def generate_pitch_values(self):
# Estimate pitch using Crepe (https://pypi.org/project/crepe/)
print(
"Estimating pitch values. This will take some time based on the power of your CPU!\n"
"Using GPU tensorflow will lead to better performance: https://www.tensorflow.org/install/gpu "
)
# Generate the paths for the file
# crepe_cmd = "crepe output/" + os.path.splitext(self.details["song_file"])[0] + "/vocals.wav"
# Calls the crepe subprocess
# os.system(crepe_cmd)
# audio, sr = a2n.audio_from_file("output/" + os.path.splitext(self.details["song_file"])[0] + "/vocals.wav")
# time, frequency, confidence, activation = crepe.predict(audio, sr, viterbi=True)
if not os.path.exists("output/" +
os.path.splitext(self.details["song_file"])[0] +
"/vocals.f0.csv"):
crepe.process_file(
"output/" + os.path.splitext(self.details["song_file"])[0] +
"/vocals.wav",
"output/" + os.path.splitext(self.details["song_file"])[0])
else:
print("Loading pitch values from cache")
# All heavy data processing has been completed, let us load in the results
csv_loc = "output/" + os.path.splitext(
self.details["song_file"])[0] + "/vocals.f0.csv"
return csv_loc
def test_sweep():
# this data contains a sine sweep
file = os.path.join(os.path.dirname(__file__), 'sweep.wav')
crepe.process_file(file)
f0_file = os.path.join(os.path.dirname(__file__), 'sweep.f0.csv')
result = np.loadtxt(f0_file, delimiter=',', skiprows=1)
# it should be confident enough about the presence of pitch in every frame
assert np.mean(result[:, 2] > 0.5) > 0.98
# the frequencies should be linear
assert np.corrcoef(result[:, 1]) > 0.99
def test_sweep():
# this data contains a sine sweep
file = os.path.join(os.path.dirname(__file__), 'sweep.wav')
model = crepe.build_and_load_model()
crepe.process_file(model, file,
crepe.parser.parse_args([file, '--skip-salience']))
f0_file = os.path.join(os.path.dirname(__file__), 'sweep.f0.csv')
result = np.loadtxt(f0_file, delimiter=',')
# the result should be confident enough about the presence of pitch in every
# frame
assert np.mean(result[:, 2] > 0.5) > 0.99
# the frequencies should be linear
assert np.corrcoef(result[:, 1]) > 0.99
def test_sweep():
crepe.process_file(file)
verify_f0()
def showTab():
try:
analyze.run("./csvFiles/"+lb.get(lb.curselection())[:-3]+"f0.csv")
except:
crepe.process_file("./savedRecordings/"+lb.get(lb.curselection()), output="csvFiles",viterbi=True)
analyze.run("./csvFiles/"+lb.get(lb.curselection())[:-3]+"f0.csv")
def test_sweep_torch():
crepe.process_file(file, backend='torch')
verify_f0()