Upload mgb2.py

mgb2.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",
+}
+
+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,
+        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.........................................................")
+
+
+            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