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remove personal code test=doc

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pull/1523/head
xiongxinlei 3 years ago
parent 7ef60ebae2
commit 0780d181d2
3 changed files with 85 additions and 49 deletions
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106
dataset/voxceleb/voxceleb1.py
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@ -11,23 +11,26 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import csv
import glob
import os
import random
from typing import Dict, List, Tuple
from typing import Dict
from typing import List
from typing import Tuple
from paddle.io import Dataset
from tqdm import tqdm
from pathos.multiprocessing import Pool
from tqdm import tqdm
from paddleaudio.backends import load as load_audio
from paddleaudio.utils import DATA_HOME, decompress, download_and_decompress
from paddleaudio.datasets.dataset import feat_funcs
from utils.utility import unpack
from paddleaudio.utils import DATA_HOME
from paddleaudio.utils import decompress
from paddleaudio.utils import download_and_decompress
from utils.utility import download
from utils.utility import unpack
__all__ = ['VoxCeleb1']
@ -60,12 +63,13 @@ class VoxCeleb1(Dataset):
]
archieves_meta = [
{
'url': 'https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt',
'md5': 'b73110731c9223c1461fe49cb48dddfc',
'url':
'https://www.robots.ox.ac.uk/~vgg/data/voxceleb/meta/veri_test2.txt',
'md5':
'b73110731c9223c1461fe49cb48dddfc',
},
]
num_speakers = 1211 # 1211 vox1, 5994 vox2, 7205 vox1+2, test speakers: 41
sample_rate = 16000
meta_info = collections.namedtuple(
@ -74,15 +78,16 @@ class VoxCeleb1(Dataset):
wav_path = os.path.join(base_path, 'wav')
subsets = ['train', 'dev', 'enrol', 'test']
def __init__(self,
subset: str = 'train',
feat_type: str = 'raw',
random_chunk: bool = True,
chunk_duration: float = 3.0, # seconds
split_ratio: float = 0.9, # train split ratio
seed: int = 0,
target_dir: str = None,
**kwargs):
def __init__(
self,
subset: str='train',
feat_type: str='raw',
random_chunk: bool=True,
chunk_duration: float=3.0, # seconds
split_ratio: float=0.9, # train split ratio
seed: int=0,
target_dir: str=None,
**kwargs):
assert subset in self.subsets, \
'Dataset subset must be one in {}, but got {}'.format(self.subsets, subset)
@ -95,8 +100,12 @@ class VoxCeleb1(Dataset):
self.chunk_duration = chunk_duration
self.split_ratio = split_ratio
self.target_dir = target_dir if target_dir else self.base_path
self.csv_path = os.path.join(target_dir, 'csv') if target_dir else os.path.join(self.base_path, 'csv')
self.meta_path = os.path.join(target_dir, 'meta') if target_dir else os.path.join(base_path, 'meta')
self.csv_path = os.path.join(
target_dir, 'csv') if target_dir else os.path.join(self.base_path,
'csv')
self.meta_path = os.path.join(
target_dir, 'meta') if target_dir else os.path.join(base_path,
'meta')
self.veri_test_file = os.path.join(self.meta_path, 'veri_test2.txt')
# self._data = self._get_data()[:1000] # KP: Small dataset test.
self._data = self._get_data()
@ -112,10 +121,14 @@ class VoxCeleb1(Dataset):
print("wav base path: {}".format(self.wav_path))
if not os.path.isdir(self.wav_path):
print("start to download the voxceleb1 dataset")
download_and_decompress( # multi-zip parts concatenate to vox1_dev_wav.zip
self.archieves_audio_dev, self.base_path, decompress=False)
download_and_decompress( # download the vox1_test_wav.zip and unzip
self.archieves_audio_test, self.base_path, decompress=True)
download_and_decompress( # multi-zip parts concatenate to vox1_dev_wav.zip
self.archieves_audio_dev,
self.base_path,
decompress=False)
download_and_decompress( # download the vox1_test_wav.zip and unzip
self.archieves_audio_test,
self.base_path,
decompress=True)
# Download all parts and concatenate the files into one zip file.
dev_zipfile = os.path.join(self.base_path, 'vox1_dev_wav.zip')
@ -131,7 +144,7 @@ class VoxCeleb1(Dataset):
if not os.path.isdir(self.meta_path):
download_and_decompress(
self.archieves_meta, self.meta_path, decompress=False)
# Data preparation.
if not os.path.isdir(self.csv_path):
os.makedirs(self.csv_path)
@ -143,8 +156,9 @@ class VoxCeleb1(Dataset):
audio_id, duration, wav, start, stop, spk_id = line.strip(
).split(',')
data.append(
self.meta_info(audio_id, float(duration), wav, int(start),
int(stop), spk_id))
self.meta_info(audio_id,
float(duration), wav,
int(start), int(stop), spk_id))
with open(os.path.join(self.meta_path, 'spk_id2label.txt'), 'r') as f:
for line in f.readlines():
@ -228,14 +242,16 @@ class VoxCeleb1(Dataset):
def generate_csv(self,
wav_files: List[str],
output_file: str,
split_chunks: bool = True):
split_chunks: bool=True):
print(f'Generating csv: {output_file}')
header = ["id", "duration", "wav", "start", "stop", "spk_id"]
with Pool(64) as p:
infos = list(
tqdm(
p.imap(lambda x: self._get_audio_info(x, split_chunks), wav_files), total=len(wav_files)))
p.imap(lambda x: self._get_audio_info(x, split_chunks),
wav_files),
total=len(wav_files)))
csv_lines = []
for info in infos:
@ -272,35 +288,39 @@ class VoxCeleb1(Dataset):
audio_files = []
speakers = set()
for path in [self.wav_path]:
for file in glob.glob(os.path.join(path, "**", "*.wav"), recursive=True):
for file in glob.glob(
os.path.join(path, "**", "*.wav"), recursive=True):
spk = file.split('/wav/')[1].split('/')[0]
if spk in test_spks:
continue
speakers.add(spk)
audio_files.append(file)
print("start to generate the {}".format(os.path.join(self.meta_path, 'spk_id2label.txt')))
print("start to generate the {}".format(
os.path.join(self.meta_path, 'spk_id2label.txt')))
# encode the train and dev speakers label to spk_id2label.txt
with open(os.path.join(self.meta_path, 'spk_id2label.txt'), 'w') as f:
for label, spk_id in enumerate(sorted(speakers)): # 1211 vox1, 5994 vox2, 7205 vox1+2
for label, spk_id in enumerate(
sorted(speakers)): # 1211 vox1, 5994 vox2, 7205 vox1+2
f.write(f'{spk_id} {label}\n')
audio_files = sorted(audio_files)
random.shuffle(audio_files)
split_idx = int(self.split_ratio * len(audio_files))
# split_ratio to train
train_files, dev_files = audio_files[:split_idx], audio_files[split_idx:]
self.generate_csv(train_files,
os.path.join(self.csv_path, 'train.csv'))
self.generate_csv(dev_files,
os.path.join(self.csv_path, 'dev.csv'))
self.generate_csv(enrol_files,
os.path.join(self.csv_path, 'enrol.csv'),
split_chunks=False)
self.generate_csv(test_files,
os.path.join(self.csv_path, 'test.csv'),
split_chunks=False)
train_files, dev_files = audio_files[:split_idx], audio_files[
split_idx:]
self.generate_csv(train_files, os.path.join(self.csv_path, 'train.csv'))
self.generate_csv(dev_files, os.path.join(self.csv_path, 'dev.csv'))
self.generate_csv(
enrol_files,
os.path.join(self.csv_path, 'enrol.csv'),
split_chunks=False)
self.generate_csv(
test_files,
os.path.join(self.csv_path, 'test.csv'),
split_chunks=False)
def __getitem__(self, idx):
return self._convert_to_record(idx)

24
examples/voxceleb/sv0/local/train.py
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@ -1,5 +1,20 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import paddle
from dataset.voxceleb.voxceleb1 import VoxCeleb1
@ -14,12 +29,13 @@ def main(args):
# stage2: data prepare
train_ds = VoxCeleb1('train', target_dir=args.data_dir)
if __name__ == "__main__":
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device',
choices=['cpu', 'gpu'],
default="cpu",
parser.add_argument('--device',
choices=['cpu', 'gpu'],
default="cpu",
help="Select which device to train model, defaults to gpu.")
parser.add_argument("--data-dir",
default="./data/",
@ -28,4 +44,4 @@ if __name__ == "__main__":
args = parser.parse_args()
# yapf: enable
main(args)
main(args)

4
examples/voxceleb/sv0/run.sh
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@ -5,6 +5,6 @@ export PPAUDIO_HOME=/home/users/xiongxinlei/exprts/v3
dir=./data/
mkdir -p ${dir}
# you can set the variable DATA_HOME to specifiy the downloaded the vox1 and vox2 dataset
/home/users/xiongxinlei/.conda/envs/xxl_base/bin/python3 \
python3 \
local/train.py \
--data-dir ${dir}
--data-dir ${dir}

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