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64ec292 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | # coding: utf-8
__author__ = "Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/"
import glob
import os
import sys
import time
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn as nn
from tqdm.auto import tqdm
# Using the embedded version of Python can also correctly import the utils module.
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_dir)
import warnings
from utils.audio_utils import denormalize_audio, draw_spectrogram, normalize_audio
from utils.model_utils import (
apply_tta,
demix,
load_start_checkpoint,
prefer_target_instrument,
)
from utils.settings import get_model_from_config, parse_args_inference
warnings.filterwarnings("ignore")
def run_folder(
model: "torch.nn.Module",
args: "argparse.Namespace",
config: dict,
device: "torch.device",
verbose: bool = False,
) -> None:
"""
Process a folder of audio files for source separation.
Parameters:
----------
model : torch.nn.Module
Pre-trained model for source separation.
args : argparse.Namespace
Arguments containing input folder, output folder, and processing options.
config : dict
Configuration object with audio and inference settings.
device : torch.device
Device for model inference (CPU or CUDA).
verbose : bool, optional
If True, prints detailed information during processing. Default is False.
"""
start_time = time.time()
model.eval()
# Recursively collect all files from input directory
mixture_paths = sorted(
glob.glob(os.path.join(args.input_folder, "**/*.*"), recursive=True)
)
mixture_paths = [p for p in mixture_paths if os.path.isfile(p)]
sample_rate: int = getattr(config.audio, "sample_rate", 44100)
print(f"Total files found: {len(mixture_paths)}. Using sample rate: {sample_rate}")
instruments: list[str] = prefer_target_instrument(config)[:]
os.makedirs(args.store_dir, exist_ok=True)
# Wrap paths with progress bar if not in verbose mode
if not verbose:
mixture_paths = tqdm(mixture_paths, desc="Total progress")
# Determine whether to use detailed progress bar
if args.disable_detailed_pbar:
detailed_pbar = False
else:
detailed_pbar = True
for path in mixture_paths:
# Get relative path from input folder
relative_path: str = os.path.relpath(path, args.input_folder)
# Extract directory and file name
dir_name: str = os.path.dirname(relative_path)
file_name: str = os.path.splitext(os.path.basename(path))[0]
try:
mix, sr = librosa.load(path, sr=sample_rate, mono=False)
except Exception as e:
print(f"Cannot read track: {format(path)}")
print(f"Error message: {str(e)}")
continue
# Convert mono audio to expected channel format if needed
if len(mix.shape) == 1:
mix = np.expand_dims(mix, axis=0)
if "num_channels" in config.audio:
if config.audio["num_channels"] == 2:
print("Convert mono track to stereo...")
mix = np.concatenate([mix, mix], axis=0)
mix_orig = mix.copy()
# Normalize input audio if enabled
if "normalize" in config.inference:
if config.inference["normalize"] is True:
mix, norm_params = normalize_audio(mix)
# Perform source separation
waveforms_orig = demix(
config, model, mix, device, model_type=args.model_type, pbar=detailed_pbar
)
# Apply test-time augmentation if enabled
if args.use_tta:
waveforms_orig = apply_tta(
config, model, mix, waveforms_orig, device, args.model_type
)
# Extract instrumental track if requested
if args.extract_instrumental:
instr = "vocals" if "vocals" in instruments else instruments[0]
waveforms_orig["instrumental"] = mix_orig - waveforms_orig[instr]
if "instrumental" not in instruments:
instruments.append("instrumental")
for instr in instruments:
estimates = waveforms_orig[instr]
# Denormalize output audio if normalization was applied
if "normalize" in config.inference:
if config.inference["normalize"] is True:
estimates = denormalize_audio(estimates, norm_params)
peak: float = float(np.abs(estimates).max())
if peak <= 1.0 and args.pcm_type != "FLOAT":
codec = "flac"
else:
codec = "wav"
subtype = args.pcm_type
# Generate output directory structure using relative paths
dirnames, fname = format_filename(
args.filename_template,
instr=instr,
start_time=int(start_time),
file_name=file_name,
dir_name=dir_name,
model_type=args.model_type,
model=os.path.splitext(os.path.basename(args.start_check_point))[0],
)
# Create output directory
output_dir: str = os.path.join(args.store_dir, *dirnames)
os.makedirs(output_dir, exist_ok=True)
output_path: str = os.path.join(output_dir, f"{fname}.{codec}")
sf.write(output_path, estimates.T, sr, subtype=subtype)
# Draw and save spectrogram if enabled
if args.draw_spectro > 0:
output_img_path = os.path.join(output_dir, f"{fname}.jpg")
draw_spectrogram(estimates.T, sr, args.draw_spectro, output_img_path)
print("Wrote file:", output_img_path)
print(f"Elapsed time: {time.time() - start_time:.2f} seconds.")
def format_filename(template, **kwargs):
"""
Formats a filename from a template. e.g "{file_name}/{instr}"
Using slashes ('/') in template will result in directories being created
Returns [dirnames, fname], i.e. an array of dir names and a single file name
"""
result = template
for k, v in kwargs.items():
result = result.replace(f"{{{k}}}", str(v))
*dirnames, fname = result.split("/")
return dirnames, fname
def proc_folder(dict_args):
args = parse_args_inference(dict_args)
device = "cpu"
if args.force_cpu:
device = "cpu"
elif torch.cuda.is_available():
print("CUDA is available, use --force_cpu to disable it.")
device = (
f"cuda:{args.device_ids[0]}"
if isinstance(args.device_ids, list)
else f"cuda:{args.device_ids}"
)
elif torch.backends.mps.is_available():
device = "mps"
print("Using device: ", device)
model_load_start_time = time.time()
torch.backends.cudnn.benchmark = True
model, config = get_model_from_config(args.model_type, args.config_path)
if "model_type" in config.training:
args.model_type = config.training.model_type
if args.start_check_point:
checkpoint = torch.load(
args.start_check_point, weights_only=False, map_location="cpu"
)
load_start_checkpoint(args, model, checkpoint, type_="inference")
print("Instruments: {}".format(config.training.instruments))
# in case multiple CUDA GPUs are used and --device_ids arg is passed
if (
isinstance(args.device_ids, list)
and len(args.device_ids) > 1
and not args.force_cpu
):
model = nn.DataParallel(model, device_ids=args.device_ids)
model = model.to(device)
print("Model load time: {:.2f} sec".format(time.time() - model_load_start_time))
run_folder(model, args, config, device, verbose=True)
if __name__ == "__main__":
proc_folder(None)
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