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# 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 libritts_statistics(data_dir):
speakers = []
distribution2speakers2pharases2utts = defaultdict(
lambda: defaultdict(lambda: defaultdict(list))
)
distribution_infos = glob(data_dir + "/*")
for distribution_info in distribution_infos:
distribution = distribution_info.split("/")[-1]
print(distribution)
speaker_infos = glob(distribution_info + "/*")
if len(speaker_infos) == 0:
continue
for speaker_info in speaker_infos:
speaker = speaker_info.split("/")[-1]
speakers.append(speaker)
pharase_infos = glob(speaker_info + "/*")
for pharase_info in pharase_infos:
pharase = pharase_info.split("/")[-1]
utts = glob(pharase_info + "/*.wav")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
distribution2speakers2pharases2utts[distribution][speaker][
pharase
].append(uid)
unique_speakers = list(set(speakers))
unique_speakers.sort()
print("Speakers: \n{}".format("\t".join(unique_speakers)))
return distribution2speakers2pharases2utts, unique_speakers
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing samples for libritts...\n")
save_dir = os.path.join(output_path, "libritts")
os.makedirs(save_dir, exist_ok=True)
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.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")
# Load
libritts_path = dataset_path
distribution2speakers2pharases2utts, unique_speakers = libritts_statistics(
libritts_path
)
# We select pharases of standard spekaer as test songs
train = []
test = []
train_index_count = 0
test_index_count = 0
train_total_duration = 0
test_total_duration = 0
for distribution, speakers2pharases2utts in tqdm(
distribution2speakers2pharases2utts.items()
):
for speaker, pharases2utts in tqdm(speakers2pharases2utts.items()):
pharase_names = list(pharases2utts.keys())
for chosen_pharase in pharase_names:
for chosen_uid in pharases2utts[chosen_pharase]:
res = {
"Dataset": "libritts",
"Singer": speaker,
"Uid": "{}#{}#{}#{}".format(
distribution, speaker, chosen_pharase, chosen_uid
),
}
res["Path"] = "{}/{}/{}/{}.wav".format(
distribution, speaker, chosen_pharase, chosen_uid
)
res["Path"] = os.path.join(libritts_path, res["Path"])
assert os.path.exists(res["Path"])
waveform, sample_rate = torchaudio.load(res["Path"])
duration = waveform.size(-1) / sample_rate
res["Duration"] = duration
if not "train" in distribution:
res["index"] = test_index_count
test_total_duration += duration
test.append(res)
test_index_count += 1
else:
res["index"] = train_index_count
train_total_duration += duration
train.append(res)
train_index_count += 1
utt2singer.write("{}\t{}\n".format(res["Uid"], res["Singer"]))
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Save train.json and test.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)
# 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)
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