Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,176 Bytes
6efc863 |
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 |
import sys
import numpy as np
import torch
import logging
import pandas as pd
import glob
logger = logging.getLogger(f'main.{__name__}')
sys.path.insert(0, '.') # nopep8
class JoinManifestSpecs(torch.utils.data.Dataset):
def __init__(self, split, spec_dir_path, mel_num=None, spec_crop_len=None,drop=0,**kwargs):
super().__init__()
self.split = split
self.batch_max_length = spec_crop_len
self.batch_min_length = 50
self.mel_num = mel_num
self.drop = drop
manifest_files = []
for dir_path in spec_dir_path.split(','):
manifest_files += glob.glob(f'{dir_path}/**/*.tsv',recursive=True)
df_list = [pd.read_csv(manifest,sep='\t') for manifest in manifest_files]
df = pd.concat(df_list,ignore_index=True)
if split == 'train':
self.dataset = df.iloc[100:]
elif split == 'valid' or split == 'val':
self.dataset = df.iloc[:100]
elif split == 'test':
df = self.add_name_num(df)
self.dataset = df
else:
raise ValueError(f'Unknown split {split}')
self.dataset.reset_index(inplace=True)
print('dataset len:', len(self.dataset))
def add_name_num(self,df):
"""each file may have different caption, we add num to filename to identify each audio-caption pair"""
name_count_dict = {}
change = []
for t in df.itertuples():
name = getattr(t,'name')
if name in name_count_dict:
name_count_dict[name] += 1
else:
name_count_dict[name] = 0
change.append((t[0],name_count_dict[name]))
for t in change:
df.loc[t[0],'name'] = df.loc[t[0],'name'] + f'_{t[1]}'
return df
def __getitem__(self, idx):
data = self.dataset.iloc[idx]
item = {}
try:
spec = np.load(data['mel_path']) # mel spec [80, 624]
except:
mel_path = data['mel_path']
print(f'corrupted:{mel_path}')
spec = np.zeros((self.mel_num,self.batch_max_length)).astype(np.float32)
if spec.shape[1] < self.batch_max_length:
# spec = np.pad(spec, ((0, 0), (0, self.batch_max_length - spec.shape[1]))) # [80, 624]
spec = np.tile(spec,reps=(self.batch_max_length//spec.shape[1])+1)
item['image'] = spec[:,:self.batch_max_length]
p = np.random.uniform(0,1)
if p > self.drop:
item["caption"] = data['caption']
else:
item["caption"] = ""
if self.split == 'test':
item['f_name'] = data['name']
return item
def __len__(self):
return len(self.dataset)
class JoinSpecsTrain(JoinManifestSpecs):
def __init__(self, specs_dataset_cfg):
super().__init__('train', **specs_dataset_cfg)
class JoinSpecsValidation(JoinManifestSpecs):
def __init__(self, specs_dataset_cfg):
super().__init__('valid', **specs_dataset_cfg)
class JoinSpecsTest(JoinManifestSpecs):
def __init__(self, specs_dataset_cfg):
super().__init__('test', **specs_dataset_cfg)
|