import os import sys import numpy as np import pyworld import torchcrepe import torch import parselmouth import tqdm from multiprocessing import Process, cpu_count current_directory = os.getcwd() sys.path.append(current_directory) from rvc.lib.utils import load_audio exp_dir = sys.argv[1] f0_method = sys.argv[2] num_processes = cpu_count() try: hop_length = int(sys.argv[3]) except ValueError: hop_length = 128 DoFormant = False Quefrency = 1.0 Timbre = 1.0 class FeatureInput: def __init__(self, sample_rate=16000, hop_size=160): self.fs = sample_rate self.hop = hop_size self.f0_method_dict = self.get_f0_method_dict() 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 mncrepe(self, method, x, p_len, hop_length): f0 = None torch_device_index = 0 torch_device = ( torch.device(f"cuda:{torch_device_index % torch.cuda.device_count()}") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu") ) audio = torch.from_numpy(x.astype(np.float32)).to(torch_device, copy=True) audio /= torch.quantile(torch.abs(audio), 0.999) audio = torch.unsqueeze(audio, dim=0) if audio.ndim == 2 and audio.shape[0] > 1: audio = torch.mean(audio, dim=0, keepdim=True).detach() audio = audio.detach() if method == "crepe": pitch = torchcrepe.predict( audio, self.fs, hop_length, self.f0_min, self.f0_max, "full", batch_size=hop_length * 2, device=torch_device, pad=True, ) p_len = p_len or x.shape[0] // hop_length source = np.array(pitch.squeeze(0).cpu().float().numpy()) source[source < 0.001] = np.nan target = np.interp( np.arange(0, len(source) * p_len, len(source)) / p_len, np.arange(0, len(source)), source, ) f0 = np.nan_to_num(target) return f0 def get_pm(self, x, p_len): f0 = ( parselmouth.Sound(x, self.fs) .to_pitch_ac( time_step=160 / 16000, voicing_threshold=0.6, pitch_floor=self.f0_min, pitch_ceiling=self.f0_max, ) .selected_array["frequency"] ) return np.pad( f0, [ [ max(0, (p_len - len(f0) + 1) // 2), max(0, p_len - len(f0) - (p_len - len(f0) + 1) // 2), ] ], mode="constant", ) def get_harvest(self, x): f0_spectral = pyworld.harvest( x.astype(np.double), fs=self.fs, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=1000 * self.hop / self.fs, ) return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.fs) def get_dio(self, x): f0_spectral = pyworld.dio( x.astype(np.double), fs=self.fs, f0_ceil=self.f0_max, f0_floor=self.f0_min, frame_period=1000 * self.hop / self.fs, ) return pyworld.stonemask(x.astype(np.double), *f0_spectral, self.fs) def get_rmvpe(self, x): if not hasattr(self, "model_rmvpe"): from rvc.lib.rmvpe import RMVPE self.model_rmvpe = RMVPE("rmvpe.pt", is_half=False, device="cpu") return self.model_rmvpe.infer_from_audio(x, thred=0.03) def get_f0_method_dict(self): return { "pm": self.get_pm, "harvest": self.get_harvest, "dio": self.get_dio, "rmvpe": self.get_rmvpe, } def compute_f0(self, path, f0_method, hop_length): x = load_audio(path, self.fs) p_len = x.shape[0] // self.hop if f0_method in self.f0_method_dict: f0 = ( self.f0_method_dict[f0_method](x, p_len) if f0_method == "pm" else self.f0_method_dict[f0_method](x) ) elif f0_method == "crepe": f0 = self.mncrepe(f0_method, x, p_len, hop_length) return f0 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(int) assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, ( f0_coarse.max(), f0_coarse.min(), ) return f0_coarse def process_paths(self, paths, f0_method, hop_length, thread_n): if len(paths) == 0: print("There are no paths to process.") return with tqdm.tqdm(total=len(paths), leave=True, position=thread_n) as pbar: description = f"Thread {thread_n} | Hop-Length {hop_length}" pbar.set_description(description) for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths): try: if os.path.exists(opt_path1 + ".npy") and os.path.exists( opt_path2 + ".npy" ): pbar.update(1) continue feature_pit = self.compute_f0(inp_path, f0_method, hop_length) np.save( opt_path2, feature_pit, allow_pickle=False, ) # nsf coarse_pit = self.coarse_f0(feature_pit) np.save( opt_path1, coarse_pit, allow_pickle=False, ) # ori pbar.update(1) except Exception as error: print(f"f0fail-{idx}-{inp_path}-{error}") if __name__ == "__main__": feature_input = FeatureInput() paths = [] input_root = f"{exp_dir}/1_16k_wavs" output_root1 = f"{exp_dir}/2a_f0" output_root2 = f"{exp_dir}/2b-f0nsf" os.makedirs(output_root1, exist_ok=True) os.makedirs(output_root2, exist_ok=True) for name in sorted(list(os.listdir(input_root))): input_path = f"{input_root}/{name}" if "spec" in input_path: continue output_path1 = f"{output_root1}/{name}" output_path2 = f"{output_root2}/{name}" paths.append([input_path, output_path1, output_path2]) processes = [] print("Using f0 method: " + f0_method) for i in range(num_processes): p = Process( target=feature_input.process_paths, args=(paths[i::num_processes], f0_method, hop_length, i), ) processes.append(p) p.start() for i in range(num_processes): processes[i].join()