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import os
import json
import argparse
import glob
import torch
import tqdm
import librosa
import soundfile as sf
import pyloudnorm as pyln
from dotmap import DotMap
from models import load_model_with_args
from separate_func import (
conv_tasnet_separate,
)
from utils import str2bool, db2linear
tqdm.monitor_interval = 0
def separate_track_with_model(
args, model, device, track_audio, track_name, meter, augmented_gain
):
with torch.no_grad():
if (
args.model_loss_params.architecture == "conv_tasnet_mask_on_output"
or args.model_loss_params.architecture == "conv_tasnet"
):
estimates = conv_tasnet_separate(
args,
model,
device,
track_audio,
track_name,
meter=meter,
augmented_gain=augmented_gain,
)
return estimates
def main():
parser = argparse.ArgumentParser(description="model test.py")
parser.add_argument("--target", type=str, default="all")
parser.add_argument("--data_root", type=str, default="./input_data")
parser.add_argument("--weight_directory", type=str, default="./weight")
parser.add_argument("--output_directory", type=str, default="./output")
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument("--save_name_as_target", type=str2bool, default=False)
parser.add_argument(
"--loudnorm_input_lufs",
type=float,
default=None,
help="If you want to use loudnorm for input",
)
parser.add_argument(
"--save_output_loudnorm",
type=float,
default=-14.0,
help="Save loudness normalized outputs or not. If you want to save, input target loudness",
)
parser.add_argument(
"--save_mixed_output",
type=float,
default=None,
help="Save original+delimited-estimation mixed output with a ratio of default 0.5 (orginal) and 1 - 0.5 (estimation)",
)
parser.add_argument(
"--save_16k_mono",
type=str2bool,
default=False,
help="Save 16k mono wav files for FAD evaluation.",
)
parser.add_argument(
"--save_histogram",
type=str2bool,
default=False,
help="Save histogram of the output. Only valid when the task is 'delimit'",
)
parser.add_argument(
"--use_singletrackset",
type=str2bool,
default=False,
help="Use SingleTrackSet if input data is too long.",
)
args, _ = parser.parse_known_args()
with open(f"{args.weight_directory}/{args.target}.json", "r") as f:
args_dict = json.load(f)
args_dict = DotMap(args_dict)
for key, value in args_dict["args"].items():
if key in list(vars(args).keys()):
pass
else:
setattr(args, key, value)
args.test_output_dir = f"{args.output_directory}"
os.makedirs(args.test_output_dir, exist_ok=True)
device = torch.device(
"cuda" if torch.cuda.is_available() and args.use_gpu else "cpu"
)
###################### Define Models ######################
our_model = load_model_with_args(args)
our_model = our_model.to(device)
target_model_path = f"{args.weight_directory}/{args.target}.pth"
checkpoint = torch.load(target_model_path, map_location=device)
our_model.load_state_dict(checkpoint)
our_model.eval()
meter = pyln.Meter(44100)
test_tracks = glob.glob(f"{args.data_root}/*.wav") + glob.glob(
f"{args.data_root}/*.mp3"
)
for track in tqdm.tqdm(test_tracks):
track_name = os.path.basename(track).replace(".wav", "").replace(".mp3", "")
track_audio, sr = librosa.load(track, sr=None, mono=False) # sr should be 44100
orig_audio = track_audio.copy()
if sr != 44100:
raise ValueError("Sample rate should be 44100")
augmented_gain = None
print("Now De-limiting : ", track_name)
if args.loudnorm_input_lufs: # If you want to use loud-normalized input
track_lufs = meter.integrated_loudness(track_audio.T)
augmented_gain = args.loudnorm_input_lufs - track_lufs
track_audio = track_audio * db2linear(augmented_gain, eps=0.0)
track_audio = (
torch.as_tensor(track_audio, dtype=torch.float32).unsqueeze(0).to(device)
)
estimates = separate_track_with_model(
args, our_model, device, track_audio, track_name, meter, augmented_gain
)
if args.save_mixed_output:
track_lufs = meter.integrated_loudness(orig_audio.T)
augmented_gain = args.save_output_loudnorm - track_lufs
orig_audio = orig_audio * db2linear(augmented_gain, eps=0.0)
mixed_output = orig_audio * args.save_mixed_output + estimates * (
1 - args.save_mixed_output
)
sf.write(
f"{args.test_output_dir}/{track_name}/{track_name}_mixed.wav",
mixed_output.T,
args.data_params.sample_rate,
)
if __name__ == "__main__":
main()
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