def mixture_feat_extractor_test():
reader = stream.StreamReader(
waveFile=wavPath,
chunkSize=480,
simulate=False,
vaDetector=None,
)
cutter = stream.ElementFrameCutter(
width=400,
shift=160,
)
extractor = feature.MixtureExtractor(
frameDim=400,
batchSize=100,
mixType=["mfcc", "fbank"],
useEnergyForFbank=False,
useEnergyForMfcc=False,
)
reader.start()
cutter.start(inPIPE=reader.outPIPE)
extractor.start(inPIPE=cutter.outPIPE)
extractor.wait()
print(extractor.outPIPE.size())
pac = extractor.outPIPE.get()
print(pac.data.shape)
def feat_extractor_test():
reader = stream.StreamReader(
waveFile=wavPath,
chunkSize=480,
simulate=False,
vaDetector=None,
)
cutter = stream.ElementFrameCutter(
width=400,
shift=160,
)
extractor = feature.MfccExtractor(
batchSize=100,
useEnergy=False,
)
reader.start()
cutter.start(inPIPE=reader.outPIPE)
extractor.start(inPIPE=cutter.outPIPE)
extractor.wait()
print(extractor.outPIPE.size())
def feat_estimator_test():
reader = stream.StreamReader(
waveFile=wavPath,
chunkSize=480,
simulate=False,
vaDetector=None,
)
cutter = stream.ElementFrameCutter(
width=400,
shift=160,
)
extractor = feature.MfccExtractor(
batchSize=100,
useEnergy=False,
)
processor = feature.FeatureProcessor(
featDim=13,
delta=2,
spliceLeft=10,
spliceRight=10,
cmvNormalizer=feature.FrameSlideCMVNormalizer(),
)
left = 5
right = 5
estimator = decode.AcousticEstimator(
featDim=819,
batchSize=100,
applySoftmax=False,
applyLog=False,
leftContext=left,
rightContext=right,
)
estimator.acoustic_function = lambda x: x[left:-right].copy()
reader.start()
cutter.start(inPIPE=reader.outPIPE)
extractor.start(inPIPE=cutter.outPIPE)
processor.start(inPIPE=extractor.outPIPE)
estimator.start(inPIPE=processor.outPIPE)
estimator.wait()
print(estimator.outPIPE.size())
def cutter_test():
reader = stream.StreamReader(
waveFile=wavPath,
chunkSize=480,
simulate=False,
vaDetector=None,
)
cutter = stream.ElementFrameCutter(
width=400,
shift=160,
)
reader.start()
cutter.start(inPIPE=reader.outPIPE)
cutter.wait()
print(cutter.outPIPE.size())
def feat_processor_test():
reader = stream.StreamReader(
waveFile=wavPath,
chunkSize=480,
simulate=False,
vaDetector=None,
)
cutter = stream.ElementFrameCutter(
width=400,
shift=160,
)
extractor = feature.MfccExtractor(
batchSize=100,
useEnergy=False,
)
processor = feature.FeatureProcessor(
featDim=13,
delta=2,
spliceLeft=10,
spliceRight=10,
cmvNormalizer=feature.FrameSlideCMVNormalizer(),
)
reader.start()
cutter.start(inPIPE=reader.outPIPE)
extractor.start(inPIPE=cutter.outPIPE)
processor.start(inPIPE=extractor.outPIPE)
processor.wait()
print(processor.outPIPE.size())
pac = processor.outPIPE.get()
print(pac.data.shape)
words = f"{rootDir}/tri3b/graph_tgsmall/words.txt"
hmm = f"{rootDir}/tri3b_ali_train_clean_5/final.mdl"
HCLG = f"{rootDir}/tri3b/graph_tgsmall/HCLG.fst"
pdfDim = decode.get_pdf_dim(hmm)
kerasmodel = make_DNN_acoustic_model(featDim, pdfDim)
kerasmodel.load_weights(kerasModel)
##########################
# Define components
##########################
# 1. Create a stream reader to read realtime stream from audio file
reader = stream.StreamReader(waveFile, simulate=True)
# 2. Cutter to cut frame
cutter = stream.ElementFrameCutter(width=400, shift=160)
# 3. MFCC feature extracting
extractor = feature.MfccExtractor(
frameDim=400,
batchSize=100,
useEnergy=False,
)
# 4. processing feature
processor = feature.FeatureProcessor(
featDim=13,
batchSize=100,
delta=delta,
spliceLeft=spliceLeft,
spliceRight=spliceRight,
cmvNormalizer=feature.FrameSlideCMVNormalizer(),
)
##########################
# Load DNN acoustic model
##########################
pdfDim = decode.get_pdf_dim(hmm)
kerasmodel = make_DNN_acoustic_model(featDim,pdfDim)
kerasmodel.load_weights(kerasModel)
##########################
# Define components
##########################
# 1. Create a stream recorder to read realtime stream from microphone
recorder = stream.StreamRecorder()
# 2. Cutter to cut frame
cutter = stream.ElementFrameCutter(batchSize=50,width=400,shift=160)
# 3. MFCC feature extracting
extractor = feature.MfccExtractor(
useEnergy=False,
)
# 4. processing feature
processor = feature.MatrixFeatureProcessor(
delta=delta,
spliceLeft=spliceLeft,
spliceRight=spliceRight,
cmvNormalizer=feature.FrameSlideCMVNormalizer(),
)
# 5. acoustic probability computer
def keras_compute(feats):
return kerasmodel(feats,training=False).numpy()