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901e06a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | from fairseq import tasks
import numpy as np
import logging
import random
from fairseq import options
import torch
import os
import soundfile as sf
from fairseq.data.audio.audio_utils import (
get_waveform,
parse_path,
)
logging.basicConfig()
logging.root.setLevel(logging.INFO)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
random.seed(1)
np.random.seed(1)
random_number_generator = np.random.RandomState(30)
def generate_random_data_sample(T, B=1, D=80):
"""Generate random data sample given the T, B, D values"""
net_input = {
"src_tokens": torch.tensor(random_number_generator.randn(B, T, D)).float(),
"src_lengths": torch.tensor([T]),
}
return {"net_input": net_input}
def generate_random_dataset(T_range_min, T_range_max, B=1, D=80, dataset_size=100):
"""Generate random dataset with T values within a given range, B, D"""
T_values = [random.randint(T_range_min, T_range_max) for i in range(dataset_size)]
dataset = []
for t in T_values:
dataset.append(generate_random_data_sample(t, B, D))
return dataset, sum(T_values) / dataset_size
def load_dataset_npy(file_name, dataset_size=None):
"""Load dataset from a .npy file."""
data = np.load(file_name, allow_pickle=True)
if dataset_size:
data = data[:dataset_size]
return data
def load_dataset_raw_to_waveforms(
file_name,
dataset_size=None,
need_waveform=True,
sample_rate=16000,
read_using_soundfile=False,
):
"""Load raw dataset from w2v tsv file. Optionally get waveforms"""
data = []
with open(file_name, "r") as fp:
lines = fp.readlines()
data = [
os.path.join(lines[0].strip(), line.strip().split("\t")[0])
for line in lines[1:]
]
if dataset_size:
data = data[:dataset_size]
if not need_waveform:
return data
features = []
if read_using_soundfile:
for _i, d in enumerate(data):
wav = sf.read(d)[0]
if wav.ndim == 2:
wav = wav.mean(-1)
features.append(torch.from_numpy(wav).float().view(1, -1))
else:
for i, d in enumerate(data):
_path, slice_ptr = parse_path(d)
if len(slice_ptr) == 0:
feat = get_waveform(
_path, always_2d=True, output_sample_rate=sample_rate
)[0]
features.append(
{
"id": i,
"net_input": {
"src_tokens": torch.tensor(feat),
"src_lengths": torch.tensor([feat.shape[1]]),
},
}
)
else:
raise Exception("Currently unsupported data format")
return features
def load_dataset_task(
args,
batch_size=1,
limit_size=None,
ref_dataset=None,
):
"""Loads dataset based on args by creating a task"""
if not args.data or not args.subset or not args.task:
raise Exception(
"Please provide necessary arguments to load the dataset - data, subset and task"
)
task = tasks.setup_task(args)
task.load_dataset(args.subset)
if not limit_size:
limit_size = len(task.dataset(args.subset))
iter = task.get_batch_iterator(
dataset=task.dataset(args.subset), max_sentences=batch_size
).next_epoch_itr(shuffle=False)
dataset = []
for i, sample in enumerate(iter):
sample = {
"id": task.datasets[args.subset].ids[sample["id"].item()],
"net_input": {
"src_tokens": sample["net_input"]["src_tokens"],
"src_lengths": sample["net_input"]["src_lengths"],
},
}
dataset.append(sample)
if i == limit_size - 1:
break
if ref_dataset:
try:
ids = get_ids_from_dataset(ref_dataset)
except Exception as e:
raise Exception(f"{e} - Cannot extract ids from reference dataset")
filtered_dataset = []
for sample in dataset:
if (
sample["id"] in ids
or sample["id"][5:] in ids
or f"dev_{sample['id']}" in ids
):
filtered_dataset.append(sample)
dataset = filtered_dataset
max_len, min_len, avg_len = get_dataset_stats(dataset)
print(
f"{args.subset} dataset stats : num_samples={len(dataset)} max_len = {max_len} min_len = {min_len} avg_len = {avg_len}"
)
return dataset
def randomly_sample_subset(dataset, size=500):
"""Randomly sample subset from a dataset"""
random_indices = [random.randint(0, len(dataset) - 1) for i in range(size)]
return [dataset[i] for i in random_indices]
def get_short_data_subset(dataset, size=500):
"""Get a subset of desired size by sorting based on src_lengths"""
return sort_dataset(dataset)[:size]
def get_long_data_subset(dataset, size=500):
"""Get a subset of desired size by sorting based on src_lengths descending"""
return sort_dataset(dataset, reverse=True)[:size]
def sort_dataset(dataset, reverse=False):
return sorted(
dataset, key=lambda x: x["net_input"]["src_lengths"].item(), reverse=reverse
)
def save_dataset_npy(dataset, file_name):
"""Save a dataset as .npy file"""
np.save(file_name, dataset)
def get_dataset_stats(dataset):
"""Get stats about dataset based on src_lengths of samples"""
max_len = 0
min_len = 100000
avg_len = 0
for d in dataset:
max_len = max(max_len, d["net_input"]["src_lengths"].item())
min_len = min(min_len, d["net_input"]["src_lengths"].item())
avg_len += d["net_input"]["src_lengths"].item()
return max_len, min_len, avg_len / len(dataset)
def make_parser():
"""
Additional args:
1. Provide the dataset dir path using --data.
2. Loading the dataset doesn't require config, provide --config-yaml to apply additional feature transforms
"""
parser = options.get_speech_generation_parser()
parser.add_argument(
"--subset",
default=None,
type=str,
required=True,
help="Subset to use for dataset generation",
)
parser.add_argument(
"--dataset-save-dir",
default=None,
type=str,
required=False,
help="Dir path in which the datasets are to be saved",
)
parser.add_argument(
"--ref-dataset",
default=None,
type=str,
required=False,
help="If provided, the ids in the reference dataset will be used to filter the new dataset generated.",
)
parser.add_argument("--dataset-save-token", default="", type=str, required=False)
options.add_generation_args(parser)
return parser
def get_ids_from_dataset(dataset):
return {sample["id"]: 1 for sample in dataset}
def cli_main():
parser = make_parser()
args = options.parse_args_and_arch(parser)
dataset = load_dataset_task(args)
random_dataset = randomly_sample_subset(dataset)
short_dataset = get_short_data_subset(dataset)
long_dataset = get_long_data_subset(dataset)
if args.dataset_save_token:
args.dataset_save_token = f"_{args.dataset_save_token}_"
if args.dataset_save_dir:
save_dataset_npy(
random_dataset,
f"{args.dataset_save_dir}/random_dataset{args.dataset_save_token}w_ids.npy",
)
save_dataset_npy(
short_dataset,
f"{args.dataset_save_dir}/short_dataset{args.dataset_save_token}w_ids.npy",
)
save_dataset_npy(
long_dataset,
f"{args.dataset_save_dir}/long_dataset{args.dataset_save_token}w_ids.npy",
)
if __name__ == "__main__":
cli_main()
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