def inference_from_microphone():
# Setup model
model = setup_classifier(load_weights_from=PATH_TO_WEIGHTS)
setup_classes_labels(load_classes_from=PATH_TO_CLASSES, model=model)
# Start keyboard listener
recording_state = Value('i', 0)
board = KeyboardMonitor(recording_state, PRINT=False)
board.start_listen(run_in_new_thread=True)
# Set up audio recorder
recorder = AudioRecorder()
# Others
tprinter = TimerPrinter() # for print
# Start loop
cnt_voice = 0
while True:
tprinter.print("Usage: keep pressing down 'R' to record audio",
T_gap=20)
board.update_key_state()
if board.has_just_pressed():
cnt_voice += 1
print("Record {}th voice".format(cnt_voice))
# start recording
recorder.start_record(folder=SAVE_AUDIO_TO)
# wait until key release
while not board.has_just_released():
board.update_key_state()
time.sleep(0.001)
# stop recording
recorder.stop_record()
# Do inference
audio = lib_datasets.AudioClass(filename=recorder.filename)
predicted_label = model.predict_audio_label(audio)
print("\nAll word labels: {}".format(model.classes))
print("\nPredicted label: {}\n".format(predicted_label))
# Shout out the results. e.g.: one is two
lib_datasets.shout_out_result(recorder.filename,
predicted_label,
preposition_word="is",
cache_folder="data/examples/")
# reset for better printing
print("\n")
tprinter.reset()
time.sleep(0.1)
def record_audio_and_classifiy(self,
is_shout_out_result=False):
model, classes = self._model, self._CLASSES
gui, recorder = self._gui, self._recorder
# -- Record audio
gui.enable_img1_self_updating()
recorder.start_record(folder=self._DST_AUDIO_FOLDER) # Start record
while not gui.is_key_released(): # Wait for key released
time.sleep(0.001)
recorder.stop_record() # Stop record
# -- Do inference
audio = lib_datasets.AudioClass(filename=recorder.filename)
probs = model.predict_audio_label_probabilities(audio)
predicted_idx = np.argmax(probs)
predicted_label = classes[predicted_idx]
max_prob = probs[predicted_idx]
print("\nAll word labels: {}".format(classes))
print("\nPredicted label: {}, probability: {}\n".format(
predicted_label, max_prob))
PROB_THRESHOLD = 0.8
final_label = predicted_label if max_prob > PROB_THRESHOLD else "none"
# -- Update the image
# Update image1: first stop self updating,
# then set recording_length and voice_intensity to zero
gui.reset_img1()
# Update image 2: the prediction results
gui.set_img2(
final_label=final_label,
predicted_label=predicted_label,
probability=max_prob,
length=audio.get_len_s(),
valid_length=audio.get_len_s(), # TODO: remove the silent voice,
)
# Update image 3: the probability of each class
gui.set_img3(probabilities=probs)
# -- Shout out the results. e.g.: two is one
if is_shout_out_result:
lib_datasets.shout_out_result(
recorder.filename, # Raw audio to shout out
final_label,
middle_word="is",
cache_folder="data/examples/")
return predicted_label, max_prob
def main(args):
model = setup_classifier(load_weights_from=args.path_to_weights)
setup_classes_labels(load_classes_from=args.path_to_classes, model=model)
filenames = get_wav_filenames(path_to_data=args.path_to_data)
print("\nStart predicting audio label:\n")
for i, name in enumerate(filenames):
audio = lib_datasets.AudioClass(filename=name)
label = model.predict_audio_label(audio)
print("{:03d}th file: Label = {:<10}, Filename = {}".format(
i, label, name))
def inference_from_microphone():
# Setup model
model, classes = lib_rnn.setup_default_RNN_model(
weight_filepath=SRC_WEIGHT_PATH, classes_txt=SRC_CLASSES_PATH)
print(f"{len(classes)} classes: {classes}")
# Start keyboard listener
keyboard = KeyboardInputFromTerminal(
hotkey="R", is_print=False, run_in_new_thread=True)
# Set up audio recorder
recorder = AudioRecorder()
# Others
timer_printer = TimerPrinter(print_period=2.0) # for print
# Start loop
cnt_voice = 0
while True:
timer_printer.print("Usage: keep pressing down 'R' to record audio")
if keyboard.is_key_pressed():
cnt_voice += 1
print("\nRecord {}th voice".format(cnt_voice))
# Record audio
recorder.start_record(folder=DST_AUDIO_FOLDER) # Start record
while not keyboard.is_key_released(): # Wait for key released
time.sleep(0.001)
recorder.stop_record() # Stop record
# Do inference
audio = lib_datasets.AudioClass(filename=recorder.filename)
predicted_label = model.predict_audio_label(audio)
print("\nAll word labels: {}".format(model.classes))
print("\nPredicted label: {}\n".format(predicted_label))
# Shout out the results. e.g.: one is two
lib_datasets.shout_out_result(recorder.filename, predicted_label,
middle_word="is",
cache_folder="data/examples/")
time.sleep(0.1)
def main(args):
# -- Init model
model, classes = lib_rnn.setup_default_RNN_model(
args.weight_path, args.classes_path)
# -- If `data_folder` is a filename, return [data_folder].
# If `data_folder` is a folder, return all .wav filenames in this folder.
filenames = lib_datasets.get_wav_filenames(
data_folder=args.data_folder, suffix=".wav")
# -- Classification
print("\nStart predicting audio label:\n")
for i, name in enumerate(filenames):
audio = lib_datasets.AudioClass(filename=name)
label = model.predict_audio_label(audio)
print("{:03d}th file: Label = {:<10}, Filename = {}".format(
i, label, name))
def reset_audio_sample_rate(filename, dst_sample_rate=16000):
data, sample_rate = sf.read(filename)
if (dst_sample_rate is not None) and (dst_sample_rate != sample_rate):
data = librosa.core.resample(data, sample_rate, dst_sample_rate)
sample_rate = dst_sample_rate
sf.write(filename, data, sample_rate)
print(f"Reset sample rate to {dst_sample_rate} for the file: {filename}")
folder = "./data/noises/"
fnames = get_filenames(folder, file_type="*.wav")
Aug = lib_augment.Augmenter
aug = Aug([
Aug.Crop(time=(0.6, 2.0)),
Aug.PadZeros(time=(0, 0.3)),
Aug.PlaySpeed(rate=(0.7, 1.5), keep_size=False),
Aug.Amplify(rate=(0.6, 1.5)),
])
for name in fnames:
audio0 = lib_datasets.AudioClass(filename=name)
for i in range(5):
print(i)
audio = copy.deepcopy(audio0)
aug(audio)
name_new = lib_commons.add_idx_suffix(name, i).split('/')[-1]
audio.write_to_file("data/data_tmp/" + name_new)
audio.play_audio()
'''
Test the function `lib_proc_audio.compute_mfcc`,
which is called by `audio.compute_mfcc()`.
'''
import matplotlib.pyplot as plt
if True: # Add ROOT and import my libraries.
import sys
import os
ROOT = os.path.dirname(os.path.abspath(__file__)) + \
"/../" # Root of the project.
sys.path.append(ROOT)
import utils.lib_datasets as lib_datasets
audio = lib_datasets.AudioClass(filename="test_data/audio_front.wav")
audio.compute_mfcc()
audio.plot_audio_and_mfcc()
plt.show()
