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901e06a | 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 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
from pathlib import Path
import shutil
import torchaudio
import soundfile as sf
from tqdm import tqdm
import pandas as pd
from examples.speech_synthesis.data_utils import extract_logmel_spectrogram
from examples.speech_to_speech.preprocessing.data_utils import gen_config_yaml
from examples.speech_to_text.data_utils import create_zip, get_zip_manifest, save_df_to_tsv
from fairseq.data.audio.audio_utils import convert_waveform
logger = logging.getLogger(__name__)
MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"]
def prepare_target_data(args, tgt_audios):
feature_name = "logmelspec80"
zip_path = args.output_root / f"{feature_name}.zip"
if zip_path.exists():
print(f"{zip_path} exists.")
return zip_path
feature_root = args.output_root / feature_name
feature_root.mkdir(exist_ok=True)
print("Extracting Mel spectrogram features...")
for tgt_audio in tqdm(tgt_audios):
sample_id = tgt_audio.stem
waveform, sample_rate = torchaudio.load(tgt_audio.as_posix())
waveform, sample_rate = convert_waveform(
waveform, sample_rate, normalize_volume=args.normalize_volume,
to_sample_rate=args.sample_rate
)
extract_logmel_spectrogram(
waveform, sample_rate, feature_root / f"{sample_id}.npy",
win_length=args.win_length, hop_length=args.hop_length,
n_fft=args.n_fft, n_mels=args.n_mels, f_min=args.f_min,
f_max=args.f_max
)
print("ZIPing features...")
create_zip(feature_root, zip_path)
shutil.rmtree(feature_root)
return zip_path
def process(args):
os.makedirs(args.output_root, exist_ok=True)
manifest = {}
tgt_audios = []
for split in args.data_split:
print(f"Processing {split}...")
manifest[split] = {c: [] for c in MANIFEST_COLUMNS}
missing_tgt_audios = []
src_audios = list(args.source_dir.glob(f"{split}/*.wav"))
for src_audio in tqdm(src_audios):
sample_id = src_audio.stem
tgt_audio = args.target_dir / split / f"{sample_id}.wav"
if not tgt_audio.is_file():
missing_tgt_audios.append(sample_id)
continue
tgt_audios.append(tgt_audio)
src_n_frames = sf.info(src_audio.as_posix()).frames
manifest[split]["id"].append(sample_id)
manifest[split]["src_audio"].append(src_audio.as_posix())
manifest[split]["src_n_frames"].append(
src_n_frames // 160
) # estimation of 10-ms frame for 16kHz audio
print(f"Processed {len(manifest[split]['id'])} samples")
if len(missing_tgt_audios) > 0:
print(
f"{len(missing_tgt_audios)} with missing target data (first 3 examples: {', '.join(missing_tgt_audios[:3])})"
)
# Extract features and pack features into ZIP
zip_path = prepare_target_data(args, tgt_audios)
print("Fetching ZIP manifest...")
tgt_audio_paths, tgt_audio_lengths = get_zip_manifest(zip_path)
print("Generating manifest...")
for split in args.data_split:
print(f"Processing {split}...")
for sample_id in tqdm(manifest[split]["id"]):
manifest[split]["tgt_audio"].append(tgt_audio_paths[sample_id])
manifest[split]["tgt_n_frames"].append(tgt_audio_lengths[sample_id])
out_manifest = args.output_root / f"{split}.tsv"
print(f"Writing manifest to {out_manifest}...")
save_df_to_tsv(pd.DataFrame.from_dict(manifest[split]), out_manifest)
# Generate config YAML
win_len_t = args.win_length / args.sample_rate
hop_len_t = args.hop_length / args.sample_rate
extra = {
"features": {
"type": "spectrogram+melscale+log",
"sample_rate": args.sample_rate,
"eps": 1e-5, "n_mels": args.n_mels, "n_fft": args.n_fft,
"window_fn": "hann", "win_length": args.win_length,
"hop_length": args.hop_length,
"win_len_t": win_len_t, "hop_len_t": hop_len_t,
"f_min": args.f_min, "f_max": args.f_max,
"n_stft": args.n_fft // 2 + 1
}
}
gen_config_yaml(
args.output_root,
audio_root=args.output_root.as_posix(),
specaugment_policy="lb",
feature_transform=["utterance_cmvn", "delta_deltas"],
extra=extra,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--source-dir", required=True, type=Path, help="source audio directory"
)
parser.add_argument(
"--target-dir", required=True, type=Path, help="target audio directory"
)
parser.add_argument(
"--data-split",
default=["train", "valid", "test"],
nargs="+",
help="data split names",
)
parser.add_argument(
"--output-root", required=True, type=Path, help="output directory"
)
# target feature related
parser.add_argument("--win-length", type=int, default=1024)
parser.add_argument("--hop-length", type=int, default=256)
parser.add_argument("--n-fft", type=int, default=1024)
parser.add_argument("--n-mels", type=int, default=80)
parser.add_argument("--f-min", type=int, default=20)
parser.add_argument("--f-max", type=int, default=8000)
parser.add_argument("--sample-rate", type=int, default=22050)
parser.add_argument("--normalize-volume", "-n", action="store_true")
args = parser.parse_args()
process(args)
if __name__ == "__main__":
main()
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