| import os | |
| import gc | |
| import sys | |
| import torch | |
| import librosa | |
| import numpy as np | |
| import torch.nn.functional as F | |
| sys.path.append(os.getcwd()) | |
| from main.library import opencl | |
| def autotune_f0(note_dict, f0, f0_autotune_strength): | |
| autotuned_f0 = np.zeros_like(f0) | |
| for i, freq in enumerate(f0): | |
| autotuned_f0[i] = freq + (min(note_dict, key=lambda x: abs(x - freq)) - freq) * f0_autotune_strength | |
| return autotuned_f0 | |
| def change_rms(source_audio, source_rate, target_audio, target_rate, rate): | |
| rms2 = F.interpolate(torch.from_numpy(librosa.feature.rms(y=target_audio, frame_length=target_rate // 2 * 2, hop_length=target_rate // 2)).float().unsqueeze(0), size=target_audio.shape[0], mode="linear").squeeze() | |
| return (target_audio * (torch.pow(F.interpolate(torch.from_numpy(librosa.feature.rms(y=source_audio, frame_length=source_rate // 2 * 2, hop_length=source_rate // 2)).float().unsqueeze(0), size=target_audio.shape[0], mode="linear").squeeze(), 1 - rate) * torch.pow(torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6), rate - 1)).numpy()) | |
| def clear_gpu_cache(): | |
| gc.collect() | |
| if torch.cuda.is_available(): torch.cuda.empty_cache() | |
| elif torch.backends.mps.is_available(): torch.mps.empty_cache() | |
| elif opencl.is_available(): opencl.pytorch_ocl.empty_cache() | |
| def extract_median_f0(f0): | |
| f0 = np.where(f0 == 0, np.nan, f0) | |
| return float(np.median(np.interp(np.arange(len(f0)), np.where(~np.isnan(f0))[0], f0[~np.isnan(f0)]))) | |
| def proposal_f0_up_key(f0, target_f0 = 155.0, limit = 12): | |
| return max(-limit, min(limit, int(np.round(12 * np.log2(target_f0 / extract_median_f0(f0)))))) | |
| def get_onnx_argument(net_g, feats, p_len, sid, pitch, pitchf, energy, pitch_guidance, energy_use): | |
| inputs = { | |
| net_g.get_inputs()[0].name: feats.cpu().numpy().astype(np.float32), | |
| net_g.get_inputs()[1].name: p_len.cpu().numpy(), | |
| net_g.get_inputs()[2].name: np.array([sid.cpu().item()], dtype=np.int64), | |
| net_g.get_inputs()[3].name: np.random.randn(1, 192, p_len).astype(np.float32) | |
| } | |
| if energy_use: | |
| if pitch_guidance: | |
| inputs.update({ | |
| net_g.get_inputs()[4].name: pitch.cpu().numpy().astype(np.int64), | |
| net_g.get_inputs()[5].name: pitchf.cpu().numpy().astype(np.float32), | |
| net_g.get_inputs()[6].name: energy.cpu().numpy().astype(np.float32) | |
| }) | |
| else: | |
| inputs.update({ | |
| net_g.get_inputs()[4].name: energy.cpu().numpy().astype(np.float32) | |
| }) | |
| else: | |
| if pitch_guidance: | |
| inputs.update({ | |
| net_g.get_inputs()[4].name: pitch.cpu().numpy().astype(np.int64), | |
| net_g.get_inputs()[5].name: pitchf.cpu().numpy().astype(np.float32) | |
| }) | |
| return inputs |