Upload my_stt_dataset.py

my_stt_dataset.py

@@ -3,127 +3,129 @@ import csv
 import datasets
 from datasets import Audio
 
-# Har xil config - 'sample' va 'full'
-class MySTTDatasetConfig(datasets.BuilderConfig):
-    def __init__(self, limit=None, **kwargs):
-        """
-        limit : int yoki None
-          Har bir splitdan qancha qatorni o'qish.
-          None bo'lsa, cheklanmagan.
-        """
-        super().__init__(**kwargs)
-        self.limit = limit
-
 
 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
+    """
+    VERSION = datasets.Version("1.0.0")
+
+    # Agar ko'p config bo'lmasa, bu qismni soddalashtiramiz.
     BUILDER_CONFIGS = [
-        MySTTDatasetConfig(
-            name="sample",
-            version=datasets.Version("1.0.0"),
-            description="Faqat har bir splitdan 10k qator ko'rsatish uchun",
-            limit=10_000,  # masalan 10 ming
-        ),
-        MySTTDatasetConfig(
-            name="full",
-            version=datasets.Version("1.0.0"),
-            description="Hech qanday cheklovsiz to'liq dataset",
-            limit=None,
-        ),
+        datasets.BuilderConfig(
+            name="uz",
+            version=VERSION,
+            description="STT dataset for Uzbek language (example).",
+        )
     ]
-    
-    DEFAULT_CONFIG_NAME = "sample"
-    
+
+    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.
+        """
         return datasets.DatasetInfo(
-            description="Speech-to-text dataset (tar ichida audio, tsvda transkript).",
-            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 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"),
+                }
+            ),
             supervised_keys=None,
+            version=self.VERSION,
         )
-    
+
     def _split_generators(self, dl_manager):
-        # TODO: train.tar, test.tar, validation.tar + tegishli TSV link yoki local path
-        # Hozircha misol tariqasida local path'lar ko'rsatamiz
-        train_tar = "Dataset_STT/audio/uz/train/train.tar"
-        train_tsv = "Dataset_STT/transcript/uz/train/train.tsv"
-        
-        val_tar = "Dataset_STT/audio/uz/validation/validation.tar"
-        val_tsv = "Dataset_STT/transcript/uz/validation/validation.tsv"
-        
-        test_tar = "Dataset_STT/audio/uz/test/test.tar"
-        test_tsv = "Dataset_STT/transcript/uz/test/test.tsv"
-        
+        """
+        Har bir split uchun: tar va tsv fayllar yo'lini belgilab, 
+        dl_manager orqali yuklab/extract qildirib, so'ng _generate_examples() ga beramiz.
+        """
+        # 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"
+
+        test_tar = "Dataset_STT/audio/uz/test.tar"
+        test_tsv = "Dataset_STT/transcript/uz/test.tsv"
+
+        val_tar = "Dataset_STT/audio/uz/validation.tar"
+        val_tsv = "Dataset_STT/transcript/uz/validation.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.
+
+        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={
-                    "tar_path": train_tar,
+                    "archive_dir": train_tar_extracted,  # tar fayl ochilib yoyilgan papka
                     "tsv_path": train_tsv,
                 },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
+                name=datasets.Split.TEST,
                 gen_kwargs={
-                    "tar_path": val_tar,
-                    "tsv_path": val_tsv,
+                    "archive_dir": test_tar_extracted,
+                    "tsv_path": test_tsv,
                 },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST,
+                name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "tar_path": test_tar,
-                    "tsv_path": test_tsv,
+                    "archive_dir": val_tar_extracted,
+                    "tsv_path": val_tsv,
                 },
             ),
         ]
-    
-    def _generate_examples(self, tar_path, tsv_path):
+
+    def _generate_examples(self, archive_dir, tsv_path):
         """
-        limit=10_000 bo'lsa, har bir splitdan 10 mingtagina qator qaytaradi.
-        Agar limit=None bo'lsa, hamma qatorni o'qiydi.
+        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.
         """
-        limit = self.config.limit
-        
-        # Tar ichidagi mp3 fayllarni avval extract qilasiz yoki on-the-fly o'qiysiz
-        # Eslatma: HF Viewer uchun eng osoni audio papkaga ochib qo'yish yoki
-        # `dl_manager.download_and_extract(...)` ishlatishdir.
-        # Bu yerda misol tariqasida tar ni ochib, audio fayllarni papkaga yoyildi deb faraz qilamiz:
-        
-        # Masalan audio papka: "Dataset_STT/audio/uz/train/unpacked"
-        # Yoki to'liq yo'li: tar_path = "Dataset_STT/audio/uz/train/train.tar"
-        # "unpacked" papkani o'zingiz oldindan tar -xvf bilan yaratishingiz kerak.
-        # Yoki tarfile moduli bilan python ichida extraction qilishingiz mumkin.
-        
-        # Soddalik uchun, tar ni allaqachon manual ravishda unpack qildik deb olamiz:
-        audio_folder = tar_path.replace(".tar", "_unpacked")  
-        # misol: "Dataset_STT/audio/uz/train/train_unpacked"
-        
-        # Keyin TSV'ni o'qiymiz:
+        # TSV ni o'qiymiz:
         with open(tsv_path, "r", encoding="utf-8") as f:
             reader = csv.DictReader(f, delimiter="\t")
             for idx, row in enumerate(reader):
-                if limit is not None and idx >= limit:
-                    break  # 10k dan oshsa, to'xtaymiz
-                
+                # tsv da shunaqa ustunlar bo'lishi kutiladi:
+                # id, sentence, duration, age, gender, accents, locale
                 audio_id = row["id"]
                 mp3_file = audio_id + ".mp3"
-                mp3_path = os.path.join(audio_folder, mp3_file)
-                
-                yield idx, {
-                    "id": audio_id,
-                    "audio": mp3_path,
-                    "sentence": row["sentence"],
-                    "duration": float(row["duration"]),
-                    "age": row["age"],
-                    "gender": row["gender"],
-                    "accents": row["accents"],
-                    "locale": row["locale"],
-                }
+                mp3_path = os.path.join(archive_dir, mp3_file)
+
+                # Agar audio fayl exist bo'lsa:
+                if os.path.isfile(mp3_path):
+                    yield idx, {
+                        "id": audio_id,
+                        "audio": mp3_path,  # Audio() -> pleyer
+                        "sentence": row.get("sentence", ""),
+                        "duration": float(row.get("duration", 0.0)),
+                        "age": row.get("age", ""),
+                        "gender": row.get("gender", ""),
+                        "accents": row.get("accents", ""),
+                        "locale": row.get("locale", ""),
+                    }
+                else:
+                    # Audio topilmasa, skip (yoki exception)
+                    continue