| 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 |
|
|
| |
| 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, |
| ) |
| coarse_pit = self.coarse_f0(feature_pit) |
| np.save( |
| opt_path1, |
| coarse_pit, |
| allow_pickle=False, |
| ) |
| 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() |
|
|
