def transcribeAudio(lang, audio_file):
"""
Call the Sphinx Java module to transcribe the audio file
Read the results and convert
Args:
lang (string): language
audio_file (string): path to the audio file:
Returns:
list(AudioWord): The start/end of each word
"""
rword = re.compile(r'^{(?P<token>[^,]+),\s(?P<prob>[^,]+),\s\[(?P<from>\d+):(?P<to>\d+)]}$')
outfile = os.path.join(config.TEMP_DIR, 'mapping.txt')
mapping = None
if os.path.exists(audio_file):
gu.removeFile(outfile)
ret = subprocess.call(['java', '-jar', config.AUDIO_TRANSCRIBER, lang, audio_file, outfile], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
if ret == 0: # success
with open(outfile, 'r') as f:
lines = f.readlines()
mapping = []
for line in lines:
m = rword.search(line)
if m:
mapping.append(AudioWord(m.group('token'), '', m.group('from'), m.group('to')))
gu.removeFile(outfile)
return mapping
def alignAudio(lang, audio_file, transcript_file):
"""
Call the Sphinx Java module to align an audiofile with a transcript file
Read the results and convert
Args:
lang (string): language
audio_file (string): path to the audio file
transcript_file (string): path to the transcript file
Returns:
list(AudioWord): Alignment status if applicable the start/end of each token
"""
rmapped = re.compile(r'^(?P<type>MAPPED):\s(?P<token>\S+)\s+\[(?P<from>\d+):(?P<to>\d+)]$')
rmissing = re.compile(r'^(?P<type>MISSING):\s(?P<token>\S+)\s+$')
outfile = os.path.join(config.TEMP_DIR, 'mapping.txt')
mapping = None
if os.path.exists(audio_file) and os.path.exists(transcript_file):
gu.removeFile(outfile)
ret = subprocess.call(['java', '-jar', config.AUDIO_ALIGNER, lang, audio_file, transcript_file, outfile], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
if ret == 0: # success
with open(outfile, 'r') as f:
lines = f.readlines()
mapping = []
for line in lines:
m = rmapped.search(line)
if m:
mapping.append(AudioWord(m.group('token'), m.group('type'), m.group('from'), m.group('to')))
else:
m = rmissing.search(line)
if m:
mapping.append(AudioWord(m.group('token'), m.group('type')))
gu.removeFile(outfile)
return mapping
def findBoundaries(lang, bookid, chapter):
"""
Find the stat and the end of a chapter
"""
start = '00:00:00.000'
end = start
audio_file, start_time, stop_time = book_manager.chapterAudio(lang, bookid, chapter)
audio_segment = AudioSegment.from_mp3(audio_file) # read the audio
audio_len = len(audio_segment)
wavfile = os.path.join(config.TEMP_DIR, 'chapter%s.wav' % chapter)
sp = utils.getSpacy(lang)
text = book_manager.bookChapter(lang, bookid, chapter)
doc = sp(text)
doc_tokens = [tkn for tkn in doc if (not tkn.is_punct) and tkn.text.strip()]
doc_start = [tok.text.lower() for tok in doc_tokens[:10]]
doc_end = [tok.text.lower() for tok in doc_tokens[-10:]]
# start of the chapter
gu.removeFile(wavfile)
encodeForSphinx(audio_file, '00:00:00', '00:01:00', wavfile) # encode audio for speech recognition
words = transcribeAudio(lang, wavfile)
trans = [word for word in words if (not word.token.startswith('<')) and (not word.token.startswith('['))]
start = msec2time(findPosition('end', 200, trans, doc_start))
gu.removeFile(wavfile)
# end of the chapter
segment_end = msec2time(audio_len)
msec_start = audio_len - 30000
segment_start = msec2time(msec_start)
encodeForSphinx(audio_file, segment_start, segment_end, wavfile) # encode audio for speech recognition
words = transcribeAudio(lang, wavfile)
trans = [word for word in words if (not word.token.startswith('<')) and (not word.token.startswith('['))]
doc_end = list(reversed(doc_end))
trans = list(reversed(trans))
end = msec2time(findPosition('begin', msec_start - 300, trans, doc_end))
return (start, start_time, end, stop_time)
def reset(fromLang, toLang):
dbName = __dbName(fromLang, toLang)
lemma_mappings[dbName] = {}
file_to_del = os.path.join(CACHE, dbName)
try:
gu.removeFile(file_to_del)
setDefault(fromLang, toLang)
except Exception as e:
print('ERROR: could not remove lemma cache under %s: %s' % (file_to_del, e))
def alignChapter(lang, bookid, chapter):
"""
Align a chapter of a book
Args:
lang (str): language
bookid (str): identifier of a book
chapter (int): the chapter to be aligned
Returns:
list of spacy tokens: the tokens with the added audio alignment information
"""
bar = IncrementalBar('Processing %s [%s] (%s)' % (bookid, lang, chapter) , max=100)
bar.start()
outfile = os.path.join(book_manager.chaptersPath(lang, bookid),book_manager.mappingFile(chapter))
audio_file, start_time, stop_time = book_manager.chapterAudio(lang, bookid, chapter)
wavfile = os.path.join(config.TEMP_DIR, 'chapter%s.wav' % chapter)
gu.removeFile(wavfile)
encodeForSphinx(audio_file, start_time, stop_time, wavfile) # encode audio for speech recognition
# get spacy models for language processing
sp = utils.getSpacy(lang)
text = book_manager.bookChapter(lang, bookid, chapter)
doc = sp(text)
# prepare sentences without punctuation
token_count = 0
doc_tokens = [tkn for tkn in doc if tkn.is_alpha and (not tkn.is_punct) and tkn.text.strip()]
token_count = len(doc_tokens)
audio_segment = AudioSegment.from_wav(wavfile) # read the audio
audio_len = len(audio_segment)
begin_tkn = 0
begin_audio = 0
startm = time2msec(start_time)
stopm = time2msec(stop_time)
l = stopm - startm
while begin_tkn < token_count:
chunk = doc_tokens[begin_tkn:begin_tkn+50]
rel_len = 1.25 * len(chunk) / token_count
end_audio = begin_audio + int(rel_len * audio_len)
last_idx, begin_audio = alignChunk(lang, audio_segment=audio_segment, audio_begin=begin_audio, audio_end=end_audio, chunk=chunk)
bar.goto(int(100.0 * begin_audio / l))
if last_idx == -1: # could not map anything
break
else:
begin_tkn += last_idx + 1
gu.removeFile(wavfile)
saveAudioMapping(doc_tokens, startm, stopm, outfile)
def alignChunk(lang, audio_segment, audio_begin, audio_end, chunk):
"""
Align a chunk of a longer text/audio
Args:
lang (str): language
audio_segment (AudioSegment): The entire audio content
audio_begin (int): The start of the audio range (ms)
audio_end (int): The end of the audio range (ms)
chunk (list of spacy Tokens): List of spacy tokens with the transcript
Returns:
(int, int): Index of the last mapped token
and the position of the last mapping in the audio
"""
temp_audio = os.path.join(config.TEMP_DIR, 'temp.wav')
temp_transcript = os.path.join(config.TEMP_DIR, 'transcript.txt')
audio = audio_segment[audio_begin:audio_end]
audio.export(temp_audio, format="wav", parameters=["-ac", "1", "-ar", "16000"])
trans = " ".join([word.lower_ for word in chunk])
with open(temp_transcript, 'w') as f:
f.write(trans)
alignment = alignAudio(lang, temp_audio, temp_transcript) # try to align the audio with the text
lastMappedEnd = -1
lastIdx = -1
if alignment is None:
gu.removeFile(temp_audio)
gu.removeFile(temp_transcript)
return (lastIdx, lastMappedEnd)
# add mapping information to the tokens
for idx, tkn in enumerate(chunk):
tkn._.spoken = alignment[idx].token
end = alignment[idx].end
end = end if end == -1 else end + audio_begin
if end > 0 and ((lastMappedEnd < 0) or (end < lastMappedEnd + 10000)):
tkn._.end = end
lastMappedEnd = end
lastIdx = idx
bgn = alignment[idx].begin
if bgn > 0:
bgn += audio_begin
tkn._.begin = bgn
gu.removeFile(temp_audio)
gu.removeFile(temp_transcript)
return (lastIdx, lastMappedEnd)