Upload my_stt_dataset.py

my_stt_dataset.py

@@ -1,85 +1,92 @@
 import os
 import csv
 import datasets
-from datasets import Audio
+from datasets import Audio, BuilderConfig
+
+# BuilderConfig ni aniqlaymiz: bu yerda til qisqartmasi va asosiy data papkasi kiritiladi.
+class STTConfig(BuilderConfig):
+    def __init__(self, language_abbr, data_dir, **kwargs):
+        """
+        Args:
+            language_abbr (str): Til qisqartmasi, masalan "uz".
+            data_dir (str): Dataset joylashgan asosiy papka (misol: "Dataset_STT").
+            **kwargs: Qolgan BuilderConfig parametrlar.
+        """
+        super().__init__(**kwargs)
+        self.language_abbr = language_abbr
+        self.data_dir = data_dir
 
 
 class MySTTDataset(datasets.GeneratorBasedBuilder):
     """
-    Common Voice uslubidagi minimal dataset skript:
-      - 3 ta tar fayl (train/test/validation)
-      - Har bir tar fayl ichida .mp3 audio
-      - Har bir split'ga mos TSV fayl (train.tsv, test.tsv, validation.tsv)
-      - Audio ustuni -> HF Viewer da "play" tugmasi
+    Common Voice uslubidagi minimal STT dataset yuklash skripti:
+      - 3 ta tar fayl (train, test, validation) ichida .mp3 audio fayllar mavjud.
+      - Har bir split uchun mos TSV fayl (train.tsv, test.tsv, validation.tsv) transkripsiyalarni o‘z ichiga oladi.
+      - 'audio' ustuni Audio() tipida bo‘lib, Hugging Face Dataset Viewer’da "play" tugmasi orqali audio eshittirish imkoniyatini beradi.
     """
     VERSION = datasets.Version("1.0.0")
 
-    # Agar ko'p config bo'lmasa, bu qismni soddalashtiramiz.
+    # Agar bir nechta konfiguratsiya bo‘lmasa, oddiy qilib bitta config ishlatamiz.
     BUILDER_CONFIGS = [
         STTConfig(
             name="uz",
             version=datasets.Version("1.0.0"),
             description="Uzbek subset of the STT dataset",
             language_abbr="uz",
-            data_dir="Dataset_STT",  # Ma'lumotlar joylashgan asosiy papka
+            data_dir="Dataset_STT",  # Bu yerga ma'lumotlar joylashgan asosiy papkani kiriting.
         )
     ]
     DEFAULT_CONFIG_NAME = "uz"
 
     def _info(self):
         """
-        Bu yerda datasetning xususiyatlari (features) e'lon qilinadi.
-        'audio' ustuni Audio() turida bo'lsa, viewer pleyer ko'rsatadi.
+        Datasetning xususiyatlari (features) aniqlanadi.
+        Agar 'audio' ustuni Audio() tipida bo‘lsa, Dataset Viewer audio pleyerni ko‘rsatadi.
         """
         return datasets.DatasetInfo(
-            description="Uzbek STT dataset: audio in .tar, transcriptions in .tsv.",
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "audio": Audio(sampling_rate=None),
-                    "sentence": datasets.Value("string"),
-                    "duration": datasets.Value("float"),
-                    "age": datasets.Value("string"),
-                    "gender": datasets.Value("string"),
-                    "accents": datasets.Value("string"),
-                    "locale": datasets.Value("string"),
-                }
-            ),
+            description="Uzbek STT dataset: audio fayllar .tar arxivda saqlanadi, transcriptions esa .tsv faylda.",
+            features=datasets.Features({
+                "id": datasets.Value("string"),
+                "audio": Audio(sampling_rate=None),  # sampling_rate=None degani audio fayldan olingan asl sampling rate saqlanadi.
+                "sentence": datasets.Value("string"),
+                "duration": datasets.Value("float"),
+                "age": datasets.Value("string"),
+                "gender": datasets.Value("string"),
+                "accents": datasets.Value("string"),
+                "locale": datasets.Value("string"),
+            }),
             supervised_keys=None,
             version=self.VERSION,
         )
 
     def _split_generators(self, dl_manager):
         """
-        Har bir split uchun: tar va tsv fayllar yo'lini belgilab, 
-        dl_manager orqali yuklab/extract qildirib, so'ng _generate_examples() ga beramiz.
+        Har bir split uchun: tar va tsv fayllar yo‘llarini belgilab, dl_manager orqali ularni yuklab yoki extract qildik.
         """
-        # local path misoli (reposingizda bo'lsa). 
-        # Agar huggingface.co'dan yuklamoqchi bo'lsangiz, URL qilishingiz mumkin
-        train_tar = "Dataset_STT/audio/uz/train.tar"
-        train_tsv = "Dataset_STT/transcript/uz/train.tsv"
+        config = self.config  # STTConfig obyekti
+        base_dir = config.data_dir  # Masalan: "Dataset_STT"
+        lang = config.language_abbr   # Masalan: "uz"
 
-        test_tar = "Dataset_STT/audio/uz/test.tar"
-        test_tsv = "Dataset_STT/transcript/uz/test.tsv"
+        # Audio va transkript fayllarining yo‘llarini shakllantiramiz:
+        train_tar = os.path.join(base_dir, "audio", lang, "train.tar")
+        train_tsv = os.path.join(base_dir, "transcript", lang, "train.tsv")
 
-        val_tar = "Dataset_STT/audio/uz/validation.tar"
-        val_tsv = "Dataset_STT/transcript/uz/validation.tsv"
+        test_tar = os.path.join(base_dir, "audio", lang, "test.tar")
+        test_tsv = os.path.join(base_dir, "transcript", lang, "test.tsv")
 
-        # Bu fayllarni download+extract (yoki local bo'lsa, faqat extract) qilamiz:
-        # Eslatma: agar localda bo'lsayu, dl_manager `is_local=True` deb topishi mumkin,
-        # ammo baribir .extract ishlaydi.
+        val_tar = os.path.join(base_dir, "audio", lang, "validation.tar")
+        val_tsv = os.path.join(base_dir, "transcript", lang, "validation.tsv")
 
+        # Tar fayllarni extract qilamiz (agar lokal bo‘lsa, dl_manager.extract mos yo‘lni qaytaradi)
         train_tar_extracted = dl_manager.extract(train_tar)
         test_tar_extracted = dl_manager.extract(test_tar)
         val_tar_extracted = dl_manager.extract(val_tar)
 
-        # Har bir splitted datasetga mos "SplitGenerator" qaytaramiz
-        # "gen_kwargs" -> _generate_examples() ga paramlar
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "archive_dir": train_tar_extracted,  # tar fayl ochilib yoyilgan papka
+                    "archive_dir": train_tar_extracted,  # Tar fayl extract qilingan papka
                     "tsv_path": train_tsv,
                 },
             ),
@@ -101,25 +108,23 @@ class MySTTDataset(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, archive_dir, tsv_path):
         """
-        Ushbu metod har bir split uchun audio+transkript juftliklarini geneate qiladi.
-          - 'archive_dir' papkada .tar dan ochilgan .mp3 fayllar mavjud.
-          - 'tsv_path' faylini qatorma-qator o'qib, 'id' -> "id.mp3" yo'lini izlaymiz.
+        TSV faylini qatorma-qator o‘qib, metadata lug‘atini yaratadi va
+        extract qilingan archive papkasidan mos .mp3 faylni topadi.
         """
-        # TSV ni o'qiymiz:
+        # TSV faylini ochamiz va DictReader yordamida o‘qiymiz.
         with open(tsv_path, "r", encoding="utf-8") as f:
             reader = csv.DictReader(f, delimiter="\t")
             for idx, row in enumerate(reader):
-                # tsv da shunaqa ustunlar bo'lishi kutiladi:
-                # id, sentence, duration, age, gender, accents, locale
+                # TSV faylida kutilayotgan ustunlar: id, sentence, duration, age, gender, accents, locale
                 audio_id = row["id"]
                 mp3_file = audio_id + ".mp3"
                 mp3_path = os.path.join(archive_dir, mp3_file)
 
-                # Agar audio fayl exist bo'lsa:
+                # Agar extract qilingan papkada audio fayl mavjud bo‘lsa:
                 if os.path.isfile(mp3_path):
                     yield idx, {
                         "id": audio_id,
-                        "audio": mp3_path,  # Audio() -> pleyer
+                        "audio": mp3_path,  # Audio() tipidagi ustun avtomatik ravishda faylni o‘qiydi va dekodlaydi.
                         "sentence": row.get("sentence", ""),
                         "duration": float(row.get("duration", 0.0)),
                         "age": row.get("age", ""),
@@ -128,5 +133,5 @@ class MySTTDataset(datasets.GeneratorBasedBuilder):
                         "locale": row.get("locale", ""),
                     }
                 else:
-                    # Audio topilmasa, skip (yoki exception)
-                    continue
+                    # Agar audio fayl topilmasa, bu yozuvni o'tkazib yuboramiz yoki xatolik chiqarish mumkin.
+                    continue