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#!/usr/bin/env python3
"""This is the preprocessing script for HuBERT model training.
The script includes:
- File list creation
- MFCC/HuBERT feature extraction
- KMeans clustering model training
- Pseudo-label generation
"""
import logging
from argparse import ArgumentParser, RawTextHelpFormatter
from pathlib import Path
import torch
from utils import create_tsv, dump_features, get_km_label, learn_kmeans
def _init_logger(debug=False):
message_fmt = "%(levelname)5s: %(funcName)10s: %(message)s" if debug else "%(message)s"
logging.basicConfig(
level=logging.DEBUG if debug else logging.INFO,
format=f"%(asctime)s: {message_fmt}",
)
def _parse_args():
parser = ArgumentParser(
description=__doc__,
formatter_class=RawTextHelpFormatter,
)
parser.add_argument("--debug", action="store_true", help="Enable debug log")
parser.add_argument("--dataset", default="librispeech", type=str, choices=["librispeech", "librilight"])
parser.add_argument(
"--root-dir",
type=Path,
help="The path to the directory where the directory ``LibriSpeech`` or ``LibriLight`` is stored.",
)
parser.add_argument("--num-rank", default=5, type=int)
parser.add_argument("--feat-type", default="mfcc", choices=["mfcc", "hubert"], type=str)
parser.add_argument(
"--layer-index",
default=6,
type=int,
help="The layer index in HuBERT model for feature extraction. (``1`` means the first layer output)",
)
parser.add_argument(
"--checkpoint-path",
default=None,
type=Path,
help="The model checkpoint of hubert_pretrain_base model.",
)
parser.add_argument("--use-gpu", default=False, type=bool)
parser.add_argument(
"--exp-dir",
type=Path,
help="The directory to store the experiment outputs.",
)
parser.add_argument(
"--num-cluster",
default=100,
type=int,
help="The number of clusters for KMeans clustering.",
)
parser.add_argument(
"--percent",
default=-1,
type=float,
help="The percent of data for KMeans clustering. If negative, use all data. (Default: -1)",
)
args = parser.parse_args()
return args
def main(args):
_init_logger(args.debug)
if not args.exp_dir.exists():
args.exp_dir.mkdir()
if args.feat_type == "mfcc":
data_dir = args.exp_dir / "data" / "mfcc"
else:
data_dir = args.exp_dir / "data" / f"{args.feat_type}_{args.layer_index}"
data_dir.mkdir(parents=True, exist_ok=True)
tsv_dir = data_dir / "tsv"
feat_dir = data_dir / "feat"
km_dir = data_dir / "km_model"
label_dir = data_dir / "label"
if args.use_gpu:
device = torch.device("cuda")
else:
device = torch.device("cpu")
# Create file lists for training and validation (optional)
create_tsv(args.root_dir, tsv_dir)
# Extract features for KMeans clustering
if not feat_dir.exists():
feat_dir.mkdir()
for split in ["train", "valid"]:
for rank in range(1, args.num_rank + 1):
dump_features(
tsv_dir / f"{args.dataset}_{split}.tsv",
feat_dir,
split,
rank,
args.num_rank,
device,
args.feat_type,
args.layer_index,
args.checkpoint_path,
16_000,
)
# Fit KMeans clustering model
learn_kmeans(
feat_dir,
"train",
args.num_rank,
km_dir,
args.num_cluster,
args.percent,
)
# Predict labels for MFCC or HuBERT features
for split in ["train", "valid"]:
get_km_label(
feat_dir,
km_dir,
label_dir,
split,
args.num_rank,
device,
)
if __name__ == "__main__":
main(_parse_args())
|
