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Running
on
Zero
| import torch as T | |
| from TTS.tts.utils.helpers import average_over_durations, generate_path, rand_segments, segment, sequence_mask | |
| def average_over_durations_test(): # pylint: disable=no-self-use | |
| pitch = T.rand(1, 1, 128) | |
| durations = T.randint(1, 5, (1, 21)) | |
| coeff = 128.0 / durations.sum() | |
| durations = T.floor(durations * coeff) | |
| diff = 128.0 - durations.sum() | |
| durations[0, -1] += diff | |
| durations = durations.long() | |
| pitch_avg = average_over_durations(pitch, durations) | |
| index = 0 | |
| for idx, dur in enumerate(durations[0]): | |
| assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5 | |
| index += dur | |
| def seqeunce_mask_test(): | |
| lengths = T.randint(10, 15, (8,)) | |
| mask = sequence_mask(lengths) | |
| for i in range(8): | |
| l = lengths[i].item() | |
| assert mask[i, :l].sum() == l | |
| assert mask[i, l:].sum() == 0 | |
| def segment_test(): | |
| x = T.range(0, 11) | |
| x = x.repeat(8, 1).unsqueeze(1) | |
| segment_ids = T.randint(0, 7, (8,)) | |
| segments = segment(x, segment_ids, segment_size=4) | |
| for idx, start_indx in enumerate(segment_ids): | |
| assert x[idx, :, start_indx : start_indx + 4].sum() == segments[idx, :, :].sum() | |
| try: | |
| segments = segment(x, segment_ids, segment_size=10) | |
| raise Exception("Should have failed") | |
| except: | |
| pass | |
| segments = segment(x, segment_ids, segment_size=10, pad_short=True) | |
| for idx, start_indx in enumerate(segment_ids): | |
| assert x[idx, :, start_indx : start_indx + 10].sum() == segments[idx, :, :].sum() | |
| def rand_segments_test(): | |
| x = T.rand(2, 3, 4) | |
| x_lens = T.randint(3, 4, (2,)) | |
| segments, seg_idxs = rand_segments(x, x_lens, segment_size=3) | |
| assert segments.shape == (2, 3, 3) | |
| assert all(seg_idxs >= 0), seg_idxs | |
| try: | |
| segments, _ = rand_segments(x, x_lens, segment_size=5) | |
| raise Exception("Should have failed") | |
| except: | |
| pass | |
| x_lens_back = x_lens.clone() | |
| segments, seg_idxs = rand_segments(x, x_lens.clone(), segment_size=5, pad_short=True, let_short_samples=True) | |
| assert segments.shape == (2, 3, 5) | |
| assert all(seg_idxs >= 0), seg_idxs | |
| assert all(x_lens_back == x_lens) | |
| def generate_path_test(): | |
| durations = T.randint(1, 4, (10, 21)) | |
| x_length = T.randint(18, 22, (10,)) | |
| x_mask = sequence_mask(x_length).unsqueeze(1).long() | |
| durations = durations * x_mask.squeeze(1) | |
| y_length = durations.sum(1) | |
| y_mask = sequence_mask(y_length).unsqueeze(1).long() | |
| attn_mask = (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2)).squeeze(1).long() | |
| print(attn_mask.shape) | |
| path = generate_path(durations, attn_mask) | |
| assert path.shape == (10, 21, durations.sum(1).max().item()) | |
| for b in range(durations.shape[0]): | |
| current_idx = 0 | |
| for t in range(durations.shape[1]): | |
| assert all(path[b, t, current_idx : current_idx + durations[b, t].item()] == 1.0) | |
| assert all(path[b, t, :current_idx] == 0.0) | |
| assert all(path[b, t, current_idx + durations[b, t].item() :] == 0.0) | |
| current_idx += durations[b, t].item() | |