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import os
import sys
import time
import torch
import shutil
import warnings
import argparse
import numpy as np
from tqdm import tqdm
from distutils.util import strtobool
sys.path.append(os.getcwd())
from main.app.variables import config, logger, translations, configs
from main.library.utils import load_audio, load_embedders_model, extract_features
warnings.filterwarnings("ignore")
F0_MIN, F0_MAX, HOP_SIZE, SAMPLE_RATE, FRAME_LENGTH = 50, 1100, 160, 16000, 2048
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument("--create_reference", action='store_true')
parser.add_argument("--audio_path", type=str, required=True)
parser.add_argument("--reference_name", type=str, default="reference")
parser.add_argument("--pitch_guidance", type=lambda x: bool(strtobool(x)), default=True)
parser.add_argument("--use_energy", type=lambda x: bool(strtobool(x)), default=False)
parser.add_argument("--version", type=str, default="v2")
parser.add_argument("--embedder_model", type=str, default="hubert_base")
parser.add_argument("--embedders_mode", type=str, default="fairseq")
parser.add_argument("--f0_method", type=str, default="rmvpe")
parser.add_argument("--f0_onnx", type=lambda x: bool(strtobool(x)), default=False)
parser.add_argument("--f0_up_key", type=int, default=0)
parser.add_argument("--filter_radius", type=int, default=3)
parser.add_argument("--f0_autotune", type=lambda x: bool(strtobool(x)), default=False)
parser.add_argument("--f0_autotune_strength", type=float, default=1)
parser.add_argument("--proposal_pitch", type=lambda x: bool(strtobool(x)), default=False)
parser.add_argument("--proposal_pitch_threshold", type=float, default=255.0)
parser.add_argument("--alpha", type=float, default=0.5)
return parser.parse_args()
def main():
args = parse_arguments()
audio_path, reference_name, pitch_guidance, use_energy, version, embedder_model, embedders_mode, f0_method, f0_onnx, f0_up_key, filter_radius, f0_autotune, f0_autotune_strength, proposal_pitch, proposal_pitch_threshold, alpha = args.audio_path, args.reference_name, args.pitch_guidance, args.use_energy, args.version, args.embedder_model, args.embedders_mode, args.f0_method, args.f0_onnx, args.f0_up_key, args.filter_radius, args.f0_autotune, args.f0_autotune_strength, args.proposal_pitch, args.proposal_pitch_threshold, args.alpha
create_reference(
audio_path,
reference_name,
pitch_guidance,
use_energy,
version,
embedder_model,
embedders_mode,
f0_method,
f0_onnx,
f0_up_key,
filter_radius,
f0_autotune,
f0_autotune_strength,
proposal_pitch,
proposal_pitch_threshold,
alpha
)
def create_reference(
audio_path,
reference_name,
pitch_guidance = True,
use_energy = False,
version = "v2",
embedder_model = "hubert_base",
embedders_mode = "fairseq",
f0_method = "rmvpe",
f0_onnx = False,
f0_up_key = 0,
filter_radius = 3,
f0_autotune = False,
f0_autotune_strength = 1,
proposal_pitch = False,
proposal_pitch_threshold = 255.0,
alpha = 0.5
):
device = config.device
is_half = config.is_half
if not audio_path:
logger.warning(translations["not_found_audio"])
sys.exit(1)
output_reference = os.path.join(configs["reference_path"], f"{reference_name}_{version}_{embedder_model}_{pitch_guidance}_{use_energy}")
if os.path.exists(output_reference): shutil.rmtree(reference_name, ignore_errors=True)
os.makedirs(output_reference)
logger.info(translations["start_create_reference"])
start_time = time.time()
with tqdm(total=5, desc=translations["create_reference"], ncols=100, unit="a") as pbar:
audio = load_audio(audio_path, sample_rate=SAMPLE_RATE)
pbar.update(1)
audio_max = np.abs(audio).max() / 0.95
if audio_max > 1: audio /= audio_max
trimmed_len = (len(audio) // 320) * 320
audio = audio[:trimmed_len]
audio_pad = torch.nn.functional.pad(
torch.from_numpy(audio).to(
torch.float16 if is_half else torch.float32
).to(device).unsqueeze(0),
(40, 40),
mode="reflect"
)
pbar.update(1)
embedder = load_embedders_model(embedder_model, embedders_mode)
if isinstance(embedder, torch.nn.Module): embedder = embedder.to(torch.float16 if is_half else torch.float32).eval().to(device)
with torch.no_grad():
feats = extract_features(embedder, audio_pad.view(1, -1), version, device=device)
np.save(os.path.join(output_reference, "feats.npy"), feats.squeeze(0).float().cpu().numpy(), allow_pickle=False)
pbar.update(1)
if pitch_guidance:
from main.library.predictors.Generator import Generator
generator = Generator(
sample_rate=SAMPLE_RATE,
hop_length=HOP_SIZE,
f0_min=F0_MIN,
f0_max=F0_MAX,
alpha=alpha,
is_half=is_half,
device=device,
f0_onnx_mode=f0_onnx,
del_onnx_model=True
)
pitch, pitchf = generator.calculator(
x_pad=config.x_pad,
f0_method=f0_method,
x=audio,
f0_up_key=f0_up_key,
p_len=audio.shape[0] // 160 + 1,
filter_radius=filter_radius,
f0_autotune=f0_autotune,
f0_autotune_strength=f0_autotune_strength,
manual_f0=None,
proposal_pitch=proposal_pitch,
proposal_pitch_threshold=proposal_pitch_threshold
)
np.save(os.path.join(output_reference, "pitch_coarse.npy"), pitch, allow_pickle=False)
np.save(os.path.join(output_reference, "pitch_fine.npy"), pitchf, allow_pickle=False)
pbar.update(1)
if use_energy:
from main.inference.extracting.rms import RMSEnergyExtractor
rms = RMSEnergyExtractor(frame_length=FRAME_LENGTH, hop_length=HOP_SIZE, center=True, pad_mode="reflect").to(device).eval()
with torch.no_grad():
energy = rms(audio_pad)
np.save(os.path.join(output_reference, "energy.npy"), energy.float().cpu().numpy(), allow_pickle=False)
pbar.update(1)
logger.info(translations["create_reference_success"].format(elapsed_time=f"{(time.time() - start_time):.2f}"))
if __name__ == "__main__": main() |