File size: 3,554 Bytes
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 | #!/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
from pathlib import Path
import soundfile as sf
from tqdm import tqdm
import pandas as pd
from examples.speech_to_speech.preprocessing.data_utils import (
gen_config_yaml,
load_units,
process_units,
)
from examples.speech_to_text.data_utils import save_df_to_tsv
logger = logging.getLogger(__name__)
MANIFEST_COLUMNS = ["id", "src_audio", "src_n_frames", "tgt_audio", "tgt_n_frames"]
def process(args):
args.output_root.mkdir(exist_ok=True)
print("Generating manifest...")
for split in args.data_split:
print(f"Processing {split}")
# load target units
target_unit_data = load_units(args.target_dir / f"{split}.txt")
manifest = {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
if sample_id not in target_unit_data:
missing_tgt_audios.append(sample_id)
continue
src_n_frames = sf.info(src_audio.as_posix()).frames
manifest["id"].append(sample_id)
manifest["src_audio"].append(src_audio.as_posix())
manifest["src_n_frames"].append(
src_n_frames // 160
) # estimation of 10-ms frame for 16kHz audio
target_units = process_units(target_unit_data[sample_id], args.reduce_unit)
manifest["tgt_audio"].append(" ".join(target_units))
manifest["tgt_n_frames"].append(len(target_units))
print(f"Processed {len(manifest['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])})"
)
out_manifest = args.output_root / f"{split}.tsv"
print(f"Writing manifest to {out_manifest}...")
save_df_to_tsv(pd.DataFrame.from_dict(manifest), out_manifest)
# Generate config YAML
gen_config_yaml(
args.output_root,
specaugment_policy="lb",
feature_transform=["utterance_cmvn"],
vocoder_type="code_hifigan",
vocoder_checkpoint=args.vocoder_checkpoint,
vocoder_cfg=args.vocoder_cfg,
)
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"
)
parser.add_argument(
"--reduce-unit",
action="store_true",
help="reduce a target unit sequence to a unique unit sequence, i.e. '1 1 1 2 2' -> '1 2'",
)
parser.add_argument(
"--vocoder-checkpoint", default=None, type=str, help="vocoder checkpoint"
)
parser.add_argument(
"--vocoder-cfg", default=None, type=str, help="vocoder config file"
)
args = parser.parse_args()
process(args)
if __name__ == "__main__":
main()
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