0a8e27413d721bf8d753e5e6061cc24f5bf6474f,tests/test_model.py,,test_local_conditioning_correctness,#,146

Before Change

    assert model.local_conditioning_enabled()
    assert not model.has_speaker_embedding()

    x = Variable(torch.from_numpy(x).contiguous())
    x = x.cuda() if use_cuda else x

    c = Variable(torch.from_numpy(c).contiguous())
    c = c.cuda() if use_cuda else c
    print(x.size(), c.size())

    model.eval()

    y_offline = model(x, c=c, softmax=True)

    // Incremental forward with forced teaching
    y_online = model.incremental_forward(
        test_inputs=x, c=c, T=None, tqdm=tqdm, softmax=True, quantize=False)

    // (1 x C x T)
    c = (y_offline - y_online).abs()
    print(c.mean(), c.max())

    try:

After Change

    assert model.local_conditioning_enabled()
    assert not model.has_speaker_embedding()

    x = torch.from_numpy(x).contiguous().to(device)

    c = torch.from_numpy(c).contiguous().to(device)
    print(x.size(), c.size())

    model.eval()

    y_offline = model(x, c=c, softmax=True)

    // Incremental forward with forced teaching
    y_online = model.incremental_forward(
        test_inputs=x, c=c, T=None, tqdm=tqdm, softmax=True, quantize=False)

    // (1 x C x T)
    c = (y_offline - y_online).abs()
    print(c.mean(), c.max())

    try:

Instances