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GeneratingDataset.py
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GeneratingDataset.py
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from __future__ import print_function
from Dataset import Dataset, DatasetSeq, convert_data_dims
from CachedDataset2 import CachedDataset2
from Util import class_idx_seq_to_1_of_k, CollectionReadCheckCovered
from Log import log
import numpy
import re
class GeneratingDataset(Dataset):
_input_classes = None
_output_classes = None
def __init__(self, input_dim, output_dim, num_seqs=float("inf"), fixed_random_seed=None, **kwargs):
"""
:param int|None input_dim:
:param int|dict[str,int|(int,int)|dict] output_dim: if dict, can specify all data-keys
:param int|float num_seqs:
:param int fixed_random_seed:
"""
super(GeneratingDataset, self).__init__(**kwargs)
assert self.shuffle_frames_of_nseqs == 0
self.num_inputs = input_dim
output_dim = convert_data_dims(output_dim, leave_dict_as_is=False)
if "data" not in output_dim and input_dim is not None:
output_dim["data"] = [input_dim * self.window, 2] # not sparse
self.num_outputs = output_dim
self.expected_load_seq_start = 0
self._num_seqs = num_seqs
self.random = numpy.random.RandomState(1)
self.fixed_random_seed = fixed_random_seed # useful when used as eval dataset
def init_seq_order(self, epoch=None, seq_list=None):
"""
:type epoch: int|None
:param seq_list: predefined order. doesn't make sense here
This is called when we start a new epoch, or at initialization.
"""
super(GeneratingDataset, self).init_seq_order(epoch=epoch)
assert not seq_list, "predefined order doesn't make sense for %s" % self.__class__.__name__
self.random.seed(self.fixed_random_seed or epoch or 1)
self._num_timesteps = 0
self.reached_final_seq = False
self.expected_load_seq_start = 0
self.added_data = []; " :type: list[DatasetSeq] "
return True
def _cleanup_old_seqs(self, seq_idx_end):
i = 0
while i < len(self.added_data):
if self.added_data[i].seq_idx >= seq_idx_end:
break
i += 1
del self.added_data[:i]
def _check_loaded_seq_idx(self, seq_idx):
if not self.added_data:
raise Exception("no data loaded yet")
start_loaded_seq_idx = self.added_data[0].seq_idx
end_loaded_seq_idx = self.added_data[-1].seq_idx
if seq_idx < start_loaded_seq_idx or seq_idx > end_loaded_seq_idx:
raise Exception("seq_idx %i not in loaded seqs range [%i,%i]" % (
seq_idx, start_loaded_seq_idx, end_loaded_seq_idx))
def _get_seq(self, seq_idx):
for data in self.added_data:
if data.seq_idx == seq_idx:
return data
return None
def is_cached(self, start, end):
# Always False, to force that we call self._load_seqs().
# This is important for our buffer management.
return False
def _load_seqs(self, start, end):
"""
:param int start: inclusive seq idx start
:param int end: exclusive seq idx end
"""
# We expect that start increase monotonic on each call
# for not-yet-loaded data.
# This will already be called with _load_seqs_superset indices.
assert start >= self.expected_load_seq_start
if start > self.expected_load_seq_start:
# Cleanup old data.
self._cleanup_old_seqs(start)
self.expected_load_seq_start = start
if self.added_data:
start = max(self.added_data[-1].seq_idx + 1, start)
if end > self.num_seqs:
end = self.num_seqs
if end >= self.num_seqs:
self.reached_final_seq = True
seqs = [self.generate_seq(seq_idx=seq_idx) for seq_idx in range(start, end)]
if self.window > 1:
for seq in seqs:
seq.features["data"] = self.sliding_window(seq.features["data"])
self._num_timesteps += sum([seq.num_frames for seq in seqs])
self.added_data += seqs
def generate_seq(self, seq_idx):
"""
:type seq_idx: int
:rtype: DatasetSeq
"""
raise NotImplementedError
def _shuffle_frames_in_seqs(self, start, end):
assert False, "Shuffling in GeneratingDataset does not make sense."
def get_num_timesteps(self):
assert self.reached_final_seq
return self._num_timesteps
@property
def num_seqs(self):
return self._num_seqs
def get_seq_length(self, sorted_seq_idx):
# get_seq_length() can be called before the seq is loaded via load_seqs().
# Thus, we just call load_seqs() ourselves here.
assert sorted_seq_idx >= self.expected_load_seq_start
self.load_seqs(self.expected_load_seq_start, sorted_seq_idx + 1)
return self._get_seq(sorted_seq_idx).num_frames
def get_data(self, seq_idx, key):
return self._get_seq(seq_idx).features[key]
def get_input_data(self, seq_idx):
return self.get_data(seq_idx, "data")
def get_targets(self, target, seq_idx):
return self.get_data(seq_idx, target)
def get_ctc_targets(self, sorted_seq_idx):
self._check_loaded_seq_idx(sorted_seq_idx)
assert self._get_seq(sorted_seq_idx).ctc_targets
def get_tag(self, sorted_seq_idx):
self._check_loaded_seq_idx(sorted_seq_idx)
return self._get_seq(sorted_seq_idx).seq_tag
class Task12AXDataset(GeneratingDataset):
"""
12AX memory task.
This is a simple memory task where there is an outer loop and an inner loop.
Description here: http://psych.colorado.edu/~oreilly/pubs-abstr.html#OReillyFrank06
"""
_input_classes = "123ABCXYZ"
_output_classes = "LR"
def __init__(self, **kwargs):
super(Task12AXDataset, self).__init__(
input_dim=len(self._input_classes),
output_dim=len(self._output_classes),
**kwargs)
def get_random_seq_len(self):
return self.random.randint(10, 100)
def generate_input_seq(self, seq_len):
"""
Somewhat made up probability distribution.
Try to make in a way that at least some "R" will occur in the output seq.
Otherwise, "R"s are really rare.
"""
seq = self.random.choice(["", "1", "2"])
while len(seq) < seq_len:
if self.random.uniform() < 0.5:
seq += self.random.choice(list("12"))
if self.random.uniform() < 0.9:
seq += self.random.choice(["AX", "BY"])
while self.random.uniform() < 0.5:
seq += self.random.choice(list(self._input_classes))
return list(map(self._input_classes.index, seq[:seq_len]))
@classmethod
def make_output_seq(cls, input_seq):
"""
:type input_seq: list[int]
:rtype: list[int]
"""
outer_state = ""
inner_state = ""
input_classes = cls._input_classes
output_seq_str = ""
for i in input_seq:
c = input_classes[i]
o = "L"
if c in "12":
outer_state = c
elif c in "AB":
inner_state = c
elif c in "XY":
if outer_state + inner_state + c in ["1AX", "2BY"]:
o = "R"
inner_state = ""
# Ignore other cases, "3CZ".
output_seq_str += o
return list(map(cls._output_classes.index, output_seq_str))
def estimate_output_class_priors(self, num_trials, seq_len=10):
"""
:type num_trials: int
:rtype: (float, float)
"""
count_l, count_r = 0, 0
for i in range(num_trials):
input_seq = self.generate_input_seq(seq_len)
output_seq = self.make_output_seq(input_seq)
count_l += output_seq.count(0)
count_r += output_seq.count(1)
return float(count_l) / (num_trials * seq_len), float(count_r) / (num_trials * seq_len)
def generate_seq(self, seq_idx):
seq_len = self.get_random_seq_len()
input_seq = self.generate_input_seq(seq_len)
output_seq = self.make_output_seq(input_seq)
features = class_idx_seq_to_1_of_k(input_seq, num_classes=len(self._input_classes))
targets = numpy.array(output_seq)
return DatasetSeq(seq_idx=seq_idx, features=features, targets=targets)
class TaskEpisodicCopyDataset(GeneratingDataset):
"""
Episodic Copy memory task.
This is a simple memory task where we need to remember a sequence.
Described in: http://arxiv.org/abs/1511.06464
Also tested for Associative LSTMs.
This is a variant where the lengths are random, both for the chars and for blanks.
"""
# Blank, delimiter and some chars.
_input_classes = " .01234567"
_output_classes = _input_classes
def __init__(self, **kwargs):
super(TaskEpisodicCopyDataset, self).__init__(
input_dim=len(self._input_classes),
output_dim=len(self._output_classes),
**kwargs)
def generate_input_seq(self):
seq = ""
# Start with random chars.
rnd_char_len = self.random.randint(1, 10)
seq += "".join([self.random.choice(list(self._input_classes[2:]))
for i in range(rnd_char_len)])
blank_len = self.random.randint(1, 100)
seq += " " * blank_len # blanks
seq += "." # 1 delim
seq += "." * (rnd_char_len + 1) # we wait for the outputs + 1 delim
return list(map(self._input_classes.index, seq))
@classmethod
def make_output_seq(cls, input_seq):
"""
:type input_seq: list[int]
:rtype: list[int]
"""
input_classes = cls._input_classes
input_mem = ""
output_seq_str = ""
state = 0
for i in input_seq:
c = input_classes[i]
if state == 0:
output_seq_str += " "
if c == " ": pass # just ignore
elif c == ".": state = 1 # start with recall now
else: input_mem += c
else: # recall from memory
# Ignore input.
if not input_mem:
output_seq_str += "."
else:
output_seq_str += input_mem[:1]
input_mem = input_mem[1:]
return list(map(cls._output_classes.index, output_seq_str))
def generate_seq(self, seq_idx):
input_seq = self.generate_input_seq()
output_seq = self.make_output_seq(input_seq)
features = class_idx_seq_to_1_of_k(input_seq, num_classes=len(self._input_classes))
targets = numpy.array(output_seq)
return DatasetSeq(seq_idx=seq_idx, features=features, targets=targets)
class TaskXmlModelingDataset(GeneratingDataset):
"""
XML modeling memory task.
This is a memory task where we need to remember a stack.
Defined in Jozefowicz et al. (2015).
Also tested for Associative LSTMs.
"""
# Blank, XML-tags and some chars.
_input_classes = " <>/abcdefgh"
_output_classes = _input_classes
def __init__(self, limit_stack_depth=4, **kwargs):
super(TaskXmlModelingDataset, self).__init__(
input_dim=len(self._input_classes),
output_dim=len(self._output_classes),
**kwargs)
self.limit_stack_depth = limit_stack_depth
def generate_input_seq(self):
# Because this is a prediction task, start with blank,
# and the output seq should predict the next char after the blank.
seq = " "
xml_stack = []
while True:
if not xml_stack or (len(xml_stack) < self.limit_stack_depth and self.random.rand() > 0.6):
tag_len = self.random.randint(1, 10)
tag = "".join([self.random.choice(list(self._input_classes[4:]))
for i in range(tag_len)])
seq += "<%s>" % tag
xml_stack += [tag]
else:
seq += "</%s>" % xml_stack.pop()
if not xml_stack and self.random.rand() > 0.2:
break
return list(map(self._input_classes.index, seq))
@classmethod
def make_output_seq(cls, input_seq):
"""
:type input_seq: list[int]
:rtype: list[int]
"""
input_classes = cls._input_classes
input_seq_str = "".join(cls._input_classes[i] for i in input_seq)
xml_stack = []
output_seq_str = ""
state = 0
for c in input_seq_str:
if c in " >":
output_seq_str += "<" # We expect an open char.
assert state != 1, repr(input_seq_str)
state = 1 # expect beginning of tag
elif state == 1: # in beginning of tag
output_seq_str += " " # We don't know yet.
assert c == "<", repr(input_seq_str)
state = 2
elif state == 2: # first char in tag
if c == "/":
assert xml_stack, repr(input_seq_str)
output_seq_str += xml_stack[-1][0]
xml_stack[-1] = xml_stack[-1][1:]
state = 4 # closing tag
else: # opening tag
output_seq_str += " " # We don't know yet.
assert c not in " <>/", repr(input_seq_str)
state = 3
xml_stack += [c]
elif state == 3: # opening tag
output_seq_str += " " # We don't know.
xml_stack[-1] += c
elif state == 4: # closing tag
assert xml_stack, repr(input_seq_str)
if not xml_stack[-1]:
output_seq_str += ">"
xml_stack.pop()
state = 0
else:
output_seq_str += xml_stack[-1][0]
xml_stack[-1] = xml_stack[-1][1:]
else:
assert False, "invalid state %i. input %r" % (state, input_seq_str)
return list(map(cls._output_classes.index, output_seq_str))
def generate_seq(self, seq_idx):
input_seq = self.generate_input_seq()
output_seq = self.make_output_seq(input_seq)
features = class_idx_seq_to_1_of_k(input_seq, num_classes=len(self._input_classes))
targets = numpy.array(output_seq)
return DatasetSeq(seq_idx=seq_idx, features=features, targets=targets)
class TaskVariableAssignmentDataset(GeneratingDataset):
"""
Variable Assignment memory task.
This is a memory task to test for key-value retrieval.
Defined in Associative LSTM paper.
"""
# Blank/Delim/End, Store/Query, and some chars for key/value.
_input_classes = " ,.SQ()abcdefgh"
_output_classes = _input_classes
def __init__(self, **kwargs):
super(TaskVariableAssignmentDataset, self).__init__(
input_dim=len(self._input_classes),
output_dim=len(self._output_classes),
**kwargs)
def generate_input_seq(self):
seq = ""
from collections import OrderedDict
store = OrderedDict()
# First the assignments.
num_assignments = self.random.randint(1, 5)
for i in range(num_assignments):
key_len = self.random.randint(2, 5)
while True: # find unique key
key = "".join([self.random.choice(list(self._input_classes[7:]))
for i in range(key_len)])
if key not in store: break
value_len = self.random.randint(1, 2)
value = "".join([self.random.choice(list(self._input_classes[7:]))
for i in range(value_len)])
if seq: seq += ","
seq += "S(%s,%s)" % (key, value)
store[key] = value
# Now one query.
key = self.random.choice(store.keys())
value = store[key]
seq += ",Q(%s)" % key
seq += "%s." % value
return list(map(self._input_classes.index, seq))
@classmethod
def make_output_seq(cls, input_seq):
"""
:type input_seq: list[int]
:rtype: list[int]
"""
input_classes = cls._input_classes
input_seq_str = "".join(cls._input_classes[i] for i in input_seq)
store = {}
key, value = "", ""
output_seq_str = ""
state = 0
for c in input_seq_str:
if state == 0:
key = ""
if c == "S": state = 1 # store
elif c == "Q": state = 2 # query
elif c in " ,": pass # can be ignored
else: assert False, "c %r in %r" % (c, input_seq_str)
output_seq_str += " "
elif state == 1: # store
assert c == "(", repr(input_seq_str)
state = 1.1
output_seq_str += " "
elif state == 1.1: # store.key
if c == ",":
assert key
value = ""
state = 1.5 # store.value
else:
assert c not in " .,SQ()", repr(input_seq_str)
key += c
output_seq_str += " "
elif state == 1.5: # store.value
if c == ")":
assert value
store[key] = value
state = 0
else:
assert c not in " .,SQ()", repr(input_seq_str)
value += c
output_seq_str += " "
elif state == 2: # query
assert c == "(", repr(input_seq_str)
state = 2.1
output_seq_str += " "
elif state == 2.1: # query.key
if c == ")":
value = store[key]
output_seq_str += value[0]
value = value[1:]
state = 2.5
else:
assert c not in " .,SQ()", repr(input_seq_str)
key += c
output_seq_str += " "
elif state == 2.5: # query result
assert c not in " .,SQ()", repr(input_seq_str)
if value:
output_seq_str += value[0]
value = value[1:]
else:
output_seq_str += "."
state = 2.6
elif state == 2.6: # query result end
assert c == ".", repr(input_seq_str)
output_seq_str += " "
else:
assert False, "invalid state %i, input %r" % (state, input_seq_str)
return list(map(cls._output_classes.index, output_seq_str))
def generate_seq(self, seq_idx):
input_seq = self.generate_input_seq()
output_seq = self.make_output_seq(input_seq)
features = class_idx_seq_to_1_of_k(input_seq, num_classes=len(self._input_classes))
targets = numpy.array(output_seq)
return DatasetSeq(seq_idx=seq_idx, features=features, targets=targets)
class DummyDataset(GeneratingDataset):
def __init__(self, input_dim, output_dim, num_seqs, seq_len=2,
input_max_value=10.0, input_shift=None, input_scale=None, **kwargs):
super(DummyDataset, self).__init__(input_dim=input_dim, output_dim=output_dim, num_seqs=num_seqs, **kwargs)
self.seq_len = seq_len
self.input_max_value = input_max_value
if input_shift is None: input_shift = -input_max_value / 2.0
self.input_shift = input_shift
if input_scale is None: input_scale = 1.0 / self.input_max_value
self.input_scale = input_scale
def generate_seq(self, seq_idx):
seq_len = self.seq_len
i1 = seq_idx
i2 = i1 + seq_len * self.num_inputs
features = numpy.array([((i % self.input_max_value) + self.input_shift) * self.input_scale
for i in range(i1, i2)]).reshape((seq_len, self.num_inputs))
i1, i2 = i2, i2 + seq_len
targets = numpy.array([i % self.num_outputs["classes"][0]
for i in range(i1, i2)])
return DatasetSeq(seq_idx=seq_idx, features=features, targets=targets)
class StaticDataset(GeneratingDataset):
"""
Provide all the data as a list of dict of numpy arrays.
"""
@classmethod
def copy_from_dataset(cls, dataset, start_seq_idx=0, max_seqs=None):
"""
:param Dataset dataset:
:param int start_seq_idx:
:param int|None max_seqs:
:rtype: StaticDataset
"""
if isinstance(dataset, StaticDataset):
return cls(
data=dataset.data, target_list=dataset.target_list,
output_dim=dataset.num_outputs, input_dim=dataset.num_inputs)
seq_idx = start_seq_idx
data = []
while dataset.is_less_than_num_seqs(seq_idx):
dataset.load_seqs(seq_idx, seq_idx + 1)
if max_seqs is not None and len(data) >= max_seqs:
break
seq_data = {key: dataset.get_data(seq_idx, key) for key in dataset.get_data_keys()}
data.append(seq_data)
seq_idx += 1
return cls(
data=data, target_list=dataset.get_target_list(),
output_dim=dataset.num_outputs, input_dim=dataset.num_inputs)
def __init__(self, data, target_list=None, output_dim=None, input_dim=None, **kwargs):
"""
:param list[dict[str,numpy.ndarray]] data: list of seqs, each provide the data for each data-key
:param int|None input_dim:
:param int|dict[str,(int,int)|list[int]] output_dim:
"""
assert len(data) > 0
self.data = data
num_seqs = len(data)
first_data = data[0]
self.data_keys = sorted(first_data.keys())
if target_list is not None:
for key in target_list:
assert key in self.data_keys
else:
target_list = self.data_keys
self.target_list = target_list
if output_dim is None:
output_dim = {}
output_dim = convert_data_dims(output_dim, leave_dict_as_is=False)
if input_dim is not None and "data" not in output_dim:
assert "data" in self.data_keys
output_dim["data"] = [input_dim, 2] # assume dense, not sparse
for key, value in first_data.items():
if key not in output_dim:
output_dim[key] = [value.shape[-1] if value.ndim >= 2 else 0, len(value.shape)]
for key in self.data_keys:
first_data_output = first_data[key]
assert len(first_data_output.shape) <= 2 # (time[,dim])
assert key in output_dim
assert output_dim[key][1] == len(first_data_output.shape)
if len(first_data_output.shape) >= 2:
assert output_dim[key][0] == first_data_output.shape[-1]
assert sorted(output_dim.keys()) == self.data_keys, "output_dim does noth match the given data"
super(StaticDataset, self).__init__(input_dim=input_dim, output_dim=output_dim, num_seqs=num_seqs, **kwargs)
def generate_seq(self, seq_idx):
data = self.data[seq_idx]
return DatasetSeq(seq_idx=seq_idx, features={key: data[key] for key in self.data_keys})
def get_data_keys(self):
return self.data_keys
def get_target_list(self):
return self.target_list
class CopyTaskDataset(GeneratingDataset):
def __init__(self, nsymbols, minlen=0, maxlen=0, minlen_epoch_factor=0, maxlen_epoch_factor=0, **kwargs):
# Sparse data.
super(CopyTaskDataset, self).__init__(input_dim=nsymbols,
output_dim={"data": [nsymbols, 1],
"classes": [nsymbols, 1]},
**kwargs)
assert nsymbols <= 256
self.nsymbols = nsymbols
self.minlen = minlen
self.maxlen = maxlen
self.minlen_epoch_factor = minlen_epoch_factor
self.maxlen_epoch_factor = maxlen_epoch_factor
def get_random_seq_len(self):
assert isinstance(self.epoch, int)
minlen = int(self.minlen + self.minlen_epoch_factor * self.epoch)
maxlen = int(self.maxlen + self.maxlen_epoch_factor * self.epoch)
assert 0 < minlen <= maxlen
return self.random.randint(minlen, maxlen + 1)
def generate_seq(self, seq_idx):
"""
:type seq_idx: int
:rtype: DatasetSeq
"""
seq_len = self.get_random_seq_len()
seq = [self.random.randint(0, self.nsymbols) for i in range(seq_len)]
seq_np = numpy.array(seq, dtype="int8")
return DatasetSeq(seq_idx=seq_idx, features=seq_np, targets={"classes": seq_np})
class _TFKerasDataset(CachedDataset2):
"""
Wraps around any dataset from tf.contrib.keras.datasets.
See: https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/keras/datasets
TODO: Should maybe be moved to a separate file. (Only here because of tf.contrib.keras.datasets.reuters).
"""
# TODO...
class _NltkCorpusReaderDataset(CachedDataset2):
"""
Wraps around any dataset from nltk.corpus.
TODO: Should maybe be moved to a separate file, e.g. CorpusReaderDataset.py or so?
"""
# TODO ...
class ExtractAudioFeatures:
"""
Currently uses librosa to extract MFCC features.
(Alternatives: python_speech_features, talkbox.features.mfcc, librosa)
We could also add support e.g. to directly extract log-filterbanks or so.
"""
def __init__(self,
window_len=0.025, step_len=0.010,
num_feature_filters=40, with_delta=False, norm_mean=None, norm_std_dev=None,
features="mfcc", random_permute=None, random_state=None):
"""
:param float window_len: in seconds
:param float step_len: in seconds
:param int num_feature_filters:
:param bool|int with_delta:
:param numpy.ndarray|str|None norm_mean: if str, will interpret as filename
:param numpy.ndarray|str|None norm_std_dev: if str, will interpret as filename
:param str features: "mfcc", "log_mel_filterbank", "log_log_mel_filterbank"
:param CollectionReadCheckCovered|dict[str]|bool|None random_permute:
:param numpy.random.RandomState|None random_state:
:return: (audio_len // int(step_len * sample_rate), (with_delta + 1) * num_feature_filters), float32
:rtype: numpy.ndarray
"""
self.window_len = window_len
self.step_len = step_len
self.num_feature_filters = num_feature_filters
if isinstance(with_delta, bool):
with_delta = 1 if with_delta else 0
assert isinstance(with_delta, int) and with_delta >= 0
self.with_delta = with_delta
if norm_mean is not None:
norm_mean = self._load_feature_vec(norm_mean)
if norm_std_dev is not None:
norm_std_dev = self._load_feature_vec(norm_std_dev)
self.norm_mean = norm_mean
self.norm_std_dev = norm_std_dev
if random_permute and not isinstance(random_permute, CollectionReadCheckCovered):
random_permute = CollectionReadCheckCovered.from_bool_or_dict(random_permute)
self.random_permute_opts = random_permute
self.random_state = random_state
self.features = features
def _load_feature_vec(self, value):
"""
:param str|None value:
:return: shape (self.num_inputs,), float32
:rtype: numpy.ndarray|None
"""
if value is None:
return None
if isinstance(value, str):
value = numpy.loadtxt(value)
assert isinstance(value, numpy.ndarray)
assert value.shape == (self.get_feature_dimension(),)
return value.astype("float32")
def get_audio_features(self, audio, sample_rate):
"""
:param numpy.ndarray audio: raw audio samples, shape (audio_len,)
:param int sample_rate: e.g. 22050
:rtype: numpy.ndarray
"""
kwargs = {
"sample_rate": sample_rate,
"window_len": self.window_len,
"step_len": self.step_len,
"num_feature_filters": self.num_feature_filters,
}
peak = numpy.max(numpy.abs(audio))
audio /= peak
if self.random_permute_opts and self.random_permute_opts.truth_value:
audio = _get_random_permuted_audio(
audio=audio,
sample_rate=sample_rate,
opts=self.random_permute_opts,
random_state=self.random_state)
kwargs["audio"] = audio
if self.features == "mfcc":
feature_data = _get_audio_features_mfcc(**kwargs)
elif self.features == "log_mel_filterbank":
feature_data = _get_audio_log_mel_filterbank(**kwargs)
elif self.features == "log_log_mel_filterbank":
feature_data = _get_audio_log_log_mel_filterbank(**kwargs)
else:
assert False, "non-supported feature type %s" % self.features
assert feature_data.ndim == 2
assert feature_data.shape[1] == self.num_feature_filters
if self.with_delta:
import librosa
deltas = [librosa.feature.delta(feature_data, order=i, axis=0).astype("float32")
for i in range(1, self.with_delta + 1)]
feature_data = numpy.concatenate([feature_data] + deltas, axis=1)
assert feature_data.shape[1] == self.get_feature_dimension()
if self.norm_mean is not None:
feature_data -= self.norm_mean[None, :]
if self.norm_std_dev is not None:
feature_data /= self.norm_std_dev[None, :]
return feature_data
def get_feature_dimension(self):
return (self.with_delta + 1) * self.num_feature_filters
def _get_audio_features_mfcc(audio, sample_rate, window_len=0.025, step_len=0.010, num_feature_filters=40):
"""
:param numpy.ndarray audio: raw audio samples, shape (audio_len,)
:param int sample_rate: e.g. 22050
:param float window_len: in seconds
:param float step_len: in seconds
:param int num_feature_filters:
:return: (audio_len // int(step_len * sample_rate), num_feature_filters), float32
:rtype: numpy.ndarray
"""
import librosa
mfccs = librosa.feature.mfcc(
audio, sr=sample_rate,
n_mfcc=num_feature_filters,
hop_length=int(step_len * sample_rate), n_fft=int(window_len * sample_rate))
energy = librosa.feature.rmse(
audio,
hop_length=int(step_len * sample_rate), frame_length=int(window_len * sample_rate))
mfccs[0] = energy # replace first MFCC with energy, per convention
assert mfccs.shape[0] == num_feature_filters # (dim, time)
mfccs = mfccs.transpose().astype("float32") # (time, dim)
return mfccs
def _get_audio_log_mel_filterbank(audio, sample_rate, window_len=0.025, step_len=0.010, num_feature_filters=80):
"""
Computes log Mel-filterbank features from an audio signal.
References:
https://github.com/jameslyons/python_speech_features/blob/master/python_speech_features/base.py
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/speech_recognition.py
:param numpy.ndarray audio: raw audio samples, shape (audio_len,)
:param int sample_rate: e.g. 22050
:param float window_len: in seconds
:param float step_len: in seconds
:param int num_feature_filters:
:return: (audio_len // int(step_len * sample_rate), num_feature_filters), float32
:rtype: numpy.ndarray
"""
import librosa
mel_filterbank = librosa.feature.melspectrogram(
audio, sr=sample_rate,
n_mels=num_feature_filters,
hop_length=int(step_len * sample_rate), n_fft=int(window_len * sample_rate))
log_noise_floor = 1e-3 # prevent numeric overflow in log
log_mel_filterbank = numpy.log(numpy.maximum(log_noise_floor, mel_filterbank))
assert log_mel_filterbank.shape[0] == num_feature_filters
log_mel_filterbank = log_mel_filterbank.transpose().astype("float32") # (time, dim)
return log_mel_filterbank
def _get_audio_log_log_mel_filterbank(audio, sample_rate, window_len=0.025, step_len=0.010, num_feature_filters=80):
"""
Computes log-log Mel-filterbank features from an audio signal.
References:
https://github.com/jameslyons/python_speech_features/blob/master/python_speech_features/base.py
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/speech_recognition.py
:param numpy.ndarray audio: raw audio samples, shape (audio_len,)
:param int sample_rate: e.g. 22050
:param float window_len: in seconds
:param float step_len: in seconds
:param int num_feature_filters:
:return: (audio_len // int(step_len * sample_rate), num_feature_filters), float32
:rtype: numpy.ndarray
"""
import librosa
mel_filterbank = librosa.feature.melspectrogram(
audio, sr=sample_rate,
n_mels=num_feature_filters,
hop_length=int(step_len * sample_rate), n_fft=int(window_len * sample_rate))
log_noise_floor = 1e-3 # prevent numeric overflow in log
log_mel_filterbank = numpy.log(numpy.maximum(log_noise_floor, mel_filterbank))
log_log_mel_filterbank = librosa.core.amplitude_to_db(log_mel_filterbank)
assert log_log_mel_filterbank.shape[0] == num_feature_filters
log_log_mel_filterbank = log_log_mel_filterbank.transpose().astype("float32") # (time, dim)
return log_log_mel_filterbank
def _get_random_permuted_audio(audio, sample_rate, opts, random_state):
"""
:param numpy.ndarray audio: raw time signal
:param int sample_rate:
:param CollectionReadCheckCovered opts:
:param numpy.random.RandomState random_state:
:return: audio randomly permuted
:rtype: numpy.ndarray
"""
import librosa
import scipy.ndimage
import warnings
audio = audio * random_state.uniform(opts.get("rnd_scale_lower", 0.8), opts.get("rnd_scale_upper", 1.0))
if random_state.uniform(0.0, 1.0) < opts.get("rnd_zoom_switch", 0.2):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# Alternative: scipy.interpolate.interp2d
factor = random_state.uniform(opts.get("rnd_zoom_lower", 0.9), opts.get("rnd_zoom_upper", 1.1))
audio = scipy.ndimage.zoom(audio, factor, order=3)
if random_state.uniform(0.0, 1.0) < opts.get("rnd_stretch_switch", 0.2):
rate = random_state.uniform(opts.get("rnd_stretch_lower", 0.9), opts.get("rnd_stretch_upper", 1.2))
audio = librosa.effects.time_stretch(audio, rate=rate)
if random_state.uniform(0.0, 1.0) < opts.get("rnd_pitch_switch", 0.2):
n_steps = random_state.uniform(opts.get("rnd_pitch_lower", -1.), opts.get("rnd_pitch_upper", 1.))
audio = librosa.effects.pitch_shift(audio, sr=sample_rate, n_steps=n_steps)
opts.assert_all_read()
return audio
class TimitDataset(CachedDataset2):
"""
DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus.
You must provide the data.
Demo:
tools/dump-dataset.py "{'class': 'TimitDataset', 'timit_dir': '...'}"
tools/dump-dataset.py "{'class': 'TimitDataset', 'timit_dir': '...', 'demo_play_audio': True, 'random_permute_audio': True}"
The full train data has 3696 utterances and the core test data has 192 utterances
(24-speaker core test set).
For some references:
https://github.com/ppwwyyxx/tensorpack/blob/master/examples/CTC-TIMIT/train-timit.py
https://www.cs.toronto.edu/~graves/preprint.pdf
https://arxiv.org/pdf/1303.5778.pdf
https://arxiv.org/pdf/0804.3269.pdf
"""
# via: https://github.com/kaldi-asr/kaldi/blob/master/egs/timit/s5/conf/phones.60-48-39.map
PhoneMapTo39 = {
'aa': 'aa', 'ae': 'ae', 'ah': 'ah', 'ao': 'aa', 'aw': 'aw', 'ax': 'ah', 'ax-h': 'ah', 'axr': 'er',
'ay': 'ay', 'b': 'b', 'bcl': 'sil', 'ch': 'ch', 'd': 'd', 'dcl': 'sil', 'dh': 'dh', 'dx': 'dx', 'eh': 'eh',
'el': 'l', 'em': 'm', 'en': 'n', 'eng': 'ng', 'epi': 'sil', 'er': 'er', 'ey': 'ey', 'f': 'f', 'g': 'g',
'gcl': 'sil', 'h#': 'sil', 'hh': 'hh', 'hv': 'hh', 'ih': 'ih', 'ix': 'ih', 'iy': 'iy', 'jh': 'jh',
'k': 'k', 'kcl': 'sil', 'l': 'l', 'm': 'm', 'n': 'n', 'ng': 'ng', 'nx': 'n', 'ow': 'ow', 'oy': 'oy',
'p': 'p', 'pau': 'sil', 'pcl': 'sil', 'q': None, 'r': 'r', 's': 's', 'sh': 'sh', 't': 't', 'tcl': 'sil',
'th': 'th', 'uh': 'uh', 'uw': 'uw', 'ux': 'uw', 'v': 'v', 'w': 'w', 'y': 'y', 'z': 'z', 'zh': 'sh'}
PhoneMapTo48 = {
'aa': 'aa', 'ae': 'ae', 'ah': 'ah', 'ao': 'ao', 'aw': 'aw', 'ax': 'ax', 'ax-h': 'ax', 'axr': 'er',
'ay': 'ay', 'b': 'b', 'bcl': 'vcl', 'ch': 'ch', 'd': 'd', 'dcl': 'vcl', 'dh': 'dh', 'dx': 'dx', 'eh': 'eh',
'el': 'el', 'em': 'm', 'en': 'en', 'eng': 'ng', 'epi': 'epi', 'er': 'er', 'ey': 'ey', 'f': 'f', 'g': 'g',
'gcl': 'vcl', 'h#': 'sil', 'hh': 'hh', 'hv': 'hh', 'ih': 'ih', 'ix': 'ix', 'iy': 'iy', 'jh': 'jh',
'k': 'k', 'kcl': 'cl', 'l': 'l', 'm': 'm', 'n': 'n', 'ng': 'ng', 'nx': 'n', 'ow': 'ow', 'oy': 'oy',
'p': 'p', 'pau': 'sil', 'pcl': 'cl', 'q': None, 'r': 'r', 's': 's', 'sh': 'sh', 't': 't', 'tcl': 'cl',
'th': 'th', 'uh': 'uh', 'uw': 'uw', 'ux': 'uw', 'v': 'v', 'w': 'w', 'y': 'y', 'z': 'z', 'zh': 'zh'}
Phones61 = PhoneMapTo39.keys()
PhoneMapTo61 = {p: p for p in Phones61}
@classmethod
def _get_phone_map(cls, num_phones=61):
"""
:param int num_phones:
:return: map 61-phone-set-phone -> num_phones-phone-set-phone
:rtype: dict[str,str|None]
"""
return {61: cls.PhoneMapTo61, 48: cls.PhoneMapTo48, 39: cls.PhoneMapTo39}[num_phones]
@classmethod
def _get_labels(cls, phone_map):
"""
:param dict[str,str|None] phone_map:
:rtype: list[str]
"""
labels = sorted(set(filter(None, phone_map.values())))
# Make 'sil' the 0 phoneme.
if "pau" in labels:
labels.remove("pau")
labels.insert(0, "pau")
else:
labels.remove("sil")
labels.insert(0, "sil")
return labels
@classmethod
def get_label_map(cls, source_num_phones=61, target_num_phones=39):
"""
:param int source_num_phones:
:param int target_num_phones:
:rtype: dict[int,int|None]
"""
src_phone_map = cls._get_phone_map(source_num_phones) # 61-phone -> src-phone
src_labels = cls._get_labels(src_phone_map) # src-idx -> src-phone
tgt_phone_map = cls._get_phone_map(target_num_phones) # 61-phone -> tgt-phone
tgt_labels = cls._get_labels(tgt_phone_map) # tgt-idx -> tgt-phone
d = {i: src_labels[i] for i in range(source_num_phones)} # src-idx -> src-phone|61-phone
if source_num_phones != 61:
src_phone_map_rev = {v: k for (k, v) in sorted(src_phone_map.items())} # src-phone -> 61-phone
d = {i: src_phone_map_rev[v] for (i, v) in d.items()} # src-idx -> 61-phone
d = {i: tgt_phone_map[v] for (i, v) in d.items()} # src-idx -> tgt-phone
d = {i: tgt_labels.index(v) if v else None for (i, v) in d.items()} # src-idx -> tgt-idx
return d
def __init__(self, timit_dir, train=True, preload=False,
num_feature_filters=40, feature_window_len=0.025, feature_step_len=0.010, with_delta=False,
norm_mean=None, norm_std_dev=None,
random_permute_audio=None, num_phones=61,
demo_play_audio=False, fixed_random_seed=None, **kwargs):
"""
:param str timit_dir: directory of TIMIT. should contain train/filelist.phn and test/filelist.core.phn
:param bool train: whether to use the train or core test data
:param bool preload: if True, here at __init__, we will wait until we loaded all the data
:param int num_feature_filters: e.g. number of MFCCs
:param bool|int with_delta: whether to add delta features (doubles the features dim). if int, up to this degree
:param str norm_mean: file with mean values which are used for mean-normalization of the final features
:param str norm_std_dev: file with std dev valeus for variance-normalization of the final features
:param None|bool|dict[str] random_permute_audio: enables permutation on the audio. see _get_random_permuted_audio
:param int num_phones: 39, 48 or 61. num labels of our classes
:param bool demo_play_audio: plays the audio. only make sense with tools/dump-dataset.py
:param None|int fixed_random_seed: if given, use this fixed random seed in every epoch
"""
super(TimitDataset, self).__init__(**kwargs)
from threading import Lock, Thread
self._lock = Lock()
self._num_feature_filters = num_feature_filters
self._feature_window_len = feature_window_len
self._feature_step_len = feature_step_len
self.num_inputs = self._num_feature_filters
if isinstance(with_delta, bool):
with_delta = 1 if with_delta else 0
assert isinstance(with_delta, int) and with_delta >= 0
self._with_delta = with_delta
self.num_inputs *= (1 + with_delta)
self._norm_mean = self._load_feature_vec(norm_mean)
self._norm_std_dev = self._load_feature_vec(norm_std_dev)
assert num_phones in {61, 48, 39}
self._phone_map = {61: self.PhoneMapTo61, 48: self.PhoneMapTo48, 39: self.PhoneMapTo39}[num_phones]
self.labels = self._get_labels(self._phone_map)
self.num_outputs = {"data": (self.num_inputs, 2), "classes": (len(self.labels), 1)}