def align_progress(audio_f, transcript, proto_langdir, nnet_dir, want_progress=False):
if len(transcript.strip()) == 0:
# Fall back on normal transcription if no transcript is provided
logging.info("Falling back on normal transcription")
for ret in _normal_transcribe(audio_f, proto_langdir, nnet_dir, want_progress=want_progress):
yield show_progress(ret)
return
vocab_path = os.path.join(proto_langdir, "graphdir/words.txt")
with open(vocab_path) as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(transcript, vocab)
ks = ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(ks, proto_langdir)
k = None
try:
k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_filename, proto_langdir)
for tran in k.transcribe_progress(audio_f):
if want_progress:
yield show_progress(tran, ms)
if not want_progress:
yield make_alignment(tran, ms)
finally:
if k:
k.stop()
os.unlink(gen_hclg_filename)
def lm_transcribe_progress(audio_f, transcript, proto_langdir, nnet_dir):
if len(transcript.strip()) == 0:
# Fall back on normal transcription if no transcript is provided
logging.info("Falling back on normal transcription")
for ret in _normal_transcribe(audio_f, proto_langdir, nnet_dir):
yield ret
return
vocab_path = os.path.join(proto_langdir, "graphdir/words.txt")
with open(vocab_path) as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(transcript, vocab)
ks = ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(ks, proto_langdir)
try:
k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_filename, proto_langdir)
ret = None
for trans in k.transcribe_progress(audio_f):
ret = diff_align.align(trans["words"], ms)
yield {
"transcript": transcript,
"words": ret,
}
finally:
k.stop()
os.unlink(gen_hclg_filename)
def lm_transcribe_progress(audio_f, transcript, proto_langdir, nnet_dir):
if len(transcript.strip()) == 0:
# Fall back on normal transcription if no transcript is provided
logging.info("Falling back on normal transcription")
for ret in _normal_transcribe(audio_f, proto_langdir, nnet_dir):
yield ret
return
vocab_path = os.path.join(proto_langdir, "graphdir/words.txt")
with open(vocab_path) as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(transcript, vocab)
ks = ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(
ks, proto_langdir)
try:
k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_filename, proto_langdir)
ret = None
for trans in k.transcribe_progress(audio_f):
ret = diff_align.align(trans["words"], ms)
yield {
"transcript": transcript,
"words": ret,
}
finally:
k.stop()
os.unlink(gen_hclg_filename)
def align_progress(audio_f,
transcript,
proto_langdir,
nnet_dir,
want_progress=False):
if len(transcript.strip()) == 0:
# Fall back on normal transcription if no transcript is provided
logging.info("Falling back on normal transcription")
for ret in _normal_transcribe(audio_f,
proto_langdir,
nnet_dir,
want_progress=want_progress):
yield show_progress(ret)
return
vocab_path = os.path.join(proto_langdir, "graphdir/words.txt")
with open(vocab_path) as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(transcript, vocab)
ks = ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(
ks, proto_langdir)
k = None
try:
k = standard_kaldi.Kaldi(nnet_dir, gen_hclg_filename, proto_langdir)
for tran in k.transcribe_progress(audio_f):
if want_progress:
yield show_progress(tran, ms)
if not want_progress:
yield make_alignment(tran, ms)
finally:
if k:
k.stop()
os.unlink(gen_hclg_filename)
def by_word(opcodes):
'''Take difflib.SequenceMatcher.get_opcodes() output and
return an equivalent opcode sequence that only modifies
one word at a time'''
for op, s1, e1, s2, e2 in opcodes:
if op == 'delete':
for i in range(s1, e1):
yield (op, i, i + 1, s2, s2)
elif op == 'insert':
for i in range(s2, e2):
yield (op, s1, s1, i, i + 1)
else:
for i1, i2 in zip(range(s1, e1), range(s2, e2)):
yield (op, i1, i1 + 1, i2, i2 + 1)
if __name__ == '__main__':
TEXT_FILE = sys.argv[1]
JSON_FILE = sys.argv[2]
OUTPUT_FILE = sys.argv[3]
with open('data/graph/words.txt') as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(open(TEXT_FILE).read(), vocab)
alignment = json.load(open(JSON_FILE))['words']
out = align(alignment, ms)
json.dump(out, open(OUTPUT_FILE, 'w'), indent=2)
def by_word(opcodes):
'''Take difflib.SequenceMatcher.get_opcodes() output and
return an equivalent opcode sequence that only modifies
one word at a time'''
for op, s1, e1, s2, e2 in opcodes:
if op == 'delete':
for i in range(s1, e1):
yield (op, i, i+1, s2, s2)
elif op == 'insert':
for i in range(s2, e2):
yield (op, s1, s1, i, i+1)
else:
for i1, i2 in zip(range(s1, e1), range(s2, e2)):
yield (op, i1, i1 + 1, i2, i2 + 1)
if __name__=='__main__':
TEXT_FILE = sys.argv[1]
JSON_FILE = sys.argv[2]
OUTPUT_FILE = sys.argv[3]
with open('data/graph/words.txt') as f:
vocab = metasentence.load_vocabulary(f)
ms = metasentence.MetaSentence(open(TEXT_FILE).read(), vocab)
alignment = json.load(open(JSON_FILE))['words']
out = align(alignment, ms)
json.dump(out, open(OUTPUT_FILE, 'w'), indent=2)