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import os |
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import json |
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import torchaudio |
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from tqdm import tqdm |
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from glob import glob |
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from utils.util import has_existed |
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def main(output_path, dataset_path): |
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print("-" * 10) |
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print("Dataset splits for ljspeech...\n") |
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save_dir = os.path.join(output_path, "ljspeech") |
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ljspeech_path = dataset_path |
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wave_files = glob(ljspeech_path + "/wavs/*.wav") |
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train_output_file = os.path.join(save_dir, "train.json") |
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test_output_file = os.path.join(save_dir, "test.json") |
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if has_existed(train_output_file): |
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return |
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utts = [] |
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for wave_file in tqdm(wave_files): |
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res = { |
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"Dataset": "ljspeech", |
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"Singer": "female1", |
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"Uid": "{}".format(wave_file.split("/")[-1].split(".")[0]), |
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} |
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res["Path"] = wave_file |
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assert os.path.exists(res["Path"]) |
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waveform, sample_rate = torchaudio.load(res["Path"]) |
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duration = waveform.size(-1) / sample_rate |
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res["Duration"] = duration |
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if duration <= 1e-8: |
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continue |
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utts.append(res) |
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test_length = len(utts) // 20 |
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train_utts = [] |
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train_index_count = 0 |
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train_total_duration = 0 |
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for i in tqdm(range(len(utts) - test_length)): |
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tmp = utts[i] |
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tmp["index"] = train_index_count |
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train_index_count += 1 |
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train_total_duration += tmp["Duration"] |
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train_utts.append(tmp) |
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test_utts = [] |
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test_index_count = 0 |
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test_total_duration = 0 |
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for i in tqdm(range(len(utts) - test_length, len(utts))): |
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tmp = utts[i] |
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tmp["index"] = test_index_count |
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test_index_count += 1 |
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test_total_duration += tmp["Duration"] |
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test_utts.append(tmp) |
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print("#Train = {}, #Test = {}".format(len(train_utts), len(test_utts))) |
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print( |
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"#Train hours= {}, #Test hours= {}".format( |
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train_total_duration / 3600, test_total_duration / 3600 |
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) |
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) |
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os.makedirs(save_dir, exist_ok=True) |
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with open(train_output_file, "w") as f: |
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json.dump(train_utts, f, indent=4, ensure_ascii=False) |
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with open(test_output_file, "w") as f: |
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json.dump(test_utts, f, indent=4, ensure_ascii=False) |
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