def mock_model(tmpdir_factory):
base_path = tmpdir_factory.mktemp("pipeline")
base_path = Path(base_path)
if not base_path.joinpath('/state').exists():
kaldi = KaldiInterface(f'{base_path}/state')
ds = kaldi.new_dataset('dataset_x')
ds.add_directory('/recordings/transcribed')
ds.select_importer('Elan')
ds.process()
pd = kaldi.new_pron_dict('pron_dict_y')
pd.link(ds)
pd.set_l2s_path('/recordings/letter_to_sound.txt')
pd.generate_lexicon()
m = kaldi.new_model('model_z')
m.link(ds, pd)
m.build_kaldi_structure() # TODO: remove this line
m.train() # may take a while
else:
kaldi = KaldiInterface.load(f'{base_path}/state')
m = kaldi.new_model('model_z', use_existing=True)
return (kaldi, m)
ds = kaldi.new_dataset('dsy')
ds.add_directory('/recordings/transcribed', filter=['eaf', 'wav'])
ds.process()
# Step 2
# ======
# Build pronunciation dictionary
pd = kaldi.new_pron_dict('pd')
pd.link(ds)
pd.set_l2s_path('/recordings/letter_to_sound.txt')
pd.generate_lexicon()
# Step 3
# ======
# Link dataset and pd to a new model, then train the model.
m = kaldi.new_model('mx')
m.link(ds, pd)
m.build_kaldi_structure()
m.train() # may take a while
# Step 4
# ======
# Make a transcription interface and transcribe unseen audio to elan.
t = kaldi.new_transcription('tx')
t.link(m)
with open('/recordings/untranscribed/audio.wav', 'rb') as faudio:
t.prepare_audio(faudio)
# t.transcribe_align()
t.transcribe()
# print(t.elan().decode('utf-8'))
print(t.text().decode('utf-8'))