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import torch
import torch.multiprocessing as mp
import os, sys
import threading
from tqdm import tqdm
import soundfile as sf
import threading
import librosa
import numpy as np
from utils import demix_track, demix_track_demucs, get_model_from_config
import traceback
import glob
import argparse
import warnings
warnings.filterwarnings("ignore")
def normalize_audio(y, target_dbfs=0):
max_amplitude = np.max(np.abs(y))
if max_amplitude < 0.1:
return y
target_amplitude = 10.0**(target_dbfs / 20.0)
scale_factor = target_amplitude / max_amplitude
normalized_audio = y * scale_factor
return normalized_audio
def inference(rank, ckpt_root, out_dir, queue: mp.Queue):
#print(f"thread {rank} start")
device = f"cuda:{rank}"
config = f"{ckpt_root}/model_bs_roformer_ep_317_sdr_12.9755.yaml"
ckpt = f"{ckpt_root}/model_bs_roformer_ep_317_sdr_12.9755.ckpt"
model, config = get_model_from_config("bs_roformer", config)
state_dict = torch.load(ckpt, map_location='cpu')
model.load_state_dict(state_dict)
model = model.to(device)
model.eval()
with torch.no_grad():
while True:
#print(texts)
filename = queue.get()
if filename is None:
break
filepath = filename[0]
filename = filepath.split('/')[-1]
try:
mix, sr = librosa.load(filepath, sr=44100, mono=False)
#mix = normalize_audio(mix, -6)
mix = mix.T
if len(mix.shape) == 1:
mix = np.stack([mix, mix], axis=-1)
mixture = torch.tensor(mix.T, dtype=torch.float32)
res = demix_track(config, model, mixture, device)
sf.write("{}/{}".format(os.path.join(out_dir, "vocal"), filename), res['vocals'].T.mean(-1), sr, subtype='FLOAT')
sf.write("{}/{}".format(os.path.join(out_dir, "bgm"), filename), mix.mean(-1) - res['vocals'].T.mean(-1), sr, subtype='FLOAT')
except Exception as e:
traceback.print_exc()
continue
def setInterval(interval):
def decorator(function):
def wrapper(*args, **kwargs):
stopped = threading.Event()
def loop(): # executed in another thread
while not stopped.wait(interval): # until stopped
function(*args, **kwargs)
t = threading.Thread(target=loop)
t.daemon = True # stop if the program exits
t.start()
return stopped
return wrapper
return decorator
last_batches = None
@setInterval(3)
def QueueWatcher(queue, bar):
global last_batches
curr_batches = queue.qsize()
bar.update(last_batches-curr_batches)
last_batches = curr_batches
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--filelist_or_dir", type=str, required=True, help="Path to save checkpoints")
parser.add_argument("--out_dir", type=str, required=True, help="Path to save checkpoints")
parser.add_argument("--ckpt_path", type=str, required=True, help="Path to save checkpoints")
parser.add_argument("--jobs", type=int, required=False, default=2, help="Path to save checkpoints")
parser.add_argument("--log_dir", type=str, required=False, default="large-v3", help="Path to save checkpoints")
parser.add_argument("--model_dir", type=str, required=False, default="large-v3", help="Path to save checkpoints")
args = parser.parse_args()
filelist_or_dir = args.filelist_or_dir
out_dir = args.out_dir
ckpt_path = args.ckpt_path
jobs = args.jobs
vad_jobs = jobs * 2
if os.path.isfile(filelist_or_dir):
filelist_name = filelist_or_dir.split('/')[-1].split('.')[0]
generator = open(filelist_or_dir).read().splitlines()
else:
filelist_name = "single"
generator = glob.glob(f"{filelist_or_dir}/*.wav")
os.makedirs(os.path.join(out_dir, "vocal"), exist_ok=True)
os.makedirs(os.path.join(out_dir, "bgm"), exist_ok=True)
gpu_num = torch.cuda.device_count()
processes = []
vad_processes = []
queue = mp.Queue()
vad_queue = mp.Queue()
for thread_num in range(jobs):
rank = thread_num % gpu_num
p = mp.Process(target=inference, args=(rank, ckpt_path, out_dir, queue))
p.start()
processes.append(p)
accum = []
for filename in tqdm(generator):
accum.append(filename)
if len(accum) == 1:
queue.put(accum.copy())
accum.clear()
for _ in range(jobs):
queue.put(None)
last_batches = queue.qsize()
bar = tqdm(total=last_batches, desc="seperation")
queue_watcher = QueueWatcher(queue, bar)
for p in processes:
p.join()
queue_watcher.set()
for p in vad_processes:
p.join()
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