File size: 15,657 Bytes
de6e35f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
"""

Taken from ESPNet, modified by Florian Lux

"""

import json
import os

import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.multiprocessing
from matplotlib.lines import Line2D

import Architectures.GeneralLayers.ConditionalLayerNorm
from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend
from Preprocessing.TextFrontend import get_language_id


def integrate_with_utt_embed(hs, utt_embeddings, projection, embedding_training):
    if not embedding_training:
        # concat hidden states with spk embeds and then apply projection
        embeddings_expanded = torch.nn.functional.normalize(utt_embeddings).unsqueeze(1).expand(-1, hs.size(1), -1)
        hs = projection(torch.cat([hs, embeddings_expanded], dim=-1))
    else:
        # in this case we don't want to normalize the embeddings to not impair the gradient flow
        hs = projection(hs, utt_embeddings)
    return hs


def float2pcm(sig, dtype='int16'):
    """

    https://gist.github.com/HudsonHuang/fbdf8e9af7993fe2a91620d3fb86a182

    """
    sig = np.asarray(sig)
    if sig.dtype.kind != 'f':
        raise TypeError("'sig' must be a float array")
    dtype = np.dtype(dtype)
    if dtype.kind not in 'iu':
        raise TypeError("'dtype' must be an integer type")
    i = np.iinfo(dtype)
    abs_max = 2 ** (i.bits - 1)
    offset = i.min + abs_max
    return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype)


def make_estimated_durations_usable_for_inference(xs, offset=1.0):
    return torch.clamp(torch.round(xs.exp() - offset), min=0).long()


def cut_to_multiple_of_n(x, n=4, return_diff=False, seq_dim=1):
    max_frames = x.shape[seq_dim] // n * n
    if return_diff:
        return x[:, :max_frames], x.shape[seq_dim] - max_frames
    return x[:, :max_frames]


def pad_to_multiple_of_n(x, n=4, seq_dim=1, pad_value=0):
    max_frames = ((x.shape[seq_dim] // n) + 1) * n
    diff = max_frames - x.shape[seq_dim]
    return torch.nn.functional.pad(x, [0, 0, 0, diff, 0, 0], mode="constant", value=pad_value)


@torch.inference_mode()
def plot_progress_spec_toucantts(net,

                                 device,

                                 save_dir,

                                 step,

                                 lang,

                                 default_emb,

                                 run_glow):
    tf = ArticulatoryCombinedTextFrontend(language=lang)
    sentence = tf.get_example_sentence(lang=lang)
    if sentence is None:
        return None
    phoneme_vector = tf.string_to_tensor(sentence).squeeze(0).to(device)
    mel, durations, pitch, energy = net.inference(text=phoneme_vector,
                                                  return_duration_pitch_energy=True,
                                                  utterance_embedding=default_emb,
                                                  lang_id=get_language_id(lang).to(device),
                                                  run_glow=run_glow)

    plot_code_spec(pitch, energy, sentence, durations, mel, os.path.join(save_dir, "visualization"), tf, step)
    return os.path.join(os.path.join(save_dir, "visualization"), f"{step}.png")


def plot_code_spec(pitch, energy, sentence, durations, mel, save_path, tf, step):
    fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(9, 8))

    expanded_pitch = list()
    expanded_energy = list()
    for p, e, d in zip(pitch.cpu().squeeze().numpy(), energy.cpu().squeeze().numpy(), durations.cpu().numpy()):
        for _ in range(d):
            expanded_energy.append(e)
            expanded_pitch.append(p)
    pitch = expanded_pitch
    energy = expanded_energy

    spec_plot_axis = ax[1]
    pitch_and_energy_axis = ax[0]

    spec_plot_axis.imshow(mel.cpu().numpy(), origin="lower", cmap='GnBu')
    pitch_and_energy_axis.yaxis.set_visible(False)
    pitch_and_energy_axis.xaxis.set_visible(False)
    spec_plot_axis.yaxis.set_visible(False)
    duration_splits, label_positions = cumsum_durations(durations.cpu().numpy())
    spec_plot_axis.xaxis.grid(True, which='minor')
    spec_plot_axis.set_xticks(label_positions, minor=False)
    phones = tf.get_phone_string(sentence, for_plot_labels=True)
    spec_plot_axis.set_xticklabels(phones)
    word_boundaries = list()
    for label_index, phone in enumerate(phones):
        if phone == "|":
            word_boundaries.append(label_positions[label_index])
    try:
        prev_word_boundary = 0
        word_label_positions = list()
        for word_boundary in word_boundaries:
            word_label_positions.append((word_boundary + prev_word_boundary) / 2)
            prev_word_boundary = word_boundary
        word_label_positions.append((duration_splits[-1] + prev_word_boundary) / 2)

        secondary_ax = spec_plot_axis.secondary_xaxis('bottom')
        secondary_ax.tick_params(axis="x", direction="out", pad=24)
        secondary_ax.set_xticks(word_label_positions, minor=False)
        secondary_ax.set_xticklabels(sentence.split())
        secondary_ax.tick_params(axis='x', colors='orange')
        secondary_ax.xaxis.label.set_color('orange')
    except ValueError:
        spec_plot_axis.set_title(sentence)
    except IndexError:
        spec_plot_axis.set_title(sentence)

    spec_plot_axis.vlines(x=duration_splits, colors="green", linestyles="solid", ymin=0, ymax=15, linewidth=1.0)
    spec_plot_axis.vlines(x=word_boundaries, colors="orange", linestyles="solid", ymin=0, ymax=15, linewidth=2.0)

    pitch_and_energy_axis.plot(pitch, color="blue")
    pitch_and_energy_axis.plot(energy, color="green")

    spec_plot_axis.set_aspect("auto")
    pitch_and_energy_axis.set_aspect("auto")

    plt.subplots_adjust(left=0.05, bottom=0.1, right=0.95, top=.95, wspace=0.0, hspace=0.0)
    os.makedirs(save_path, exist_ok=True)
    plt.savefig(os.path.join(save_path, f"{step}.png"), dpi=100)
    plt.clf()
    plt.close()


def plot_spec_tensor(spec, save_path, name):
    fig, spec_plot_axis = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))
    spec_plot_axis.imshow(spec.detach().cpu().numpy(), origin="lower", cmap='GnBu')
    spec_plot_axis.yaxis.set_visible(False)
    spec_plot_axis.set_aspect("auto")
    plt.subplots_adjust(left=0.05, bottom=0.1, right=0.95, top=.95, wspace=0.0, hspace=0.0)
    os.makedirs(save_path, exist_ok=True)
    plt.savefig(os.path.join(save_path, f"{name}.png"), dpi=100)
    plt.clf()
    plt.close()


def cumsum_durations(durations):
    out = [0]
    for duration in durations:
        out.append(duration + out[-1])
    centers = list()
    for index, _ in enumerate(out):
        if index + 1 < len(out):
            centers.append((out[index] + out[index + 1]) // 2)
    return out, centers


def delete_old_checkpoints(checkpoint_dir, keep=5):
    checkpoint_list = list()
    for el in os.listdir(checkpoint_dir):
        if el.endswith(".pt"):
            try:
                checkpoint_list.append(int(el.replace("checkpoint_", "").replace(".pt", "")))
            except ValueError:
                pass
    if len(checkpoint_list) <= keep:
        return
    else:
        checkpoint_list.sort(reverse=False)
        checkpoints_to_delete = [os.path.join(checkpoint_dir, "checkpoint_{}.pt".format(step)) for step in
                                 checkpoint_list[:-keep]]
        for old_checkpoint in checkpoints_to_delete:
            os.remove(os.path.join(old_checkpoint))


def plot_grad_flow(named_parameters):
    """

    Plots the gradients flowing through different layers in the net during training.

    Can be used for checking for possible gradient vanishing / exploding problems.



    Usage: Plug this function after loss.backwards() and unscaling as

    "plot_grad_flow(self.model.named_parameters())" to visualize the gradient flow

    """
    ave_grads = []
    max_grads = []
    layers = []
    for n, p in named_parameters:
        if p.requires_grad and ("bias" not in n):
            layers.append(n)
            ave_grads.append(p.grad.abs().mean())
            max_grads.append(p.grad.abs().max())
    plt.bar(np.arange(len(max_grads)), max_grads, alpha=0.1, lw=1, color="c")
    plt.bar(np.arange(len(max_grads)), ave_grads, alpha=0.1, lw=1, color="b")
    plt.hlines(0, 0, len(ave_grads) + 1, lw=2, color="k")
    plt.xticks(range(0, len(ave_grads), 1), layers, rotation="vertical")
    plt.xlim(left=0, right=len(ave_grads))
    plt.ylim(bottom=-0.001, top=0.02)  # zoom in on the lower gradient regions
    plt.xlabel("Layers")
    plt.ylabel("Gradient")
    plt.title("Gradient Flow")
    plt.grid(True)
    plt.legend([Line2D([0], [0], color="c", lw=4),
                Line2D([0], [0], color="b", lw=4),
                Line2D([0], [0], color="k", lw=4)], ['max-gradient', 'mean-gradient', 'zero-gradient'])
    plt.show()


def get_most_recent_checkpoint(checkpoint_dir, verbose=True):
    checkpoint_list = list()
    for el in os.listdir(checkpoint_dir):
        if el.endswith(".pt") and el != "best.pt" and el != "embedding_function.pt":
            try:
                checkpoint_list.append(int(el.split(".")[0].split("_")[1]))
            except ValueError:
                pass
    if len(checkpoint_list) == 0:
        print("No previous checkpoints found, cannot reload.")
        return None
    checkpoint_list.sort(reverse=True)
    if verbose:
        print("Reloading checkpoint_{}.pt".format(checkpoint_list[0]))
    return os.path.join(checkpoint_dir, "checkpoint_{}.pt".format(checkpoint_list[0]))


def make_pad_mask(lengths, xs=None, length_dim=-1, device=None):
    """

    Make mask tensor containing indices of padded part.



    Args:

        lengths (LongTensor or List): Batch of lengths (B,).

        xs (Tensor, optional): The reference tensor.

            If set, masks will be the same shape as this tensor.

        length_dim (int, optional): Dimension indicator of the above tensor.

            See the example.



    Returns:

        Tensor: Mask tensor containing indices of padded part.

                dtype=torch.uint8 in PyTorch 1.2-

                dtype=torch.bool in PyTorch 1.2+ (including 1.2)



    """
    if length_dim == 0:
        raise ValueError("length_dim cannot be 0: {}".format(length_dim))

    if not isinstance(lengths, list):
        lengths = lengths.tolist()
    bs = int(len(lengths))
    if xs is None:
        maxlen = int(max(lengths))
    else:
        maxlen = xs.size(length_dim)

    if device is not None:
        seq_range = torch.arange(0, maxlen, dtype=torch.int64, device=device)
    else:
        seq_range = torch.arange(0, maxlen, dtype=torch.int64)
    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
    mask = seq_range_expand >= seq_length_expand

    if xs is not None:
        assert xs.size(0) == bs, (xs.size(0), bs)

        if length_dim < 0:
            length_dim = xs.dim() + length_dim
        # ind = (:, None, ..., None, :, , None, ..., None)
        ind = tuple(slice(None) if i in (0, length_dim) else None for i in range(xs.dim()))
        mask = mask[ind].expand_as(xs).to(xs.device)
    return mask


def make_non_pad_mask(lengths, xs=None, length_dim=-1, device=None):
    """

    Make mask tensor containing indices of non-padded part.



    Args:

        lengths (LongTensor or List): Batch of lengths (B,).

        xs (Tensor, optional): The reference tensor.

            If set, masks will be the same shape as this tensor.

        length_dim (int, optional): Dimension indicator of the above tensor.

            See the example.



    Returns:

        ByteTensor: mask tensor containing indices of padded part.

                    dtype=torch.uint8 in PyTorch 1.2-

                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)



    """
    return ~make_pad_mask(lengths, xs, length_dim, device=device)


def initialize(model, init):
    """

    Initialize weights of a neural network module.



    Parameters are initialized using the given method or distribution.



    Args:

        model: Target.

        init: Method of initialization.

    """

    # weight init
    for p in model.parameters():
        if p.dim() > 1:
            if init == "xavier_uniform":
                torch.nn.init.xavier_uniform_(p.data)
            elif init == "xavier_normal":
                torch.nn.init.xavier_normal_(p.data)
            elif init == "kaiming_uniform":
                torch.nn.init.kaiming_uniform_(p.data, nonlinearity="relu")
            elif init == "kaiming_normal":
                torch.nn.init.kaiming_normal_(p.data, nonlinearity="relu")
            else:
                raise ValueError("Unknown initialization: " + init)
    # bias init
    for p in model.parameters():
        if p.dim() == 1:
            p.data.zero_()

    # reset some modules with default init
    for m in model.modules():
        if isinstance(m, (torch.nn.Embedding,
                          torch.nn.LayerNorm,
                          Architectures.GeneralLayers.ConditionalLayerNorm.ConditionalLayerNorm,
                          Architectures.GeneralLayers.ConditionalLayerNorm.SequentialWrappableConditionalLayerNorm
                          )):
            m.reset_parameters()


def pad_list(xs, pad_value):
    """

    Perform padding for the list of tensors.



    Args:

        xs (List): List of Tensors [(T_1, `*`), (T_2, `*`), ..., (T_B, `*`)].

        pad_value (float): Value for padding.



    Returns:

        Tensor: Padded tensor (B, Tmax, `*`).



    """
    n_batch = len(xs)
    max_len = max(x.size(0) for x in xs)
    pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)

    for i in range(n_batch):
        pad[i, : xs[i].size(0)] = xs[i]

    return pad


def curve_smoother(curve):
    if len(curve) < 3:
        return curve
    new_curve = list()
    for index in range(len(curve)):
        if curve[index] != 0:
            current_value = curve[index]
            if index != len(curve) - 1:
                if curve[index + 1] != 0:
                    next_value = curve[index + 1]
                else:
                    next_value = curve[index]
            if index != 0:
                if curve[index - 1] != 0:
                    prev_value = curve[index - 1]
                else:
                    prev_value = curve[index]
            else:
                prev_value = curve[index]
            smooth_value = (current_value * 3 + prev_value + next_value) / 5
            new_curve.append(smooth_value)
        else:
            new_curve.append(0)
    return new_curve


def remove_elements(tensor, indexes):
    # Create a boolean mask where True represents the elements to keep
    print("\n\n\n")
    print(tensor.shape)
    print(indexes)
    mask = torch.ones(tensor.size(0), dtype=torch.bool)
    mask[indexes] = False

    # Use the mask to select the elements to keep
    result = tensor[mask, :]
    print(result.shape)
    return result


def load_json_from_path(path):
    with open(path, "r", encoding="utf8") as f:
        obj = json.loads(f.read())

    return obj

if __name__ == '__main__':
    data = np.random.randn(50)
    plt.plot(data, color="b")
    smooth = curve_smoother(data)
    plt.plot(smooth, color="g")
    plt.show()