Spaces:
Runtime error
Runtime error
| import os | |
| import librosa | |
| import pyworld | |
| import utils | |
| import numpy as np | |
| from scipy.io import wavfile | |
| class FeatureInput(object): | |
| def __init__(self, samplerate=16000, hop_size=160): | |
| self.fs = samplerate | |
| self.hop = hop_size | |
| self.f0_bin = 256 | |
| self.f0_max = 1100.0 | |
| self.f0_min = 50.0 | |
| self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) | |
| self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) | |
| def compute_f0(self, path): | |
| x, sr = librosa.load(path, sr=self.fs) | |
| assert sr == self.fs | |
| f0, t = pyworld.dio( | |
| x.astype(np.double), | |
| fs=sr, | |
| f0_ceil=800, | |
| frame_period=1000 * self.hop / sr, | |
| ) | |
| f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs) | |
| for index, pitch in enumerate(f0): | |
| f0[index] = round(pitch, 1) | |
| return f0 | |
| # for numpy # code from diffsinger | |
| def coarse_f0(self, f0): | |
| f0_mel = 1127 * np.log(1 + f0 / 700) | |
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * ( | |
| self.f0_bin - 2 | |
| ) / (self.f0_mel_max - self.f0_mel_min) + 1 | |
| # use 0 or 1 | |
| f0_mel[f0_mel <= 1] = 1 | |
| f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1 | |
| f0_coarse = np.rint(f0_mel).astype(np.int) | |
| assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( | |
| f0_coarse.max(), | |
| f0_coarse.min(), | |
| ) | |
| return f0_coarse | |
| # for tensor # code from diffsinger | |
| def coarse_f0_ts(self, f0): | |
| f0_mel = 1127 * (1 + f0 / 700).log() | |
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * ( | |
| self.f0_bin - 2 | |
| ) / (self.f0_mel_max - self.f0_mel_min) + 1 | |
| # use 0 or 1 | |
| f0_mel[f0_mel <= 1] = 1 | |
| f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1 | |
| f0_coarse = (f0_mel + 0.5).long() | |
| assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( | |
| f0_coarse.max(), | |
| f0_coarse.min(), | |
| ) | |
| return f0_coarse | |
| def save_wav(self, wav, path): | |
| wav *= 32767 / max(0.01, np.max(np.abs(wav))) * 0.6 | |
| wavfile.write(path, self.fs, wav.astype(np.int16)) | |
| if __name__ == "__main__": | |
| wavPath = "./data/waves" | |
| outPath = "./data/label" | |
| if not os.path.exists("./data/label"): | |
| os.mkdir("./data/label") | |
| # define model and load checkpoint | |
| hps = utils.get_hparams_from_file("./configs/singing_base.json") | |
| featureInput = FeatureInput(hps.data.sampling_rate, hps.data.hop_length) | |
| vits_file = open("./filelists/vc_file.txt", "w", encoding="utf-8") | |
| for spks in os.listdir(wavPath): | |
| if os.path.isdir(f"./{wavPath}/{spks}"): | |
| os.makedirs(f"./{outPath}/{spks}") | |
| for file in os.listdir(f"./{wavPath}/{spks}"): | |
| if file.endswith(".wav"): | |
| file = file[:-4] | |
| audio_path = f"./{wavPath}/{spks}/{file}.wav" | |
| featur_pit = featureInput.compute_f0(audio_path) | |
| coarse_pit = featureInput.coarse_f0(featur_pit) | |
| np.save( | |
| f"{outPath}/{spks}/{file}_pitch.npy", | |
| coarse_pit, | |
| allow_pickle=False, | |
| ) | |
| np.save( | |
| f"{outPath}/{spks}/{file}_nsff0.npy", | |
| featur_pit, | |
| allow_pickle=False, | |
| ) | |
| path_audio = f"./data/waves/{spks}/{file}.wav" | |
| path_spkid = f"./data/spkid/{spks}.npy" | |
| path_label = ( | |
| f"./data/phone/{spks}/{file}.npy" # phone means ppg & hubert | |
| ) | |
| path_pitch = f"./data/label/{spks}/{file}_pitch.npy" | |
| path_nsff0 = f"./data/label/{spks}/{file}_nsff0.npy" | |
| print( | |
| f"{path_audio}|{path_spkid}|{path_label}|{path_pitch}|{path_nsff0}", | |
| file=vits_file, | |
| ) | |
| vits_file.close() | |