Delete mg.trial4.py

mg.trial4.py

@@ -1,187 +0,0 @@
-
-import datasets
-import os
-
-
-_DESCRIPTION = "MGB2 speech recognition dataset AR"
-_HOMEPAGE = "https://arabicspeech.org/mgb2/"
-_LICENSE = "MGB-2 License agreement"
-
-
-
-
-
-_DATA_ARCHIVE_ROOT = "https://huggingface.co/datasets/taqwa92/mg.trial4/tree/main/audio/ar/test"
-_DATA_ARCHIVE_ROOT1 ="https://huggingface.co/datasets/taqwa92/mg.trial4/tree/main/transcript/ar/test"
-
-_DATA_URL = {
-    "test": _DATA_ARCHIVE_ROOT + "test.zip",
-    
-}
-
-_TEXT_URL = {
-    "test": _DATA_ARCHIVE_ROOT1 + "test.tsv",
-    
-
-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),
-                "text": datasets.Value("string"),
-            }
-        ),
-        supervised_keys=None,
-        homepage=_HOMEPAGE,
-        license=_LICENSE,
-       
-    )
-
-    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.........................................................")
-
-
-            test_txt_files=dl_manager.extract(txt_archive['test']);
-            print("txt file list .....................................",txt_archive['test'])
-
-
-            print("txt file names .....................................",test_txt_files)
-
-            
-            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.extract(wav_archive['test']),
-                    "txt_files": test_txt_files,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "path_to_txt": dev_dir + "/txt",
-                    "path_to_wav": dev_dir + "/wav",
-                    "wav_files": dl_manager.extract(wav_archive['dev']),
-                    "txt_files": dl_manager.extract(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.extract(wav_archive['train']),
-                    "txt_files": dl_manager.extract(txt_archive['train']),
-                },
-            ),
-        ]
-    print("end of generation.........................................................")
-      
-
-
-    
-    def _generate_examples(self, path_to_txt, path_to_wav, wav_files, txt_files):
-        """ 
-        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 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()
-                
-                examples[wav_path] = {
-                    "text": txt,
-                    "path": wav_path,
-                }
-
-        for wf in wav_files:
-            for item in wf:
-                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 path.find(path_to_wav) > -1:
-                    wav_path = os.path.split(path)[1].strip()
-                    audio = {"path": path, "bytes": wav_data}
-                    yield id_, {**examples[wav_path], "audio": audio}
-                    id_ += 1