def _write_pickle_to_table_empty(wspecifier, logger):
'''Special case when pickle file(s) was/were empty'''
# doesn't matter what type we choose; we're not writing anything
try:
kaldi_open(wspecifier, 'bm', 'w')
except IOError as error:
logger.error(error.message, exc_info=True)
return 1
logger.warning('No entries were written (pickle file(s) was/were empty)')
return 0
def test_write_table_to_pickle(values, temp_file_1_name, temp_file_2_name):
if len(values):
kaldi_dtype = infer_kaldi_data_type(values[0]).value
else:
kaldi_dtype = 'bm'
with kaldi_open('ark:' + temp_file_1_name, kaldi_dtype, 'w') as writer:
for num, value in enumerate(values):
writer.write(str(num), value)
ret_code = command_line.write_table_to_pickle(
['ark:' + temp_file_1_name, temp_file_2_name, '-i', kaldi_dtype])
assert ret_code == 0
num_entries = 0
pickle_file = open(temp_file_2_name, 'rb')
num_entries = 0
try:
while True:
key, value = pickle.load(pickle_file)
num_entries = int(key) + 1
try:
values[num_entries - 1].dtype
assert np.allclose(value, values[num_entries - 1])
except AttributeError:
assert value == values[num_entries - 1]
except EOFError:
pass
assert num_entries == len(values)
def _write_pickle_to_table_value_only(options, logger):
'''write_pickle_to_table when only value_in has been specified'''
from six.moves import cPickle as pickle
try:
if options.value_in.endswith('.gz'):
import gzip
value_in = gzip.open(options.value_in, 'rb')
else:
value_in = open(options.value_in, 'rb')
except IOError as error:
logger.error(error.message, exc_info=True)
return 1
try:
key, value = pickle.load(value_in)
except pickle.UnpicklingError as error:
logger.error(error.message, exc_info=True)
return 1
except EOFError:
value_in.close()
return _write_pickle_to_table_empty(options.wspecifier, logger)
out_type = options.out_type
try:
writer = kaldi_open(options.wspecifier, out_type, 'w')
except IOError as error:
value_in.close()
logger.error(error.message, exc_info=True)
return 1
num_entries = 0
try:
while True:
if out_type.is_floating_point:
if out_type.is_double:
try:
value = value.astype(np.float64, copy=False)
except AttributeError:
pass
else:
try:
value = value.astype(np.float32, copy=False)
except AttributeError:
pass
writer.write(key, value)
num_entries += 1
if num_entries % 10 == 0:
logger.info('Processed {} entries'.format(num_entries))
logger.debug('Processed key {}'.format(key))
key, value = pickle.load(value_in)
except EOFError:
pass
except (IOError, ValueError, TypeError, pickle.UnpicklingError) as error:
if hasattr(error, 'message'):
logger.error(error.message, exc_info=True)
else:
logger.error('error', exc_info=True)
return 1
finally:
value_in.close()
logger.info("Wrote {} entries".format(num_entries))
return 0
def __init__(self, rxfilename=None, along_axis=0):
self.along_axis = along_axis
if KaldiDataType.BaseMatrix.is_double:
self.stats_dtype = np.float64
else:
self.stats_dtype = np.float32
if rxfilename:
with kaldi_open(rxfilename) as stats_file:
self.stats = stats_file.read('bm')
else:
self.stats = None
def test_normalize_feat_lens(temp_file_1_name, temp_file_2_name,
temp_file_3_name):
feats_a = np.random.random((10, 4))
feats_b = np.random.random((5, 4))
feats_c = np.random.random((4, 4))
with kaldi_open('ark:' + temp_file_1_name, 'dm', 'w') as feats_in_writer:
feats_in_writer.write('A', feats_a)
feats_in_writer.write('B', feats_b)
feats_in_writer.write('C', feats_c)
with kaldi_open('ark:' + temp_file_2_name, 'i', 'w') as len_in_writer:
len_in_writer.write('A', 9)
len_in_writer.write('B', 7)
len_in_writer.write('C', 4)
ret_code = command_line.normalize_feat_lens([
'ark:' + temp_file_1_name,
'ark:' + temp_file_2_name,
'ark:' + temp_file_3_name,
'--type=dm',
'--pad-mode=zero',
])
assert ret_code == 0
with kaldi_open('ark:' + temp_file_3_name, 'dm') as feats_out_reader:
out_a = next(feats_out_reader)
out_b = next(feats_out_reader)
out_c = next(feats_out_reader)
assert out_a.shape == (9, 4)
assert np.allclose(out_a, feats_a[:9])
assert out_b.shape == (7, 4)
assert np.allclose(out_b[:5], feats_b)
assert np.allclose(out_b[5:], 0)
assert out_c.shape == (4, 4)
assert np.allclose(out_c, feats_c)
ret_code = command_line.normalize_feat_lens([
'ark:' + temp_file_1_name,
'ark:' + temp_file_2_name,
'ark:' + temp_file_3_name,
'--type=dm',
'--tolerance=1',
'--strict=true',
])
assert ret_code == 1
def test_write_table_to_torch_dir(temp_dir):
import torch
out_dir = os.path.join(temp_dir, 'test_write_table_to_torch_dir')
os.makedirs(out_dir)
rwspecifier = 'ark:' + os.path.join(out_dir, 'table.ark')
a = torch.rand(10, 4)
b = torch.rand(5, 2)
c = torch.rand(5, 100)
with kaldi_open(rwspecifier, 'bm', mode='w') as table:
table.write('a', a.numpy())
table.write('b', b.numpy())
table.write('c', c.numpy())
assert not command_line.write_table_to_torch_dir([rwspecifier, out_dir])
assert torch.allclose(c, torch.load(os.path.join(out_dir, 'c.pt')))
assert torch.allclose(b, torch.load(os.path.join(out_dir, 'b.pt')))
assert torch.allclose(a, torch.load(os.path.join(out_dir, 'a.pt')))
def test_write_torch_dir_to_table(temp_dir):
import torch
in_dir = os.path.join(temp_dir, 'test_write_torch_dir_to_table')
rwspecifier = 'ark:' + os.path.join(in_dir, 'table.ark')
os.makedirs(in_dir)
a = torch.rand(5, 4)
b = torch.rand(4, 3)
c = torch.rand(3, 2)
torch.save(a, os.path.join(in_dir, 'a.pt'))
torch.save(b, os.path.join(in_dir, 'b.pt'))
torch.save(c, os.path.join(in_dir, 'c.pt'))
assert not command_line.write_torch_dir_to_table([in_dir, rwspecifier])
with kaldi_open(rwspecifier, 'bm') as table:
keys, vals = zip(*table.items())
keys = tuple(keys)
vals = tuple(vals)
assert keys == ('a', 'b', 'c')
assert len(vals) == 3
for dval, tval in zip((a, b, c), vals):
assert torch.allclose(dval, torch.from_numpy(tval))
def test_write_pickle_to_table(values, temp_file_1_name, temp_file_2_name):
if len(values):
kaldi_dtype = infer_kaldi_data_type(values[0]).value
else:
kaldi_dtype = 'bm'
with open(temp_file_1_name, 'wb') as pickle_file:
for num, value in enumerate(values):
pickle.dump((str(num), value), pickle_file)
ret_code = command_line.write_pickle_to_table(
[temp_file_1_name, 'ark:' + temp_file_2_name, '-o', kaldi_dtype])
assert ret_code == 0
kaldi_reader = kaldi_open('ark:' + temp_file_2_name, kaldi_dtype, 'r')
num_entries = 0
for key, value in kaldi_reader.items():
num_entries = int(key) + 1
try:
values[num_entries - 1].dtype
assert np.allclose(value, values[num_entries - 1])
except AttributeError:
assert value == values[num_entries - 1]
assert num_entries == len(values)
def compute_error_rate(args=None):
'''Compute error rates between reference and hypothesis token vectors
Two common error rates in speech are the word (WER) and phone (PER),
though the computation is the same. Given a reference and hypothesis
sequence, the error rate is
>>> error_rate = (substitutions + insertions + deletions) / (
... ref_tokens * 100)
Where the number of substitutions (e.g. ``A B C -> A D C``),
deletions (e.g. ``A B C -> A C``), and insertions (e.g.
``A B C -> A D B C``) are determined by Levenshtein distance.
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(sys.argv[0])
options = _compute_error_rate_parse_args(args, logger)
global_edit = 0
global_token_count = 0
global_sents = 0
global_processed = 0
inserts = dict()
deletes = dict()
subs = dict()
totals = dict()
def _err_on_utt_id(utt_id, missing_rxspecifier):
msg = "Utterance '{}' absent in '{}'".format(
utt_id, missing_rxspecifier)
if options.strict:
logger.error(msg)
return 1
else:
logger.warning(msg)
return 0
return_tables = options.print_tables or not options.include_inserts_in_cost
with kaldi_open(options.ref_rspecifier, 'tv') as ref_table, \
kaldi_open(options.hyp_rspecifier, 'tv') as hyp_table:
while not ref_table.done() and not hyp_table.done():
global_sents += 1
if ref_table.key() > hyp_table.key():
if _err_on_utt_id(hyp_table.key(), options.ref_rspecifier):
return 1
hyp_table.move()
elif hyp_table.key() > ref_table.key():
if _err_on_utt_id(ref_table.key(), options.hyp_rspecifier):
return 1
ref_table.move()
else:
logger.debug('Processing {}: ref [{}] hyp [{}]'.format(
ref_table.key(),
' '.join(ref_table.value()),
' '.join(hyp_table.value())))
global_token_count += len(ref_table.value())
res = kaldi_eval_util.edit_distance(
ref_table.value(), hyp_table.value(),
return_tables=return_tables,
insertion_cost=options.insertion_cost,
deletion_cost=options.deletion_cost,
substitution_cost=options.substitution_cost,
)
if return_tables:
global_edit += res[0]
for global_dict, utt_dict in zip(
(inserts, deletes, subs, totals), res[1:]):
for token in ref_table.value() + hyp_table.value():
global_dict.setdefault(token, 0)
for token, count in utt_dict.items():
global_dict[token] += count
else:
global_edit += res
global_processed += 1
ref_table.move()
hyp_table.move()
while not ref_table.done():
if _err_on_utt_id(ref_table.key(), options.hyp_rspecifier):
return 1
global_sents += 1
ref_table.move()
while not hyp_table.done():
if _err_on_utt_id(hyp_table.key(), options.ref_rspecifier):
return 1
global_sents += 1
hyp_table.move()
if options.out_path is None:
out_file = sys.stdout
else:
out_file = open(options.out_path, 'w')
print(
"Processed {}/{}.".format(global_processed, global_sents),
file=out_file, end=' '
)
if not options.include_inserts_in_cost:
global_edit -= sum(inserts.values())
if options.report_accuracy:
print(
'Accuracy: {:.2f}%'.format(
(1 - global_edit / global_token_count) * 100),
file=out_file,
)
else:
print(
'Error rate: {:.2f}%'.format(
global_edit / global_token_count * 100),
file=out_file,
)
if options.print_tables:
print(
"Total insertions: {}, deletions: {}, substitutions: {}".format(
sum(inserts.values()), sum(deletes.values()),
sum(subs.values())),
file=out_file,
)
print("", file=out_file)
tokens = list(set(inserts) | set(deletes) | set(subs))
tokens.sort()
token_len = max(max(len(token) for token in tokens), 5)
max_count = max(
chain(inserts.values(), deletes.values(), subs.values()))
max_count_len = int(log10(max_count) + 1)
divider_str = '+' + ('-' * (token_len + 1))
divider_str += ('+' + ('-' * (max_count_len + 9))) * 4
divider_str += '+'
format_str = '|{{:<{}}}|'.format(token_len + 1)
format_str += 4 * '{{:>{}}}({{:05.2f}}%)|'.format(max_count_len + 1)
print(
'|{2:<{0}}|{3:>{1}}(%)|{4:>{1}}(%)|{5:>{1}}(%)|{6:>{1}}(%)|'
''.format(
token_len + 1, max_count_len + 6, 'token', 'inserts',
'deletes', 'subs', 'errs',
),
file=out_file,
)
print(divider_str, file=out_file)
print(divider_str, file=out_file)
for token in tokens:
i, d, s = inserts[token], deletes[token], subs[token]
t = totals[token]
print(
format_str.format(
token,
i, i / t * 100,
d, d / t * 100,
s, s / t * 100,
i + d + s, (i + d + s) / t * 100,
),
file=out_file
)
print(divider_str, file=out_file)
return 0
def write_table_to_pickle(args=None):
'''Write a kaldi table to pickle file(s)
The inverse is write-pickle-to-table
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(logger)
try:
options = _write_table_to_pickle_parse_args(args, logger)
except SystemExit as ex:
return ex.code
out_type = options.out_type
if out_type is None:
if options.in_type.is_floating_point:
if options.in_type.is_double:
out_type = np.float64
else:
out_type = np.float32
else:
out_type = np.str
from six.moves import cPickle as pickle
try:
reader = kaldi_open(options.rspecifier, options.in_type, 'r')
if options.value_out.endswith('.gz'):
import gzip
value_out = gzip.open(options.value_out, 'wb')
else:
value_out = open(options.value_out, 'wb')
if options.key_out:
if options.key_out.endswith('.gz'):
import gzip
key_out = gzip.open(options.key_out, 'wt')
else:
key_out = open(options.key_out, 'w')
else:
key_out = None
except IOError as error:
logger.error(error.message, exc_info=True)
return 1
num_entries = 0
try:
for key, value in reader.items():
num_entries += 1
if not np.issubdtype(out_type, np.dtype(str).type):
value = value.astype(out_type)
if key_out:
pickle.dump(value, value_out)
pickle.dump(key, key_out)
else:
pickle.dump((key, value), value_out)
if num_entries % 10 == 0:
logger.info('Processed {} entries'.format(num_entries))
logger.debug('Processed key {}'.format(key))
except (IOError, ValueError) as error:
logger.error(error.message, exc_info=True)
return 1
finally:
value_out.close()
if key_out:
key_out.close()
if num_entries == 0:
logger.warning("No entries were written (table was empty)")
else:
logger.info("Wrote {} entries".format(num_entries))
return 0
def write_torch_dir_to_table(args=None):
'''Write a data directory containing PyTorch data files to a Kaldi table
Reads from a folder in the format:
::
folder/
<file_prefix><key_1><file_suffix>
<file_prefix><key_2><file_suffix>
...
Where each file contains a PyTorch tensor. The contents of the file
``<file_prefix><key_1><file_suffix>`` will be written as a value in
a Kaldi table with key ``<key_1>``
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(logger)
try:
options = _write_torch_dir_to_table_parse_args(args, logger)
except SystemExit as ex:
return ex.code
if not os.path.isdir(options.dir):
print("'{}' is not a directory".format(options.dir), file=sys.stderr)
return 1
import torch
is_bool = False
if options.out_type in {
enums.KaldiDataType.BaseMatrix, enums.KaldiDataType.BaseVector,
enums.KaldiDataType.WaveMatrix, enums.KaldiDataType.Base,
enums.KaldiDataType.BasePairVector
}:
if options.out_type.is_double:
torch_type = torch.double
else:
torch_type = torch.float
elif options.out_type in {
enums.KaldiDataType.FloatMatrix, enums.KaldiDataType.FloatVector
}:
torch_type = torch.float
elif options.out_type in {
enums.KaldiDataType.DoubleMatrix, enums.KaldiDataType.Double
}:
torch_type = torch.double
elif options.out_type in {
enums.KaldiDataType.Int32, enums.KaldiDataType.Int32Vector,
enums.KaldiDataType.Int32VectorVector
}:
torch_type = torch.int
elif options.out_type == enums.KaldiDataType.Boolean:
torch_type = torch.uint8
is_bool = True
else:
print('Do not know how to convert {} from torch type'.format(
options.out_type),
file=sys.stderr)
return 1
neg_fsl = -len(options.file_suffix)
if not neg_fsl:
neg_fsl = None
fpl = len(options.file_prefix)
utt_ids = sorted(
os.path.basename(x)[fpl:neg_fsl] for x in os.listdir(options.dir) if
x.startswith(options.file_prefix) and x.endswith(options.file_suffix))
with kaldi_open(options.wspecifier, options.out_type, mode='w') as table:
for utt_id in utt_ids:
val = torch.load(
os.path.join(
options.dir,
options.file_prefix + utt_id + options.file_suffix))
val = val.cpu().type(torch_type).numpy()
if is_bool:
val = bool(val) # make sure val is a scalar!
table.write(utt_id, val)
return 0
def write_table_to_torch_dir(args=None):
'''Write a Kaldi table to a series of PyTorch data files in a directory
Writes to a folder in the format:
::
folder/
<file_prefix><key_1><file_suffix>
<file_prefix><key_2><file_suffix>
...
The contents of the file ``<file_prefix><key_1><file_suffix>`` will be
a PyTorch tensor corresponding to the entry in the table for ``<key_1>``
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(logger)
try:
options = _write_table_to_torch_dir_parse_args(args, logger)
except SystemExit as ex:
return ex.code
out_type = options.out_type
if out_type is None:
if options.in_type in {
enums.KaldiDataType.BaseMatrix, enums.KaldiDataType.BaseVector,
enums.KaldiDataType.WaveMatrix, enums.KaldiDataType.Base,
enums.KaldiDataType.BasePairVector
}:
if options.in_type.is_double:
out_type = 'double'
else:
out_type = 'float'
elif options.in_type in {
enums.KaldiDataType.FloatMatrix,
enums.KaldiDataType.FloatVector
}:
out_type = 'float'
elif options.in_type in {
enums.KaldiDataType.DoubleMatrix, enums.KaldiDataType.Double
}:
out_type = 'double'
elif options.in_type in {
enums.KaldiDataType.Int32, enums.KaldiDataType.Int32Vector,
enums.KaldiDataType.Int32VectorVector
}:
out_type = 'int'
elif options.in_type == enums.KaldiDataType.Boolean:
out_type = 'byte'
else:
print('Do not know how to convert {} to torch type'.format(
options.in_type),
file=sys.stderr)
return 1
import torch
if out_type == 'float':
out_type = torch.float
elif out_type == 'double':
out_type = torch.double
elif out_type == 'half':
out_type = torch.half
elif out_type == 'byte':
out_type = torch.uint8
elif out_type == 'char':
out_type = torch.int8
elif out_type == 'short':
out_type = torch.short
elif out_type == 'int':
out_type = torch.int
elif out_type == 'long':
out_type = torch.long
try:
os.makedirs(options.dir)
except FileExistsError:
pass
with kaldi_open(options.rspecifier, options.in_type) as table:
for key, value in table.items():
value = torch.tensor(value).type(out_type)
torch.save(
value,
os.path.join(options.dir,
options.file_prefix + key + options.file_suffix))
return 0
def _write_pickle_to_table_key_value(options, logger):
try:
logger.info('Opening {}'.format(options.value_in))
if options.value_in.endswith('.gz'):
import gzip
value_in = gzip.open(options.value_in, 'rb')
else:
value_in = open(options.value_in, 'rb')
logger.info('Opening {}'.format(options.key_in))
if options.key_in.endswith('.gz'):
import gzip
key_in = gzip.open(options.key_in, 'rt')
else:
key_in = open(options.key_in, 'r')
except IOError as error:
logger.error(error.message, exc_info=True)
return 1
try:
value = pickle.load(value_in)
except pickle.UnpicklingError as error:
value_in.close()
key_in.close()
logger.error(error.message, exc_info=True)
return 1
except EOFError:
value_in.close()
try:
pickle.load(key_in)
logger.error('Number of keys (1) and values (0) do not match')
return 1
except pickle.UnpicklingError as error:
key_in.close()
logger.error(error.message, exc_info=True)
return 1
key_in.close()
return _write_pickle_to_table_empty(options.wspecifier, logger)
try:
key = pickle.load(key_in)
except EOFError:
value_in.close()
key_in.close()
logger.error('Number of keys (0) and values (1) do not match')
return 1
except pickle.UnpicklingError as error:
value_in.close()
key_in.close()
logger.error(error.message, exc_info=True)
return 1
out_type = options.out_type
try:
logging.info('Opening {}'.format(options.wspecifier))
writer = kaldi_open(options.wspecifier, out_type, 'w')
except IOError as error:
value_in.close()
key_in.close()
logger.error(error.message, exc_info=True)
return 1
num_entries = 0
try:
while True:
if out_type.is_floating_point:
if out_type.is_double:
try:
value = value.astype(np.float64, copy=False)
except AttributeError:
pass # will happen implicitly
else:
try:
value = value.astype(np.float32, copy=False)
except AttributeError:
pass # will happen implicitly
writer.write(key, value)
num_entries += 1
if num_entries % 10 == 0:
logger.info('Processed {} entries'.format(num_entries))
logger.debug('Processed key {}'.format(key))
key = pickle.load(key_in)
value = pickle.load(value_in)
except EOFError:
pass
except (IOError, ValueError, TypeError, pickle.UnpicklingError) as error:
logger.error(error.message, exc_info=True)
return 1
try:
pickle.load(value_in)
value_in.close()
key_in.close()
logger.error('Number of keys ({}) and values ({}) do not match'.format(
num_entries, num_entries + 1))
return 1
except EOFError:
pass
except (IOError, pickle.UnpicklingError) as error:
value_in.close()
key_in.close()
logger.error(error.message, exc_info=True)
return 1
try:
pickle.load(key_in)
value_in.close()
key_in.close()
logger.error('Number of keys ({}) and values ({}) do not match'.format(
num_entries + 1, num_entries))
return 1
except EOFError:
pass
except (IOError, pickle.UnpicklingError) as error:
logger.error(error.message, exc_info=True)
return 1
finally:
value_in.close()
key_in.close()
logger.info("Wrote {} entries".format(num_entries))
return 0
def compute_error_rate(args=None):
'''Compute error rates between reference and hypothesis token vectors
Two common error rates in speech are the word (WER) and phone (PER),
though the computation is the same. Given a reference and hypothesis
sequence, the error rate is
>>> error_rate = (substitutions + insertions + deletions) / (
... ref_tokens * 100)
Where the number of substitutions (e.g. ``A B C -> A D C``),
deletions (e.g. ``A B C -> A C``), and insertions (e.g.
``A B C -> A D B C``) are determined by Levenshtein distance.
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(sys.argv[0])
options = _compute_error_rate_parse_args(args, logger)
global_edit = 0
global_token_count = 0
global_sents = 0
global_processed = 0
inserts = dict()
deletes = dict()
subs = dict()
totals = dict()
def _err_on_utt_id(utt_id, missing_rxspecifier):
msg = "Utterance '{}' absent in '{}'".format(utt_id,
missing_rxspecifier)
if options.strict:
logger.error(msg)
return 1
else:
logger.warning(msg)
return 0
return_tables = options.print_tables or not options.include_inserts_in_cost
with kaldi_open(options.ref_rspecifier, 'tv') as ref_table, \
kaldi_open(options.hyp_rspecifier, 'tv') as hyp_table:
while not ref_table.done() and not hyp_table.done():
global_sents += 1
if ref_table.key() > hyp_table.key():
if _err_on_utt_id(hyp_table.key(), options.ref_rspecifier):
return 1
hyp_table.move()
elif hyp_table.key() > ref_table.key():
if _err_on_utt_id(ref_table.key(), options.hyp_rspecifier):
return 1
ref_table.move()
else:
logger.debug('Processing {}: ref [{}] hyp [{}]'.format(
ref_table.key(), ' '.join(ref_table.value()),
' '.join(hyp_table.value())))
global_token_count += len(ref_table.value())
res = kaldi_eval_util.edit_distance(
ref_table.value(),
hyp_table.value(),
return_tables=return_tables,
insertion_cost=options.insertion_cost,
deletion_cost=options.deletion_cost,
substitution_cost=options.substitution_cost,
)
if return_tables:
global_edit += res[0]
for global_dict, utt_dict in zip(
(inserts, deletes, subs, totals), res[1:]):
for token in ref_table.value() + hyp_table.value():
global_dict.setdefault(token, 0)
for token, count in utt_dict.items():
global_dict[token] += count
else:
global_edit += res
global_processed += 1
ref_table.move()
hyp_table.move()
while not ref_table.done():
if _err_on_utt_id(ref_table.key(), options.hyp_rspecifier):
return 1
global_sents += 1
ref_table.move()
while not hyp_table.done():
if _err_on_utt_id(hyp_table.key(), options.ref_rspecifier):
return 1
global_sents += 1
hyp_table.move()
if options.out_path is None:
out_file = sys.stdout
else:
out_file = open(options.out_path, 'w')
print("Processed {}/{}.".format(global_processed, global_sents),
file=out_file,
end=' ')
if not options.include_inserts_in_cost:
global_edit -= sum(inserts.values())
if options.report_accuracy:
print(
'Accuracy: {:.2f}%'.format(
(1 - global_edit / global_token_count) * 100),
file=out_file,
)
else:
print(
'Error rate: {:.2f}%'.format(global_edit / global_token_count *
100),
file=out_file,
)
if options.print_tables:
print(
"Total insertions: {}, deletions: {}, substitutions: {}".format(
sum(inserts.values()), sum(deletes.values()),
sum(subs.values())),
file=out_file,
)
print("", file=out_file)
tokens = list(set(inserts) | set(deletes) | set(subs))
tokens.sort()
token_len = max(max(len(token) for token in tokens), 5)
max_count = max(
chain(inserts.values(), deletes.values(), subs.values()))
max_count_len = int(log10(max_count) + 1)
divider_str = '+' + ('-' * (token_len + 1))
divider_str += ('+' + ('-' * (max_count_len + 9))) * 4
divider_str += '+'
format_str = '|{{:<{}}}|'.format(token_len + 1)
format_str += 4 * '{{:>{}}}({{:05.2f}}%)|'.format(max_count_len + 1)
print(
'|{2:<{0}}|{3:>{1}}(%)|{4:>{1}}(%)|{5:>{1}}(%)|{6:>{1}}(%)|'
''.format(
token_len + 1,
max_count_len + 6,
'token',
'inserts',
'deletes',
'subs',
'errs',
),
file=out_file,
)
print(divider_str, file=out_file)
print(divider_str, file=out_file)
for token in tokens:
i, d, s = inserts[token], deletes[token], subs[token]
t = totals[token]
print(format_str.format(
token,
i,
i / t * 100,
d,
d / t * 100,
s,
s / t * 100,
i + d + s,
(i + d + s) / t * 100,
),
file=out_file)
print(divider_str, file=out_file)
return 0
def save(self, wxfilename):
'''Save statistics to extended file name'''
with kaldi_open(wxfilename, mode='w') as stats_file:
stats_file.write(self.stats, 'bm')
def normalize_feat_lens(args=None):
'''Ensure features match some reference lengths
Incoming features are either clipped or padded to match
reference lengths (stored as an int32 table), if they are within
tolerance.
'''
logger = logging.getLogger(sys.argv[0])
if not logger.handlers:
logger.addHandler(logging.StreamHandler())
register_logger_for_kaldi(sys.argv[0])
options = _normalize_feat_lens_parse_args(args, logger)
if options.pad_mode == 'zero':
options.pad_mode = 'constant'
feats_in = kaldi_open(options.feats_in_rspecifier, options.type, mode='r')
len_in = kaldi_open(options.len_in_rspecifier, 'i', mode='r+')
feats_out = kaldi_open(options.feats_out_wspecifier,
options.type,
mode='w')
total_utts = 0
processed_utts = 0
for utt_id, feats in feats_in.items():
total_utts += 1
if utt_id not in len_in:
msg = "Utterance '{}' absent in '{}'".format(
utt_id, options.len_in_rspecifier)
if options.strict:
logger.error(msg)
return 1
else:
logger.warning(msg)
continue
exp_feat_len = len_in[utt_id]
act_feat_len = len(feats)
logger.debug('{} exp len: {} act len: {}'.format(
utt_id, exp_feat_len, act_feat_len))
if act_feat_len < exp_feat_len:
if act_feat_len < exp_feat_len - options.tolerance:
msg = '{} has feature length {}, which is below the '
msg += 'tolerance ({}) of the expected length {}'
msg = msg.format(utt_id, act_feat_len, options.tolerance,
exp_feat_len)
if options.strict:
logger.error(msg)
return 1
else:
logger.warning(msg)
continue
# for matrices or vectors, this cast shouldn't be necessary.
# If the user tries some special type like token vectors,
# however, this *might* work as intended
feats = np.array(feats, copy=False)
pad_list = [(0, 0)] * len(feats.shape)
if options.side == 'right':
pad_list[0] = (0, exp_feat_len - act_feat_len)
elif options.side == 'left':
pad_list[0] = (exp_feat_len - act_feat_len, 0)
else:
pad_list[0] = ((exp_feat_len - act_feat_len) // 2,
(exp_feat_len - act_feat_len + 1) // 2)
feats = np.pad(feats, pad_list, options.pad_mode)
elif act_feat_len > exp_feat_len:
if act_feat_len > exp_feat_len + options.tolerance:
msg = '{} has feature length {}, which is above the '
msg += 'tolerance ({}) of the expected length {}'
msg = msg.format(utt_id, act_feat_len, options.tolerance,
exp_feat_len)
if options.strict:
logger.error(msg)
return 1
else:
logger.warning(msg)
continue
if options.side == 'right':
feats = feats[:exp_feat_len - act_feat_len]
elif options.side == 'left':
feats = feats[exp_feat_len - act_feat_len:]
else:
feats = feats[(exp_feat_len - act_feat_len) //
2:(exp_feat_len - act_feat_len + 1) // 2]
feats_out.write(utt_id, feats)
processed_utts += 1
logger.info('Processed {}/{} utterances'.format(processed_utts,
total_utts))
return 0
