Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,227 Bytes
c968fc3 |
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 |
# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import json
import torchaudio
from tqdm import tqdm
from glob import glob
from collections import defaultdict
from utils.util import has_existed
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing samples for hifitts...\n")
save_dir = os.path.join(output_path, "hifitts")
os.makedirs(save_dir, exist_ok=True)
print("Saving to ", save_dir)
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.json")
valid_output_file = os.path.join(save_dir, "valid.json")
singer_dict_file = os.path.join(save_dir, "singers.json")
utt2singer_file = os.path.join(save_dir, "utt2singer")
if has_existed(train_output_file):
return
utt2singer = open(utt2singer_file, "w")
hifitts_path = dataset_path
speakers = []
train = []
test = []
valid = []
train_index_count = 0
test_index_count = 0
valid_index_count = 0
train_total_duration = 0
test_total_duration = 0
valid_total_duration = 0
distribution_infos = glob(hifitts_path + "/*.json")
for distribution_info in tqdm(
distribution_infos, desc="Extracting metadata from distributions"
):
distribution = distribution_info.split("/")[-1].split(".")[0]
speaker_id = distribution.split("_")[0]
speakers.append(speaker_id)
with open(distribution_info, "r", encoding="utf-8") as file:
for line in file:
entry = json.loads(line)
utt_path = entry.get("audio_filepath")
chosen_book = utt_path.split("/")[-2]
chosen_uid = utt_path.split("/")[-1].split(".")[0]
duration = entry.get("duration")
text = entry.get("text_normalized")
path = os.path.join(hifitts_path, utt_path)
assert os.path.exists(path)
res = {
"Dataset": "hifitts",
"Singer": speaker_id,
"Uid": "{}#{}#{}#{}".format(
distribution, speaker_id, chosen_book, chosen_uid
),
"Text": text,
"Path": path,
"Duration": duration,
}
if "train" in distribution:
res["index"] = train_index_count
train_total_duration += duration
train.append(res)
train_index_count += 1
elif "test" in distribution:
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_index_count += 1
elif "dev" in distribution:
res["index"] = valid_index_count
valid_total_duration += duration
valid.append(res)
valid_index_count += 1
utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
unique_speakers = list(set(speakers))
unique_speakers.sort()
print("Speakers: \n{}".format("\t".join(unique_speakers)))
print(
"#Train = {}, #Test = {}, #Valid = {}".format(len(train), len(test), len(valid))
)
print(
"#Train hours= {}, #Test hours= {}, #Valid hours= {}".format(
train_total_duration / 3600,
test_total_duration / 3600,
valid_total_duration / 3600,
)
)
# Save train.json, test.json, valid.json
with open(train_output_file, "w") as f:
json.dump(train, f, indent=4, ensure_ascii=False)
with open(test_output_file, "w") as f:
json.dump(test, f, indent=4, ensure_ascii=False)
with open(valid_output_file, "w") as f:
json.dump(valid, f, indent=4, ensure_ascii=False)
# Save singers.json
singer_lut = {name: i for i, name in enumerate(unique_speakers)}
with open(singer_dict_file, "w") as f:
json.dump(singer_lut, f, indent=4, ensure_ascii=False)
|