def process_file(wav_file, class_labels, to_csv, num_files, output_file,
ten_seconds, num_predictions, threshold):
print(wav_file)
files = tf.io.gfile.glob(wav_file)
total_predictions = []
examples = []
print('Number of predictions ' + str(num_predictions))
print('Threshold ' + str(threshold))
if not files:
raise IOError("Unable to find input files. data_pattern='" + wav_file +
"'")
if num_files == 0:
num_files = len(files)
for i in range(0, num_files):
if not files[i]:
i = i + 1
sr, data = wavfile.read(files[i])
if data.dtype != np.int16:
raise TypeError('Bad sample type: %r' % data.dtype)
print(i)
print(files[i])
# local import to reduce start-up time
from audio.processor import WavProcessor, format_predictions
with WavProcessor() as proc:
if ten_seconds == False:
predictions = proc.get_predictions(wav_file, sr, data,
num_predictions, threshold,
class_labels)
print('Predictions')
print(format_predictions(predictions))
if to_csv == True:
examples.append(int(len(data) / 44100))
total_predictions.append(predictions)
else:
predictions = proc.get_predictions2(sr, data, num_predictions,
threshold, class_labels)
print('Predictions')
for i in range(0, len(predictions)):
print(str(i) + ' ' + format_predictions(predictions[i]))
if to_csv == True:
examples.append(int(len(data) / 44100))
total_predictions.append(predictions)
if to_csv == True:
from audio.processor import WavProcessor, format_predictions
with WavProcessor() as proc:
if ten_seconds == False:
proc.toCSV(examples, wav_file, num_files, output_file,
total_predictions)
else:
proc.toCSV2(examples, wav_file, num_files, output_file,
total_predictions)
def _process_loop(self):
with WavProcessor() as proc:
self._ask_data_event.set()
while self.is_running:
if self._process_buf is None:
# Waiting for data to process
time.sleep(self._processor_sleep_time)
continue
self._ask_data_event.clear()
if self._save_path:
f_path = os.path.join(
self._save_path, 'record_{:.0f}.wav'.format(time.time())
)
wavfile.write(f_path, self._sample_rate, self._process_buf)
logger.info('"{}" saved'.format(f_path))
logger.info('Start processing')
predictions = proc.get_predictions(
self._sample_rate, self._process_buf)
formatted = format_predictions(predictions)
logger.info('Predictions: {}'.format(formatted))
self.events_queue.append((datetime.datetime.now(), formatted))
self._send_dh(predictions)
logger.info('Stop processing')
self._process_buf = None
self._ask_data_event.set()
def _process_loop(self):
with WavProcessor() as proc:
self._ask_data.set()
while True:
if self._process_buf is None:
# Waiting for data to process
time.sleep(self._processor_sleep_time)
continue
self._ask_data.clear()
if self._save_path:
f_path = os.path.join(
self._save_path,
'record_{:.0f}.wav'.format(time.time()))
wavfile.write(f_path, self._sample_rate, self._process_buf)
logger.info('"{}" saved.'.format(f_path))
logger.info('Start processing.')
predictions = proc.get_predictions(self._sample_rate,
self._process_buf)
logger.info('Predictions: {}'.format(
format_predictions(predictions)))
logger.info('Stop processing.')
self._process_buf = None
self._ask_data.set()
def _process_loop(self):
with WavProcessor() as proc:
self._ask_data.set()
while True:
if self._process_buf is None:
# Waiting for data to process
time.sleep(self._processor_sleep_time)
continue
self._ask_data.clear()
if self._save_path:
f_path = os.path.join(
self._save_path, 'record_{:.0f}.wav'.format(time.time())
)
wavfile.write(f_path, self._sample_rate, self._process_buf)
logger.info('"{}" saved.'.format(f_path))
logger.info('Start processing.')
predictions = proc.get_predictions(
self._sample_rate, self._process_buf)
logger.info(
'Predictions: {}'.format(format_predictions(predictions))
)
logger.info('Stop processing.')
self._process_buf = None
self._ask_data.set()
def process_file(wav_file):
sr, data = wavfile.read(wav_file)
if data.dtype != np.int16:
raise TypeError('Bad sample type: %r' % data.dtype)
# local import to reduce start-up time
from audio.processor import WavProcessor, format_predictions
with WavProcessor() as proc:
predictions = proc.get_predictions(sr, data)
print(format_predictions(predictions))
def process_file(wav_file, ckpt_file, labels_file):
sr, data = wavfile.read(wav_file)
print(wav_file, end=' ')
if data.dtype != np.int16:
raise TypeError('Bad sample type: %r' % data.dtype)
predictions = []
with WavProcessor(ckpt_file, labels_file) as proc:
try:
predictions = proc.get_predictions(sr, data)
print(format_predictions(predictions))
except:
print('CORRUPTED!')
return predictions
def _process_loop(self):
with WavProcessor() as proc:
self._ask_data.set()
while True:
if self._process_buf is None:
# Waiting for data to process
time.sleep(self._processor_sleep_time)
continue
self._ask_data.clear()
if self._save_path:
f_path = os.path.join(
self._save_path,
'record_{:.0f}.wav'.format(time.time()))
wavfile.write(f_path, self._sample_rate, self._process_buf)
logger.info('"{}" saved.'.format(f_path))
logger.info('Start processing.')
predictions = proc.get_predictions(self._sample_rate,
self._process_buf)
logger.info('Predictions: {}'.format(
format_predictions(predictions)))
my_dict = {x[0]: x[1] for x in predictions}
msg_json = {
"app_id": "openears",
"dev_id": "TEST001",
"payload_fields": my_dict,
"time": int(time.time() * 1e3)
}
msg_str = json.dumps(msg_json)
auth = {"username": mqtt_user, "password": mqtt_password}
publish.single("pipeline/openears/TEST001",
payload=msg_str,
hostname=mqtt_host,
port=mqtt_port,
auth=auth)
print('message sent')
logger.info('Stop processing.')
self._process_buf = None
self._ask_data.set()