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| import os | |
| import sys | |
| sys.path.append(os.getcwd()) | |
| import json | |
| from concurrent.futures import ProcessPoolExecutor | |
| from importlib.resources import files | |
| from pathlib import Path | |
| from tqdm import tqdm | |
| import soundfile as sf | |
| from datasets.arrow_writer import ArrowWriter | |
| import numpy as np | |
| import torch | |
| import torchaudio | |
| def deal_with_audio_dir(audio_dir): | |
| sub_result, durations = [], [] | |
| vocab_set = set() | |
| audio_lists = list(audio_dir.rglob("*.wav")) | |
| for line in audio_lists: | |
| text_path = line.with_suffix(".normalized.txt") | |
| text = open(text_path, "r").read().strip() | |
| duration = sf.info(line).duration | |
| if duration < 0.4 or duration > 30: | |
| continue | |
| sub_result.append({"audio_path": str(line), "text": text, "duration": duration}) | |
| durations.append(duration) | |
| vocab_set.update(list(text)) | |
| return sub_result, durations, vocab_set | |
| def main(): | |
| result = [] | |
| duration_list = [] | |
| text_vocab_set = set() | |
| # process raw data | |
| #executor = ProcessPoolExecutor(max_workers=max_workers) | |
| #futures = [] | |
| # | |
| #for subset in tqdm(SUB_SET): | |
| # dataset_path = Path(os.path.join(dataset_dir, subset)) | |
| # [ | |
| # futures.append(executor.submit(deal_with_audio_dir, audio_dir)) | |
| # for audio_dir in dataset_path.iterdir() | |
| # if audio_dir.is_dir() | |
| # ] | |
| #for future in tqdm(futures, total=len(futures)): | |
| # sub_result, durations, vocab_set = future.result() | |
| # result.extend(sub_result) | |
| # duration_list.extend(durations) | |
| # text_vocab_set.update(vocab_set) | |
| #executor.shutdown() | |
| train_scp = "/ailab-train/speech/zhanghaomin/datas/v2cdata/test.scp" | |
| v2a_path = "/ailab-train/speech/zhanghaomin/codes3/MMAudio-main/output_v2c_s44/" | |
| #v2a_path = "/ailab-train/speech/zhanghaomin/codes3/v2a_v2cdata/" | |
| with open(train_scp, "r") as fr: | |
| for line in fr.readlines(): | |
| video, txt, audio = line.strip().split("\t") | |
| ####v2a_audio = v2a_path + video.replace("/", "__") + ".flac" | |
| v2a_audio = v2a_path + video.replace("/", "__")[:-4] + ".wav" | |
| if not os.path.exists(video) or not os.path.exists(audio) or not os.path.exists(v2a_audio): | |
| print(video, audio, v2a_audio) | |
| continue | |
| waveform, sr = torchaudio.load(audio) | |
| duration = waveform.shape[-1] / sr | |
| waveform_v2a, sr_v2a = torchaudio.load(v2a_audio) | |
| duration_v2a = waveform_v2a.shape[-1] / sr_v2a | |
| if duration_v2a >= duration: | |
| waveform_v2a = waveform_v2a[:, :int(sr_v2a*duration)] | |
| else: | |
| waveform_v2a = torch.cat([waveform_v2a, torch.zeros([waveform_v2a.shape[0], int(sr_v2a*duration)-waveform_v2a.shape[1]])], dim=1) | |
| duration_v2a = duration | |
| energy_v2a = [] | |
| for i in range(int(duration_v2a/(256/24000))): | |
| energy_v2a.append(waveform_v2a[0,int(i*sr_v2a*(256/24000)):int((i+1)*sr_v2a*(256/24000))].abs().mean()) | |
| energy_v2a = np.array(energy_v2a) | |
| energy_v2a = energy_v2a / max(energy_v2a) | |
| #print(len(energy_v2a), max(energy_v2a), min(energy_v2a), energy_v2a.mean()) | |
| np.savez(v2a_audio+".npz", energy_v2a) | |
| energy = [] | |
| for i in range(int(duration/(256/24000))): | |
| energy.append(waveform[0,int(i*sr*(256/24000)):int((i+1)*sr*(256/24000))].abs().mean()) | |
| energy = np.array(energy) | |
| energy = energy / max(energy) | |
| #print(len(energy), max(energy), min(energy), energy.mean()) | |
| np.savez(audio+".npz", energy) | |
| d = {} | |
| d["audio_path"] = audio | |
| d["text"] = txt | |
| d["duration"] = duration | |
| d["energy"] = v2a_audio+".npz" | |
| result.append(d) | |
| duration_list.append(duration) | |
| text_vocab_set.update(list(txt)) | |
| print(len(result), result[:2]) # 354218 [{'audio_path': '/ailab-train/speech/zhanghaomin/datas/libritts/LibriTTS/train-clean-100/7635/105409/7635_105409_000088_000000.wav', 'text': '"There is no \'but.\' I said, do you remember?"', 'duration': 2.31}, {'audio_path': '/ailab-train/speech/zhanghaomin/datas/libritts/LibriTTS/train-clean-100/7635/105409/7635_105409_000061_000002.wav', 'text': 'They know it.', 'duration': 0.76}] | |
| print(len(duration_list), duration_list[:2]) # 354218 [2.31, 0.76] | |
| print(len(text_vocab_set)) # 78 | |
| # save preprocessed dataset to disk | |
| if not os.path.exists(f"{save_dir}"): | |
| os.makedirs(f"{save_dir}") | |
| print(f"\nSaving to {save_dir} ...") | |
| with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer: | |
| for line in tqdm(result, desc="Writing to raw.arrow ..."): | |
| writer.write(line) | |
| # dup a json separately saving duration in case for DynamicBatchSampler ease | |
| with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f: | |
| json.dump({"duration": duration_list}, f, ensure_ascii=False) | |
| # vocab map, i.e. tokenizer | |
| with open(f"{save_dir}/vocab.txt", "w") as f: | |
| for vocab in sorted(text_vocab_set): | |
| f.write(vocab + "\n") | |
| print(f"\nFor {dataset_name}, sample count: {len(result)}") | |
| print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}") | |
| print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours") | |
| if __name__ == "__main__": | |
| max_workers = 36 | |
| tokenizer = "char" # "pinyin" | "char" | |
| #SUB_SET = ["train-clean-100", "train-clean-360", "train-other-500"] | |
| #dataset_dir = "/ailab-train/speech/zhanghaomin/datas/libritts/LibriTTS" | |
| #dataset_name = f"LibriTTS_{'_'.join(SUB_SET)}_{tokenizer}".replace("train-clean-", "").replace("train-other-", "") | |
| dataset_name = "v2c_s44_test_char" | |
| save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}" | |
| print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n") | |
| main() | |
| # For LibriTTS_100_360_500_char, sample count: 354218 | |
| # For LibriTTS_100_360_500_char, vocab size is: 78 | |
| # For LibriTTS_100_360_500_char, total 554.09 hours | |