gen_wavernn.py

from wavernn.utils.dsp import *
from wavernn.models.fatchord_version import WaveRNN
from wavernn.utils.paths import Paths
from wavernn.utils.display import simple_table
import torch
import argparse
from pathlib import Path


def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, save_path: Path):

    k = model.get_step() // 1000

    for i, (m, x) in enumerate(test_set, 1):

        if i > samples: break

        print('\n| Generating: %i/%i' % (i, samples))

        x = x[0].numpy()

        bits = 16 if hp.voc_mode == 'MOL' else hp.bits

        if hp.mu_law and hp.voc_mode != 'MOL':
            x = decode_mu_law(x, 2**bits, from_labels=True)
        else:
            x = label_2_float(x, bits)

        save_wav(x, save_path/f'{k}k_steps_{i}_target.wav')

        batch_str = f'gen_batched_target{target}_overlap{overlap}' if batched else 'gen_NOT_BATCHED'
        save_str = str(save_path/f'{k}k_steps_{i}_{batch_str}.wav')

        _ = model.generate(m, save_str, batched, target, overlap, hp.mu_law)


def generate(model, spectrogram, batched, target, overlap, save_str=None):
    mel = normalize(spectrogram)
    if mel.ndim != 2 or mel.shape[0] != hp.num_mels:
        raise ValueError(f'Expected a numpy array shaped (n_mels, n_hops), but got {wav.shape}!')
    _max = np.max(mel)
    _min = np.min(mel)
    if _max >= 1.01 or _min <= -0.01:
        raise ValueError(f'Expected spectrogram range in [0,1] but was instead [{_min}, {_max}]')
    mel = torch.tensor(mel).unsqueeze(0)
    return model.generate(mel, save_str, batched, target, overlap, hp.mu_law)


def gen_from_file(model: WaveRNN, load_path: Path, save_path: Path, batched, target, overlap):
    mel = np.load(load_path)
    generate(model, mel, batched, target, overlap, save_str=save_path)


if __name__ == "__main__":

    parser = argparse.ArgumentParser(description='Generate WaveRNN Samples')
    parser.add_argument('--batched', '-b', dest='batched', action='store_true', help='Fast Batched Generation')
    parser.add_argument('--unbatched', '-u', dest='batched', action='store_false', help='Slow Unbatched Generation')
    parser.add_argument('--samples', '-s', type=int, help='[int] number of utterances to generate')
    parser.add_argument('--target', '-t', type=int, help='[int] number of samples in each batch index')
    parser.add_argument('--overlap', '-o', type=int, help='[int] number of crossover samples')
    parser.add_argument('--file', '-f', type=str, required=True, help='[string/path] for testing a wav outside dataset')
    parser.add_argument('--output', '-p', type=str, help='output file')
    parser.add_argument('--voc_weights', '-w', type=str, help='[string/path] Load in different WaveRNN weights')
    parser.add_argument('--force_cpu', '-c', action='store_true', help='Forces CPU-only training, even when in CUDA capable environment')
    parser.add_argument('--hp_file', metavar='FILE', default='hparams.py', help='The file to use for the hyperparameters')

    parser.set_defaults(batched=None)

    args = parser.parse_args()

    hp.configure(args.hp_file)  # Load hparams from file
    # set defaults for any arguments that depend on hparams
    if args.target is None:
        args.target = hp.voc_target
    if args.overlap is None:
        args.overlap = hp.voc_overlap
    if args.batched is None:
        args.batched = hp.voc_gen_batched
    if args.samples is None:
        args.samples = hp.voc_gen_at_checkpoint

    batched = args.batched
    samples = args.samples
    target = args.target
    overlap = args.overlap
    file = args.file

    if not args.force_cpu and torch.cuda.is_available():
        device = torch.device('cuda')
    else:
        device = torch.device('cpu')
    print('Using device:', device)

    print('\nInitialising Model...\n')

    model = WaveRNN(rnn_dims=hp.voc_rnn_dims,
                    fc_dims=hp.voc_fc_dims,
                    bits=hp.bits,
                    pad=hp.voc_pad,
                    upsample_factors=hp.voc_upsample_factors,
                    feat_dims=hp.num_mels,
                    compute_dims=hp.voc_compute_dims,
                    res_out_dims=hp.voc_res_out_dims,
                    res_blocks=hp.voc_res_blocks,
                    hop_length=hp.hop_length,
                    sample_rate=hp.sample_rate,
                    mode=hp.voc_mode).to(device)

    paths = Paths(hp.data_path, hp.voc_model_id)

    voc_weights = args.voc_weights if args.voc_weights else paths.voc_latest_weights

    model.load(voc_weights)

    simple_table([('Generation Mode', 'Batched' if batched else 'Unbatched'),
                  ('Target Samples', target if batched else 'N/A'),
                  ('Overlap Samples', overlap if batched else 'N/A')])

    file = Path(file).expanduser()
    gen_from_file(model, file, args.output, batched, target, overlap)
    
    print('\n\nExiting...\n')