| import numpy as np |
| import torch |
| import librosa |
| import soundfile as sf |
| from rvc.lib.predictors.f0 import RMVPE |
| from transformers import HubertModel |
|
|
|
|
| def cf0(f0): |
| f0_bin = 256 |
| f0_max = 1100.0 |
| f0_min = 50.0 |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) |
| """Convert F0 to coarse F0.""" |
| f0_mel = 1127 * np.log(1 + f0 / 700) |
| f0_mel = np.clip( |
| (f0_mel - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1, |
| 1, |
| f0_bin - 1, |
| ) |
| return np.rint(f0_mel).astype(int) |
|
|
|
|
| ref = r"reference.wav" |
| audio, sr = librosa.load(ref, sr=16000) |
| trimmed_len = (len(audio) // 320) * 320 |
| |
| audio = audio[:trimmed_len] |
|
|
| print("audio", audio.shape) |
| rmvpe_model = RMVPE(device="cpu", sample_rate=16000, hop_size=160) |
| f0 = rmvpe_model.get_f0(audio, filter_radius=0.03) |
| print("f0", f0.shape) |
| f0c = cf0(f0) |
| print("f0c", f0c.shape) |
|
|
| cv_path = r"rvc\models\embedders\contentvec" |
| cv_model = HubertModel.from_pretrained(cv_path) |
|
|
| spin_path = r"rvc\models\embedders\spin" |
| spin_model = HubertModel.from_pretrained(spin_path) |
|
|
| spin2_path = r"rvc\models\embedders\spin-v2" |
| spin2_model = HubertModel.from_pretrained(spin2_path) |
|
|
| feats = torch.from_numpy(audio).to(torch.float32).to("cpu") |
| feats = torch.nn.functional.pad(feats.unsqueeze(0), (40, 40), mode="reflect") |
| feats = feats.view(1, -1) |
|
|
| with torch.no_grad(): |
| cv_feats = cv_model(feats)["last_hidden_state"] |
| cv_feats = cv_feats.squeeze(0).float().cpu().numpy() |
| print("cv", cv_feats.shape) |
|
|
| spin_feats = spin_model(feats)["last_hidden_state"] |
| spin_feats = spin_feats.squeeze(0).float().cpu().numpy() |
| print("spin", spin_feats.shape) |
|
|
| spin2_feats = spin2_model(feats)["last_hidden_state"] |
| spin2_feats = spin2_feats.squeeze(0).float().cpu().numpy() |
| print("spin-v2", spin2_feats.shape) |
|
|
| np.save(r"logs\reference\contentvec\feats.npy", cv_feats) |
| np.save(r"logs\reference\spin\feats.npy", spin_feats) |
| np.save(r"logs\reference\spin-v2\feats.npy", spin2_feats) |
| np.save(r"logs\reference\pitch_coarse.npy", f0c) |
| np.save(r"logs\reference\pitch_fine.npy", f0) |
|
|
