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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 torch.nn import CTCLoss
from torch.nn.utils.rnn import pack_padded_sequence
from torch.nn.utils.rnn import pad_packed_sequence
from Preprocessing.TextFrontend 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_features=128,
num_symbols=145,
lstm_dim=512,
conv_dim=512):
super().__init__()
self.convs = nn.ModuleList([
BatchNormConv(n_features, 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="eng")
self.ctc_loss = CTCLoss(blank=144, zero_infinity=True)
self.vector_to_id = dict()
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.inference_mode()
def inference(self, features, tokens, save_img_for_debug=None, train=False, pathfinding="MAS", return_ctc=False):
if not train:
tokens_indexed = self.tf.text_vectors_to_id_sequence(text_vector=tokens) # first we need to convert the articulatory vectors to IDs, so we can apply dijkstra or viterbi
tokens = np.asarray(tokens_indexed)
else:
tokens = tokens.cpu().detach().numpy()
pred = self(features.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]
# run monotonic alignment search
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, 5))
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
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 features 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
if __name__ == '__main__':
tf = ArticulatoryCombinedTextFrontend(language="eng")
from Preprocessing.HiFiCodecAudioPreprocessor import CodecAudioPreprocessor
cap = CodecAudioPreprocessor(input_sr=-1)
dummy_codebook_indexes = torch.randint(low=0, high=1023, size=[9, 20])
codebook_frames = cap.indexes_to_codec_frames(dummy_codebook_indexes)
alignment = Aligner().inference(codebook_frames.transpose(0, 1), tokens=tf.string_to_tensor("Hello world"))
print(alignment.shape)
plt.imshow(alignment, origin="lower", cmap="GnBu")
plt.show()
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