preprocessors/hifitts.py

# 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)