add state 0 to prepare the voxcele data and augment data

examples/voxceleb/README.md

@@ -6,3 +6,56 @@ sv0 - speaker verfication with softmax backend etc, all python code
 
 sv1 - dependence on kaldi, speaker verfication with plda/sc backend, 
       more info refer to the sv1/readme.txt
+
+
+## VoxCeleb2 preparation
+
+VoxCeleb2 audio files are released in m4a format. All the VoxCeleb2 m4a audio files must be converted in wav files before feeding them in PaddleSpeech. 
+Please, follow these steps to prepare the dataset correctly:
+
+1. Download Voxceleb2.
+You can find download instructions here: http://www.robots.ox.ac.uk/~vgg/data/voxceleb/
+
+2. Convert .m4a to wav
+VoxCeleb2 stores files with the m4a audio format. To use them in PaddleSpeech,  you have to convert all the m4a audio files into wav files.
+
+``` shell
+ffmpeg -y -i %s -ac 1 -vn -acodec pcm_s16le -ar 16000 %s
+```
+
+``` shell
+# copy this to root directory of data and 
+# chmod a+x convert.sh
+# ./convert.sh
+# https://unix.stackexchange.com/questions/103920/parallelize-a-bash-for-loop
+
+open_sem(){
+    mkfifo pipe-$$
+    exec 3<>pipe-$$
+    rm pipe-$$
+    local i=$1
+    for((;i>0;i--)); do
+        printf %s 000 >&3
+    done
+}
+run_with_lock(){
+    local x
+    read -u 3 -n 3 x && ((0==x)) || exit $x
+    (
+     ( "$@"; )
+    printf '%.3d' $? >&3
+    )&
+}
+
+N=32 # number of vCPU
+open_sem $N
+for f in $(find . -name "*.m4a"); do
+    run_with_lock ffmpeg -loglevel panic -i "$f" -ar 16000 "${f%.*}.wav"
+done
+```
+
+You can do the conversion using ffmpeg  https://gist.github.com/seungwonpark/4f273739beef2691cd53b5c39629d830). This operation might take several hours and should be only once.
+
+3. Put all the wav files in a folder called `wav`. You should have something like `voxceleb2/wav/id*/*.wav` (e.g, `voxceleb2/wav/id00012/21Uxsk56VDQ/00001.wav`)
+
+4. 

examples/voxceleb/sv0/local/data_prepare.py

@@ -0,0 +1,60 @@
+import argparse
+import os
+
+import numpy as np
+import paddle
+from paddle.io import BatchSampler
+from paddle.io import DataLoader
+from paddle.io import DistributedBatchSampler
+
+from paddleaudio.datasets.voxceleb import VoxCeleb1
+from paddleaudio.features.core import melspectrogram
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.vector.io.augment import build_augment_pipeline
+from paddlespeech.vector.io.augment import waveform_augment
+from paddlespeech.vector.io.batch import feature_normalize
+from paddlespeech.vector.io.batch import waveform_collate_fn
+from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
+from paddlespeech.vector.modules.loss import AdditiveAngularMargin
+from paddlespeech.vector.modules.loss import LogSoftmaxWrapper
+from paddlespeech.vector.modules.lr import CyclicLRScheduler
+from paddlespeech.vector.modules.sid_model import SpeakerIdetification
+from paddlespeech.vector.training.seeding import seed_everything
+from paddlespeech.vector.utils.time import Timer
+
+logger = Log(__name__).getlog()
+
+def main(args):
+    # stage0: set the cpu device, all data prepare process will be done in cpu mode
+    paddle.set_device("cpu")
+    # set the random seed, it is a must for multiprocess training
+    seed_everything(args.seed)
+
+    # stage 1: generate the voxceleb csv file
+    # Note: this may occurs c++ execption, but the program will execute fine
+    # so we can ignore the execption 
+    train_dataset = VoxCeleb1('train', target_dir=args.data_dir)
+    dev_dataset = VoxCeleb1('dev', target_dir=args.data_dir)
+
+    # stage 2: generate the augment noise csv file
+    if args.augment:
+        augment_pipeline = build_augment_pipeline(target_dir=args.data_dir)
+
+if __name__ == "__main__":
+    # yapf: disable
+    parser = argparse.ArgumentParser(__doc__)
+    parser.add_argument("--seed",
+                        default=0,
+                        type=int,
+                        help="random seed for paddle, numpy and python random package")
+    parser.add_argument("--data-dir",
+                        default="./data/",
+                        type=str,
+                        help="data directory")
+    parser.add_argument("--augment",
+                        action="store_true",
+                        default=False,
+                        help="Apply audio augments.")
+    args = parser.parse_args()
+    # yapf: enable
+    main(args)                    

examples/voxceleb/sv0/run.sh

@@ -20,10 +20,10 @@ exp_dir=exp/ecapa-tdnn/           # experiment directory
 mkdir -p ${dir}
 mkdir -p ${exp_dir}
 
-# if [ $stage -le 0 ]; then 
-#      # stage 0: data prepare for vox1 and vox2, vox2 must be converted from m4a to wav
-#      # todo
-# fi 
+if [ $stage -le 0 ]; then 
+     # stage 0: data prepare for vox1 and vox2, vox2 must be converted from m4a to wav
+     python3 local/data_prepare.py --data-dir ${dir} --augment
+fi 
 
 if [ $stage -le 1 ]; then
      # stage 1: train the speaker identification model

paddleaudio/datasets/rirs_noises.py

@@ -69,8 +69,9 @@ class OpenRIRNoise(Dataset):
         self.random_chunk = random_chunk
         self.chunk_duration = chunk_duration
 
-        self.csv_path = os.path.join(target_dir, "open_rir_noise",
-                                     "csv") if target_dir else self.csv_path
+        OpenRIRNoise.csv_path = os.path.join(
+            target_dir, "open_rir_noise",
+            "csv") if target_dir else self.csv_path
         self._data = self._get_data()
         super(OpenRIRNoise, self).__init__()
 

paddleaudio/datasets/voxceleb.py

@@ -16,6 +16,7 @@ import csv
 import glob
 import os
 import random
+from multiprocessing import cpu_count
 from typing import Dict
 from typing import List
 from typing import Tuple
@@ -28,8 +29,8 @@ from paddleaudio.backends import load as load_audio
 from paddleaudio.datasets.dataset import feat_funcs
 from paddleaudio.utils import DATA_HOME
 from paddleaudio.utils import decompress
-from paddlespeech.vector.utils.download import download_and_decompress
 from paddlespeech.s2t.utils.log import Log
+from paddlespeech.vector.utils.download import download_and_decompress
 from utils.utility import download
 from utils.utility import unpack
 
@@ -105,14 +106,15 @@ class VoxCeleb1(Dataset):
         self.random_chunk = random_chunk
         self.chunk_duration = chunk_duration
         self.split_ratio = split_ratio
-        self.target_dir = target_dir if target_dir else self.base_path
+        self.target_dir = target_dir if target_dir else VoxCeleb1.base_path
+
+        # if we set the target dir, we will change the vox data info data from base path to target dir
         VoxCeleb1.csv_path = os.path.join(
-            target_dir, 'csv') if target_dir else os.path.join(self.base_path,
-                                                               'csv')
+            target_dir, "voxceleb", 'csv') if target_dir else VoxCeleb1.csv_path
         VoxCeleb1.meta_path = os.path.join(
-            target_dir, 'meta') if target_dir else os.path.join(self.base_path,
-                                                                'meta')
-        VoxCeleb1.veri_test_file = os.path.join(self.meta_path,
+            target_dir, "voxceleb",
+            'meta') if target_dir else VoxCeleb1.meta_path
+        VoxCeleb1.veri_test_file = os.path.join(VoxCeleb1.meta_path,
                                                 'veri_test2.txt')
         # self._data = self._get_data()[:1000]  # KP: Small dataset test.
         self._data = self._get_data()
@@ -255,8 +257,9 @@ class VoxCeleb1(Dataset):
                      split_chunks: bool=True):
         logger.info(f'Generating csv: {output_file}')
         header = ["id", "duration", "wav", "start", "stop", "spk_id"]
-
-        with Pool(64) as p:
+        # 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),
@@ -277,20 +280,20 @@ class VoxCeleb1(Dataset):
     def prepare_data(self):
         # Audio of speakers in veri_test_file should not be included in training set.
         logger.info("start to prepare the data csv file")
-        enrol_files = set()
+        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(' ')
-                enrol_files.add(os.path.join(self.wav_path, enrol_file))
+                enroll_files.add(os.path.join(self.wav_path, enrol_file))
                 test_files.add(os.path.join(self.wav_path, test_file))
-            enrol_files = sorted(enrol_files)
+            enroll_files = sorted(enroll_files)
             test_files = sorted(test_files)
 
         # get the enroll and test speakers
         test_spks = set()
-        for file in (enrol_files + test_files):
+        for file in (enroll_files + test_files):
             spk = file.split('/wav/')[1].split('/')[0]
             test_spks.add(spk)
 
@@ -306,8 +309,9 @@ class VoxCeleb1(Dataset):
                 speakers.add(spk)
                 audio_files.append(file)
 
-        logger.info("start to generate the {}".format(
-            os.path.join(self.meta_path, 'spk_id2label.txt')))
+        logger.info(
+            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(
@@ -323,8 +327,9 @@ class VoxCeleb1(Dataset):
 
         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,
+            enroll_files,
             os.path.join(self.csv_path, 'enrol.csv'),
             split_chunks=False)
         self.generate_csv(

paddlespeech/vector/io/augment.py

@@ -840,7 +840,7 @@ def build_augment_pipeline(target_dir=None) -> List[paddle.nn.Layer]:
     """
     logger.info("start to build the augment pipeline")
     noise_dataset = OpenRIRNoise('noise', target_dir=target_dir)
-    rir_dataset = OpenRIRNoise('rir')
+    rir_dataset = OpenRIRNoise('rir', target_dir=target_dir)
 
     wavedrop = TimeDomainSpecAugment(
         sample_rate=16000,