import time import os import argparse import torch import numpy as np import glob from pathlib import Path from tqdm import tqdm from ppg_extractor import load_model import librosa import soundfile as sf from utils.load_yaml import HpsYaml from encoder.audio import preprocess_wav from encoder import inference as speacker_encoder from vocoder.hifigan import inference as vocoder from ppg2mel import MelDecoderMOLv2 from utils.f0_utils import compute_f0, f02lf0, compute_mean_std, get_converted_lf0uv def _build_ppg2mel_model(model_config, model_file, device): ppg2mel_model = MelDecoderMOLv2( **model_config["model"] ).to(device) ckpt = torch.load(model_file, map_location=device) ppg2mel_model.load_state_dict(ckpt["model"]) ppg2mel_model.eval() return ppg2mel_model @torch.no_grad() def convert(args): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") output_dir = args.output_dir os.makedirs(output_dir, exist_ok=True) step = os.path.basename(args.ppg2mel_model_file)[:-4].split("_")[-1] # Build models print("Load PPG-model, PPG2Mel-model, Vocoder-model...") ppg_model = load_model( Path('./ppg_extractor/saved_models/24epoch.pt'), device, ) ppg2mel_model = _build_ppg2mel_model(HpsYaml(args.ppg2mel_model_train_config), args.ppg2mel_model_file, device) # vocoder.load_model('./vocoder/saved_models/pretrained/g_hifigan.pt', "./vocoder/hifigan/config_16k_.json") vocoder.load_model('./vocoder/saved_models/24k/g_02830000.pt') # Data related ref_wav_path = args.ref_wav_path ref_wav = preprocess_wav(ref_wav_path) ref_fid = os.path.basename(ref_wav_path)[:-4] # TODO: specify encoder speacker_encoder.load_model(Path("encoder/saved_models/pretrained_bak_5805000.pt")) ref_spk_dvec = speacker_encoder.embed_utterance(ref_wav) ref_spk_dvec = torch.from_numpy(ref_spk_dvec).unsqueeze(0).to(device) ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav))) source_file_list = sorted(glob.glob(f"{args.wav_dir}/*.wav")) print(f"Number of source utterances: {len(source_file_list)}.") total_rtf = 0.0 cnt = 0 for src_wav_path in tqdm(source_file_list): # Load the audio to a numpy array: src_wav, _ = librosa.load(src_wav_path, sr=16000) src_wav_tensor = torch.from_numpy(src_wav).unsqueeze(0).float().to(device) src_wav_lengths = torch.LongTensor([len(src_wav)]).to(device) ppg = ppg_model(src_wav_tensor, src_wav_lengths) lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True) min_len = min(ppg.shape[1], len(lf0_uv)) ppg = ppg[:, :min_len] lf0_uv = lf0_uv[:min_len] start = time.time() _, mel_pred, att_ws = ppg2mel_model.inference( ppg, logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device), spembs=ref_spk_dvec, ) src_fid = os.path.basename(src_wav_path)[:-4] wav_fname = f"{output_dir}/vc_{src_fid}_ref_{ref_fid}_step{step}.wav" mel_len = mel_pred.shape[0] rtf = (time.time() - start) / (0.01 * mel_len) total_rtf += rtf cnt += 1 # continue mel_pred= mel_pred.transpose(0, 1) y, output_sample_rate = vocoder.infer_waveform(mel_pred.cpu()) sf.write(wav_fname, y.squeeze(), output_sample_rate, "PCM_16") print("RTF:") print(total_rtf / cnt) def get_parser(): parser = argparse.ArgumentParser(description="Conversion from wave input") parser.add_argument( "--wav_dir", type=str, default=None, required=True, help="Source wave directory.", ) parser.add_argument( "--ref_wav_path", type=str, required=True, help="Reference wave file path.", ) parser.add_argument( "--ppg2mel_model_train_config", "-c", type=str, default=None, required=True, help="Training config file (yaml file)", ) parser.add_argument( "--ppg2mel_model_file", "-m", type=str, default=None, required=True, help="ppg2mel model checkpoint file path" ) parser.add_argument( "--output_dir", "-o", type=str, default="vc_gens_vctk_oneshot", help="Output folder to save the converted wave." ) return parser def main(): parser = get_parser() args = parser.parse_args() convert(args) if __name__ == "__main__": main()