def create_bundle(
alphabet_path,
lm_path,
vocab_path,
package_path,
force_utf8,
default_alpha,
default_beta,
):
words = set()
vocab_looks_char_based = True
with open(vocab_path) as fin:
for line in fin:
for word in line.split():
words.add(word.encode("utf-8"))
if len(word) > 1:
vocab_looks_char_based = False
print("{} unique words read from vocabulary file.".format(len(words)))
cbm = "Looks" if vocab_looks_char_based else "Doesn't look"
print("{} like a character based model.".format(cbm))
if force_utf8 != None: # pylint: disable=singleton-comparison
use_utf8 = force_utf8.value
else:
use_utf8 = vocab_looks_char_based
print("Using detected UTF-8 mode: {}".format(use_utf8))
if use_utf8:
serialized_alphabet = UTF8Alphabet().serialize()
else:
if not alphabet_path:
raise RuntimeError("No --alphabet path specified, can't continue.")
serialized_alphabet = Alphabet(alphabet_path).serialize()
alphabet = NativeAlphabet()
err = alphabet.deserialize(serialized_alphabet, len(serialized_alphabet))
if err != 0:
raise RuntimeError("Error loading alphabet: {}".format(err))
scorer = Scorer()
scorer.set_alphabet(alphabet)
scorer.set_utf8_mode(use_utf8)
scorer.reset_params(default_alpha, default_beta)
err = scorer.load_lm(lm_path)
if err != ds_ctcdecoder.DS_ERR_SCORER_NO_TRIE:
print('Error loading language model file: 0x{:X}.'.format(err))
print(
'See the error codes section in https://deepspeech.readthedocs.io for a description.'
)
sys.exit(1)
scorer.fill_dictionary(list(words))
shutil.copy(lm_path, package_path)
# append, not overwrite
if scorer.save_dictionary(package_path, True):
print("Package created in {}".format(package_path))
else:
print("Error when creating {}".format(package_path))
sys.exit(1)
def create_bundle(
alphabet_path,
lm_path,
vocab_path,
package_path,
force_utf8,
default_alpha,
default_beta,
):
words = set()
vocab_looks_char_based = True
with open(vocab_path) as fin:
for line in fin:
for word in line.split():
words.add(word.encode("utf-8"))
if len(word) > 1:
vocab_looks_char_based = False
print("{} unique words read from vocabulary file.".format(len(words)))
print("{} like a character based model.".format(
"Looks" if vocab_looks_char_based else "Doesn't look"))
if force_utf8 != None: # pylint: disable=singleton-comparison
use_utf8 = force_utf8.value
print("Forcing UTF-8 mode = {}".format(use_utf8))
else:
use_utf8 = vocab_looks_char_based
if use_utf8:
serialized_alphabet = UTF8Alphabet().serialize()
else:
if not alphabet_path:
print("No --alphabet path specified, can't continue.")
sys.exit(1)
serialized_alphabet = Alphabet(alphabet_path).serialize()
alphabet = NativeAlphabet()
err = alphabet.deserialize(serialized_alphabet, len(serialized_alphabet))
if err != 0:
print("Error loading alphabet: {}".format(err))
sys.exit(1)
scorer = Scorer()
scorer.set_alphabet(alphabet)
scorer.set_utf8_mode(use_utf8)
scorer.reset_params(default_alpha, default_beta)
scorer.load_lm(lm_path)
scorer.fill_dictionary(list(words))
shutil.copy(lm_path, package_path)
scorer.save_dictionary(package_path, True) # append, not overwrite
print("Package created in {}".format(package_path))
def create_bundle(
alphabet_path,
lm_path,
vocab_path,
package_path,
force_utf8,
default_alpha,
default_beta,
):
words = set()
with open(vocab_path) as fin:
for line in fin:
for word in line.split():
words.add(word.encode("utf-8"))
if not alphabet_path:
raise RuntimeError("No --alphabet path specified, can't continue.")
serialized_alphabet = Alphabet(alphabet_path).serialize()
alphabet = NativeAlphabet()
err = alphabet.deserialize(serialized_alphabet, len(serialized_alphabet))
if err != 0:
raise RuntimeError("Error loading alphabet: {}".format(err))
scorer = Scorer()
scorer.set_alphabet(alphabet)
scorer.reset_params(default_alpha, default_beta)
scorer.load_lm(lm_path)
# TODO: Why is this not working?
# err = scorer.load_lm(lm_path)
# if err != ds_ctcdecoder.DS_ERR_SCORER_NO_TRIE:
# print('Error loading language model file: 0x{:X}.'.format(err))
# print('See the error codes section in https://deepspeech.readthedocs.io for a description.')
# sys.exit(1)
scorer.fill_dictionary(list(words))
shutil.copy(lm_path, package_path)
# This is the problem!
scorer.save_dictionary(package_path, True) # append, not overwrite
print("Package created in {}".format(package_path))
def main():
alphabet_txt = os.path.join(LANG.model_dir, 'alphabet.txt')
raw_txt_gz = os.path.join(LANG.model_dir, 'raw.txt.gz')
unprepared_txt = os.path.join(LANG.model_dir, 'unprepared.txt')
prepared_txt = os.path.join(LANG.model_dir, 'prepared.txt')
vocabulary_txt = os.path.join(LANG.model_dir, 'vocabulary.txt')
unfiltered_arpa = os.path.join(LANG.model_dir, 'unfiltered.arpa')
filtered_arpa = os.path.join(LANG.model_dir, 'filtered.arpa')
lm_binary = os.path.join(LANG.model_dir, 'lm.binary')
kenlm_scorer = os.path.join(LANG.model_dir, 'kenlm.scorer')
temp_prefix = os.path.join(LANG.model_dir, 'tmp')
section('Writing alphabet file', empty_lines_before=1)
with open(alphabet_txt, 'w', encoding='utf-8') as alphabet_file:
alphabet_file.write('\n'.join(LANG.alphabet) + '\n')
redo = ARGS.force_download
section('Downloading text data')
redo = maybe_download(LANG.text_url, raw_txt_gz, force=redo)
section('Unzipping text data')
redo = maybe_ungzip(raw_txt_gz, unprepared_txt, force=redo)
redo = redo or ARGS.force_prepare
section('Preparing text and building vocabulary')
if redo or not os.path.isfile(prepared_txt) or not os.path.isfile(vocabulary_txt):
redo = True
announce('Preparing {} shards of "{}"...'.format(ARGS.workers, unprepared_txt))
counters = Queue(ARGS.workers)
source_bytes = os.path.getsize(unprepared_txt)
aggregator_process = Process(target=aggregate_counters, args=(vocabulary_txt, source_bytes, counters))
aggregator_process.start()
counter_processes = list(map(lambda index: Process(target=count_words, args=(index, counters)),
range(ARGS.workers)))
try:
for p in counter_processes:
p.start()
for p in counter_processes:
p.join()
counters.put(STOP_TOKEN)
aggregator_process.join()
print('')
partials = list(map(lambda i: get_partial_path(i), range(ARGS.workers)))
join_files(partials, prepared_txt)
for partial in partials:
os.unlink(partial)
except KeyboardInterrupt:
aggregator_process.terminate()
for p in counter_processes:
p.terminate()
raise
else:
announce('Files "{}" and \n\t"{}" existing - not preparing'.format(prepared_txt, vocabulary_txt))
redo = redo or ARGS.force_generate
section('Building unfiltered language model')
if redo or not os.path.isfile(unfiltered_arpa):
redo = True
lmplz_args = [
KENLM_BIN + '/lmplz',
'--temp_prefix', temp_prefix,
'--memory', '80%',
'--discount_fallback',
'--limit_vocab_file', vocabulary_txt,
'--text', prepared_txt,
'--arpa', unfiltered_arpa,
'--skip', 'symbols',
'--order', str(LANG.order)
]
if len(LANG.prune) > 0:
lmplz_args.append('--prune')
lmplz_args.extend(list(map(str, LANG.prune)))
subprocess.check_call(lmplz_args)
else:
announce('File "{}" existing - not generating'.format(unfiltered_arpa))
section('Filtering language model')
if redo or not os.path.isfile(filtered_arpa):
redo = True
with open(vocabulary_txt, 'rb') as vocabulary_file:
vocabulary_content = vocabulary_file.read()
subprocess.run([
KENLM_BIN + '/filter',
'single',
'model:' + unfiltered_arpa,
filtered_arpa
], input=vocabulary_content, check=True)
else:
announce('File "{}" existing - not filtering'.format(filtered_arpa))
section('Generating binary representation')
if redo or not os.path.isfile(lm_binary):
redo = True
subprocess.check_call([
KENLM_BIN + '/build_binary',
'-a', '255',
'-q', '8',
'-v',
'trie',
filtered_arpa,
lm_binary
])
else:
announce('File "{}" existing - not generating'.format(lm_binary))
section('Building scorer')
if redo or not os.path.isfile(kenlm_scorer):
redo = True
words = set()
vocab_looks_char_based = True
with open(vocabulary_txt) as vocabulary_file:
for line in vocabulary_file:
for word in line.split():
words.add(word.encode())
if len(word) > 1:
vocab_looks_char_based = False
announce("{} unique words read from vocabulary file.".format(len(words)))
announce(
"{} like a character based model.".format(
"Looks" if vocab_looks_char_based else "Doesn't look"
)
)
if ARGS.alphabet_mode == 'auto':
use_utf8 = vocab_looks_char_based
elif ARGS.alphabet_mode == 'utf8':
use_utf8 = True
else:
use_utf8 = False
serialized_alphabet = get_serialized_utf8_alphabet() if use_utf8 else LANG.get_serialized_alphabet()
from ds_ctcdecoder import Scorer, Alphabet
alphabet = Alphabet()
err = alphabet.deserialize(serialized_alphabet, len(serialized_alphabet))
if err != 0:
announce('Error loading alphabet: {}'.format(err))
sys.exit(1)
scorer = Scorer()
scorer.set_alphabet(alphabet)
scorer.set_utf8_mode(use_utf8)
scorer.reset_params(LANG.alpha, LANG.beta)
scorer.load_lm(lm_binary)
scorer.fill_dictionary(list(words))
shutil.copy(lm_binary, kenlm_scorer)
scorer.save_dictionary(kenlm_scorer, True) # append, not overwrite
announce('Package created in {}'.format(kenlm_scorer))
announce('Testing package...')
scorer = Scorer()
scorer.load_lm(kenlm_scorer)
else:
announce('File "{}" existing - not building'.format(kenlm_scorer))
