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+ # -*- coding: utf-8 -*-
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+ """Untitled2.ipynb
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+
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+ Automatically generated by Colaboratory.
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+
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+ Original file is located at
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+ https://colab.research.google.com/drive/1Jy8fwFO774TM_FTwK-0to2L0qHoUAT-U
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+ """
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+
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+ # -*- coding: utf-8 -*-
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+ """MGB2.ipynb
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+ Automatically generated by Colaboratory.
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+ Original file is located at
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+ https://colab.research.google.com/drive/15ejoy2EWN9bj2s5ORQRZb5aTmFlcgA9d
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+ """
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+
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+ import datasets
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+ import os
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+
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+
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+ _DESCRIPTION = "MGB2 speech recognition dataset AR"
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+ _HOMEPAGE = "https://arabicspeech.org/mgb2/"
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+ _LICENSE = "MGB-2 License agreement"
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+ _CITATION = """@misc{https://doi.org/10.48550/arxiv.1609.05625,
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+ doi = {10.48550/ARXIV.1609.05625},
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+
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+ url = {https://arxiv.org/abs/1609.05625},
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+
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+ author = {Ali, Ahmed and Bell, Peter and Glass, James and Messaoui, Yacine and Mubarak, Hamdy and Renals, Steve and Zhang, Yifan},
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+
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+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
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+
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+ title = {The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition},
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+
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+ publisher = {arXiv},
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+
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+ year = {2016},
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+
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+ copyright = {arXiv.org perpetual, non-exclusive license}
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+ }
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+ """
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+ _DATA_ARCHIVE_ROOT = "Data/archives/"
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+ _DATA_URL = {
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+ "test": _DATA_ARCHIVE_ROOT + "mgb2_wav.test.zip",
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+ "dev": _DATA_ARCHIVE_ROOT + "mgb2_wav.dev.zip",
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+ "train": _DATA_ARCHIVE_ROOT + "mgb2_wav.train.zip",
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+
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+ #"train": [_DATA_ARCHIVE_ROOT + f"mgb2_wav_{x}.train.tar.gz" for x in range(48)], # we have 48 archives
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+ }
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+ _TEXT_URL = {
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+ "test": _DATA_ARCHIVE_ROOT + "mgb2_txt.test.zip",
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+ "dev": _DATA_ARCHIVE_ROOT + "mgb2_txt.dev.zip",
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+ "train": _DATA_ARCHIVE_ROOT + "mgb2_txt.train.zip",
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+ }
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+
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+
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+
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+ def absoluteFilePaths(directory):
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+ for dirpath,_,filenames in os.walk(directory):
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+ for f in filenames:
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+ yield os.path.abspath(os.path.join(dirpath, f))
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+
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+
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+ class MGDB2Dataset(datasets.GeneratorBasedBuilder):
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "path": datasets.Value("string"),
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+ "audio": datasets.Audio(sampling_rate=16_000),
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+ "sentence": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage=_HOMEPAGE,
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+ license=_LICENSE,
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ wav_archive = dl_manager.download(_DATA_URL)
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+ txt_archive = dl_manager.download(_TEXT_URL)
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+ test_dir = "dataset/test"
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+ dev_dir = "dataset/dev"
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+ train_dir = "dataset/train"
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+
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+
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+ print("Starting write datasets.........................................................")
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+
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+
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+ if dl_manager.is_streaming:
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+ print("from streaming.........................................................")
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+
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+
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+
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "path_to_txt": test_dir + "/txt",
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+ "path_to_wav": test_dir + "/wav",
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+ "wav_files": dl_manager.iter_archive(wav_archive['test']),
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+ "txt_files": dl_manager.iter_archive(txt_archive['test']),
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "path_to_txt": dev_dir + "/txt",
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+ "path_to_wav": dev_dir + "/wav",
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+ "wav_files": dl_manager.iter_archive(wav_archive['dev']),
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+ "txt_files": dl_manager.iter_archive(txt_archive['dev']),
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "path_to_txt": train_dir + "/txt",
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+ "path_to_wav": train_dir + "/wav",
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+ "wav_files": dl_manager.iter_archive(wav_archive['train']),
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+ "txt_files": dl_manager.iter_archive(txt_archive['train']),
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+ },
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+ ),
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+ ]
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+ else:
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+ print("from non streaming.........................................................")
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+
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+
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+ dstZipFileName=txt_archive['test']
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+
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+ sz=os.path.getsize(dstZipFileName)
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+
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+ print("file size=",sz)
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+
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+
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+ #test_txt_files=dl_manager.extract(txt_archive['test']);
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+
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+ #flist=os.listdir(test_txt_files)
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+
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+ #print(flist)
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+
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+ #f = open(test_txt_files, 'r')
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+ #file_contents = f.read()
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+ #print (file_contents)
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+ #f.close()
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+
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "path_to_txt": test_dir + "/txt",
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+ "path_to_wav": test_dir + "/wav",
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+ "wav_files": absoluteFilePaths(dl_manager.extract(wav_archive['test'])),
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+ "txt_files": absoluteFilePaths(dl_manager.extract(txt_archive['test'])),
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+ "data_type":2,
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={
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+ "path_to_txt": dev_dir + "/txt",
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+ "path_to_wav": dev_dir + "/wav",
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+ "wav_files": absoluteFilePaths(dl_manager.extract(wav_archive['dev'])),
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+ "txt_files": absoluteFilePaths(dl_manager.extract(txt_archive['dev'])),
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+ "data_type":1,
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "path_to_txt": train_dir + "/txt",
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+ "path_to_wav": train_dir + "/wav",
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+ "wav_files": absoluteFilePaths(dl_manager.extract(wav_archive['train'])),
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+ "txt_files": absoluteFilePaths(dl_manager.extract(txt_archive['train'])),
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+ "data_type":0,
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+ },
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+ ),
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+ ]
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+ print("end of generation.........................................................")
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+
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+
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+ #0 --> train
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+ #1--> validation
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+ #2-->test
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+
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+ def _generate_examples(self, path_to_txt, path_to_wav, wav_files, txt_files,data_type):
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+ """
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+ This assumes that the text directory alphabetically precedes the wav dir
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+ The file names for wav and text seem to match and are unique
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+ We can use them for the dictionary matching them
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+ """
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+
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+ print("start of generate examples.........................................................")
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+
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+ print("txt file names............................",txt_files)
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+ print("wav_files names....................................",wav_files)
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+
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+ examples = {}
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+ id_ = 0
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+ # need to prepare the transcript - wave map
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+ for item in txt_files:
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+
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+
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+ #print("copying txt file...............",item)
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+
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+ if type(item) is tuple:
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+ # iter_archive will return path and file
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+ path, f = item
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+ txt = f.read().decode(encoding="utf-8").strip()
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+ else:
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+ # extract will return path only
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+ path = item
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+ with open(path, encoding="utf-8") as f:
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+ txt = f.read().strip()
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+
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+ #if os.path.exists(path_to_txt)==False:
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+ # os.makedirs(path_to_txt)
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+ #if path.find(path_to_txt) > -1:
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+ # construct the wav path
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+ # which is used as an identifier
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+ wav_path = os.path.split(path)[1].replace("_utf8", "").replace(".txt", ".wav").strip()
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+ #print(wav_path)
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+ examples[wav_path] = {
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+ "sentence": txt,
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+ "path": wav_path,
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+ }
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+
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+ #for wf in wav_files:
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+ #print(wf)
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+ for item in wav_files:#wf:
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+ #print(item)
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+ if type(item) is tuple:
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+ path, f = item
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+ wav_data = f.read()
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+ else:
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+ path = item
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+ with open(path, "rb") as f:
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+ wav_data = f.read()
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+ #if os.path.exists(path_to_wav)==False:
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+ # os.makedirs(path_to_wav)
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+ #if path.find(path_to_wav) > -1:
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+ wav_path = os.path.split(path)[1].strip()
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+ if not (wav_path in examples):
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+ print("wav file mismatch:",wav_path)
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+ continue
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+ audio = {"path": path, "bytes": wav_data}
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+ yield id_, {**examples[wav_path], "audio": audio}
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+ id_ += 1