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import torch as T |
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from TTS.tts.utils.helpers import average_over_durations, generate_path, rand_segments, segment, sequence_mask |
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def average_over_durations_test(): |
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pitch = T.rand(1, 1, 128) |
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durations = T.randint(1, 5, (1, 21)) |
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coeff = 128.0 / durations.sum() |
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durations = T.floor(durations * coeff) |
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diff = 128.0 - durations.sum() |
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durations[0, -1] += diff |
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durations = durations.long() |
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pitch_avg = average_over_durations(pitch, durations) |
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index = 0 |
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for idx, dur in enumerate(durations[0]): |
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assert abs(pitch_avg[0, 0, idx] - pitch[0, 0, index : index + dur.item()].mean()) < 1e-5 |
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index += dur |
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def seqeunce_mask_test(): |
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lengths = T.randint(10, 15, (8,)) |
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mask = sequence_mask(lengths) |
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for i in range(8): |
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l = lengths[i].item() |
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assert mask[i, :l].sum() == l |
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assert mask[i, l:].sum() == 0 |
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def segment_test(): |
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x = T.range(0, 11) |
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x = x.repeat(8, 1).unsqueeze(1) |
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segment_ids = T.randint(0, 7, (8,)) |
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segments = segment(x, segment_ids, segment_size=4) |
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for idx, start_indx in enumerate(segment_ids): |
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assert x[idx, :, start_indx : start_indx + 4].sum() == segments[idx, :, :].sum() |
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try: |
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segments = segment(x, segment_ids, segment_size=10) |
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raise Exception("Should have failed") |
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except: |
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pass |
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segments = segment(x, segment_ids, segment_size=10, pad_short=True) |
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for idx, start_indx in enumerate(segment_ids): |
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assert x[idx, :, start_indx : start_indx + 10].sum() == segments[idx, :, :].sum() |
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def rand_segments_test(): |
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x = T.rand(2, 3, 4) |
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x_lens = T.randint(3, 4, (2,)) |
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segments, seg_idxs = rand_segments(x, x_lens, segment_size=3) |
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assert segments.shape == (2, 3, 3) |
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assert all(seg_idxs >= 0), seg_idxs |
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try: |
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segments, _ = rand_segments(x, x_lens, segment_size=5) |
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raise Exception("Should have failed") |
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except: |
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pass |
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x_lens_back = x_lens.clone() |
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segments, seg_idxs = rand_segments(x, x_lens.clone(), segment_size=5, pad_short=True, let_short_samples=True) |
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assert segments.shape == (2, 3, 5) |
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assert all(seg_idxs >= 0), seg_idxs |
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assert all(x_lens_back == x_lens) |
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def generate_path_test(): |
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durations = T.randint(1, 4, (10, 21)) |
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x_length = T.randint(18, 22, (10,)) |
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x_mask = sequence_mask(x_length).unsqueeze(1).long() |
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durations = durations * x_mask.squeeze(1) |
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y_length = durations.sum(1) |
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y_mask = sequence_mask(y_length).unsqueeze(1).long() |
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attn_mask = (T.unsqueeze(x_mask, -1) * T.unsqueeze(y_mask, 2)).squeeze(1).long() |
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print(attn_mask.shape) |
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path = generate_path(durations, attn_mask) |
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assert path.shape == (10, 21, durations.sum(1).max().item()) |
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for b in range(durations.shape[0]): |
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current_idx = 0 |
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for t in range(durations.shape[1]): |
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assert all(path[b, t, current_idx : current_idx + durations[b, t].item()] == 1.0) |
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assert all(path[b, t, :current_idx] == 0.0) |
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assert all(path[b, t, current_idx + durations[b, t].item() :] == 0.0) |
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current_idx += durations[b, t].item() |
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