def __init__(self, resources, nthreads=2):
self.available = False
if nthreads <= 0: return
if not os.path.exists(resources.full_hclg_path): return
queue = kaldi_queue.build(resources, nthreads=nthreads)
self.mtt = MultiThreadedTranscriber(queue, nthreads=nthreads)
self.available = True
def __init__(self, resources, nthreads=2):
self.available = False
if nthreads <= 0: return
if not os.path.exists(resources.full_hclg_path): return
queue = kaldi_queue.build(resources, nthreads=nthreads)
self.mtt = MultiThreadedTranscriber(queue, nthreads=nthreads)
self.available = True
def __init__(self, resources, transcript, nthreads=4, **kwargs):
self.kwargs = kwargs
self.nthreads = nthreads
self.transcript = transcript
self.resources = resources
self.ms = metasentence.MetaSentence(transcript, resources.vocab)
ks = self.ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(ks, resources.proto_langdir, **kwargs)
self.queue = kaldi_queue.build(resources, hclg_path=gen_hclg_filename, nthreads=nthreads)
self.mtt = MultiThreadedTranscriber(self.queue, nthreads=nthreads)
def __init__(self, resources, transcript, nthreads=4, **kwargs):
self.kwargs = kwargs
self.nthreads = nthreads
self.transcript = transcript
self.resources = resources
self.ms = metasentence.MetaSentence(transcript, resources.vocab)
ks = self.ms.get_kaldi_sequence()
gen_hclg_filename = language_model.make_bigram_language_model(ks, resources.proto_langdir, **kwargs)
self.queue = kaldi_queue.build(resources, hclg_path=gen_hclg_filename, nthreads=nthreads)
self.mtt = MultiThreadedTranscriber(self.queue, nthreads=nthreads)
def test_transcriber(self):
from gentle import resampled, kaldi_queue, standard_kaldi, Resources
from gentle.transcriber import MultiThreadedTranscriber
resources = Resources()
k_queue = kaldi_queue.build(resources, 1)
trans = MultiThreadedTranscriber(k_queue)
with resampled('examples/data/lucier.mp3', 10.5, 2.5) as filename:
words, duration = trans.transcribe(filename)
self.assertEqual(words[0].word, "different")
def test_transcriber(self):
import subprocess
from gentle import resampled, kaldi_queue, standard_kaldi, Resources
from gentle.transcriber import MultiThreadedTranscriber
standard_kaldi.STDERR = subprocess.STDOUT
resources = Resources()
k_queue = kaldi_queue.build(resources, 1)
trans = MultiThreadedTranscriber(k_queue)
with resampled(self.audio, 10.5, 2.5) as filename:
words, duration = trans.transcribe(filename)
self.assertEqual(words[0].word, "different")
def test_transcriber(self):
import subprocess
from gentle import resampled, kaldi_queue, standard_kaldi, Resources
from gentle.transcriber import MultiThreadedTranscriber
standard_kaldi.STDERR = subprocess.STDOUT
resources = Resources()
k_queue = kaldi_queue.build(resources, 1)
trans = MultiThreadedTranscriber(k_queue)
with resampled(self.audio, 10.5, 2.5) as filename:
words, duration = trans.transcribe(filename)
self.assertEqual(words[0].word, "different")
words = [
words[i] for i in range(len(words) - 1)
if not words[i].corresponds(words[i + 1])
]
return words, duration
if __name__ == '__main__':
# full transcription
import json
import sys
import logging
logging.getLogger().setLevel('INFO')
import gentle
from gentle import standard_kaldi
from gentle import kaldi_queue
resources = gentle.Resources()
k_queue = kaldi_queue.build(resources, 3)
trans = MultiThreadedTranscriber(k_queue)
with gentle.resampled(sys.argv[1]) as filename:
words, duration = trans.transcribe(filename)
open(sys.argv[2],
'w').write(transcription.Transcription(words=words).to_json())
# 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, duration
if __name__=='__main__':
# full transcription
import json
import sys
import logging
logging.getLogger().setLevel('INFO')
import gentle
from gentle import standard_kaldi
from gentle import kaldi_queue
resources = gentle.Resources()
k_queue = kaldi_queue.build(resources, 3)
trans = MultiThreadedTranscriber(k_queue)
with gentle.resampled(sys.argv[1]) as filename:
words, duration = trans.transcribe(filename)
open(sys.argv[2], 'w').write(transcription.Transcription(words=words).to_json())
