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"""
taken and adapted from https://github.com/as-ideas/DeepForcedAligner
"""
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.multiprocessing
import torch.nn as nn
from scipy.sparse import coo_matrix
from scipy.sparse.csgraph import dijkstra
from torch.nn import CTCLoss
from torch.nn.utils.rnn import pack_padded_sequence
from torch.nn.utils.rnn import pad_packed_sequence
from Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend
class BatchNormConv(nn.Module):
def __init__(self, in_channels: int, out_channels: int, kernel_size: int):
super().__init__()
self.conv = nn.Conv1d(
in_channels, out_channels, kernel_size,
stride=1, padding=kernel_size // 2, bias=False)
self.bnorm = nn.BatchNorm1d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
x = x.transpose(1, 2)
x = self.conv(x)
x = self.relu(x)
x = self.bnorm(x)
x = x.transpose(1, 2)
return x
class Aligner(torch.nn.Module):
def __init__(self,
n_mels=80,
num_symbols=145,
lstm_dim=512,
conv_dim=512):
super().__init__()
self.convs = nn.ModuleList([
BatchNormConv(n_mels, conv_dim, 3),
nn.Dropout(p=0.5),
BatchNormConv(conv_dim, conv_dim, 3),
nn.Dropout(p=0.5),
BatchNormConv(conv_dim, conv_dim, 3),
nn.Dropout(p=0.5),
BatchNormConv(conv_dim, conv_dim, 3),
nn.Dropout(p=0.5),
BatchNormConv(conv_dim, conv_dim, 3),
nn.Dropout(p=0.5),
])
self.rnn = torch.nn.LSTM(conv_dim, lstm_dim, batch_first=True, bidirectional=True)
self.proj = torch.nn.Linear(2 * lstm_dim, num_symbols)
self.tf = ArticulatoryCombinedTextFrontend(language="en")
self.ctc_loss = CTCLoss(blank=144, zero_infinity=True)
self.vector_to_id = dict()
for phone in self.tf.phone_to_vector:
self.vector_to_id[tuple(self.tf.phone_to_vector[phone])] = self.tf.phone_to_id[phone]
def forward(self, x, lens=None):
for conv in self.convs:
x = conv(x)
if lens is not None:
x = pack_padded_sequence(x, lens.cpu(), batch_first=True, enforce_sorted=False)
x, _ = self.rnn(x)
if lens is not None:
x, _ = pad_packed_sequence(x, batch_first=True)
x = self.proj(x)
return x
@torch.no_grad()
def label_speech(self, speech):
# theoretically possible, but doesn't work well at all. Would probably require a beamsearch
probabilities_of_phones_over_frames = self(speech.unsqueeze(0)).squeeze()[:, :73]
smoothed_phone_probs_over_frames = list()
for index, _ in enumerate(probabilities_of_phones_over_frames):
access_safe_prev_index = max(0, index - 1)
access_safe_next_index = min(index + 1, len(probabilities_of_phones_over_frames) - 1)
smoothed_probs = (probabilities_of_phones_over_frames[access_safe_prev_index] +
probabilities_of_phones_over_frames[access_safe_next_index] +
probabilities_of_phones_over_frames[index]) / 3
smoothed_phone_probs_over_frames.append(smoothed_probs.unsqueeze(0))
print(torch.cat(smoothed_phone_probs_over_frames))
_, phone_ids_over_frames = torch.max(torch.cat(smoothed_phone_probs_over_frames), dim=1)
phone_ids = torch.unique_consecutive(phone_ids_over_frames)
phones = list()
for id_of_phone in phone_ids:
phones.append(self.tf.id_to_phone[int(id_of_phone)])
return "".join(phones)
@torch.inference_mode()
def inference(self, mel, tokens, save_img_for_debug=None, train=False, pathfinding="MAS", return_ctc=False):
if not train:
tokens_indexed = list() # first we need to convert the articulatory vectors to IDs, so we can apply dijkstra or viterbi
for vector in tokens:
tokens_indexed.append(self.vector_to_id[tuple(vector.cpu().detach().numpy().tolist())])
tokens = np.asarray(tokens_indexed)
else:
tokens = tokens.cpu().detach().numpy()
pred = self(mel.unsqueeze(0))
if return_ctc:
ctc_loss = self.ctc_loss(pred.transpose(0, 1).log_softmax(2), torch.LongTensor(tokens), torch.LongTensor([len(pred[0])]),
torch.LongTensor([len(tokens)])).item()
pred = pred.squeeze().cpu().detach().numpy()
pred_max = pred[:, tokens]
path_probs = 1. - pred_max
adj_matrix = to_adj_matrix(path_probs)
if pathfinding == "MAS":
alignment_matrix = binarize_alignment(pred_max)
if save_img_for_debug is not None:
phones = list()
for index in tokens:
for phone in self.tf.phone_to_id:
if self.tf.phone_to_id[phone] == index:
phones.append(phone)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 4))
ax.imshow(alignment_matrix, interpolation='nearest', aspect='auto', origin="lower", cmap='cividis')
ax.set_ylabel("Mel-Frames")
ax.set_xticks(range(len(pred_max[0])))
ax.set_xticklabels(labels=phones)
ax.set_title("MAS Path")
plt.tight_layout()
fig.savefig(save_img_for_debug)
fig.clf()
plt.close()
if return_ctc:
return alignment_matrix, ctc_loss
return alignment_matrix
elif pathfinding == "dijkstra":
dist_matrix, predecessors, *_ = dijkstra(csgraph=adj_matrix,
directed=True,
indices=0,
return_predecessors=True)
path = []
pr_index = predecessors[-1]
while pr_index != 0:
path.append(pr_index)
pr_index = predecessors[pr_index]
path.reverse()
# append first and last node
path = [0] + path + [dist_matrix.size - 1]
cols = path_probs.shape[1]
mel_text = {}
# collect indices (mel, text) along the path
for node_index in path:
i, j = from_node_index(node_index, cols)
mel_text[i] = j
path_plot = np.zeros_like(pred_max)
for i in mel_text:
path_plot[i][mel_text[i]] = 1.0
if save_img_for_debug is not None:
phones = list()
for index in tokens:
for phone in self.tf.phone_to_id:
if self.tf.phone_to_id[phone] == index:
phones.append(phone)
fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(10, 9))
ax[0].imshow(pred_max, interpolation='nearest', aspect='auto', origin="lower")
ax[1].imshow(path_plot, interpolation='nearest', aspect='auto', origin="lower", cmap='cividis')
ax[0].set_ylabel("Mel-Frames")
ax[1].set_ylabel("Mel-Frames")
ax[0].set_xticks(range(len(pred_max[0])))
ax[0].set_xticklabels(labels=phones)
ax[1].set_xticks(range(len(pred_max[0])))
ax[1].set_xticklabels(labels=phones)
ax[0].set_title("Path Probabilities")
ax[1].set_title("Dijkstra Path")
plt.tight_layout()
fig.savefig(save_img_for_debug)
fig.clf()
plt.close()
if return_ctc:
return path_plot, ctc_loss
return path_plot
def binarize_alignment(alignment_prob):
"""
# Implementation by:
# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/alignment.py
# https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/FastPitch/fastpitch/attn_loss_function.py
Binarizes alignment with MAS.
"""
# assumes mel x text
opt = np.zeros_like(alignment_prob)
alignment_prob = alignment_prob + (np.abs(alignment_prob).max() + 1.0) # make all numbers positive and add an offset to avoid log of 0 later
alignment_prob * alignment_prob * (1.0 / alignment_prob.max()) # normalize to (0, 1]
attn_map = np.log(alignment_prob)
attn_map[0, 1:] = -np.inf
log_p = np.zeros_like(attn_map)
log_p[0, :] = attn_map[0, :]
prev_ind = np.zeros_like(attn_map, dtype=np.int64)
for i in range(1, attn_map.shape[0]):
for j in range(attn_map.shape[1]): # for each text dim
prev_log = log_p[i - 1, j]
prev_j = j
if j - 1 >= 0 and log_p[i - 1, j - 1] >= log_p[i - 1, j]:
prev_log = log_p[i - 1, j - 1]
prev_j = j - 1
log_p[i, j] = attn_map[i, j] + prev_log
prev_ind[i, j] = prev_j
# now backtrack
curr_text_idx = attn_map.shape[1] - 1
for i in range(attn_map.shape[0] - 1, -1, -1):
opt[i, curr_text_idx] = 1
curr_text_idx = prev_ind[i, curr_text_idx]
opt[0, curr_text_idx] = 1
return opt
def to_node_index(i, j, cols):
return cols * i + j
def from_node_index(node_index, cols):
return node_index // cols, node_index % cols
def to_adj_matrix(mat):
rows = mat.shape[0]
cols = mat.shape[1]
row_ind = []
col_ind = []
data = []
for i in range(rows):
for j in range(cols):
node = to_node_index(i, j, cols)
if j < cols - 1:
right_node = to_node_index(i, j + 1, cols)
weight_right = mat[i, j + 1]
row_ind.append(node)
col_ind.append(right_node)
data.append(weight_right)
if i < rows - 1 and j < cols:
bottom_node = to_node_index(i + 1, j, cols)
weight_bottom = mat[i + 1, j]
row_ind.append(node)
col_ind.append(bottom_node)
data.append(weight_bottom)
if i < rows - 1 and j < cols - 1:
bottom_right_node = to_node_index(i + 1, j + 1, cols)
weight_bottom_right = mat[i + 1, j + 1]
row_ind.append(node)
col_ind.append(bottom_right_node)
data.append(weight_bottom_right)
adj_mat = coo_matrix((data, (row_ind, col_ind)), shape=(rows * cols, rows * cols))
return adj_mat.tocsr()
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