def test_simple(self):
txt = """<document>
<s xml:lang="atj">Kwei! Tan e ici matisihin?</s>
</document>
"""
xml = etree.fromstring(txt)
tokenized = tokenize_xml.tokenize_xml(xml)
print(etree.tounicode(tokenized))
def test_mixed_lang(self):
txt = """<document>
<s xml:lang="atj">Kwei! Tan e ici matisihin?</s>
<s xml:lang="fra">Bonjour! Comment ça va?</s>
</document>
"""
xml = etree.fromstring(txt)
tokenized = tokenize_xml.tokenize_xml(xml)
print(etree.tounicode(tokenized))
def test_comments(self):
txt = """<document>
<s xml:lang="atj">Kwei! (<w xml:lang="fra">Bonjour</w>!)</s>
<s xml:lang="atj">Tan e ici matisihin?</s>
</document>
"""
xml = etree.fromstring(txt)
tokenized = tokenize_xml.tokenize_xml(xml)
print(etree.tounicode(tokenized))
def end_to_end(xml,
input_filename,
unit,
word_unit,
out_orth,
mapping_dir=None):
xml = add_lang_ids(xml, mapping_dir, unit="p")
xml = tokenize_xml(xml, mapping_dir)
xml = add_ids(xml)
converted_xml = convert_xml(xml,
word_unit,
out_orth,
mapping_dir=mapping_dir)
#save_xml("test.xml", converted_xml)
fsg = make_fsg(converted_xml, input_filename, unit)
pronouncing_dictionary = make_dict(converted_xml, input_filename, unit)
return xml, fsg, pronouncing_dictionary
def align_audio(xml_path, wav_path, unit='w', save_temps=None):
"""
Align an XML input file to an audio file.
Args:
xml_path: Path to XML input file in TEI-like format
wav_path: Path to audio input (WAV or MP3)
unit: Element to create alignments for.
save_temps: Basename for intermediate output files (or
None if they won't be saved)
"""
results = {"words": []}
# First do G2P
xml = etree.parse(xml_path).getroot()
xml = add_lang_ids(xml, mapping_dir, unit="s")
xml = tokenize_xml(xml)
if save_temps:
save_xml(save_temps + '.tokenized.xml', xml)
results['tokenized'] = xml = add_ids(xml)
if save_temps:
save_xml(save_temps + '.ids.xml', xml)
xml = convert_xml(xml)
if save_temps:
save_xml(save_temps + '.g2p.xml', xml)
# Now generate dictionary and FSG
dict_data = make_dict(xml, xml_path, unit=unit)
if save_temps:
dict_file = io.open(save_temps + '.dict', 'wb')
else:
dict_file = NamedTemporaryFile(prefix='readalongs_dict_', delete=False)
dict_file.write(dict_data.encode('utf-8'))
dict_file.flush()
fsg_data = make_fsg(xml, xml_path, unit=unit)
if save_temps:
fsg_file = io.open(save_temps + '.fsg', 'wb')
else:
fsg_file = NamedTemporaryFile(prefix='readalongs_fsg_', delete=False)
fsg_file.write(fsg_data.encode('utf-8'))
fsg_file.flush()
# Now do alignment
cfg = pocketsphinx.Decoder.default_config()
model_path = pocketsphinx.get_model_path()
cfg.set_boolean('-remove_noise', False)
cfg.set_boolean('-remove_silence', False)
cfg.set_string('-hmm', os.path.join(model_path, 'en-us'))
cfg.set_string('-dict', dict_file.name)
cfg.set_string('-fsg', fsg_file.name)
# cfg.set_string('-samprate', "no no")
cfg.set_float('-beam', 1e-100)
cfg.set_float('-wbeam', 1e-80)
_, wav_ext = os.path.splitext(wav_path)
if wav_ext == '.wav':
with wave.open(wav_path) as wav:
logging.info("Read %s: %d frames (%f seconds) audio"
% (wav_path, wav.getnframes(), wav.getnframes()
/ wav.getframerate()))
raw_data = wav.readframes(wav.getnframes())
# Downsampling is (probably) not necessary
cfg.set_float('-samprate', wav.getframerate())
else: # Try pydub, it might fail
audio = pydub.AudioSegment.from_file(wav_path)
audio = audio.set_channels(1).set_sample_width(2)
# Downsampling is (probably) not necessary
cfg.set_float('-samprate', audio.frame_rate)
raw_data = audio.raw_data
frame_points = int(cfg.get_float('-samprate')
* cfg.get_float('-wlen'))
fft_size = 1
while fft_size < frame_points:
fft_size = fft_size << 1
cfg.set_int('-nfft', fft_size)
ps = pocketsphinx.Decoder(cfg)
frame_size = 1.0 / cfg.get_int('-frate')
def frames_to_time(frames):
return frames * frame_size
ps.start_utt()
ps.process_raw(raw_data, no_search=False, full_utt=True)
ps.end_utt()
for seg in ps.seg():
start = frames_to_time(seg.start_frame)
end = frames_to_time(seg.end_frame + 1)
if seg.word in ('<sil>', '[NOISE]'):
continue
else:
results["words"].append({
"id": seg.word,
"start": start,
"end": end
})
logging.info("Segment: %s (%.3f : %.3f)",
seg.word, start, end)
final_end = end
# FIXME: should have the same number of outputs as inputs
if len(results['words']) == 0:
raise RuntimeError("Alignment Failed, please examine "
"dictionary and input audio and text.")
# Split adjoining silence/noise between words
last_end = 0.0
last_word = dict()
for word in results['words']:
silence = word['start'] - last_end
midpoint = last_end + silence / 2
if silence > 0:
if last_word:
last_word['end'] = midpoint
word['start'] = midpoint
last_word = word
last_end = word['end']
silence = final_end - last_end
if silence > 0:
if last_word is not None:
last_word['end'] += silence / 2
dict_file.close()
os.unlink(dict_file.name)
fsg_file.close()
os.unlink(fsg_file.name)
return results
def testConvert(self):
xml = tokenize_xml(self.xml)
xml = add_ids(xml)
xml = convert_xml(xml)
self.assertEqual(etree.tounicode(xml), EXPECTED_CONVERTED)
def testAddIDs(self):
xml = tokenize_xml(self.xml)
xml = add_ids(xml)
self.assertEqual(etree.tounicode(xml), EXPECTED_IDS)
def testTokenize(self):
xml = tokenize_xml(self.xml)
self.assertEqual(etree.tounicode(xml), EXPECTED_TOKENIZED)
