Spaces:
Sleeping
Sleeping
File size: 7,518 Bytes
eb9c81a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | from typing import Any
import random
from pathlib import Path
import librosa
import numpy as np
import torch
from sklearn.model_selection import train_test_split
import pytorch_lightning as pl
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split
def load_audio(full_path, sampling_rate=16000):
data, sampling_rate = librosa.load(full_path, sr = sampling_rate)
return data, sampling_rate
class ResynthesisDataset(Dataset):
def __init__(
self,
training_files,
segment_size,
code_hop_size,
sampling_rate
):
self.audio_files = training_files
self.segment_size = segment_size
self.code_hop_size = code_hop_size
self.sampling_rate = sampling_rate
random.seed(1234)
def _sample_interval(self, seqs, seq_len=None):
N = max([v.shape[-1] for v in seqs])
if seq_len is None:
seq_len = self.segment_size if self.segment_size > 0 else N
hops = [N // v.shape[-1] for v in seqs]
lcm = np.lcm.reduce(hops)
# Randomly pickup with the batch_max_steps length of the part
interval_start = 0
interval_end = N // lcm - seq_len // lcm
start_step = random.randint(interval_start, interval_end)
new_seqs = []
for i, v in enumerate(seqs):
start = start_step * (lcm // hops[i])
end = (start_step + seq_len // lcm) * (lcm // hops[i])
new_seqs += [v[..., start:end]]
return new_seqs
def __getitem__(self, index):
wav_fpath = self.audio_files[index]
audio, sampling_rate = load_audio(wav_fpath, self.sampling_rate)
if sampling_rate != self.sampling_rate:
import resampy
audio = resampy.resample(audio, sampling_rate, self.sampling_rate)
# audio = audio / MAX_WAV_VALUE
# audio = normalize(audio) * 0.95
audio = audio / (max(abs(audio)) + 0.00001) * 0.9
# Trim audio ending
code_length = min(audio.shape[0] // self.code_hop_size, tokens.shape[-1])
audio = audio[:code_length * self.code_hop_size]
while audio.shape[0] < self.segment_size:
audio = np.hstack([audio, audio])
audio = torch.FloatTensor(audio)
audio = audio.unsqueeze(0)
assert audio.size(1) >= self.segment_size, "Padding not supported!!"
audio = self._sample_interval([audio])
return audio.squeeze(0), str(wav_fpath)
def __len__(self):
return len(self.audio_files)
class PasrMultilingualDataModule(pl.LightningDataModule):
"""
A DataModule implements 5 key methods:
def prepare_data(self):
# things to do on 1 GPU/TPU (not on every GPU/TPU in DDP)
# download data, pre-process, split, save to disk, etc...
def setup(self, stage):
# things to do on every process in DDP
# load data, set variables, etc...
def train_dataloader(self):
# return train dataloader
def val_dataloader(self):
# return validation dataloader
def test_dataloader(self):
# return test dataloader
def teardown(self):
# called on every process in DDP
# clean up after fit or test
This allows you to share a full dataset without explaining how to download,
split, transform and process the data.
Read the docs:
https://lightning.ai/docs/pytorch/latest/data/datamodule.html
"""
def __init__(
self,
data_dir: str = "data",
batch_size: int = 16,
num_workers: int = 4,
pin_memory: bool = True,
segment_size: int = 20480,
code_hop_size: int = 320,
sampling_rate: int = 16000,
):
super().__init__()
# this line allows to access init params with 'self.hparams' attribute
# also ensures init params will be stored in ckpt
self.save_hyperparameters()
# data transformations
# self.transforms = T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
self.data_train: Dataset = None
self.data_val: Dataset = None
self.data_test: Dataset = None
@property
def num_classes(self):
return self.hparams.num_codes
def prepare_data(self):
"""Download data if needed.
Do not use it to assign state (self.x = y).
"""
pass
def setup(self, stage: str = None):
"""Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
This method is called by lightning with both `trainer.fit()` and `trainer.test()`, so be
careful not to execute things like random split twice!
"""
training_files = list(Path(self.hparams.data_dir).rglob("*.wav"))
training_files, self.validation_files, _, _ = train_test_split(training_files, training_files, test_size=0.001, random_state=42)
self.training_files, self.test_files, _, _ = train_test_split(training_files, training_files, test_size=0.0001, random_state=42)
# load and split datasets only if not loaded already
if not self.data_train and not self.data_val and not self.data_test:
self.data_train = PasrMultilingualDataset(
training_files=self.training_files,
segment_size=self.hparams.segment_size,
code_hop_size=self.hparams.code_hop_size,
sampling_rate=self.hparams.sampling_rate,
)
self.data_val = PasrMultilingualDataset(
training_files=self.validation_files,
segment_size=self.hparams.segment_size,
code_hop_size=self.hparams.code_hop_size,
sampling_rate=self.hparams.sampling_rate,
)
self.data_test = PasrMultilingualDataset(
training_files=self.test_files,
segment_size=self.hparams.segment_size,
code_hop_size=self.hparams.code_hop_size,
sampling_rate=self.hparams.sampling_rate,
)
def train_dataloader(self):
return DataLoader(
dataset=self.data_train,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=True,
)
def val_dataloader(self):
return DataLoader(
dataset=self.data_val,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
def test_dataloader(self):
return DataLoader(
dataset=self.data_test,
batch_size=self.hparams.batch_size,
num_workers=self.hparams.num_workers,
pin_memory=self.hparams.pin_memory,
shuffle=False,
)
def teardown(self, stage: str = None):
"""Clean up after fit or test."""
pass
def state_dict(self):
"""Extra things to save to checkpoint."""
return {}
def load_state_dict(self, state_dict: dict[str, Any]):
"""Things to do when loading checkpoint."""
pass
if __name__ == "__main__":
dm = ResynthesisDataset()
dm.prepare_data()
dm.setup()
for batch in dm.train_dataloader():
print(batch[0].shape)
print(batch[1].shape)
break
|