def handle_source(json_data):
data = str(json_data['data'])
data = data[1:-1]
global graph
np_wav = np.fromstring(data, dtype=np.int16, sep=',') / \
32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
print('Successfully convert to NP rep', np_wav)
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
print('Db...', db)
# Make predictions
print('Making prediction...')
x = waveform_to_examples(np_wav, RATE)
predictions = []
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
print('Successfully reshape x', x.shape)
# pred = model.predict(x)
# predictions.append(pred)
print('Prediction: Speech (50%)')
socketio.emit('audio_label', {
'label': 'Unrecognized Sound',
'accuracy': '1.0'
})
def audio_samples(in_data, frame_count, time_info, status_flags):
global graph
np_wav = np.fromstring(in_data, dtype=np.int16) / \
32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
# Make predictions
x = waveform_to_examples(np_wav, RATE)
predictions = []
with graph.as_default():
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
print('Reshape x successful', x.shape)
pred = model.predict(x)
predictions.append(pred)
print('Prediction succeeded')
for prediction in predictions:
context_prediction = np.take(
prediction[0], [homesounds.labels[x] for x in active_context])
m = np.argmax(context_prediction)
if (context_prediction[m] > PREDICTION_THRES
and db > DBLEVEL_THRES):
print("Prediction: %s (%0.2f)" %
(homesounds.to_human_labels[active_context[m]],
context_prediction[m]))
return (in_data, pyaudio.paContinue)
def handle_source(json_data):
data = str(json_data['data'])
data = data[1:-1]
global graph
np_wav = np.fromstring(data, dtype=np.int16, sep=',') / \
32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
# Make predictions
x = waveform_to_examples(np_wav, RATE)
predictions = []
with graph.as_default():
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
pred = model.predict(x)
predictions.append(pred)
for prediction in predictions:
context_prediction = np.take(
prediction[0], [homesounds.labels[x] for x in active_context])
m = np.argmax(context_prediction)
print('Max prediction', str(
homesounds.to_human_labels[active_context[m]]), str(context_prediction[m]))
if (context_prediction[m] > PREDICTION_THRES and db > DBLEVEL_THRES):
socketio.emit('audio_label',
{
'label': str(homesounds.to_human_labels[active_context[m]]),
'accuracy': str(context_prediction[m]),
'db': str(db)
},
room=request.sid)
print("Prediction: %s (%0.2f)" % (
homesounds.to_human_labels[active_context[m]], context_prediction[m]))
def audio_samples(in_data, frame_count, time_info, status_flags):
global session
global interpolators
global audio_rms
global candidate
global m
np_wav = np.fromstring(in_data,
dtype=np.int16) / 32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
interp = interpolators[30]
interp.animate(interp.end, db, 1.0)
# Make Predictions
x = waveform_to_examples(np_wav, RATE)
predictions = []
with session.graph.as_default():
set_session(session)
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
pred = model.predict(x)
predictions.append(pred)
for prediction in predictions:
m = np.argmax(prediction[0])
candidate = (ubicoustics.to_human_labels[label[m]], prediction[0,
m])
num_classes = len(prediction[0])
for k in range(num_classes):
interp = interpolators[k]
prev = interp.end
interp.animate(prev, prediction[0, k], 1.0)
return (in_data, pyaudio.paContinue)
def audio_samples(in_data):
# get float data from string array
in_data = in_data.replace("[", "").replace("]", "")
in_data = in_data.split(",")
in_data = np.array(in_data)
in_data = in_data.astype(np.float)
np_wav = in_data / 32768.0 #Convert to [-1.0, +1.0]
#np_wav = np.fromstring(in_data, dtype=np.int16, sep=',') / 32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
# Make predictions
x = waveform_to_examples(np_wav, RATE)
predictions = []
with graph.as_default():
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
pred = model.predict(x)
predictions.append(pred)
for prediction in predictions:
context_prediction = np.take(
prediction[0], [homesounds.labels[x] for x in active_context])
m = np.argmax(context_prediction)
if (context_prediction[m] > PREDICTION_THRES
and db > DBLEVEL_THRES):
#print ("Prediction: %s (%0.2f)" % (homesounds.to_human_labels[active_context[m]], context_prediction[m]))
return ("Prediction: %s (%0.2f)" %
(homesounds.to_human_labels[active_context[m]],
context_prediction[m]))
def audio_samples(in_data):
global graph
print("======")
np_wav = np.array(struct.unpack(
'h' * RATE, in_data)) / 32768.0 # Convert to [-1.0, +1.0]
print(np_wav)
print(len(np_wav))
# Compute RMS and convert to dB
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
# Make predictions
x = waveform_to_examples(np_wav, RATE)
predictions = []
with graph.as_default():
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
pred = model.predict(x)
predictions.append(pred)
for prediction in predictions:
context_prediction = np.take(
prediction[0], [homesounds.labels[x] for x in active_context])
m = np.argmax(context_prediction)
if (context_prediction[m] > PREDICTION_THRES
and db > DBLEVEL_THRES):
print("Prediction: %s (%0.2f)" %
(homesounds.to_human_labels[active_context[m]],
context_prediction[m]))
return homesounds.to_human_labels[active_context[m]]
def handle_source(json_data):
data = str(json_data['data'])
data = data[1:-1]
global graph
np_wav = np.fromstring(data, dtype=np.int16, sep=',') / \
32768.0 # Convert to [-1.0, +1.0]
# Compute RMS and convert to dB
print('Successfully convert to NP rep', np_wav)
rms = np.sqrt(np.mean(np_wav**2))
db = dbFS(rms)
print('Db...', db)
# Make predictions
print('Making prediction...')
x = waveform_to_examples(np_wav, RATE)
predictions = []
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
print('Successfully reshape x', x.shape)
pred = model.predict(x)
predictions.append(pred)
with graph.as_default():
if x.shape[0] != 0:
x = x.reshape(len(x), 96, 64, 1)
print('Successfully reshape x', x)
# pred = model.predict(x)
# predictions.append(pred)
for prediction in predictions:
context_prediction = np.take(
prediction[0], [homesounds.labels[x] for x in active_context])
m = np.argmax(context_prediction)
print('Max prediction', str(
homesounds.to_human_labels[active_context[m]]), str(context_prediction[m]))
if (context_prediction[m] > PREDICTION_THRES and db > DBLEVEL_THRES):
socketio.emit('audio_label',
{'label': str(homesounds.to_human_labels[active_context[m]]),
'accuracy': str(context_prediction[m])})
print("Prediction: %s (%0.2f)" % (
homesounds.to_human_labels[active_context[m]], context_prediction[m]))
socket.emit('audio_label',
{
'label': 'Unrecognized Sound',
'accuracy': '1.0'
})
def background_thread():
"""Example of how to send server generated events to clients."""
count = 0
while True:
socketio.sleep(10)
count += 1
socketio.emit('my_response',
{'data': 'Server generated event', 'count': count},
namespace='/test')
@app.route('/')
def index():
return render_template('index.html',)
@socketio.on('send_message')
def handle_source(json_data):
print('Receive message...' + str(json_data['message']))
text = json_data['message'].encode('ascii', 'ignore')
socketio.emit('echo', {'echo': 'Server Says: ' + str(text)})
print('Sending message back..')
@socketio.on('disconnect_request', namespace='/test')
def disconnect_request():
@copy_current_request_context
def can_disconnect():
disconnect()
session['receive_count'] = session.get('receive_count', 0) + 1
# for this emit we use a callback function
# when the callback function is invoked we know that the message has been
# received and it is safe to disconnect
emit('my_response',
{'data': 'Disconnected!', 'count': session['receive_count']},
callback=can_disconnect)
@socketio.on('connect', namespace='/test')
def test_connect():
global thread
with thread_lock:
if thread is None:
thread = socketio.start_background_task(background_thread)
emit('my_response', {'data': 'Connected', 'count': 0})
@socketio.on('disconnect', namespace='/test')
def test_disconnect():
print('Client disconnected', request.sid)
if __name__ == '__main__':
socketio.run(app, debug=True)
