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Running
on
Zero
# 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 librosa | |
from tqdm import tqdm | |
from glob import glob | |
from collections import defaultdict | |
from utils.util import has_existed | |
def get_lines(file): | |
with open(file, "r") as f: | |
lines = f.readlines() | |
lines = [l.strip() for l in lines] | |
return lines | |
def vctk_statistics(data_dir): | |
speakers = [] | |
speakers2utts = defaultdict(list) | |
speaker_infos = glob(data_dir + "/wav48_silence_trimmed" + "/*") | |
for speaker_info in speaker_infos: | |
speaker = speaker_info.split("/")[-1] | |
if speaker == "log.txt": | |
continue | |
speakers.append(speaker) | |
utts = glob(speaker_info + "/*") | |
for utt in utts: | |
uid = ( | |
utt.split("/")[-1].split("_")[1] | |
+ "_" | |
+ utt.split("/")[-1].split("_")[2].split(".")[0] | |
) | |
speakers2utts[speaker].append(uid) | |
unique_speakers = list(set(speakers)) | |
unique_speakers.sort() | |
print("Speakers: \n{}".format("\t".join(unique_speakers))) | |
return speakers2utts, unique_speakers | |
def vctk_speaker_infos(data_dir): | |
file = os.path.join(data_dir, "speaker-info.txt") | |
lines = get_lines(file) | |
ID2speakers = defaultdict() | |
for l in tqdm(lines): | |
items = l.replace(" ", "") | |
if items[:2] == "ID": | |
# The header line | |
continue | |
if items[0] == "p": | |
id = items[:4] | |
gender = items[6] | |
elif items[0] == "s": | |
id = items[:2] | |
gender = items[4] | |
if gender == "F": | |
speaker = "female_{}".format(id) | |
elif gender == "M": | |
speaker = "male_{}".format(id) | |
ID2speakers[id] = speaker | |
return ID2speakers | |
def main(output_path, dataset_path, TEST_NUM_OF_EVERY_SPEAKER=3): | |
print("-" * 10) | |
print("Preparing test samples for vctk...") | |
save_dir = os.path.join(output_path, "vctk") | |
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 | |
vctk_dir = dataset_path | |
ID2speakers = vctk_speaker_infos(vctk_dir) | |
speaker2utts, unique_speakers = vctk_statistics(vctk_dir) | |
# We select speakers of standard samples as test utts | |
train = [] | |
test = [] | |
train_index_count = 0 | |
test_index_count = 0 | |
test_speaker_count = defaultdict(int) | |
train_total_duration = 0 | |
test_total_duration = 0 | |
for i, speaker in enumerate(speaker2utts.keys()): | |
for chosen_uid in tqdm( | |
speaker2utts[speaker], | |
desc="Speaker {}/{}, #Train = {}, #Test = {}".format( | |
i + 1, len(speaker2utts), train_index_count, test_index_count | |
), | |
): | |
res = { | |
"Dataset": "vctk", | |
"Singer": ID2speakers[speaker], | |
"Uid": "{}#{}".format(ID2speakers[speaker], chosen_uid), | |
} | |
res["Path"] = "{}/{}_{}.flac".format(speaker, speaker, chosen_uid) | |
res["Path"] = os.path.join(vctk_dir, "wav48_silence_trimmed", res["Path"]) | |
assert os.path.exists(res["Path"]) | |
duration = librosa.get_duration(filename=res["Path"]) | |
res["Duration"] = duration | |
if test_speaker_count[speaker] < TEST_NUM_OF_EVERY_SPEAKER: | |
res["index"] = test_index_count | |
test_total_duration += duration | |
test.append(res) | |
test_index_count += 1 | |
test_speaker_count[speaker] += 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) | |