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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 os
import glob
from tqdm import tqdm
import torchaudio
import pandas as pd
from glob import glob
from collections import defaultdict
from utils.io import save_audio
from utils.util import has_existed
from preprocessors import GOLDEN_TEST_SAMPLES
def save_utterance(output_file, waveform, fs, start, end, overlap=0.1):
"""
waveform: [#channel, audio_len]
start, end, overlap: seconds
"""
start = int((start - overlap) * fs)
end = int((end + overlap) * fs)
utterance = waveform[:, start:end]
save_audio(output_file, utterance, fs)
def split_to_utterances(language_dir, output_dir):
print("Splitting to utterances for {}...".format(language_dir))
wav_dir = os.path.join(language_dir, "wav")
phoneme_dir = os.path.join(language_dir, "txt")
annot_dir = os.path.join(language_dir, "csv")
pitches = set()
for wav_file in tqdm(glob("{}/*.wav".format(wav_dir))):
# Load waveform
song_name = wav_file.split("/")[-1].split(".")[0]
waveform, fs = torchaudio.load(wav_file)
# Load utterances
phoneme_file = os.path.join(phoneme_dir, "{}.txt".format(song_name))
with open(phoneme_file, "r") as f:
lines = f.readlines()
utterances = [l.strip().split() for l in lines]
utterances = [utt for utt in utterances if len(utt) > 0]
# Load annotation
annot_file = os.path.join(annot_dir, "{}.csv".format(song_name))
annot_df = pd.read_csv(annot_file)
pitches = pitches.union(set(annot_df["pitch"]))
starts = annot_df["start"].tolist()
ends = annot_df["end"].tolist()
syllables = annot_df["syllable"].tolist()
# Split
curr = 0
for i, phones in enumerate(utterances):
sz = len(phones)
assert phones[0] == syllables[curr]
assert phones[-1] == syllables[curr + sz - 1]
s = starts[curr]
e = ends[curr + sz - 1]
curr += sz
save_dir = os.path.join(output_dir, song_name)
os.makedirs(save_dir, exist_ok=True)
output_file = os.path.join(save_dir, "{:04d}.wav".format(i))
save_utterance(output_file, waveform, fs, start=s, end=e)
def _main(dataset_path):
"""
Split to utterances
"""
utterance_dir = os.path.join(dataset_path, "utterances")
for lang in ["english", "korean"]:
split_to_utterances(os.path.join(dataset_path, lang), utterance_dir)
def get_test_songs():
golden_samples = GOLDEN_TEST_SAMPLES["csd"]
# every item is a tuple (language, song)
golden_songs = [s.split("_")[:2] for s in golden_samples]
# language_song, eg: en_001a
return golden_songs
def csd_statistics(data_dir):
languages = []
songs = []
languages2songs = defaultdict(lambda: defaultdict(list))
folder_infos = glob(data_dir + "/*")
for folder_info in folder_infos:
folder_info_split = folder_info.split("/")[-1]
language = folder_info_split[:2]
song = folder_info_split[2:]
languages.append(language)
songs.append(song)
utts = glob(folder_info + "/*")
for utt in utts:
uid = utt.split("/")[-1].split(".")[0]
languages2songs[language][song].append(uid)
unique_languages = list(set(languages))
unique_songs = list(set(songs))
unique_languages.sort()
unique_songs.sort()
print(
"csd: {} languages, {} utterances ({} unique songs)".format(
len(unique_languages), len(songs), len(unique_songs)
)
)
print("Languages: \n{}".format("\t".join(unique_languages)))
return languages2songs
def main(output_path, dataset_path):
print("-" * 10)
print("Preparing test samples for csd...\n")
if not os.path.exists(os.path.join(dataset_path, "utterances")):
print("Spliting into utterances...\n")
_main(dataset_path)
save_dir = os.path.join(output_path, "csd")
train_output_file = os.path.join(save_dir, "train.json")
test_output_file = os.path.join(save_dir, "test.json")
if has_existed(test_output_file):
return
# Load
csd_path = os.path.join(dataset_path, "utterances")
language2songs = csd_statistics(csd_path)
test_songs = get_test_songs()
# We select songs of standard samples as test songs
train = []
test = []
train_index_count = 0
test_index_count = 0
train_total_duration = 0
test_total_duration = 0
for language, songs in tqdm(language2songs.items()):
song_names = list(songs.keys())
for chosen_song in song_names:
for chosen_uid in songs[chosen_song]:
res = {
"Dataset": "csd",
"Singer": "Female1_{}".format(language),
"Uid": "{}_{}_{}".format(language, chosen_song, chosen_uid),
}
res["Path"] = "{}{}/{}.wav".format(language, chosen_song, chosen_uid)
res["Path"] = os.path.join(csd_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 [language, chosen_song] in test_songs:
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
print("#Train = {}, #Test = {}".format(len(train), len(test)))
print(
"#Train hours= {}, #Test hours= {}".format(
train_total_duration / 3600, test_total_duration / 3600
)
)
# Save
os.makedirs(save_dir, exist_ok=True)
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)
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