def transcribe(self, wavfile, progress_cb=None):
wav_obj = wave.open(wavfile, 'r')
duration = wav_obj.getnframes() / float(wav_obj.getframerate())
n_chunks = int(
math.ceil(duration / float(self.chunk_len - self.overlap_t)))
chunks = []
def transcribe_chunk(idx):
wav_obj = wave.open(wavfile, 'r')
start_t = idx * (self.chunk_len - self.overlap_t)
# Seek
wav_obj.setpos(int(start_t * wav_obj.getframerate()))
# Read frames
buf = wav_obj.readframes(
int(self.chunk_len * wav_obj.getframerate()))
k = self.kaldi_queue.get()
k.push_chunk(buf)
ret = k.get_final()
k.reset()
self.kaldi_queue.put(k)
chunks.append({"start": start_t, "words": ret})
logging.info('%d/%d' % (len(chunks), n_chunks))
if progress_cb is not None:
progress_cb({
"message": ' '.join([X['word'] for X in ret]),
"percent": len(chunks) / float(n_chunks)
})
pool = Pool(min(n_chunks, self.nthreads))
pool.map(transcribe_chunk, range(n_chunks))
pool.close()
chunks.sort(key=lambda x: x['start'])
# Combine chunks
words = []
for c in chunks:
chunk_start = c['start']
for wd in c['words']:
wd['start'] += chunk_start
words.append(transcription.Word(**wd))
# Remove overlap: Sort by time, then filter out any Word entries in
# the list that are adjacent to another entry corresponding to the same
# word in the audio.
words.sort(key=lambda word: word.start)
words.append(transcription.Word(word="__dummy__"))
words = [
words[i] for i in range(len(words) - 1)
if not words[i].corresponds(words[i + 1])
]
return words
def realign(chunk):
wav_obj = wave.open(wavfile, 'r')
if chunk["start"] is None:
start_t = 0
else:
start_t = chunk["start"].end
if chunk["end"] is None:
end_t = wav_obj.getnframes() / float(wav_obj.getframerate())
else:
end_t = chunk["end"].start
duration = end_t - start_t
if duration < 0.01 or duration > 60:
logging.debug("cannot realign %d words with duration %f" %
(len(chunk['words']), duration))
return
# Create a language model
offset_offset = chunk['words'][0].startOffset
chunk_len = chunk['words'][-1].endOffset - offset_offset
chunk_transcript = ms.raw_sentence[offset_offset:offset_offset +
chunk_len].encode("utf-8")
chunk_ms = metasentence.MetaSentence(chunk_transcript, resources.vocab)
chunk_ks = chunk_ms.get_kaldi_sequence()
chunk_gen_hclg_filename = language_model.make_bigram_language_model(
chunk_ks, resources.proto_langdir)
k = standard_kaldi.Kaldi(resources.nnet_gpu_path,
chunk_gen_hclg_filename,
resources.proto_langdir)
wav_obj = wave.open(wavfile, 'r')
wav_obj.setpos(int(start_t * wav_obj.getframerate()))
buf = wav_obj.readframes(int(duration * wav_obj.getframerate()))
k.push_chunk(buf)
ret = [transcription.Word(**wd) for wd in k.get_final()]
k.stop()
word_alignment = diff_align.align(ret, chunk_ms)
# Adjust startOffset, endOffset, and timing to match originals
for wd in word_alignment:
if wd.end is not None:
# Apply timing offset
wd.start += start_t
wd.end += start_t
if wd.endOffset is not None:
wd.startOffset += offset_offset
wd.endOffset += offset_offset
# "chunk" should be replaced by "words"
realignments.append({"chunk": chunk, "words": word_alignment})
if progress_cb is not None:
progress_cb(
{"percent": len(realignments) / float(len(to_realign))})
def realign_sub(chunk):
with wave.open(wavfile, "rb") as wav_obj:
if chunk["start"] is None:
start_t = 0
else:
start_t = chunk["start"].end
if chunk["end"] is None:
end_t = wav_obj.getnframes() / float(wav_obj.getframerate())
else:
end_t = chunk["end"].start
duration = end_t - start_t
# XXX: the minimum length seems bigger now (?)
if duration < 0.75 or duration > 60:
logging.debug(
"cannot realign %d words with duration %f",
len(chunk["words"]),
duration,
)
return
# Create a language model
offset_offset = chunk["words"][0].startOffset
chunk_len = chunk["words"][-1].endOffset - offset_offset
chunk_transcript = ms.raw_sentence[offset_offset:offset_offset +
chunk_len].encode("utf-8")
chunk_ms = metasentence.MetaSentence(chunk_transcript, resources.vocab)
chunk_ks = chunk_ms.get_kaldi_sequence()
chunk_gen_hclg_filename = language_model.make_bigram_language_model(
chunk_ks, resources.proto_langdir)
k = standard_kaldi.Kaldi(resources.nnet_gpu_path,
chunk_gen_hclg_filename,
resources.proto_langdir)
with wave.open(wavfile, "rb") as wav_obj:
wav_obj.setpos(int(start_t * wav_obj.getframerate()))
buf = wav_obj.readframes(int(duration * wav_obj.getframerate()))
k.push_chunk(buf)
ret = [transcription.Word(**wd) for wd in k.get_final()]
k.stop()
word_alignment = diff_align.align(ret, chunk_ms)
for wd in word_alignment:
wd.shift(time=start_t, offset=offset_offset)
# "chunk" should be replaced by "words"
realignments.append({"chunk": chunk, "words": word_alignment})
if progress_cb is not None:
progress_cb(
{"percent": len(realignments) / float(len(to_realign))})
def transcribe(self, wavfile, progress_cb=None):
wav_obj = wave.open(wavfile, 'r')
duration = wav_obj.getnframes() / float(wav_obj.getframerate())
n_chunks = int(
math.ceil(duration / float(self.chunk_len - self.overlap_t)))
chunks = []
def transcribe_chunk(idx):
wav_obj = wave.open(wavfile, 'r')
start_t = idx * (self.chunk_len - self.overlap_t)
# Seek
wav_obj.setpos(int(start_t * wav_obj.getframerate()))
# Read frames
buf = wav_obj.readframes(
int(self.chunk_len * wav_obj.getframerate()))
k = self.kaldi_queue.get()
k.push_chunk(buf)
ret = k.get_final()
k.reset()
self.kaldi_queue.put(k)
chunks.append({"start": start_t, "words": ret})
logging.info('%d/%d' % (len(chunks), n_chunks))
if progress_cb is not None:
progress_cb({
"message": ' '.join([X['word'] for X in ret]),
"percent": len(chunks) / float(n_chunks)
})
pool = Pool(min(n_chunks, self.nthreads))
pool.map(transcribe_chunk, range(n_chunks))
pool.close()
chunks.sort(key=lambda x: x['start'])
# Combine chunks
words = []
for c in chunks:
chunk_start = c['start']
chunk_end = chunk_start + self.chunk_len
chunk_words = [
transcription.Word(**wd).shift(time=chunk_start)
for wd in c['words']
]
# At chunk boundary cut points the audio often contains part of a
# word, which can get erroneously identified as one or more different
# in-vocabulary words. So discard one or more words near the cut points
# (they'll be covered by the ovlerap anyway).
#
trim = min(0.25 * self.overlap_t, 0.5)
if c is not chunks[0]:
while len(chunk_words) > 1:
chunk_words.pop(0)
if chunk_words[0].end > chunk_start + trim:
break
if c is not chunks[-1]:
while len(chunk_words) > 1:
chunk_words.pop()
if chunk_words[-1].start < chunk_end - trim:
break
words.extend(chunk_words)
# Remove overlap: Sort by time, then filter out any Word entries in
# the list that are adjacent to another entry corresponding to the same
# word in the audio.
words.sort(key=lambda word: word.start)
words.append(transcription.Word(word="__dummy__"))
words = [
words[i] for i in range(len(words) - 1)
if not words[i].corresponds(words[i + 1])
]
return words
def realign(chunk):
nonlocal ignored
if chunk["start"] is None:
start_t = 0
else:
start_t = chunk["start"].end
if chunk["end"] is None:
end_t = final_end_t
else:
end_t = chunk["end"].start
duration = end_t - start_t
# XXX: the minimum length seems bigger now (?)
if duration < 0.75 or duration > 60:
logging.debug("cannot realign %d words with duration %f" %
(len(chunk["words"]), duration))
ignored += 1
return
# Create a language model
offset_offset = chunk["words"][0].startOffset
chunk_len = chunk["words"][-1].endOffset - offset_offset
chunk_transcript = ms.raw_sentence[offset_offset:offset_offset +
chunk_len].encode("utf-8")
realign_transcript = chunk_transcript.decode("utf-8").replace(
"\n", " ")
logging.debug("realign transcript: %s", realign_transcript)
chunk_ms = metasentence.MetaSentence(chunk_transcript, resources.vocab)
chunk_ks = chunk_ms.get_kaldi_sequence()
chunk_gen_hclg_filename = language_model.make_bigram_language_model(
chunk_ks, resources.proto_langdir)
wav_obj = wave.open(wavfile, "rb")
wav_obj.setpos(int(start_t * wav_obj.getframerate()))
buf = wav_obj.readframes(int(duration * wav_obj.getframerate()))
wav_obj.close()
retries = 5
while retries > 0:
try:
k = standard_kaldi.Kaldi(
resources.nnet_gpu_path,
chunk_gen_hclg_filename,
resources.proto_langdir,
)
k.push_chunk(buf)
ret = [transcription.Word(**wd) for wd in k.get_final()]
k.stop()
break
except BrokenPipeError:
retries -= 1
if retries == 0:
raise
word_alignment = diff_align.align(ret, chunk_ms)
for wd in word_alignment:
wd.shift(time=start_t, offset=offset_offset)
# "chunk" should be replaced by "words"
realignments.append({"chunk": chunk, "words": word_alignment})
if progress_cb is not None:
progress_cb({
"percent":
(ignored + len(realignments)) / float(len(to_realign))
})
