predict.py

import argparse
import os

import data.utils
import model.utils as model_utils

from test import predict_song
from model.waveunet import Waveunet

def main(args):
    # MODEL
    num_features = [args.features*i for i in range(1, args.levels+1)] if args.feature_growth == "add" else \
                   [args.features*2**i for i in range(0, args.levels)]
    target_outputs = int(args.output_size * args.sr)
    model = Waveunet(args.channels, num_features, args.channels, args.instruments, kernel_size=args.kernel_size,
                     target_output_size=target_outputs, depth=args.depth, strides=args.strides,
                     conv_type=args.conv_type, res=args.res, separate=args.separate)

    if args.cuda:
        model = model_utils.DataParallel(model)
        print("move model to gpu")
        model.cuda()

    print("Loading model from checkpoint " + str(args.load_model))
    state = model_utils.load_model(model, None, args.load_model, args.cuda)
    print('Step', state['step'])

    preds = predict_song(args, args.input, model)

    output_folder = os.path.dirname(args.input) if args.output is None else args.output
    for inst in preds.keys():
        data.utils.write_wav(os.path.join(output_folder, os.path.basename(args.input) + "_" + inst + ".wav"), preds[inst], args.sr)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--instruments', type=str, nargs='+', default=["bass", "drums", "other", "vocals"],
                        help="List of instruments to separate (default: \"bass drums other vocals\")")
    parser.add_argument('--cuda', action='store_true',
                        help='Use CUDA (default: False)')
    parser.add_argument('--features', type=int, default=32,
                        help='Number of feature channels per layer')
    parser.add_argument('--load_model', type=str, default='checkpoints/waveunet/model',
                        help='Reload a previously trained model')
    parser.add_argument('--batch_size', type=int, default=4,
                        help="Batch size")
    parser.add_argument('--levels', type=int, default=6,
                        help="Number of DS/US blocks")
    parser.add_argument('--depth', type=int, default=1,
                        help="Number of convs per block")
    parser.add_argument('--sr', type=int, default=44100,
                        help="Sampling rate")
    parser.add_argument('--channels', type=int, default=2,
                        help="Number of input audio channels")
    parser.add_argument('--kernel_size', type=int, default=5,
                        help="Filter width of kernels. Has to be an odd number")
    parser.add_argument('--output_size', type=float, default=2.0,
                        help="Output duration")
    parser.add_argument('--strides', type=int, default=4,
                        help="Strides in Waveunet")
    parser.add_argument('--conv_type', type=str, default="gn",
                        help="Type of convolution (normal, BN-normalised, GN-normalised): normal/bn/gn")
    parser.add_argument('--res', type=str, default="fixed",
                        help="Resampling strategy: fixed sinc-based lowpass filtering or learned conv layer: fixed/learned")
    parser.add_argument('--separate', type=int, default=1,
                        help="Train separate model for each source (1) or only one (0)")
    parser.add_argument('--feature_growth', type=str, default="double",
                        help="How the features in each layer should grow, either (add) the initial number of features each time, or multiply by 2 (double)")

    parser.add_argument('--input', type=str, default=os.path.join("audio_examples", "Cristina Vane - So Easy", "mix.mp3"),
                        help="Path to input mixture to be separated")
    parser.add_argument('--output', type=str, default=None, help="Output path (same folder as input path if not set)")

    args = parser.parse_args()

    main(args)