# Copyright (c) 2021 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 collections
import csv
import glob
import os
import random
from multiprocessing import cpu_count
from typing import List
from paddle.io import Dataset
from pathos.multiprocessing import Pool
from tqdm import tqdm
from ..backends import load as load_audio
from ..utils import DATA_HOME
from ..utils import decompress
from ..utils.download import download_and_decompress
from .dataset import feat_funcs
__all__ = ['VoxCeleb']
class VoxCeleb(Dataset):
source_url = 'https://thor.robots.ox.ac.uk/~vgg/data/voxceleb/vox1a/'
archieves_audio_dev = [
{
'url': source_url + 'vox1_dev_wav_partaa',
'md5': 'e395d020928bc15670b570a21695ed96',
},
{
'url': source_url + 'vox1_dev_wav_partab',
'md5': 'bbfaaccefab65d82b21903e81a8a8020',
},
{
'url': source_url + 'vox1_dev_wav_partac',
'md5': '017d579a2a96a077f40042ec33e51512',
},
{
'url': source_url + 'vox1_dev_wav_partad',
'md5': '7bb1e9f70fddc7a678fa998ea8b3ba19',
},
]
archieves_audio_test = [
{
'url': source_url + 'vox1_test_wav.zip',
'md5': '185fdc63c3c739954633d50379a3d102',
},
]
archieves_meta = [
{
'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(
'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
base_path = os.path.join(DATA_HOME, 'vox1')
wav_path = os.path.join(base_path, 'wav')
meta_path = os.path.join(base_path, 'meta')
veri_test_file = os.path.join(meta_path, 'veri_test2.txt')
csv_path = os.path.join(base_path, 'csv')
subsets = ['train', 'dev', 'enroll', '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,
vox2_base_path=None,
**kwargs):
"""VoxCeleb data prepare and get the specific dataset audio info
Args:
subset (str, optional): dataset name, such as train, dev, enroll or test. Defaults to 'train'.
feat_type (str, optional): feat type, such raw, melspectrogram(fbank) or mfcc . Defaults to 'raw'.
random_chunk (bool, optional): random select a duration from audio. Defaults to True.
chunk_duration (float, optional): chunk duration if random_chunk flag is set. Defaults to 3.0.
target_dir (str, optional): data dir, audio info will be stored in this directory. Defaults to None.
vox2_base_path (_type_, optional): vox2 directory. vox2 data must be converted from m4a to wav. Defaults to None.
"""
assert subset in self.subsets, \
'Dataset subset must be one in {}, but got {}'.format(self.subsets, subset)
self.subset = subset
self.spk_id2label = {}
self.feat_type = feat_type
self.feat_config = kwargs
self.random_chunk = random_chunk
self.chunk_duration = chunk_duration
self.split_ratio = split_ratio
self.target_dir = target_dir if target_dir else VoxCeleb.base_path
self.vox2_base_path = vox2_base_path
# if we set the target dir, we will change the vox data info data from base path to target dir
VoxCeleb.csv_path = os.path.join(
target_dir, "voxceleb", 'csv') if target_dir else VoxCeleb.csv_path
VoxCeleb.meta_path = os.path.join(
target_dir, "voxceleb",
'meta') if target_dir else VoxCeleb.meta_path
VoxCeleb.veri_test_file = os.path.join(VoxCeleb.meta_path,
'veri_test2.txt')
# self._data = self._get_data()[:1000] # KP: Small dataset test.
self._data = self._get_data()
super(VoxCeleb, self).__init__()
# Set up a seed to reproduce training or predicting result.
# random.seed(seed)
def _get_data(self):
# Download audio files.
# We need the users to decompress all vox1/dev/wav and vox1/test/wav/ to vox1/wav/ dir
# so, we check the vox1/wav dir status
print(f"wav base path: {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 all parts and concatenate the files into one zip file.
dev_zipfile = os.path.join(self.base_path, 'vox1_dev_wav.zip')
print(f'Concatenating all parts to: {dev_zipfile}')
os.system(
f'cat {os.path.join(self.base_path, "vox1_dev_wav_parta*")} > {dev_zipfile}'
)
# Extract all audio files of dev and test set.
decompress(dev_zipfile, self.base_path)
# Download meta files.
if not os.path.isdir(self.meta_path):
print("prepare the meta data")
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)
self.prepare_data()
data = []
print(
f"read the {self.subset} from {os.path.join(self.csv_path, f'{self.subset}.csv')}"
)
with open(os.path.join(self.csv_path, f'{self.subset}.csv'), 'r') as rf:
for line in rf.readlines()[1:]:
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))
with open(os.path.join(self.meta_path, 'spk_id2label.txt'), 'r') as f:
for line in f.readlines():
spk_id, label = line.strip().split(' ')
self.spk_id2label[spk_id] = int(label)
return data
def _convert_to_record(self, idx: int):
sample = self._data[idx]
record = {}
# To show all fields in a namedtuple: `type(sample)._fields`
for field in type(sample)._fields:
record[field] = getattr(sample, field)
waveform, sr = load_audio(record['wav'])
# random select a chunk audio samples from the audio
if self.random_chunk:
num_wav_samples = waveform.shape[0]
num_chunk_samples = int(self.chunk_duration * sr)
start = random.randint(0, num_wav_samples - num_chunk_samples - 1)
stop = start + num_chunk_samples
else:
start = record['start']
stop = record['stop']
waveform = waveform[start:stop]
assert self.feat_type in feat_funcs.keys(), \
f"Unknown feat_type: {self.feat_type}, it must be one in {list(feat_funcs.keys())}"
feat_func = feat_funcs[self.feat_type]
feat = feat_func(
waveform, sr=sr, **self.feat_config) if feat_func else waveform
record.update({'feat': feat})
if self.subset in ['train',
'dev']: # Labels are available in train and dev.
record.update({'label': self.spk_id2label[record['spk_id']]})
return record
@staticmethod
def _get_chunks(seg_dur, audio_id, audio_duration):
num_chunks = int(audio_duration / seg_dur) # all in milliseconds
chunk_lst = [
audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
for i in range(num_chunks)
]
return chunk_lst
def _get_audio_info(self, wav_file: str,
split_chunks: bool) -> List[List[str]]:
waveform, sr = load_audio(wav_file)
spk_id, sess_id, utt_id = wav_file.split("/")[-3:]
audio_id = '-'.join([spk_id, sess_id, utt_id.split(".")[0]])
audio_duration = waveform.shape[0] / sr
ret = []
if split_chunks: # Split into pieces of self.chunk_duration seconds.
uniq_chunks_list = self._get_chunks(self.chunk_duration, audio_id,
audio_duration)
for chunk in uniq_chunks_list:
s, e = chunk.split("_")[-2:] # Timestamps of start and end
start_sample = int(float(s) * sr)
end_sample = int(float(e) * sr)
# id, duration, wav, start, stop, spk_id
ret.append([
chunk, audio_duration, wav_file, start_sample, end_sample,
spk_id
])
else: # Keep whole audio.
ret.append([
audio_id, audio_duration, wav_file, 0, waveform.shape[0], spk_id
])
return ret
def generate_csv(self,
wav_files: List[str],
output_file: str,
split_chunks: bool=True):
print(f'Generating csv: {output_file}')
header = ["id", "duration", "wav", "start", "stop", "spk_id"]
# Note: this may occurs c++ execption, but the program will execute fine
# so we can ignore the execption
with Pool(cpu_count()) as p:
infos = list(
tqdm(
p.imap(lambda x: self._get_audio_info(x, split_chunks),
wav_files),
total=len(wav_files)))
csv_lines = []
for info in infos:
csv_lines.extend(info)
with open(output_file, mode="w") as csv_f:
csv_writer = csv.writer(
csv_f, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL)
csv_writer.writerow(header)
for line in csv_lines:
csv_writer.writerow(line)
def prepare_data(self):
# Audio of speakers in veri_test_file should not be included in training set.
print("start to prepare the data csv file")
enroll_files = set()
test_files = set()
# get the enroll and test audio file path
with open(self.veri_test_file, 'r') as f:
for line in f.readlines():
_, enrol_file, test_file = line.strip().split(' ')
enroll_files.add(os.path.join(self.wav_path, enrol_file))
test_files.add(os.path.join(self.wav_path, test_file))
enroll_files = sorted(enroll_files)
test_files = sorted(test_files)
# get the enroll and test speakers
test_spks = set()
for file in (enroll_files + test_files):
spk = file.split('/wav/')[1].split('/')[0]
test_spks.add(spk)
# get all the train and dev audios file path
audio_files = []
speakers = set()
print("Getting file list...")
for path in [self.wav_path, self.vox2_base_path]:
# if vox2 directory is not set and vox2 is not a directory
# we will not process this directory
if not path or not os.path.exists(path):
print(f"{path} is an invalid path, please check again, "
"and we will ignore the vox2 base path")
continue
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(
f"start to generate the {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
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(
enroll_files,
os.path.join(self.csv_path, 'enroll.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)
def __len__(self):
return len(self._data)