Spaces:
Runtime error
Runtime error
File size: 5,370 Bytes
ae8e1dd |
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 |
import os.path
import random
import numpy as np
import torch
import re
import torch.utils.data
import json
import kaldiio
from tqdm import tqdm
class BaseCollate:
def __init__(self, n_frames_per_step=1):
self.n_frames_per_step = n_frames_per_step
def collate_text_mel(self, batch: [dict]):
"""
:param batch: list of dicts
"""
utt = list(map(lambda x: x['utt'], batch))
input_lengths, ids_sorted_decreasing = torch.sort(
torch.LongTensor([len(x['text']) for x in batch]),
dim=0, descending=True)
max_input_len = input_lengths[0]
text_padded = torch.LongTensor(len(batch), max_input_len)
text_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
text = batch[ids_sorted_decreasing[i]]['text']
text_padded[i, :text.size(0)] = text
# Right zero-pad mel-spec
num_mels = batch[0]['mel'].size(0)
max_target_len = max([x['mel'].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
# include mel padded
mel_padded = torch.FloatTensor(len(batch), num_mels, max_target_len)
mel_padded.zero_()
output_lengths = torch.LongTensor(len(batch))
for i in range(len(ids_sorted_decreasing)):
mel = batch[ids_sorted_decreasing[i]]['mel']
mel_padded[i, :, :mel.size(1)] = mel
output_lengths[i] = mel.size(1)
utt_name = np.array(utt)[ids_sorted_decreasing].tolist()
if isinstance(utt_name, str):
utt_name = [utt_name]
res = {
"utt": utt_name,
"text_padded": text_padded,
"input_lengths": input_lengths,
"mel_padded": mel_padded,
"output_lengths": output_lengths,
}
return res, ids_sorted_decreasing
class SpkIDCollate(BaseCollate):
def __call__(self, batch, *args, **kwargs):
base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
spk_ids = torch.LongTensor(list(map(lambda x: x["spk_ids"], batch)))
spk_ids = spk_ids[ids_sorted_decreasing]
base_data.update({
"spk_ids": spk_ids
})
return base_data
class SpkIDCollateWithEmo(BaseCollate):
def __call__(self, batch, *args, **kwargs):
base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
spk_ids = torch.LongTensor(list(map(lambda x: x["spk_ids"], batch)))
spk_ids = spk_ids[ids_sorted_decreasing]
emo_ids = torch.LongTensor(list(map(lambda x: x['emo_ids'], batch)))
emo_ids = emo_ids[ids_sorted_decreasing]
base_data.update({
"spk_ids": spk_ids,
"emo_ids": emo_ids
})
return base_data
class XvectorCollate(BaseCollate):
def __call__(self, batch, *args, **kwargs):
base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
xvectors = torch.cat(list(map(lambda x: x["xvector"].unsqueeze(0), batch)), dim=0)
xvectors = xvectors[ids_sorted_decreasing]
base_data.update({
"xvector": xvectors
})
return base_data
class SpkIDCollateWithPE(BaseCollate):
def __call__(self, batch, *args, **kwargs):
base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
spk_ids = torch.LongTensor(list(map(lambda x: x["spk_ids"], batch)))
spk_ids = spk_ids[ids_sorted_decreasing]
num_var = batch[0]["var"].size(0)
max_target_len = max([x["var"].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
var_padded = torch.FloatTensor(len(batch), num_var, max_target_len)
var_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
var = batch[ids_sorted_decreasing[i]]["var"]
var_padded[i, :, :var.size(1)] = var
base_data.update({
"spk_ids": spk_ids,
"var_padded": var_padded
})
return base_data
class XvectorCollateWithPE(BaseCollate):
def __call__(self, batch, *args, **kwargs):
base_data, ids_sorted_decreasing = self.collate_text_mel(batch)
xvectors = torch.cat(list(map(lambda x: x["xvector"].unsqueeze(0), batch)), dim=0)
xvectors = xvectors[ids_sorted_decreasing]
num_var = batch[0]["var"].size(0)
max_target_len = max([x["var"].size(1) for x in batch])
if max_target_len % self.n_frames_per_step != 0:
max_target_len += self.n_frames_per_step - max_target_len % self.n_frames_per_step
assert max_target_len % self.n_frames_per_step == 0
var_padded = torch.FloatTensor(len(batch), num_var, max_target_len)
var_padded.zero_()
for i in range(len(ids_sorted_decreasing)):
var = batch[ids_sorted_decreasing[i]]["var"]
var_padded[i, :, :var.size(1)] = var
base_data.update({
"xvector": xvectors,
"var_padded": var_padded
})
return base_data
|