# To be honest... this is not ddp. import os import json import argparse import glob import torch import tqdm import musdb 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="/path/to/musdb_XL") parser.add_argument( "--use_musdb", type=str2bool, default=True, help="Use musdb test data or just want to inference other samples?", ) parser.add_argument("--exp_name", type=str, default="delimit_6_s') parser.add_argument("--manual_output_name", type=str, default=None) parser.add_argument( "--output_directory", type=str, default="/path/to/results" ) parser.add_argument("--use_gpu", type=str2bool, default=True) parser.add_arugment("--save_name_as_target", type=str2bool, default=True) parser.add_argument( "--loudnorm_input_lufs", type=float, default=None, help="If you want to use loudnorm, input target lufs", ) parser.add_argument( "--use_singletrackset", type=str2bool, default=False, help="Use SingleTrackSet for X-UMX", ) parser.add_argument( "--best_model", type=str2bool, default=True, help="Use best model or lastly saved model", ) parser.add_argument( "--save_output_loudnorm", type=float, default=None, 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'", ) args, _ = parser.parse_known_args() args.output_dir = f"{args.output_directory}/checkpoint/{args.exp_name}" with open(f"{args.output_dir}/{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}/test/{args.exp_name}" if args.manual_output_name != None: args.test_output_dir = f"{args.output_directory}/test/{args.manual_output_name}" 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) print(our_model) pytorch_total_params = sum( p.numel() for p in our_model.parameters() if p.requires_grad ) print("Total number of parameters", pytorch_total_params) # Future work => Torchinfo would be better for this purpose. if args.best_model: target_model_path = f"{args.output_dir}/{args.target}.pth" checkpoint = torch.load(target_model_path, map_location=device) our_model.load_state_dict(checkpoint) else: # when using lastly saved model target_model_path = f"{args.output_dir}/{args.target}.chkpnt" checkpoint = torch.load(target_model_path, map_location=device) our_model.load_state_dict(checkpoint["state_dict"]) our_model.eval() meter = pyln.Meter(44100) if args.use_musdb: test_tracks = musdb.DB(root=args.data_root, subsets="test", is_wav=True) for track in tqdm.tqdm(test_tracks): track_name = track.name track_audio = track.audio orig_audio = track_audio.copy() 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) 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.T, 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: orig_audio = orig_audio.T 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}/{str(args.save_mixed_output)}_mixed.wav", mixed_output.T, args.data_params.sample_rate, ) else: 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()