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#!/usr/bin/env python3
"""Generate feature statistics for training set.
Example:
python global_stats.py --model-type librispeech --dataset-path /home/librispeech
"""
import json
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from common import (
MODEL_TYPE_LIBRISPEECH,
MODEL_TYPE_MUSTC,
MODEL_TYPE_TEDLIUM3,
piecewise_linear_log,
spectrogram_transform,
)
from must.dataset import MUSTC
logger = logging.getLogger()
def parse_args():
parser = ArgumentParser(description=__doc__, formatter_class=RawTextHelpFormatter)
parser.add_argument(
"--model-type", type=str, choices=[MODEL_TYPE_LIBRISPEECH, MODEL_TYPE_TEDLIUM3, MODEL_TYPE_MUSTC], required=True
)
parser.add_argument(
"--dataset-path",
required=True,
type=pathlib.Path,
help="Path to dataset. "
"For LibriSpeech, all of 'train-clean-360', 'train-clean-100', and 'train-other-500' must exist.",
)
parser.add_argument(
"--output-path",
default=pathlib.Path("global_stats.json"),
type=pathlib.Path,
help="File to save feature statistics to. (Default: './global_stats.json')",
)
return parser.parse_args()
def generate_statistics(samples):
E_x = 0
E_x_2 = 0
N = 0
for idx, sample in enumerate(samples):
mel_spec = spectrogram_transform(sample[0].squeeze()).transpose(1, 0)
scaled_mel_spec = piecewise_linear_log(mel_spec)
sum = scaled_mel_spec.sum(0)
sq_sum = scaled_mel_spec.pow(2).sum(0)
M = scaled_mel_spec.size(0)
E_x = E_x * (N / (N + M)) + sum / (N + M)
E_x_2 = E_x_2 * (N / (N + M)) + sq_sum / (N + M)
N += M
if idx % 100 == 0:
logger.info(f"Processed {idx}")
return E_x, (E_x_2 - E_x**2) ** 0.5
def get_dataset(args):
if args.model_type == MODEL_TYPE_LIBRISPEECH:
return torch.utils.data.ConcatDataset(
[
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-360"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-clean-100"),
torchaudio.datasets.LIBRISPEECH(args.dataset_path, url="train-other-500"),
]
)
elif args.model_type == MODEL_TYPE_TEDLIUM3:
return torchaudio.datasets.TEDLIUM(args.dataset_path, release="release3", subset="train")
elif args.model_type == MODEL_TYPE_MUSTC:
return MUSTC(args.dataset_path, subset="train")
else:
raise ValueError(f"Encountered unsupported model type {args.model_type}.")
def cli_main():
args = parse_args()
dataset = get_dataset(args)
dataloader = torch.utils.data.DataLoader(dataset, num_workers=4)
mean, stddev = generate_statistics(iter(dataloader))
json_str = json.dumps({"mean": mean.tolist(), "invstddev": (1 / stddev).tolist()}, indent=2)
with open(args.output_path, "w") as f:
f.write(json_str)
if __name__ == "__main__":
cli_main()
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