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import sys,os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import librosa
import argparse
import numpy as np
import parselmouth
# pip install praat-parselmouth
def compute_f0_mouth(path):
x, sr = librosa.load(path, sr=16000)
assert sr == 16000
lpad = 1024 // 160
rpad = lpad
f0 = parselmouth.Sound(x, sr).to_pitch_ac(
time_step=160 / sr,
voicing_threshold=0.5,
pitch_floor=30,
pitch_ceiling=1000).selected_array['frequency']
f0 = np.pad(f0, [[lpad, rpad]], mode='constant')
return f0
def compute_f0_crepe(filename):
import torch
import torchcrepe
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
audio, sr = librosa.load(filename, sr=16000)
assert sr == 16000
audio = torch.tensor(np.copy(audio))[None]
audio = audio + torch.randn_like(audio) * 0.001
# Here we'll use a 20 millisecond hop length
hop_length = 320
fmin = 50
fmax = 1000
model = "full"
batch_size = 512
pitch = torchcrepe.predict(
audio,
sr,
hop_length,
fmin,
fmax,
model,
batch_size=batch_size,
device=device,
return_periodicity=False,
)
pitch = np.repeat(pitch, 2, -1) # 320 -> 160 * 2
pitch = torchcrepe.filter.mean(pitch, 5)
pitch = pitch.squeeze(0)
return pitch
def save_csv_pitch(pitch, path):
with open(path, "w", encoding='utf-8') as pitch_file:
for i in range(len(pitch)):
t = i * 10
minute = t // 60000
seconds = (t - minute * 60000) // 1000
millisecond = t % 1000
print(
f"{minute}m {seconds}s {millisecond:3d},{int(pitch[i])}", file=pitch_file)
def load_csv_pitch(path):
pitch = []
with open(path, "r", encoding='utf-8') as pitch_file:
for line in pitch_file.readlines():
pit = line.strip().split(",")[-1]
pitch.append(int(pit))
return pitch
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-w", "--wav", help="wav", dest="wav")
parser.add_argument("-p", "--pit", help="pit", dest="pit") # csv for excel
args = parser.parse_args()
print(args.wav)
print(args.pit)
pitch = compute_f0_mouth(args.wav)
save_csv_pitch(pitch, args.pit)
#tmp = load_csv_pitch(args.pit)
#save_csv_pitch(tmp, "tmp.csv")
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