File size: 7,889 Bytes
7934b29 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
# 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.
import argparse
import json
import os
from glob import glob
import numpy as np
from scipy.io import wavfile
from tqdm import tqdm
parser = argparse.ArgumentParser(description="Cut audio on the segments based on segments")
parser.add_argument("--output_dir", type=str, help="Path to output directory", required=True)
parser.add_argument(
"--alignment",
type=str,
required=True,
help="Path to a data directory with alignments or a single .txt file with timestamps - result of the ctc-segmentation",
)
parser.add_argument("--threshold", type=float, default=-5, help="Minimum score value accepted")
parser.add_argument("--offset", type=int, default=0, help="Offset, s")
parser.add_argument("--batch_size", type=int, default=64, help="Batch size for inference")
parser.add_argument(
"--edge_duration",
type=float,
help="Duration of audio for mean absolute value calculation at the edges, s",
default=0.05,
)
parser.add_argument("--sample_rate", type=int, help="Sample rate, Hz", default=16000)
parser.add_argument(
"--max_duration",
type=int,
help="Maximum audio duration (seconds). Samples that are longer will be dropped",
default=60,
)
def process_alignment(alignment_file: str, manifest: str, clips_dir: str, args):
""" Cut original audio file into audio segments based on alignment_file
Args:
alignment_file: path to the file with segmented text and corresponding time stamps.
The first line of the file contains the path to the original audio file
manifest: path to .json manifest to save segments metadata
clips_dir: path to a directory to save audio clips
args: main script args
"""
if not os.path.exists(alignment_file):
raise ValueError(f"{alignment_file} not found")
base_name = os.path.basename(alignment_file).replace("_segments.txt", "")
# read the segments, note the first line contains the path to the original audio
segments = []
ref_text_processed = []
ref_text_no_preprocessing = []
ref_text_normalized = []
with open(alignment_file, "r") as f:
for line in f:
line = line.split("|")
# read audio file name from the first line
if len(line) == 1:
audio_file = line[0].strip()
continue
ref_text_processed.append(line[1].strip())
ref_text_no_preprocessing.append(line[2].strip())
ref_text_normalized.append(line[3].strip())
line = line[0].split()
segments.append((float(line[0]) + args.offset / 1000, float(line[1]) + args.offset / 1000, float(line[2])))
# cut the audio into segments and save the final manifests at output_dir
sampling_rate, signal = wavfile.read(audio_file)
original_duration = len(signal) / sampling_rate
num_samples = int(args.edge_duration * args.sample_rate)
low_score_dur = 0
high_score_dur = 0
with open(manifest, "a", encoding="utf8") as f:
for i, (st, end, score) in enumerate(segments):
segment = signal[round(st * sampling_rate) : round(end * sampling_rate)]
duration = len(segment) / sampling_rate
if duration > args.max_duration:
continue
if duration > 0:
text_processed = ref_text_processed[i].strip()
text_no_preprocessing = ref_text_no_preprocessing[i].strip()
text_normalized = ref_text_normalized[i].strip()
if score >= args.threshold:
high_score_dur += duration
audio_filepath = os.path.join(clips_dir, f"{base_name}_{i:04}.wav")
wavfile.write(audio_filepath, sampling_rate, segment)
assert len(signal.shape) == 1 and sampling_rate == args.sample_rate, "check sampling rate"
info = {
"audio_filepath": audio_filepath,
"duration": duration,
"text": text_processed,
"text_no_preprocessing": text_no_preprocessing,
"text_normalized": text_normalized,
"score": round(score, 2),
"start_abs": float(np.mean(np.abs(segment[:num_samples]))),
"end_abs": float(np.mean(np.abs(segment[-num_samples:]))),
}
json.dump(info, f, ensure_ascii=False)
f.write("\n")
else:
low_score_dur += duration
# keep track of duration of the deleted segments
del_duration = 0
begin = 0
for i, (st, end, _) in enumerate(segments):
if st - begin > 0.01:
segment = signal[int(begin * sampling_rate) : int(st * sampling_rate)]
duration = len(segment) / sampling_rate
del_duration += duration
begin = end
segment = signal[int(begin * sampling_rate) :]
duration = len(segment) / sampling_rate
del_duration += duration
stats = (
args.output_dir,
base_name,
round(original_duration),
round(high_score_dur),
round(low_score_dur),
round(del_duration),
)
return stats
if __name__ == "__main__":
args = parser.parse_args()
print("Splitting audio files into segments...")
if os.path.isdir(args.alignment):
alignment_files = glob(f"{args.alignment}/*_segments.txt")
else:
alignment_files = [args.alignment]
# create a directory to store segments with alignement confindence score avove the threshold
args.output_dir = os.path.abspath(args.output_dir)
clips_dir = os.path.join(args.output_dir, "clips")
manifest_dir = os.path.join(args.output_dir, "manifests")
os.makedirs(clips_dir, exist_ok=True)
os.makedirs(manifest_dir, exist_ok=True)
manifest = os.path.join(manifest_dir, "manifest.json")
if os.path.exists(manifest):
os.remove(manifest)
stats_file = os.path.join(args.output_dir, "stats.tsv")
with open(stats_file, "w") as f:
f.write("Folder\tSegment\tOriginal dur (s)\tHigh quality dur (s)\tLow quality dur (s)\tDeleted dur (s)\n")
high_score_dur = 0
low_score_dur = 0
del_duration = 0
original_dur = 0
for alignment_file in tqdm(alignment_files):
stats = process_alignment(alignment_file, manifest, clips_dir, args)
original_dur += stats[-4]
high_score_dur += stats[-3]
low_score_dur += stats[-2]
del_duration += stats[-1]
stats = "\t".join([str(t) for t in stats]) + "\n"
f.write(stats)
f.write(f"Total\t\t{round(high_score_dur)}\t{round(low_score_dur)}\t{del_duration}")
print(f"Original duration : {round(original_dur / 60)}min")
print(f"High score segments: {round(high_score_dur / 60)}min ({round(high_score_dur/original_dur*100)}%)")
print(f"Low score segments : {round(low_score_dur / 60)}min ({round(low_score_dur/original_dur*100)}%)")
print(f"Deleted segments : {round(del_duration / 60)}min ({round(del_duration/original_dur*100)}%)")
print(f"Stats saved at {stats_file}")
print(f"Manifest saved at {manifest}")
|