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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 hifitts_statistics(data_dir):
speakers = []
distribution2books2utts = defaultdict(
lambda:defaultdict(list)
)
distribution_infos = glob(data_dir + "/*.json")
for distribution_info in distribution_infos:
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)
text_normalized = entry.get("text_normalized")
audio_path = entry.get("audio_filepath")
book = audio_path.split("/")[-2]
distribution2books2utts[distribution][book].append((text_normalized, audio_path))
unique_speakers = list(set(speakers))
unique_speakers.sort()
print("Speakers: \n{}".format("\t".join(unique_speakers)))
return distribution2books2utts, unique_speakers
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")
# Load
hifitts_path = dataset_path
distribution2books2utts, unique_speakers = hifitts_statistics(
hifitts_path
)
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
for distribution, books2utts in tqdm(
distribution2books2utts.items(),
desc=f"Distribution"
):
speaker = distribution.split("_")[0]
book_names = list(books2utts.keys())
for chosen_book in tqdm(book_names, desc=f"chosen_book"):
for text, utt_path in tqdm(books2utts[chosen_book], desc=f"utterance"):
chosen_uid = utt_path.split("/")[-1].split(".")[0]
res = {
"Dataset":"hifitts",
"Singer":speaker,
"Uid": "{}#{}#{}#{}".format(
distribution, speaker, chosen_book, chosen_uid
),
"Text": text
}
res["Path"] = os.path.join(hifitts_path, utt_path)
assert os.path.exists(res["Path"])
waveform, sample_rate = torchaudio.load(res["Path"])
duration = waveform.size(-1) / sample_rate
res["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"]))
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) |