def test_analyze_long_file_formants(acoustic_corpus_path, formants_func):
segments = [(1, 2, 0)]
output = analyze_long_file(acoustic_corpus_path, segments, formants_func)
print(sorted(output[(1, 2, 0)].keys()))
assert (all(x >= 1 for x in output[(1, 2, 0)].keys()))
assert (all(x <= 2 for x in output[(1, 2, 0)].keys()))
output = analyze_long_file(acoustic_corpus_path,
segments,
formants_func,
padding=0.5)
print(sorted(output[(1, 2, 0)].keys()))
assert (all(x >= 1 for x in output[(1, 2, 0)].keys()))
assert (all(x <= 2 for x in output[(1, 2, 0)].keys()))
def analyze_pitch(corpus_context, sound_file, stop_check = None):
if not os.path.exists(sound_file.filepath):
return
algorithm = corpus_context.config.pitch_algorithm
if corpus_context.has_pitch(sound_file.discourse.name, algorithm):
return
if algorithm == 'reaper':
if getattr(corpus_context.config, 'reaper_path', None) is not None:
pitch_function = partial(ReaperPitch, reaper = corpus_context.config.reaper_path,
time_step = 0.01, freq_lims = (50,500))
else:
return
elif algorithm == 'praat':
if getattr(corpus_context.config, 'praat_path', None) is not None:
pitch_function = partial(PraatPitch, praatpath = corpus_context.config.praat_path,
time_step = 0.01, freq_lims = (75,500))
else:
return
else:
pitch_function = partial(ASPitch, time_step = 0.01, freq_lims = (75,500), window_shape = 'gaussian')
atype = corpus_context.hierarchy.highest
prob_utt = getattr(corpus_context, atype)
q = corpus_context.query_graph(prob_utt)
q = q.filter(prob_utt.discourse.name == sound_file.discourse.name)
q = q.preload(prob_utt.discourse, prob_utt.speaker)
utterances = q.all()
segments = []
for u in utterances:
segments.append((u.begin, u.end, u.channel))
output = analyze_long_file(sound_file.vowel_filepath, segments, pitch_function, padding = 1)
for k, track in output.items():
corpus_context.save_pitch(sound_file, track, channel = k[-1], source = algorithm)
def analyze_formants(corpus_context, sound_file, stop_check = None):
if not os.path.exists(sound_file.filepath):
return
algorithm = corpus_context.config.formant_algorithm
if corpus_context.has_formants(sound_file.discourse.name, algorithm):
return
if algorithm == 'praat':
if getattr(corpus_context.config, 'praat_path', None) is not None:
formant_function = partial(PraatFormants,
praatpath = corpus_context.config.praat_path,
max_freq = 5500, num_formants = 5, win_len = 0.025,
time_step = 0.01)
else:
return
else:
formant_function = partial(ASFormants, freq_lims = (0, 5500),
time_step = 0.01, num_formants = 5,
win_len = 0.025, window_shape = 'gaussian')
atype = corpus_context.hierarchy.highest
prob_utt = getattr(corpus_context, atype)
q = corpus_context.query_graph(prob_utt)
q = q.filter(prob_utt.discourse.name == sound_file.discourse.name)
utterances = q.all()
segments = []
for i, u in enumerate(utterances):
segments.append((u.begin, u.end, u.channel))
output = analyze_long_file(sound_file.vowel_filepath, segments, formant_function, padding = 1)
for k, track in output.items():
corpus_context.save_formants(sound_file, track, channel = k[-1], source = algorithm)
corpus_context.sql_session.flush()
def analyze_pitch(corpus_context, sound_file, stop_check=None):
filepath = os.path.expanduser(sound_file.vowel_filepath)
if not os.path.exists(filepath):
return
algorithm = corpus_context.config.pitch_algorithm
if corpus_context.has_pitch(sound_file.discourse.name, algorithm):
return
if algorithm == 'reaper':
if getattr(corpus_context.config, 'reaper_path', None) is not None:
pitch_function = partial(ReaperPitch,
reaper=corpus_context.config.reaper_path,
time_step=0.01,
freq_lims=(50, 500))
else:
return
elif algorithm == 'praat':
if getattr(corpus_context.config, 'praat_path', None) is not None:
pitch_function = partial(
PraatPitch,
praatpath=corpus_context.config.praat_path,
time_step=0.01,
freq_lims=(75, 500))
else:
return
else:
pitch_function = partial(ASPitch,
time_step=0.01,
freq_lims=(75, 500),
window_shape='gaussian')
atype = corpus_context.hierarchy.highest
prob_utt = getattr(corpus_context, atype)
q = corpus_context.query_graph(prob_utt)
q = q.filter(prob_utt.discourse.name == sound_file.discourse.name)
q = q.preload(prob_utt.discourse, prob_utt.speaker)
utterances = q.all()
segments = []
for u in utterances:
segments.append((u.begin, u.end, u.channel))
output = analyze_long_file(filepath, segments, pitch_function, padding=1)
for k, track in output.items():
corpus_context.save_pitch(sound_file,
track,
channel=k[-1],
source=algorithm)
def analyze_formants(corpus_context, sound_file, stop_check=None):
filepath = os.path.expanduser(sound_file.vowel_filepath)
if not os.path.exists(filepath):
return
algorithm = corpus_context.config.formant_algorithm
if corpus_context.has_formants(sound_file.discourse.name, algorithm):
return
if algorithm == 'praat':
if getattr(corpus_context.config, 'praat_path', None) is not None:
formant_function = partial(
PraatFormants,
praatpath=corpus_context.config.praat_path,
max_freq=5500,
num_formants=5,
win_len=0.025,
time_step=0.01)
else:
return
else:
formant_function = partial(ASFormants,
freq_lims=(0, 5500),
time_step=0.01,
num_formants=5,
win_len=0.025,
window_shape='gaussian')
atype = corpus_context.hierarchy.highest
prob_utt = getattr(corpus_context, atype)
q = corpus_context.query_graph(prob_utt)
q = q.filter(prob_utt.discourse.name == sound_file.discourse.name)
utterances = q.all()
segments = []
for i, u in enumerate(utterances):
segments.append((u.begin, u.end, u.channel))
output = analyze_long_file(filepath, segments, formant_function, padding=1)
for k, track in output.items():
corpus_context.save_formants(sound_file,
track,
channel=k[-1],
source=algorithm)
corpus_context.sql_session.flush()
