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my_dataset.py

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+import os
+import csv
+import datasets
+from tqdm import tqdm  # لإظهار تقدم القراءة في ملفات metadata
+
+_DESCRIPTION = "A speech dataset designed for automatic speech recognition (ASR), structured like Mozilla Common Voice."
+_CITATION = "No citation available yet."
+
+class MyDatasetConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(MyDatasetConfig, self).__init__(**kwargs)
+
+class MyDataset(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000  # مثل Common Voice
+
+    BUILDER_CONFIGS = [
+        MyDatasetConfig(
+            name="default",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features({
+            "client_id": datasets.Value("string"),
+            "path": datasets.Value("string"),
+            "audio": datasets.features.Audio(sampling_rate=16000),
+            "text": datasets.Value("string"),
+            "up_votes": datasets.Value("int64"),
+            "down_votes": datasets.Value("int64"),
+            "age": datasets.Value("string"),
+            "gender": datasets.Value("string"),
+            "accent": datasets.Value("string"),
+            "locale": datasets.Value("string"),
+            "segment": datasets.Value("string"),
+            "variant": datasets.Value("string"),
+        })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            citation=_CITATION,
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = self.config.data_dir
+
+        splits = ["train", "validation", "test"]
+        split_generators = []
+
+        for split in splits:
+            audio_tar = os.path.join(data_dir, f"{split}_audio")
+            metadata_csv = os.path.join(data_dir, f"{split}_metadata.csv")
+
+            if os.path.exists(audio_tar) and os.path.exists(metadata_csv):
+                split_generators.append(
+                    datasets.SplitGenerator(
+                        name=getattr(datasets.Split, split.upper()),
+                        gen_kwargs={
+                            "archives": dl_manager.iter_archive(audio_tar),
+                            "metadata_path": metadata_csv,
+                        },
+                    )
+                )
+
+        return split_generators
+
+    def _generate_examples(self, archives, metadata_path):
+        # قراءة الميتاداتا في dict
+        metadata = {}
+        data_fields = list(self._info().features.keys())
+        
+        with open(metadata_path, encoding="utf-8-sig") as f:
+            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
+            reader.fieldnames = [name.strip().replace('"', '') for name in reader.fieldnames]
+
+            for row in tqdm(reader, desc="Loading metadata..."):
+                row = {k.replace('"', ''): v.replace('"', '') for k, v in row.items()}
+                if not row["file_name"].endswith(".wav"):
+                    row["file_name"] += ".wav"
+
+                # تحويل accents إلى accent (كما في Common Voice 8.0)
+                if "accents" in row:
+                    row["accent"] = row["accents"]
+                    del row["accents"]
+
+                # ملء الحقول الناقصة بقيم فارغة أو افتراضية
+                for field in data_fields:
+                    if field not in row:
+                        # للأعداد (up_votes, down_votes) نضع صفر، الباقي فراغ
+                        if field in ["up_votes", "down_votes"]:
+                            row[field] = 0
+                        else:
+                            row[field] = ""
+
+                metadata[row["file_name"]] = row
+
+
+        # قراءة ملفات الصوت من الأرشيف وتوليد الأمثلة
+        for i, audio_archive in enumerate(archives):
+            for path_in_tar, file_obj in audio_archive:
+                _, filename = os.path.split(path_in_tar)
+
+                if filename in metadata:
+                    example = dict(metadata[filename])
+
+                    # ضبط مسار الصوت (لتحميله محلياً أو عبر streaming)
+                    example["audio"] = {"path": path_in_tar, "bytes": file_obj.read()}
+                    example["path"] = path_in_tar
+
+                    yield path_in_tar, example