mirror of
https://github.com/Vision-CAIR/MiniGPT-4.git
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216 lines
8.2 KiB
Python
216 lines
8.2 KiB
Python
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import argparse
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import librosa
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import json
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import webdataset as wds
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import numpy as np
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import os
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import random
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import io
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from glob import glob
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from itertools import islice
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import scipy.signal as sps
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import soundfile as sf
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from tqdm import tqdm
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import tarfile
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def tardir(
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file_path, tar_name, n_entry_each, audio_ext=".flac", text_ext=".json", shuffle=True, start_idx=0, delete_file=False
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):
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"""
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This function create the tars that includes the audio and text files in the same folder
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@param file_path | string | the path where audio and text files located
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@param tar_name | string | the tar name
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@param n_entry_each | int | how many pairs of (audio, text) will be in a tar
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@param audio_ext | string | the extension of the audio
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@param text_ext | string | the extension of the text
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@param shuffle | boolean | True to shuffle the file sequence before packing up
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@param start_idx | int | the start index of the tar
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@param delete_file | boolean | True to delete the audio and text files after packing up
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"""
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filelist = glob(file_path+'/*'+audio_ext)
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if shuffle:
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random.shuffle(filelist)
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count = 0
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n_split = len(filelist) // n_entry_each
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if n_split * n_entry_each != len(filelist):
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n_split += 1
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size_dict = {
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os.path.basename(tar_name) + "{:06d}".format(i) + ".tar": n_entry_each
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for i in range(n_split)
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}
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if n_split * n_entry_each != len(filelist):
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size_dict[os.path.basename(tar_name) + "{:06d}".format(n_split - 1) + ".tar"] = (
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len(filelist) - (n_split - 1) * n_entry_each
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)
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for i in tqdm(range(start_idx, n_split + start_idx), desc='Creating .tar file:'):
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with tarfile.open(tar_name + "{:06d}".format(i) + ".tar", "w") as tar_handle:
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for j in range(count, len(filelist)):
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audio = filelist[j]
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basename = ".".join(audio.split(".")[:-1])
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text_file_path = os.path.join(file_path, basename + text_ext)
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audio_file_path = os.path.join(file_path, audio)
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tar_handle.add(audio_file_path)
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tar_handle.add(text_file_path)
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if delete_file:
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os.remove(audio_file_path)
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os.remove(text_file_path)
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if (j + 1) % n_entry_each == 0:
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count = j + 1
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break
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tar_handle.close()
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# Serializing json
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json_object = json.dumps(size_dict, indent=4)
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# Writing to sample.json
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with open(os.path.join(os.path.dirname(tar_name), "sizes.json"), "w") as outfile:
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outfile.write(json_object)
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return size_dict
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def packup(input, output, filename, dataclass='all', num_element=512, start_idx=0, delete_file=False):
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if not os.path.exists(os.path.join(input, dataclass)):
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print(
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"Dataclass {} does not exist, this folder does not exist. Skipping it.".format(
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dataclass
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)
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)
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return
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if os.path.exists(os.path.join(output, dataclass)):
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tardir(
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os.path.join(input, dataclass),
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os.path.join(output, dataclass, filename),
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num_element,
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start_idx=start_idx,
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delete_file=delete_file,
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)
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else:
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os.makedirs(os.path.join(output, dataclass))
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tardir(
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os.path.join(input, dataclass),
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os.path.join(output, dataclass, filename),
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num_element,
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start_idx=start_idx,
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delete_file=delete_file,
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)
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return
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def load_from_tar(
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file_path,
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file_path_type="local",
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audio_ext="flac",
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text_ext="json",
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samplerate=32000,
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mono=True,
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max_len=1000000,
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dtype="float64",
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res_type="kaiser_best",
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):
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"""
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This function load the tar files to 3 entry tuple (audios, texts, names) accordingly
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@param file_path | string | the path where audio and text files located
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@param file_path_type | string | this is meant to control the prefix of the address in case people forget to include it
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if file_path_type is "local" and 'file:\\' is not shown as a prefix, it will be added automatically
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@param audio_ext | string | the extension of the audio
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@param text_ext | string | the extension of the text
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@param samplerate | int | the sample rate of the audio
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@param mono | boolean | if the audio is in mono channel
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@param max_len | int | max len of the audio, if exceed, will random crop; elif deficit, will pad
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@param dtype | string | the type of the dtype of the audio sample representation
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@param res_type | string | the resample method
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"""
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if file_path_type == "local" and ("file:\\" not in file_path):
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file_path = "file:\\" + file_path
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dataset = wds.WebDataset(file_path)
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audios = []
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texts = []
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names = []
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for sample in dataset:
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for key, value in sample.items():
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if key == audio_ext:
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audio_data, orig_sr = sf.read(io.BytesIO(value))
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if samplerate is not None:
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audio_data = librosa.resample(
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audio_data,
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orig_sr=orig_sr,
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target_sr=samplerate,
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res_type=res_type,
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)
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if len(audio_data) > max_len:
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overflow = len(audio_data) - max_len
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idx = np.random.randint(0, overflow + 1)
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if np.random.rand() > 0.5:
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audio_data = audio_data[idx : idx + max_len]
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else:
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audio_data = audio_data[
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len(audio_data)
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+ 1
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- idx
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- max_len : len(audio_data)
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+ 1
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- idx
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]
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else:
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audio_data = np.pad(
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audio_data,
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(0, max_len - len(audio_data)),
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mode="constant",
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constant_values=0,
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)
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if mono:
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audio_data = librosa.to_mono(audio_data)
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audios.append((audio_data, samplerate))
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elif key == text_ext:
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texts.append(value)
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elif key == "__key__":
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names.append(value)
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return audios, texts, names
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--input",
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type=str,
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help="input folder, expecting subdirectory like train, valid or test",
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)
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parser.add_argument(
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"--output",
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type=str,
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help="output, generating tar files at output/dataclass/filename_{}.tar",
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)
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parser.add_argument(
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"--filename",
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type=str,
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default="",
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help="the filename of the tar, generating tar files at output/dataclass/filename_{}.tar",
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)
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parser.add_argument(
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"--dataclass", type=str, default="all", help="train or test or valid or all"
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)
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parser.add_argument(
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"--num_element", type=int, default=512, help="pairs of (audio, text) to be included in a single tar"
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)
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parser.add_argument(
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"--start_idx", type=int, default=0, help="start index of the tar"
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)
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parser.add_argument(
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"--delete_file", action='store_true', help="delete the input file when making tars"
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)
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args = parser.parse_args()
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if args.dataclass == "all":
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for x in ["train", "valid", "test"]:
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packup(args.input, args.output, args.filename, x, args.num_element, args.start_idx, args.delete_file)
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elif args.dataclass == "none":
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os.makedirs(args.output, exist_ok=True)
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tardir(
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args.input,
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os.path.join(args.output, args.filename),
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args.num_element,
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start_idx=args.start_idx,
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delete_file=args.delete_file,
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)
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else: # if dataclass is in other name
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packup(args.input, args.output, args.filename, args.dataclass, args.num_element, args.start_idx, args.delete_file)
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