def log_softmax(data, axis=1):
'''
The log-softmax function.
Args:
<data>: a Numpy array.
<axis>: the dimension to softmax.
Return:
A new array.
'''
declare.is_classes("data", data, np.ndarray)
if len(data.shape) == 1:
axis = 0
declare.in_boundary("axis", axis, 0, len(data.shape) - 1)
dataShape = list(data.shape)
dataShape[axis] = 1
maxValue = data.max(axis, keepdims=True)
dataNor = data - maxValue
dataExp = np.exp(dataNor)
dataExpSum = np.sum(dataExp, axis)
dataExpSumLog = np.log(dataExpSum) + maxValue.reshape(dataExpSum.shape)
return data - dataExpSumLog.reshape(dataShape)
def ctc_greedy_search(prob, vocabs, blankID=None):
'''
The best path decoding algorithm.
Args:
<prob>: An exkaldi probability object. This probalility should be an output of Neural Network with CTC loss fucntion.
<vocabs>: a list of vocabulary.
<blankID>: specify the ID of blank symbol. If None, use the last dimentionality of <prob>.
Return:
An exkaldi Transcription object of decoding results.
'''
declare.is_classes("vocabs", vocabs, list)
declare.is_probability("prob", prob)
if type_name(prob) == "BytesProb":
prob = prob.to_numpy()
elif type_name(prob) == "IndexTable":
prob = prob.read_record("prob").to_numpy()
probDim = prob.dim
if len(vocabs) == probDim:
if blankID is None:
blankID = probDim - 1
declare.is_positive_int("blankID", blackID)
declare.in_boundary("blankID", blackID, 0, probDim - 1)
elif len(vocabs) == probDim - 1:
if blankID == None:
blankID = probDim - 1
else:
assert blankID == probDim - 1, f"The dimensibality of probability is {probDim} but only have {len(vocabs)} words. In this case, blank ID must be {probDim-1} but got {blankID}"
else:
raise WrongDataFormat(
f"The dimensibality of probability {probDim} does not match the numbers of words {len(vocabs)}."
)
results = Transcription(name="bestPathResult")
for utt, pb in prob.items:
declare.is_classes("prob", prob, np.ndarray)
declare.is_classes("the rank of matrix shape", len(pb.shape),
"expected rank", 2)
best_path = np.argmax(pb, 1)
best_chars_collapsed = [
vocabs[ID] for ID, _ in groupby(best_path) if ID != blankID
]
try:
results[utt] = " ".join(best_chars_collapsed)
except Exception as e:
e.args = ("<vocab> might has non-string items.\n" + e.args[0], )
raise e
return results
def softmax(data, axis=1):
'''
The softmax function.
Args:
<data>: a Numpy array.
<axis>: the dimension to softmax.
Return:
A new array.
'''
declare.is_classes("data", data, np.ndarray)
if len(data.shape) == 1:
axis = 0
declare.in_boundary("axis", axis, 0, len(data.shape) - 1)
maxValue = data.max(axis, keepdims=True)
dataNor = data - maxValue
dataExp = np.exp(dataNor)
dataExpSum = np.sum(dataExp, axis, keepdims=True)
return dataExp / dataExpSum
def ctc_prefix_beam_search(prob,
vocabs,
blankID=None,
beam=5,
cutoff=0.999,
strick=1.0,
lmFile=None,
alpha=1.0,
beta=0):
'''
Prefix beam search decoding algorithm. Lm score is supported.
Args:
<prob>: An exkaldi postprobability object. This probalility should be an output of Neural Network with CTC loss fucntion.
We expect the probability didn't pass any activation function, or it may generate wrong results.
<vocabs>: a list of vocabulary.
<blankID>: specify the ID of blank symbol. If None, use the last dimentionality of <prob>.
<beam>: the beam size.
<cutoff>: the sum threshold to cut off dimensions whose probability is extremely small.
<strick>: When the decoding results of two adjacent frames are the same, the probability of latter will be reduced.
<lmFile>: If not None, add language model score to beam.
<alpha>: the weight of LM score.
<beta>: the length normaoliztion weight of LM score.
Return:
An exkaldi Transcription object of decoding results.
'''
declare.is_classes("vocabs", vocabs, [tuple, list])
declare.is_probability("prob", prob)
if type_name(prob) == "BytesProb":
prob = prob.to_numpy()
elif type_name(prob) == "IndexTable":
prob = prob.read_record("prob").to_numpy()
if lmFile is not None:
declare.is_file("lmFile", lmFile)
else:
lmFile = "none"
probDim = prob.dims
if len(vocabs) == probDim:
if blankID is None:
blankID = probDim - 1
declare.is_positive_int("blankID", blackID)
declare.in_boundary("blankID", blackID, 0, probDim - 1)
elif len(vocabs) == probDim - 1:
if blankID == None:
blankID = probDim - 1
else:
assert blankID == probDim - 1, f"The dimensibality of probability is {probDim} but only have {len(vocabs)} words. In this case, blank ID must be {probDim-1} but got {blankID}"
else:
raise WrongDataFormat(
f"The dimensibality of probability {probDim} does not match the numbers of words {len(vocabs)}."
)
for ID, word in enumerate(vocabs):
if len(word.strip()) == 0:
raise WrongDataFormat(f"Found a vocab {word} unavaliable.")
num_classes = len(vocabs)
vocabs = " ".join(vocabs)
sources = [
vocabs.encode(),
]
uttTemp = []
for utt, pb in prob.items:
declare.is_classes("prob", prob, np.ndarray)
declare.is_classes("the rank of matrix shape", len(pb.shape),
"expected rank", 2)
pb = softmax(pb, axis=1)
sources.append(f" {pb.shape[0]} ".encode() +
pb.astype("float32").tobytes())
sources = b"".join(sources)
cmd = os.path.join(sys.prefix, "exkaldisrc", "tools",
"prefix_beam_search_decode")
cmd += " --num_files {}".format(prob.lens[0])
cmd += " --num_classes {}".format(num_classes)
cmd += " --blank_id {}".format(blankID)
cmd += " --lm_model {}".format(lmFile)
cmd += " --beam_size {}".format(beam)
cmd += " --cutoff_prob {}".format(cutoff)
cmd += " --alpha {}".format(alpha)
cmd += " --beta {}".format(beta)
out, err, _ = run_shell_command(cmd,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
inputs=sources)
if len(out) == 0:
raise Exception("Failed to beam search decode.", err.decode())
else:
results = Transcription(name="beamSearchResults")
out = out.decode().strip().split("file")
results = []
for index, re in enumerate(out[1:]):
re = re.strip().split("\n")
if len(re) <= 1:
results.append([
"",
])
else:
results[uttTemp[index]] = " ".join(re[1].strip().split()[1:])
return results
def __init__(self,
indexTable,
processFunc,
batchSize,
chunks='auto',
otherArgs=None,
shuffle=False,
retainData=0.0):
declare.is_index_table("indexTable", indexTable)
declare.is_callable("processFunc", processFunc)
declare.is_positive_int("batchSize", batchSize)
declare.is_bool("shuffle", shuffle)
declare.in_boundary("retainData", retainData, minV=0.0, maxV=0.9)
self.processFunc = processFunc
self._batchSize = batchSize
self.otherArgs = otherArgs
self._shuffle = shuffle
self._chunks = chunks
if chunks != 'auto':
declare.is_positive_int("chunks", chunks)
totalDataNumber = len(indexTable)
trainDataNumber = int(totalDataNumber * (1 - retainData))
evalDataNumber = totalDataNumber - trainDataNumber
scpTable = indexTable.shuffle()
self.trainTable = scpTable.subset(nHead=trainDataNumber)
self.evalTable = scpTable.subset(nTail=evalDataNumber)
if chunks == 'auto':
#Compute the chunks automatically
sampleTable = self.trainTable.subset(nHead=10)
meanSize = sum(
[indexInfo.dataSize
for indexInfo in sampleTable.values()]) / 10
autoChunkSize = math.ceil(
104857600 / meanSize) # 100MB = 102400KB = 104857600 B
self._chunks = trainDataNumber // autoChunkSize
if self._chunks == 0:
self._chunks = 1
self.make_dataset_bag(shuffle=False)
self._epoch = 0
self.load_dataset(0)
self.currentDataset = self.nextDataset
self.nextDataset = None
self.epochSize = len(self.currentDataset)
self.countEpochSizeFlag = True
self.currentPosition = 0
self.currentEpochPosition = 0
self._isNewEpoch = False
self._isNewChunk = False
self.datasetIndex = 0
if self._chunks > 1:
self.datasetIndex = 1
self.loadDatasetThread = threading.Thread(target=self.load_dataset,
args=(1, ))
self.loadDatasetThread.start()
def __init__(self,
indexTable,
processFunc,
batchSize,
chunks='auto',
otherArgs=None,
shuffle=False,
retainData=0.0):
'''
Args:
_indexTable_: an ExKaldi IndexTable object whose <filePath> info is necessary.
_processFunc_: a function receive a IndexTable object return return an iterable dataset object.
It at least need two arguments to receive ( the data iteator itself, a IndexTable object of a chunk data ).
_batchSize_: mini batch size.
_chunks_: how many chunks to split.
_otherArgs_: other arguments to send to <processFunc>.
_shuffle_: If True, shuffle a batch data.
_retainData_: a probability value. how much data to retained for evaluation.
'''
declare.is_index_table("indexTable", indexTable)
declare.is_callable("processFunc", processFunc)
declare.is_positive_int("batchSize", batchSize)
declare.is_bool("shuffle", shuffle)
declare.in_boundary("retainData", retainData, minV=0.0, maxV=0.9)
self.__processFunc = processFunc
self.__batchSize = batchSize
self.__otherArgs = otherArgs
self.__shuffle = shuffle
self.__chunks = chunks
if chunks != 'auto':
declare.is_positive_int("chunks", chunks)
totalDataNumber = len(indexTable)
trainDataNumber = int(totalDataNumber * (1 - retainData))
evalDataNumber = totalDataNumber - trainDataNumber
scpTable = indexTable.shuffle()
self.__trainTable = scpTable.subset(nHead=trainDataNumber)
if evalDataNumber > 0:
self.__evalTable = scpTable.subset(nTail=evalDataNumber)
else:
self.__evalTable = None
if chunks == 'auto':
#Compute the chunks automatically
sampleTable = self.__trainTable.subset(nHead=10)
meanSize = sum(
[indexInfo.dataSize
for indexInfo in sampleTable.values()]) / 10
autoChunkSize = math.ceil(
104857600 / meanSize) # 100MB = 102400KB = 104857600 B
self.__chunks = trainDataNumber // autoChunkSize
if self.__chunks == 0:
self.__chunks = 1
# split train dataset into N chunks
self.__make_dataset_bag(shuffle=False)
# initialize some parameters
self.__epoch = 0
self.__currentPosition = 0
self.__currentEpochPosition = 0
self.__isNewEpoch = False
self.__isNewChunk = False
self.__datasetIndex = 0
# load the first chunk data
self.__load_dataset(0)
self.__currentDataset = self.__nextDataset
self.__nextDataset = None
# accumulate counts
self.__epochSize = len(self.__currentDataset)
self.__countEpochSizeFlag = True
# try to load the next chunk
if self.__chunks > 1:
self.__datasetIndex = 1
self.__loadDatasetThread = threading.Thread(
target=self.__load_dataset, args=(1, ))
self.__loadDatasetThread.start()
