def on_epoch_end(self, epoch, logs={}):
monitor_values = logs[self.key_monitor]
if len(monitor_values) >= 2:
io.log('{}: {} --> {}'.format(self.key_monitor, self.best_val,
monitor_values[-1]))
if len(monitor_values) < 2 or self.monitor_op(monitor_values[-1],
self.best_val):
io.log('saving weights')
self.model.save_weights(self.weights_path, overwrite=True)
self.best_val = monitor_values[-1]
io.log('best {}: {}'.format(self.key_monitor, self.best_val))
self.wait = 0
else:
if self.wait >= self.patience or self.curr_lr < self.min_lr:
self.model.stop_training = True
self.wait += 1
if self.restore > 0:
io.log('loading weights from last epoch')
self.model.load_weights(self.weights_path)
if self.k > 1.:
self.curr_lr /= self.k
K.set_value(self.model.optimizer.lr, self.curr_lr)
io.log('lr: {} --> {}'.format(self.curr_lr * self.k,
self.curr_lr))
def validate_rnn(model, buf_valid, metrics=['acc'], stateful=False,
report_interval=20):
"""
Evaluate against a dataset. Return a list of values, one for each metric.
`metrics` must be a list.
"""
metrics = [init_metric(m) for m in metrics]
chunks_read = 0
while True:
# Load data chunk
X = buf_valid.read_next_chunk()
if X is None:
break
report = chunks_read % report_interval == 0
chunks_read += 1
# Validate
valid_Xs, valid_ys, valid_eobs, utt_indices = X
if report:
io.log('Validating on chunk {} ({} utts, max dur {})'.format(
buf_valid.get_progress(), len(utt_indices), valid_Xs[0].shape[1]
))
for valid_X, valid_y, valid_eob in zip(valid_Xs, valid_ys, valid_eobs):
preds = model.predict(
valid_X, batch_size=len(utt_indices), verbose=0
)
for i in range(len(valid_eob)):
y_true = valid_y[i][buf_valid.get_delay():valid_eob[i]]
y_pred = preds[i][buf_valid.get_delay():valid_eob[i]]
for m in metrics:
m.accum(y_true, y_pred)
if stateful:
model.reset_states()
return [m.eval() for m in metrics]
def pre_execute(self):
# Hack to fix recursion depth exceeded
sys.setrecursionlimit(1000000)
io.log('args: {}'.format(vars(self.args)))
self._init_output_dir()
self._init_records()
self._init_best_err()
def __get_word_labels(text, utt):
""" Return word-level error labels for text. Each error label is an
array of strings, containing:
<wrd_start> <wrd_end> <label1> <label1> ...
"""
if len(text) == 0 or text[0] is None:
return None
wlabels_raw = []
widx = -1
for idx, w in zip(range(len(text)), text):
if __is_word(w):
widx += 1
wlabel = [widx]
err_indices = __find_errs(text, idx)
if len(err_indices) == 0:
wlabel.append('C/{}'.format(w))
else:
io.log('**WARN** {} - word with multiple errors: {}'.format(
utt, text[idx:err_indices[-1] + 1]))
# Word target is shared across different error types
target = __parse_target(text, idx, err_indices[-1] + 1, utt)
for err_idx in err_indices:
err_code = __parse_err_code(text, err_idx, utt)
wlabel.append('{}/{}'.format(err_code, target))
wlabels_raw.append(wlabel)
return __finalize_wlabels(wlabels_raw, text, utt)
def _init_base_err(self):
""" Compute validation results using base model.
"""
args = self.args
if 'base' in self.records:
valid_errs = self.records['base'][1:]
self.base_err = valid_errs[args.validate_on]
return
metrics = ['acc']
if args.task != 'classification':
metrics = ['mean_squared_error']
io.log('Reporting base model results, metrics = {}'.format(metrics))
# Convert to multi-task to conform with data provider
mt_model = Model(input=self.base_model.input,
output=[self.base_model.output] * 2)
train_err = -1 # Don't compute error on training set, too expensive
valid_errs = []
for buf_valid in self.buf_valids:
valid_errs.append(
learning_mtrnn.validate_mtrnn(mt_model,
buf_valid,
metrics=metrics)[0][0])
io.log('--> base: train err {}, valid err {}'.format(
train_err, valid_errs[args.validate_on]))
# Update records and clean up
self._update_records('base', [train_err] + valid_errs)
self.base_err = valid_errs[args.validate_on]
del mt_model
def eval_dnn(dnn, buf_dataset, shared_ds=None):
""" Return the mini-batch error rate on the given dataset.
:type dnn: models.dnn.DNN
:param dnn: The DNN to use for evaluation
:type buf_dataset: chaipy.data.temporal.BufferedTemporalData
:param buf_dataset: The dataset to evaluate on
:type shared_ds: tuple (see BufferedTemporalData.make_shared)
:param shared_ds: (optional) The shared dataset to use. If not set, will
be automatically created from buf_dataset.
:rtype: float
:return: The mean mini-batch error rate (percentage)
"""
if shared_ds is None:
shared_ds = buf_dataset.make_shared()
x, shared_x, y, shared_y = shared_ds[:4]
# Compile validation function
io.log('... getting the validation function')
valid_fn = dnn.build_validation_function((shared_x, shared_y),
batch_size=dnn.cfg.batch_size)
io.log('Got it!')
# Get error
errors = _validate(valid_fn, buf_dataset, dnn.cfg.batch_size, shared_ds)
return 100 * numpy.mean(errors)
def __proc_compound_words(text, utt):
""" Compound words are linked by '+', '-', or '_'. We need to break them
apart. The tricky thing is to preserve special form markers after breaking.
"""
if len(text) == 0 or text[0] is None:
return text
new_text = []
for idx, w in zip(range(len(text)), text):
if __is_word(w) and not w.startswith('<') and \
any([c in w for c in ['+', '_', '-']]):
if any([c in w for c in ['@o', '@s', '@b', '@si', '@i']]):
io.log('**WARN** {} - not breaking {}'.format(utt, w))
new_text.append(w)
else:
err_indices = __find_errs(text, idx)
if len(err_indices) != 0:
io.log('**WARN** {} - compound with errs: {}'.format(
utt, text[idx:err_indices[-1] + 1]))
ary = w.split('@')
new_words = ary[0].replace('+', ' ')
new_words = new_words.replace('_', ' ')
new_words = new_words.replace('-', ' ')
nw_ary = new_words.split()
for i, nw in zip(range(len(nw_ary)), nw_ary):
new_text.append('@'.join([nw] + ary[1:]))
if len(err_indices) != 0 and i < len(nw_ary) - 1:
new_text.append('[* ->]')
else:
new_text.append(w)
return new_text
def _train(self, model, lrate, optimizer, model_name, model_dir,
resume_json, **kwargs):
""" Train model.
"""
args = self.args
loss = []
validate_metrics = {}
for i in range(len(args.task)):
if args.task[i] == 'classification':
loss.append('sparse_categorical_crossentropy')
validate_metrics[i] = ['acc']
else:
loss.append('mean_squared_error')
validate_metrics[i] = ['mean_squared_error']
metrics = ['acc']
if args.task[args.primary_task] != 'classification':
metrics = ['mean_squared_error']
io.log('Loss: {}, Metrics: {}, Primary Task: {}, Task Weights: {}'.format(
loss, validate_metrics, args.primary_task, args.task_weights
))
train_errs, valid_errs = learning_mtrnn.train_mtrnn_v2(
model, self.buf_train, self.buf_valids, lrate,
validate_on=args.validate_on, optimizer=optimizer, save_dir=model_dir,
pre_validate=resume_json is not None, restore=True,
loss=loss, metrics=metrics, validate_metrics=validate_metrics,
ntasks=len(args.task), primary_task=args.primary_task,
task_weights=args.task_weights
)
io.log('--> {}: train err {}, valid err {}'.format(
model_name, train_errs[-1], valid_errs[args.validate_on][-1]
))
return (train_errs, valid_errs)
def _train(self, model, lrate, optimizer, model_name, model_dir,
resume_json, **kwargs):
""" Train model.
"""
args = self.args
loss = 'sparse_categorical_crossentropy'
metrics = ['acc']
if args.task != 'classification':
loss = 'mean_squared_error'
metrics = ['mean_squared_error']
io.log('Loss: {}, Metrics: {}'.format(loss, metrics))
train_errs, valid_errs = learning_rnn.train_rnn_v2(
model,
self.buf_train,
self.buf_valids,
lrate,
validate_on=args.validate_on,
optimizer=optimizer,
save_dir=model_dir,
pre_validate=resume_json is not None,
restore=True,
loss=loss,
metrics=metrics)
io.log('--> {}: train err {}, valid err {}'.format(
model_name, train_errs[-1], valid_errs[args.validate_on][-1]))
return (train_errs, valid_errs)
def _init_ivectors(self):
""" Add i-vectors if applicable.
"""
args = self.args
self.ivectors = None
if args.ivectors is not None:
io.log('Loading i-vectors from {}'.format(args.ivectors))
self.ivectors = ivector_ark_read(args.ivectors)
def _init_base_model(self):
""" Initialize base model.
"""
args = self.args
io.log('Initializing base model, json: {}, weights: {}'.format(
args.base_model_json, args.base_model_weights))
self.base_model = model_from_json(io.json_load(args.base_model_json))
self.base_model.load_weights(args.base_model_weights)
def train_gmms(group2array, n_components=1, cov_type='diag', ordered=True):
""" Train a GMM for each group. Return group to GMM mapping. """
gmms = OrderedDict() if ordered else {}
for group in group2array:
log('Fitting GMM for {}'.format(group))
gmms[group] = GMM(n_components=n_components, covariance_type=cov_type)
gmms[group].fit(group2array[group])
return gmms
def _save_model(self, model, model_name, results):
""" Save model to disk if applicable.
"""
args = self.args
valid_errs = results[1:]
if args.save_all or valid_errs[args.validate_on] < self.best_err:
model_fname = os.path.join(args.output_dir, model_name)
io.log('Saving final model to {}'.format(model_fname))
io.json_save('{}.json'.format(model_fname), model.to_json())
model.save_weights('{}.weights'.format(model_fname))
def _ext_model(self, model_name):
""" Check if model configuration has already been computed.
"""
if model_name not in self.records:
return False
train_err = self.records[model_name][0]
valid_errs = self.records[model_name][1:]
io.log('--> {}: train err {}, valid err {}'.format(
model_name, train_err, valid_errs[self.args.validate_on]))
return True
def main():
desc = 'Extract features with DNN. Output to Kaldi ark.'
parser = common.init_argparse(desc)
parser.add_argument('model_in', help='Model that can be read by load_dnn')
parser.add_argument('feats_scp', help='scp of input features')
parser.add_argument('ark_out', help='Output ark file')
parser.add_argument('--output-layer', type=int, default=-2,
help='Layer to use for extracting features. ' + \
'Negative index can be used. For example, ' + \
'-1 means the last layer, and so on.')
parser.add_argument('--context',
type=int,
default=8,
help='Number of context frames for splicing')
parser.add_argument('--padding', default='replicate',
help='What to do with out-of-bound frames. Valid ' + \
'values: [replicate|zero]')
parser.add_argument('--ivectors', help='Utterance i-vectors to append')
parser.add_argument('--chunk-size',
default='300m',
help='Chunk size for data buffering')
args = parser.parse_args()
io.log('Initializing dataset')
ivectors = None if args.ivectors is None else \
io.ivector_ark_read(args.ivectors, dtype=theano.config.floatX)
dataset = init_dataset(args.feats_scp, args.context, args.padding,
ivectors)
io.log('Initializing model')
dnn = load_dnn(args.model_in)
# Initializing shared_ds according to chunk_size
num_items = get_num_items(args.chunk_size, theano.config.floatX)
max_frames = num_items / dataset.get_dim()
max_utt_frames = np.max(
map(dataset.get_num_frames_by_utt_name, dataset.get_utt_names()))
common.CHK_GE(max_frames, max_utt_frames)
x = np.zeros((max_frames, dataset.get_dim()), dtype=theano.config.floatX)
io.log('...getting extraction function')
extract_fn = dnn.build_extract_feat_function(args.output_layer)
io.log('Got it!')
io.log('** Begin outputting to {} **'.format(args.ark_out))
ark_out = KaldiWriteOut(args.ark_out)
utt_names, utt_frames, total_frames = [], [], 0
for utt in dataset.get_utt_names():
frames = dataset.get_num_frames_by_utt_name(utt)
if total_frames + frames > max_frames:
__extract(extract_fn, ark_out, dataset, x, utt_names, utt_frames)
utt_names, utt_frames, total_frames = [], [], 0
utt_names.append(utt)
utt_frames.append(frames)
total_frames += frames
__extract(extract_fn, ark_out, dataset, x, utt_names, utt_frames)
ark_out.close()
def validate_mtrnn(model,
buf_valid,
ntasks=None,
metrics=['acc'],
stateful=False,
report_interval=20):
"""
Evaluate against a dataset. Return a list of lists, one for each task.
Each task-specific list is a list of values, one for each metric.
`metrics` can be a list (same metrics applied to all tasks) or a dict
that maps from task ID to a list of task-specific metrics.
"""
if ntasks is None:
ntasks = len(buf_valid.dataset().get_frame_labels())
mt_metrics = []
for t in range(ntasks):
st_metrics = metrics
if type(metrics) != list:
assert type(metrics) == dict
st_metrics = metrics[t]
mt_metrics.append([init_metric(m) for m in st_metrics])
chunks_read = 0
while True:
# Load data chunk
X = buf_valid.read_next_chunk()
if X is None:
break
report = chunks_read % report_interval == 0
chunks_read += 1
# Validate
valid_Xs, valid_ys, valid_eobs, utt_indices = X
if report:
io.log('Validating on chunk {} ({} utts, max dur {})'.format(
buf_valid.get_progress(), len(utt_indices),
valid_Xs[0].shape[1]))
for valid_X, valid_y, valid_eob in zip(valid_Xs, valid_ys, valid_eobs):
preds = model.predict(valid_X,
batch_size=len(utt_indices),
verbose=0)
# Special handling for single-task
if ntasks == 1:
preds = [preds]
for t in range(ntasks):
for i in range(len(valid_eob)):
y_true = valid_y[t][i][buf_valid.get_delay():valid_eob[i]]
y_pred = preds[t][i][buf_valid.get_delay():valid_eob[i]]
for m in mt_metrics[t]:
m.accum(y_true, y_pred)
if stateful:
model.reset_states()
valid_metrics = []
for t in range(ntasks):
valid_metrics.append([m.eval() for m in mt_metrics[t]])
return valid_metrics
def __separate_blocks(text, utt):
""" For blocks of text like <he is playing>, the angular brackets need
to be separated so we can easily retrieve the whole block if needed.
"""
if len(text) == 0 or text[0] is None:
return text
new_text = []
for w in text:
# Check for 1-letter word (stand-alone brackets are allowed)
if len(w) == 1:
new_text.append(w)
# If start of block, separate the opening bracket
elif w.startswith('<') and not w.endswith('>'):
new_text.append(w[0])
new_text.append(w[1:])
# If end of block, separate the closing bracket
elif w.endswith('>') and not w.startswith('<'):
new_text.append(w[:len(w) - 1])
new_text.append(w[len(w) - 1])
elif w == "(?)":
new_text.append('&=unk')
# If one whole block, separate both brackets
elif w.startswith('<') and w.endswith('>'):
common.CHK_NEQ(w, '<>')
io.log('**WARN** {} - rare stand-alone block: {}'.format(utt, w))
new_text.append(w[0])
new_text.append(w[1:len(w) - 1])
new_text.append(w[len(w) - 1])
else:
new_text.append(w)
# Make sure there are matching brackets. Unlike in explanations, in word
# blocks opening brackets don't have to match immediately.
opening = common.find_all(new_text, '<')
closing = common.find_all(new_text, '>')
if len(opening) != len(closing):
with open('/z/mkperez/HD_Project/Aphasia_Bank_data/overlap_log.txt',
'a') as wf:
wf.write("prob: %s\n" % (utt))
wf.write("prob text: %s\n" % (text))
# raise ValueError('{} - has {} < but {} >: {}'.format(
# utt, len(opening), len(closing), new_text
# ))
elif len(opening) != 0:
used = []
for c in closing:
matched = False
for o in [x for x in reversed(opening) if x < c and x not in used]:
matched = True
used.append(o)
break
if not matched:
raise ValueError('{} - unmatched >: {}'.format(
utt, new_text[:c + 1]))
return new_text
def _init_best_err(self):
""" Determine the best model thus far.
"""
self.best_model, self.best_err = None, FLT_MAX
for model_name in self.records:
valid_errs = self.records[model_name][1:]
if valid_errs[self.args.validate_on] < self.best_err:
self.best_model = model_name
self.best_err = valid_errs[self.args.validate_on]
io.log('Best existing model: {}, Best existing err: {}'.format(
self.best_model, self.best_err))
def run(ivectors, train_group2ivecs, test_ivecs, cov_type):
all_data = OrderedDict()
for group in train_group2ivecs:
train_data = np.asarray(map(lambda x: ivectors[x],
train_group2ivecs[group]))
all_data[group] = train_data
io.log('Training GMMs')
gmms = train_gmms(all_data, n_components=1, cov_type=cov_type, ordered=True)
io.log('Getting predictions')
test_data = np.vstack(map(lambda x: ivectors[x], test_ivecs))
return predict_gmm(test_data, gmms)
def main():
desc = 'Convert from alignment with length to regular alignments. Output to stdout.'
parser = common.init_argparse(desc)
parser.add_argument('ali_with_length', help='Alignment with lengths')
args = parser.parse_args()
ali = ali_with_length_read(args.ali_with_length, ordered=True, expand=True)
io.log('Read {} aligment with lengths'.format(len(ali)))
for key in ali:
print '{} {}'.format(key, ' '.join(ali[key]))
def phone2word_ali(key, phone_tokens, words, lexicon, sil_label, length):
""" Print word alignment to stdout. If there are several possible
alignments, arbitrarily choose the first one.
"""
word_alis = get_alignments(words, phone_tokens, lexicon)
if word_alis is None:
raise ValueError('{} - failed to align {} to {}'.format(
key, words, phone_tokens))
if len(word_alis) > 1:
io.log('WARNING - {} has multiple ({}) alignments: {} ({})'.format(
key, len(word_alis), word_alis, words))
__print_ali(key, word_alis[0], sil_label, length)
def __parse_err_code(text, idx, utt):
err_code = text[idx].replace('[*', '').replace(']', '').strip()
# Uncategorized error
if err_code in ['']:
return 'X'
# Check that error is of known type
if not any(
[err_code.startswith(c)
for c in ['p', 's', 'n', 'd', 'm', 'f', '->']]):
io.log('WARNING: {} - invalid err_code {} ({})'.format(
utt, err_code, text[:idx + 1]))
return err_code
def _save_model(self, model, model_name, results):
""" Save model to disk if applicable.
"""
args = self.args
valid_errs = results[1:]
if args.save_all or valid_errs[args.validate_on] < self.best_err:
model_fname = os.path.join(args.output_dir, model_name)
io.log('Saving final model to {}'.format(model_fname))
# Remove auxiliary output prior to saving
st_model = Model(input=model.input, output=model.output[0])
io.json_save('{}.json'.format(model_fname), st_model.to_json())
st_model.save_weights('{}.weights'.format(model_fname))
del st_model
def __parse_target(text, start, end, utt):
targets = [w for w in text[start:end] if w.startswith('[:')]
if len(targets) == 0:
return '?'
if len(targets) > 1:
io.log('**WARN** {} - multiple targets {}, using 1st item ({})'.format(
utt, targets, text[:end]))
target = targets[0]
target = [target.replace('[:', '').replace(']', '').strip()]
target = __clean_word_tokens(target, utt)
target = __proc_compound_words(target, utt)
target = ' '.join(target)
return target if target.lower() != 'x@n' else '?'
def _init_records(self):
""" Setup result caching for parameter combinations. Load existing
results from disk if possible. Each line will look like this:
<model_name> <train_err> <valid_err> [<valid_err> ...]
"""
self.records_fname = os.path.join(self.args.output_dir, 'summary.txt')
self.records = OrderedDict()
if os.path.exists(self.records_fname):
io.log('Loading existing records from {}'.format(
self.records_fname))
self.records = io.dict_read(self.records_fname,
ordered=True,
lst=True,
fn=float)
def main(args):
# Check and set variables
common.CHK_GT(args.frames_to_stack, 0)
if args.frames_to_skip is None:
args.frames_to_skip = args.frames_to_stack
common.CHK_GT(args.frames_to_skip, 0)
ds = TemporalData.from_kaldi(args.scp)
io.log('Loaded dataset containing {} utts'.format(len(ds.get_utt_names())))
io.log('Outputting stacked features (stack: {}, skip: {}) to stdout...'.
format(args.frames_to_stack, args.frames_to_skip))
for utt_name in ds.get_utt_names():
data = ds.get_data_by_utt_name(utt_name)
stacked = stack_data(data, args.frames_to_stack, args.frames_to_skip)
print_matrix(utt_name, stacked)
def _init_resume_json(self, model_dir):
""" Load json for resuming training if possible.
"""
resume_json = None
resume_fname = os.path.join(model_dir, 'resume.json')
if os.path.exists(resume_fname):
resume_json = io.json_load(resume_fname)
# Check that json contains enough information
assert 'weights' in resume_json
assert 'lrate' in resume_json
assert 'epoch' in resume_json
# Make path absolute
resume_json['weights'] = os.path.join(model_dir,
resume_json['weights'])
io.log('Resuming training: {}'.format(resume_json))
return resume_json
def main():
desc = 'Convert from speaker i-vectors to utt-ivectors. Output to stdout.'
parser = common.init_argparse(desc)
parser.add_argument('spk_ivectors', help='File containing spk i-vectors.')
parser.add_argument('utt2spk', help='Kaldi utt2spk mapping.')
args = parser.parse_args()
spk_ivectors = ivector_ark_read(args.spk_ivectors)
utt2spk = io.dict_read(args.utt2spk, ordered=True)
spk2utt = common.make_reverse_index(utt2spk, ordered=True)
wrote = 0
for spk in spk2utt.keys():
for utt in spk2utt[spk]:
print_vector(utt, spk_ivectors[spk])
wrote += 1
io.log('Wrote {} utt i-vectors for {} spks'.format(wrote, len(spk2utt)))
def get_log_priors(class_frame_counts, prior_floor):
if class_frame_counts is None:
return None
with open(class_frame_counts, 'r') as f:
count_str = f.readline().strip().replace('[', '').replace(']', '')
priors = np.asarray(count_str.split(), dtype=np.float32)
priors = priors / np.sum(priors)
# Add a small value before doing log to avoid NaN
log_priors = np.log(priors + 1e-20)
# Floor pdf
num_floored = 0
for i in range(len(log_priors)):
if priors[i] < prior_floor:
log_priors[i] = math.sqrt(FLT_MAX)
num_floored += 1
io.log('Floored {} pdf-priors (hard-set to {}, which disables DNN output)'.
format(num_floored, math.sqrt(FLT_MAX)))
return log_priors
def init_lrate(cls,
init_lr,
min_lr,
depoch,
dstart,
dstop,
max_epoch=None,
resume_json=None):
io.log('.....using {}'.format(cls))
lrate = cls(start_rate=init_lr,
min_lrate_stop=min_lr,
min_epoch_decay_start=depoch,
min_derror_decay_start=dstart,
min_derror_stop=dstop,
max_epoch=max_epoch)
if resume_json is not None:
io.log('Resuming learning rate from {}'.format(resume_json))
lrate = parse_lrate_json(resume_json, lrate)
return lrate
