Update parquet files

Data/archives/mgb2_wav.test.zip

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Data/archives/mgb2_wav.train.zip

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Data/archives/mgb2_txt.test.zip → default/mg2_data-test-00000-of-00002.parquet

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Data/archives/mgb2_txt.train.zip → default/mg2_data-test-00001-of-00002.parquet

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Data/archives/mgb2_wav.dev.zip → default/mg2_data-train-00000-of-00004.parquet

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default/mg2_data-train-00001-of-00004.parquet

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default/mg2_data-train-00002-of-00004.parquet

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Data/archives/mgb2_txt.dev.zip → default/mg2_data-train-00003-of-00004.parquet

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default/mg2_data-validation-00000-of-00002.parquet

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default/mg2_data-validation-00001-of-00002.parquet

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mg2_data.py

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