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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()
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