(100 * dev.epochProgress), dev.chunk,
int(100 * dev.chunkProgress), i, int(usedTime),
"%.4f" % (batchLoss), "%.2f" % (1 / ut)),
end=" " * 5 + '\r')
i += 1
# If forwarded all data, break
if dev.isNewEpoch:
break
print()
# We compute WER score from 4th epoch
if e >= 2:
minWER = wer_fun(model, testFeat, normalizeBias)
supporter.send_report({'test_wer': minWER})
# one epoch is over so collect information
supporter.collect_report(plot=True)
# Save model
def saveFunc(archs):
fileName, model = archs
torch.save(model.state_dict(), fileName)
supporter.save_arch(saveFunc, arch={'GRU': model})
# Change learning rate
if len(lr) > 0 and supporter.judge('epoch', '>=', lr[0][0]):
for param_group in optimizer.param_groups:
param_group['lr'] = lr[0][1]
lr.pop(0)
print("GRU Acoustic Model training done.")
print("The final model has been saved as:", supporter.finalArch["GRU"])
print('Over System Time:', datetime.datetime.now().strftime("%Y-%m-%d %X"))
def train_model():
global args
print("\n############## Parameters Configure ##############")
# Show configure information and write them to file
def configLog(message,f):
print(message)
f.write(message+'\n')
f = open(args.outDir+'/configure',"w")
configLog('Start System Time:{}'.format(datetime.datetime.now().strftime("%Y-%m-%d %X")),f)
configLog('Host Name:{}'.format(socket.gethostname()),f)
configLog('Fix Random Seed:{}'.format(args.randomSeed),f)
configLog('Mini Batch Size:{}'.format(args.batchSize),f)
configLog('GPU ID:{}'.format(args.gpu),f)
configLog('Train Epochs:{}'.format(args.epoch),f)
configLog('Output Folder:{}'.format(args.outDir),f)
configLog('Dropout Rate:{}'.format(args.dropout),f)
configLog('Use CMVN:{}'.format(args.useCMVN),f)
configLog('Splice N Frames:{}'.format(args.splice),f)
configLog('Add N Deltas:{}'.format(args.delta),f)
configLog('Normalize Chunk:{}'.format(args.normalizeChunk),f)
configLog('Normalize AMP:{}'.format(args.normalizeAMP),f)
configLog('Decode Minimum Active:{}'.format(args.minActive),f)
configLog('Decode Maximum Active:{}'.format(args.maxActive),f)
configLog('Decode Maximum Memory:{}'.format(args.maxMemory),f)
configLog('Decode Beam:{}'.format(args.beam),f)
configLog('Decode Lattice Beam:{}'.format(args.latBeam),f)
configLog('Decode Acoustic Weight:{}'.format(args.acwt),f)
configLog('Decode minimum Language Weight:{}'.format(args.minLmwt),f)
configLog('Decode maximum Language Weight:{}'.format(args.maxLmwt),f)
f.close()
print("\n############## Train DNN Acoustic Model ##############")
#------------------------ STEP 1: Prepare Training and Validation Data -----------------------------
print('Prepare Data Iterator...')
# Prepare fMLLR feature files
trainScpFile = args.TIMITpath + '/data-fmllr-tri3/train/feats.scp'
devScpFile = args.TIMITpath + '/data-fmllr-tri3/dev/feats.scp'
# Prepare training labels (alignment data)
trainAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali/ali.*.gz'
trainLabelPdf = E.load_ali(trainAliFile)
trainLabelPho = E.load_ali(trainAliFile,returnPhone=True)
for i in trainLabelPho.keys():
trainLabelPho[i] = trainLabelPho[i] - 1
# Prepare validation labels (alignment data)
devAliFile = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_dev/ali.*.gz'
devLabelPdf = E.load_ali(devAliFile)
devLabelPho = E.load_ali(devAliFile,returnPhone=True)
for i in devLabelPho.keys():
devLabelPho[i] = devLabelPho[i] - 1
# prepare CMVN files
trainUttSpk = args.TIMITpath + '/data-fmllr-tri3/train/utt2spk'
trainCmvnState = args.TIMITpath + '/data-fmllr-tri3/train/cmvn.ark'
devUttSpk = args.TIMITpath + '/data-fmllr-tri3/dev/utt2spk'
devCmvnState = args.TIMITpath + '/data-fmllr-tri3/dev/cmvn.ark'
# Now we try to make training-iterator and validation-iterator.
# Firstly, customize a function to process feature data.
def loadChunkData(iterator, feat, otherArgs):
# <feat> is a KaldiArk object
global args
uttSpk, cmvnState, labelPdf, labelPho = otherArgs
# use CMVN
if args.useCMVN:
feat = E.use_cmvn(feat, cmvnState, uttSpk)
# Add delta
if args.delta > 0:
feat = E.add_delta(feat, args.delta)
# Splice front-back n frames
if args.splice > 0:
feat = feat.splice(args.splice)
# Transform to KaldiDict
feat = feat.array
# Normalize
if args.normalizeChunk:
feat = feat.normalize()
# Concatenate data-label in dimension
datas = feat.concat([labelPdf,labelPho], axis=1)
# Transform to trainable numpy data
datas,_ = datas.merge(keepDim=False, sortFrame=False)
return datas
# Then get data iterators
train = E.DataIterator(trainScpFile,loadChunkData,args.batchSize,chunks=5,shuffle=True,otherArgs=(trainUttSpk,trainCmvnState,trainLabelPdf,trainLabelPho))
print('Generate train dataset done. Chunks:{} / Batch size:{}'.format(train.chunks,train.batchSize))
dev = E.DataIterator(devScpFile,loadChunkData,args.batchSize,chunks=1,shuffle=False,otherArgs=(devUttSpk,devCmvnState,devLabelPdf,devLabelPho))
print('Generate validation dataset done. Chunks:{} / Batch size:{}.'.format(dev.chunks,dev.batchSize))
#--------------------------------- STEP 2: Prepare Model --------------------------
print('Prepare Model...')
# Initialize model
featDim = 40
if args.delta>0:
featDim *= (args.delta + 1)
if args.splice > 0:
featDim *= ( 2 * args.splice + 1 )
model = MLP(featDim, trainLabelPdf.target, trainLabelPho.target)
if args.gpu >=0:
model.to_gpu(args.gpu)
# Initialize optimizer
lr = [(0,0.08),(10,0.04),(15,0.02),(17,0.01),(19,0.005),(22,0.0025),(25,0.001)]
print('Learning Rate (epoch,newLR):',lr)
optimizer = chainer.optimizers.MomentumSGD(lr[0][1],momentum=0.0)
lr.pop(0)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer_hooks.WeightDecay(0.0))
# Prepare a supporter to help handling training information.
supporter = E.Supporter(args.outDir)
#------------------ STEP 3: Prepare Decoding Test Data and Function ------------------
print('Prepare decoding test data...')
# fMLLR file of test data
testFilePath = args.TIMITpath + '/data-fmllr-tri3/test/feats.scp'
testFeat = E.load(testFilePath)
# Using CMVN
if args.useCMVN:
testUttSpk = args.TIMITpath + '/data-fmllr-tri3/test/utt2spk'
testCmvnState = args.TIMITpath + '/data-fmllr-tri3/test/cmvn.ark'
testFeat = E.use_cmvn(testFeat,testCmvnState,testUttSpk)
# Add delta
if args.delta > 0:
testFeat = E.add_delta(testFeat,args.delta)
# Splice frames
if args.splice > 0:
testFeat = testFeat.splice(args.splice)
# Transform to array
testFeat = testFeat.array
# Normalize
if args.normalizeChunk:
testFeat = testFeat.normalize()
# Normalize acoustic model output
if args.normalizeAMP:
# Compute pdf counts in order to normalize acoustic model posterior probability.
countFile = args.outDir+'/pdfs_counts.txt'
# Get statistics file
if not os.path.isfile(countFile):
_ = E.analyze_counts(aliFile=trainAliFile,outFile=countFile)
with open(countFile) as f:
line = f.readline().strip().strip("[]").strip()
# Get AMP bias value
counts = np.array(list(map(float,line.split())),dtype=np.float32)
normalizeBias = np.log(counts/np.sum(counts))
else:
normalizeBias = 0
# Now, design a function to compute WER score
def wer_fun(model,testFeat,normalizeBias):
global args
# Use decode test data to forward network
temp = E.KaldiDict()
print('(testing) Forward network',end=" "*20+'\r')
with chainer.using_config('train',False),chainer.no_backprop_mode():
for utt in testFeat.keys():
data = cp.array(testFeat[utt],dtype=cp.float32)
out1,out2 = model(data)
out = F.log_softmax(out1,axis=1)
out.to_cpu()
temp[utt] = out.array - normalizeBias
# Tansform KaldiDict to KaldiArk format
print('(testing) Transform to ark',end=" "*20+'\r')
amp = temp.ark
# Decoding to obtain a lattice
hmm = args.TIMITpath + '/exp/dnn4_pretrain-dbn_dnn_ali_test/final.mdl'
hclg = args.TIMITpath + '/exp/tri3/graph/HCLG.fst'
lexicon = args.TIMITpath + '/exp/tri3/graph/words.txt'
print('(testing) Generate Lattice',end=" "*20+'\r')
lattice = E.decode_lattice(amp,hmm,hclg,lexicon,args.minActive,args.maxActive,args.maxMemory,args.beam,args.latBeam,args.acwt)
# Change language weight from 1 to 10, get the 1best words.
print('(testing) Get 1-best words',end=" "*20+'\r')
outs = lattice.get_1best(lmwt=args.minLmwt,maxLmwt=args.maxLmwt,outFile=args.outDir+'/outRaw.txt')
# If reference file is not existed, make it.
phonemap = args.TIMITpath + '/conf/phones.60-48-39.map'
outFilter = args.TIMITpath + '/local/timit_norm_trans.pl -i - -m {} -from 48 -to 39'.format(phonemap)
if not os.path.isfile(args.outDir+'/test_filt.txt'):
refText = args.TIMITpath + '/data/test/text'
cmd = 'cat {} | {} > {}/test_filt.txt'.format(refText,outFilter,args.outDir)
(_,_) = E.run_shell_cmd(cmd)
# Score WER and find the smallest one.
print('(testing) Score',end=" "*20+'\r')
minWER = None
for k in range(args.minLmwt,args.maxLmwt+1,1):
cmd = 'cat {} | {} > {}/test_prediction_filt.txt'.format(outs[k],outFilter,args.outDir)
(_,_) = E.run_shell_cmd(cmd)
os.remove(outs[k])
score = E.wer('{}/test_filt.txt'.format(args.outDir),"{}/test_prediction_filt.txt".format(args.outDir),mode='all')
if minWER == None or score['WER'] < minWER:
minWER = score['WER']
os.remove("{}/test_prediction_filt.txt".format(args.outDir))
return minWER
#-------------------------- STEP 4: Train Model ---------------------------
# While first epoch, the epoch size is computed gradually, so the prograss information will be inaccurate.
print('Now Start to Train')
print('Note that: The first epoch will be doing the statistics of total data size gradually.')
print('Note that: We will evaluate the WER of test dataset after epoch which will cost a few seconds.')
# Preprocessing batch data which is getten from data iterator
def convert(batch):
batch = cp.array(batch,dtype=cp.float32)
data = batch[:,0:-2]
label1 = cp.array(batch[:,-2],dtype=cp.int32)
label2 = cp.array(batch[:,-1],dtype=cp.int32)
return data,label1,label2
# We will save model during training loop, so prepare a model-save function
def saveFunc(archs):
global args
fileName, model = archs
copymodel = model.copy()
if args.gpu >= 0:
copymodel.to_cpu()
chainer.serializers.save_npz(fileName, copymodel)
# Start training loop
for e in range(args.epoch):
supporter.send_report({'epoch':e})
print()
i = 1
usedTime = 0
# Train
while True:
start = time.time()
# Get data >> Forward network >> Loss back propagation >> Update
batch = train.next()
data,label1,label2 = convert(batch)
with chainer.using_config('Train',True):
h1,h2 = model(data)
L1 = F.softmax_cross_entropy(h1,label1)
L2 = F.softmax_cross_entropy(h2,label2)
loss = L1 + L2
acc = F.accuracy(F.softmax(h1,axis=1),label1)
model.cleargrads()
loss.backward()
optimizer.update()
# Compute time cost
ut = time.time() - start
usedTime += ut
print("(training) Epoch:{}>>>{}% Chunk:{}>>>{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s".format(e,int(100*train.epochProgress),train.chunk,int(100*train.chunkProgress),i,int(usedTime),"%.4f"%(float(loss.array)),"%.2f"%(1/ut)), end=" "*5+'\r')
i += 1
supporter.send_report({'train_loss':float(loss.data),'train_acc':float(acc.data)})
# If forward all data, break
if train.isNewEpoch:
break
print()
i = 1
usedTime = 0
# Validate
while True:
start = time.time()
# Get data >> Forward network >> Score
batch = dev.next()
data,label1,label2 = convert(batch)
with chainer.using_config('train',False),chainer.no_backprop_mode():
h1,h2 = model(data)
loss = F.softmax_cross_entropy(h1,label1)
acc = F.accuracy(F.softmax(h1,axis=1),label1)
# Compute time cost
ut = time.time() - start
usedTime += ut
print("(Validating) Epoch:{}>>>{}% Chunk:{}>>>{}% Iter:{} Used-time:{}s Batch-loss:{} Speed:{}iters/s".format(e,int(100*dev.epochProgress),dev.chunk,int(100*dev.chunkProgress),i,int(usedTime),"%.4f"%(float(loss.array)),"%.2f"%(1/ut)), end=" "*5+'\r')
i += 1
supporter.send_report({'dev_loss':float(loss.data),'dev_acc':float(acc.data)})
# If forward all data, break
if dev.isNewEpoch:
break
print()
# Compute WER score
WERscore = wer_fun(model,testFeat,normalizeBias)
supporter.send_report({'test_wer':WERscore,'lr':optimizer.lr})
# Collect all information of this epoch that is reported before, and show them at display
supporter.collect_report(plot=True)
# Save model
supporter.save_arch(saveFunc,{'MLP':model})
# Change learning rate
if len(lr) > 0 and supporter.judge('epoch','>=',lr[0][0]):
optimizer.lr = lr[0][1]
lr.pop(0)
print("DNN Acoustic Model training done.")
print("The final model has been saved as:",supporter.finalArch)
print('Over System Time:',datetime.datetime.now().strftime("%Y-%m-%d %X"))