Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,411 Bytes
dd9600d |
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 |
# @ hwang258@jhu.edu
import os
import json
import torch
import random
import logging
import shutil
import typing as tp
import numpy as np
import torchaudio
import sys
from torch.utils.data import Dataset
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
def read_json(path):
with open(path, 'r') as f:
return json.load(f)
class CapSpeech(Dataset):
def __init__(
self,
dataset_dir: str = None,
clap_emb_dir: str = None,
t5_folder_name: str = "t5",
phn_folder_name: str = "g2p",
manifest_name: str = "manifest",
json_name: str = "jsons",
dynamic_batching: bool = True,
text_pad_token: int = -1,
audio_pad_token: float = 0.0,
split: str = "val",
sr: int = 24000,
norm_audio: bool = False,
vocab_file: str = None,
):
super().__init__()
self.dataset_dir = dataset_dir
self.clap_emb_dir = clap_emb_dir
self.t5_folder_name = t5_folder_name
self.phn_folder_name = phn_folder_name
self.manifest_name = manifest_name
self.json_name = json_name
self.dynamic_batching = dynamic_batching
self.text_pad_token = text_pad_token
self.audio_pad_token = torch.tensor(audio_pad_token)
self.split = split
self.sr = sr
self.norm_audio = norm_audio
assert self.split in ['train', 'train_small', 'val', 'test']
manifest_fn = os.path.join(self.dataset_dir, self.manifest_name, self.split+".txt")
meta = read_json(os.path.join(self.dataset_dir, self.json_name, self.split + ".json"))
self.meta = {item["segment_id"]: item["audio_path"] for item in meta}
with open(manifest_fn, "r") as rf:
data = [l.strip().split("\t") for l in rf.readlines()]
# data = [item for item in data if item[2] == 'none'] # remove sound effects
self.data = [item[0] for item in data]
self.tag_list = [item[1] for item in data]
logging.info(f"number of data points for {self.split} split: {len(self.data)}")
# phoneme vocabulary
if vocab_file is None:
vocab_fn = os.path.join(self.dataset_dir, "vocab.txt")
else:
vocab_fn = vocab_file
with open(vocab_fn, "r") as f:
temp = [l.strip().split(" ") for l in f.readlines() if len(l) != 0]
self.phn2num = {item[1]:int(item[0]) for item in temp}
def __len__(self):
return len(self.data)
def _load_audio(self, audio_path):
try:
y, sr = torchaudio.load(audio_path)
if y.shape[0] > 1:
y = y.mean(dim=0, keepdim=True)
if sr != self.sr:
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=self.sr)
y = resampler(y)
if self.norm_audio:
eps = 1e-9
max_val = torch.max(torch.abs(y))
y = y / (max_val + eps)
if torch.isnan(y.mean()):
return None
return y
except:
return None
def _load_phn_enc(self, index):
try:
seg_id = self.data[index]
pf = os.path.join(self.dataset_dir, self.phn_folder_name, seg_id+".txt")
audio_path = self.meta[seg_id]
cf = os.path.join(self.dataset_dir, self.t5_folder_name, seg_id+".npz")
tagf = os.path.join(self.clap_emb_dir, self.tag_list[index]+'.npz')
with open(pf, "r") as p:
phns = [l.strip() for l in p.readlines()]
assert len(phns) == 1, phns
x = [self.phn2num[item] for item in phns[0].split(" ")]
c = np.load(cf)['arr_0']
c = torch.tensor(c).squeeze()
tag = np.load(tagf)['arr_0']
tag = torch.tensor(tag).squeeze()
y = self._load_audio(audio_path)
if y is not None:
return x, y, c, tag
return None, None, None, None
except:
return None, None, None, None
def __getitem__(self, index):
x, y, c, tag = self._load_phn_enc(index)
if x is None:
return {
"x": None,
"x_len": None,
"y": None,
"y_len": None,
"c": None,
"c_len": None,
"tag": None
}
x_len, y_len, c_len = len(x), len(y[0]), len(c)
y_len = y_len / self.sr
if y_len * self.sr / 256 <= x_len:
return {
"x": None,
"x_len": None,
"y": None,
"y_len": None,
"c": None,
"c_len": None,
"tag": None
}
x = torch.LongTensor(x)
return {
"x": x,
"x_len": x_len,
"y": y,
"y_len": y_len,
"c": c,
"c_len": c_len,
"tag": tag
}
def collate(self, batch):
out = {key:[] for key in batch[0]}
for item in batch:
if item['x'] == None: # deal with load failure
continue
if item['c'].ndim != 2:
continue
for key, val in item.items():
out[key].append(val)
res = {}
res["x"] = torch.nn.utils.rnn.pad_sequence(out["x"], batch_first=True, padding_value=self.text_pad_token)
res["x_lens"] = torch.LongTensor(out["x_len"])
if self.dynamic_batching:
res['y'] = torch.nn.utils.rnn.pad_sequence([item.transpose(1,0) for item in out['y']],padding_value=self.audio_pad_token)
res['y'] = res['y'].permute(1,2,0) # T B K -> B K T
else:
res['y'] = torch.stack(out['y'], dim=0)
res["y_lens"] = torch.Tensor(out["y_len"])
res['c'] = torch.nn.utils.rnn.pad_sequence(out['c'], batch_first=True)
res["c_lens"] = torch.LongTensor(out["c_len"])
res["tag"] = torch.stack(out['tag'], dim=0)
return res
if __name__ == "__main__":
# debug
import argparse
from torch.utils.data import DataLoader
from accelerate import Accelerator
dataset = CapSpeech(
dataset_dir="./data/capspeech",
clap_emb_dir="./data/clap_embs/",
split="val"
)
|