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# Copyright (c) 2020, NVIDIA CORPORATION. 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.
"""
Usage:
python process_vad_data.py \
--out_dir=<output path to where the generated manifest should be stored> \
--speech_data_root=<path where the speech data are stored> \
--background_data_root=<path where the background data are stored> \
--rebalance_method=<'under' or 'over' or 'fixed'> \
--log
(Optional --demo (for demonstration in tutorial). If you want to use your own background noise data, make sure to delete --demo)
"""
import argparse
import glob
import json
import logging
import os
import tarfile
import urllib.request
import librosa
import numpy as np
import soundfile as sf
from sklearn.model_selection import train_test_split
sr = 16000
# google speech command v2
URL = "http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz"
def __maybe_download_file(destination: str, source: str):
"""
Downloads source to destination if it doesn't exist.
If exists, skips download
Args:
destination: local filepath
source: url of resource
Returns:
"""
if not os.path.exists(destination):
logging.info(f"{destination} does not exist. Downloading ...")
urllib.request.urlretrieve(source, filename=destination + '.tmp')
os.rename(destination + '.tmp', destination)
logging.info(f"Downloaded {destination}.")
else:
logging.info(f"Destination {destination} exists. Skipping.")
return destination
def extract_file(filepath: str, data_dir: str):
try:
tar = tarfile.open(filepath)
tar.extractall(data_dir)
tar.close()
except Exception:
logging.info('Not extracting. Maybe already there?')
def __extract_all_files(filepath: str, data_root: str, data_dir: str):
if not os.path.exists(data_dir):
extract_file(filepath, data_dir)
else:
logging.info(f'Skipping extracting. Data already there {data_dir}')
def split_train_val_test(data_dir, file_type, test_size=0.1, val_size=0.1, demo=False):
X = []
if file_type == "speech":
for o in os.listdir(data_dir):
if os.path.isdir(os.path.join(data_dir, o)) and o.split("/")[-1] != "_background_noise_":
X.extend(glob.glob(os.path.join(data_dir, o) + '/*.wav'))
if demo:
logging.info(
f"For Demonstration, we use {int(len(X)/100)}/{len(X)} speech data. Make sure to remove --demo flag when you actually train your model!"
)
X = np.random.choice(X, int(len(X) / 100), replace=False)
else:
for o in os.listdir(data_dir):
if os.path.isdir(os.path.join(data_dir, o)):
X.extend(glob.glob(os.path.join(data_dir, o) + '/*.wav'))
else: # for using "_background_noise_" from google speech commands as background data
if o.endswith(".wav"):
X.append(os.path.join(data_dir, o))
X_train, X_test = train_test_split(X, test_size=test_size, random_state=1)
val_size_tmp = val_size / (1 - test_size)
X_train, X_val = train_test_split(X_train, test_size=val_size_tmp, random_state=1)
with open(os.path.join(data_dir, file_type + "_training_list.txt"), "w") as outfile:
outfile.write("\n".join(X_train))
with open(os.path.join(data_dir, file_type + "_testing_list.txt"), "w") as outfile:
outfile.write("\n".join(X_test))
with open(os.path.join(data_dir, file_type + "_validation_list.txt"), "w") as outfile:
outfile.write("\n".join(X_val))
logging.info(f'Overall: {len(X)}, Train: {len(X_train)}, Validatoin: {len(X_val)}, Test: {len(X_test)}')
logging.info(f"Finish spliting train, val and test for {file_type}. Write to files!")
def process_google_speech_train(data_dir):
X = []
for o in os.listdir(data_dir):
if os.path.isdir(os.path.join(data_dir, o)) and o.split("/")[-1] != "_background_noise_":
X.extend(glob.glob(os.path.join(data_dir, o) + '/*.wav'))
short_files = [i.split(data_dir)[1] for i in files]
with open(os.path.join(data_dir, 'testing_list.txt'), 'r') as allfile:
testing_list = allfile.read().splitlines()
with open(os.path.join(data_dir, 'validation_list.txt'), 'r') as allfile:
validation_list = allfile.read().splitlines()
exist_set = set(testing_list).copy()
exist_set.update(set(validation_list))
training_list = [i for i in short_files if i not in exist_set]
with open(os.path.join(data_dir, "training_list.txt"), "w") as outfile:
outfile.write("\n".join(training_list))
logging.info(
f'Overall: {len(files)}, Train: {len(training_list)}, Validatoin: {len(validation_list)}, Test: {len(testing_list)}'
)
def write_manifest(
out_dir,
files,
prefix,
manifest_name,
start=0.0,
end=None,
duration_stride=1.0,
duration_max=None,
duration_limit=100.0,
filter_long=False,
):
"""
Given a list of files, segment each file and write them to manifest with restrictions.
Args:
out_dir: directory of generated manifest
files: list of files to be processed
prefix: label of samples
manifest_name: name of generated manifest
start: beginning of audio of generating segment
end: end of audio of generating segment
duration_stride: stride for segmenting audio samples
duration_max: duration for each segment
duration_limit: duration threshold for filtering out long audio samples
filter_long: boolean to determine whether to filter out long audio samples
Returns:
"""
seg_num = 0
skip_num = 0
if duration_max is None:
duration_max = 1e9
if not os.path.exists(out_dir):
logging.info(f'Outdir {out_dir} does not exist. Creat directory.')
os.mkdir(out_dir)
output_path = os.path.join(out_dir, manifest_name + '.json')
with open(output_path, 'w') as fout:
for file in files:
label = prefix
try:
x, _sr = librosa.load(file, sr=sr)
duration = librosa.get_duration(x, sr=sr)
except Exception:
continue
if filter_long and duration > duration_limit:
skip_num += 1
continue
offsets = []
durations = []
if duration > duration_max:
current_offset = start
while current_offset < duration:
if end is not None and current_offset > end:
break
difference = duration - current_offset
if difference < duration_max:
break
offsets.append(current_offset)
durations.append(duration_max)
current_offset += duration_stride
else:
# Duration is not long enough! Skip
skip_num += 1
for duration, offset in zip(durations, offsets):
metadata = {
'audio_filepath': file,
'duration': duration,
'label': label,
'text': '_', # for compatibility with ASRAudioText
'offset': offset,
}
json.dump(metadata, fout)
fout.write('\n')
fout.flush()
seg_num += 1
return skip_num, seg_num, output_path
def load_list_write_manifest(
data_dir,
out_dir,
filename,
prefix,
start,
end,
duration_stride=1.0,
duration_max=1.0,
duration_limit=100.0,
filter_long=True,
):
filename = prefix + '_' + filename
file_path = os.path.join(data_dir, filename)
with open(file_path, 'r') as allfile:
files = allfile.read().splitlines()
manifest_name = filename.split('_list.txt')[0] + '_manifest'
skip_num, seg_num, output_path = write_manifest(
out_dir,
files,
prefix,
manifest_name,
start,
end,
duration_stride,
duration_max,
duration_limit,
filter_long=True,
)
return skip_num, seg_num, output_path
def rebalance_json(data_dir, data_json, num, prefix):
data = []
seg = 0
for line in open(data_json, 'r'):
data.append(json.loads(line))
filename = data_json.split('/')[-1]
fout_path = os.path.join(data_dir, prefix + "_" + filename)
if len(data) >= num:
selected_sample = np.random.choice(data, num, replace=False)
else:
selected_sample = np.random.choice(data, num, replace=True)
with open(fout_path, 'a') as fout:
for i in selected_sample:
seg += 1
json.dump(i, fout)
fout.write('\n')
fout.flush()
logging.info(f'Get {seg}/{num} to {fout_path} from {data_json}')
return fout_path
def generate_variety_noise(data_dir, filename, prefix):
curr_dir = data_dir.split("_background_noise_")[0]
silence_path = os.path.join(curr_dir, "_background_noise_more")
if not os.path.exists(silence_path):
os.mkdir(silence_path)
silence_stride = 1000 # stride = 1/16 seconds
sampling_rate = 16000
silence_files = []
rng = np.random.RandomState(0)
filename = prefix + '_' + filename
file_path = os.path.join(data_dir, filename)
with open(file_path, 'r') as allfile:
files = allfile.read().splitlines()
for file in files:
y, sr = librosa.load(file, sr=sampling_rate)
for i in range(
0, len(y) - sampling_rate, silence_stride * 100
): # stride * 100 to generate less samples for demo
file_name = "{}_{}.wav".format(file.split("/")[-1], i)
y_slice = y[i : i + sampling_rate]
magnitude = rng.uniform(0.0, 1.0)
y_slice *= magnitude
out_file_path = os.path.join(silence_path, file_name)
sf.write(out_file_path, y_slice, sr)
silence_files.append(out_file_path)
new_list_file = os.path.join(silence_path, filename)
with open(new_list_file, "w") as outfile:
outfile.write("\n".join(silence_files))
logging.info(f"Generate {len(out_file_path)} background files for {file_path}. => {new_list_file} !")
return len(silence_files)
def main():
parser = argparse.ArgumentParser(description='Speech and backgound data download and preprocess')
parser.add_argument("--out_dir", required=False, default='./manifest/', type=str)
parser.add_argument("--speech_data_root", required=True, default=None, type=str)
parser.add_argument("--background_data_root", required=True, default=None, type=str)
parser.add_argument('--test_size', required=False, default=0.1, type=float)
parser.add_argument('--val_size', required=False, default=0.1, type=float)
parser.add_argument('--seg_len', required=False, default=0.63, type=float)
parser.add_argument('--log', required=False, action='store_true')
parser.add_argument('--rebalance_method', required=False, default=None, type=str)
parser.add_argument('--demo', required=False, action='store_true')
parser.set_defaults(log=False, generate=False)
args = parser.parse_args()
if not args.rebalance_method:
rebalance = False
else:
if args.rebalance_method != 'over' and args.rebalance_method != 'under' and args.rebalance_method != 'fixed':
raise NameError("Please select a valid sampling method: over/under/fixed.")
else:
rebalance = True
if args.log:
logging.basicConfig(level=logging.DEBUG)
# Download speech data
speech_data_root = args.speech_data_root
data_set = "google_speech_recognition_v2"
speech_data_folder = os.path.join(speech_data_root, data_set)
background_data_folder = args.background_data_root
logging.info(f"Working on: {data_set}")
# Download and extract speech data
if not os.path.exists(speech_data_folder):
file_path = os.path.join(speech_data_root, data_set + ".tar.bz2")
logging.info(f"Getting {data_set}")
__maybe_download_file(file_path, URL)
logging.info(f"Extracting {data_set}")
__extract_all_files(file_path, speech_data_root, speech_data_folder)
logging.info(f"Split speech data!")
# dataset provide testing.txt and validation.txt feel free to split data using that with process_google_speech_train
split_train_val_test(speech_data_folder, "speech", args.test_size, args.val_size, args.demo)
logging.info(f"Split background data!")
split_train_val_test(background_data_folder, "background", args.test_size, args.val_size)
out_dir = args.out_dir
# Process Speech manifest
logging.info(f"=== Write speech data to manifest!")
skip_num_val, speech_seg_num_val, speech_val = load_list_write_manifest(
speech_data_folder, out_dir, 'validation_list.txt', 'speech', 0.2, 0.8, args.seg_len, args.seg_len
)
skip_num_test, speech_seg_num_test, speech_test = load_list_write_manifest(
speech_data_folder, out_dir, 'testing_list.txt', 'speech', 0.2, 0.8, 0.01, args.seg_len
)
skip_num_train, speech_seg_num_train, speech_train = load_list_write_manifest(
speech_data_folder, out_dir, 'training_list.txt', 'speech', 0.2, 0.8, args.seg_len, args.seg_len
)
logging.info(f'Val: Skip {skip_num_val} samples. Get {speech_seg_num_val} segments! => {speech_val} ')
logging.info(f'Test: Skip {skip_num_test} samples. Get {speech_seg_num_test} segments! => {speech_test}')
logging.info(f'Train: Skip {skip_num_train} samples. Get {speech_seg_num_train} segments!=> {speech_train}')
# Process background manifest
# if we select to generate more background noise data
if args.demo:
logging.info("Start generating more background noise data")
generate_variety_noise(background_data_folder, 'validation_list.txt', 'background')
generate_variety_noise(background_data_folder, 'training_list.txt', 'background')
generate_variety_noise(background_data_folder, 'testing_list.txt', 'background')
background_data_folder = os.path.join(
background_data_folder.split("_background_noise_")[0], "_background_noise_more"
)
logging.info(f"=== Write background data to manifest!")
skip_num_val, background_seg_num_val, background_val = load_list_write_manifest(
background_data_folder, out_dir, 'validation_list.txt', 'background', 0, None, 0.15, args.seg_len
)
skip_num_test, background_seg_num_test, background_test = load_list_write_manifest(
background_data_folder, out_dir, 'testing_list.txt', 'background', 0, None, 0.01, args.seg_len
)
skip_num_train, background_seg_num_train, background_train = load_list_write_manifest(
background_data_folder, out_dir, 'training_list.txt', 'background', 0, None, 0.15, args.seg_len
)
logging.info(f'Val: Skip {skip_num_val} samples. Get {background_seg_num_val} segments! => {background_val}')
logging.info(f'Test: Skip {skip_num_test} samples. Get {background_seg_num_test} segments! => {background_test}')
logging.info(
f'Train: Skip {skip_num_train} samples. Get {background_seg_num_train} segments! => {background_train}'
)
min_val, max_val = min(speech_seg_num_val, background_seg_num_val), max(speech_seg_num_val, background_seg_num_val)
min_test, max_test = (
min(speech_seg_num_test, background_seg_num_test),
max(speech_seg_num_test, background_seg_num_test),
)
min_train, max_train = (
min(speech_seg_num_train, background_seg_num_train),
max(speech_seg_num_train, background_seg_num_train),
)
logging.info('Finish generating manifest!')
if rebalance:
# Random Oversampling: Randomly duplicate examples in the minority class.
# Random Undersampling: Randomly delete examples in the majority class.
if args.rebalance_method == 'under':
logging.info(f"Rebalancing number of samples in classes using {args.rebalance_method} sampling.")
logging.info(f'Val: {min_val} Test: {min_test} Train: {min_train}!')
rebalance_json(out_dir, background_val, min_val, 'balanced')
rebalance_json(out_dir, background_test, min_test, 'balanced')
rebalance_json(out_dir, background_train, min_train, 'balanced')
rebalance_json(out_dir, speech_val, min_val, 'balanced')
rebalance_json(out_dir, speech_test, min_test, 'balanced')
rebalance_json(out_dir, speech_train, min_train, 'balanced')
if args.rebalance_method == 'over':
logging.info(f"Rebalancing number of samples in classes using {args.rebalance_method} sampling.")
logging.info(f'Val: {max_val} Test: {max_test} Train: {max_train}!')
rebalance_json(out_dir, background_val, max_val, 'balanced')
rebalance_json(out_dir, background_test, max_test, 'balanced')
rebalance_json(out_dir, background_train, max_train, 'balanced')
rebalance_json(out_dir, speech_val, max_val, 'balanced')
rebalance_json(out_dir, speech_test, max_test, 'balanced')
rebalance_json(out_dir, speech_train, max_train, 'balanced')
if args.rebalance_method == 'fixed':
fixed_test, fixed_val, fixed_train = 200, 100, 500
logging.info(f"Rebalancing number of samples in classes using {args.rebalance_method} sampling.")
logging.info(f'Val: {fixed_val} Test: {fixed_test} Train: {fixed_train}!')
rebalance_json(out_dir, background_val, fixed_val, 'balanced')
rebalance_json(out_dir, background_test, fixed_test, 'balanced')
rebalance_json(out_dir, background_train, fixed_train, 'balanced')
rebalance_json(out_dir, speech_val, fixed_val, 'balanced')
rebalance_json(out_dir, speech_test, fixed_test, 'balanced')
rebalance_json(out_dir, speech_train, fixed_train, 'balanced')
else:
logging.info("Don't rebalance number of samples in classes.")
if __name__ == '__main__':
main()