Upload my_stt_dataset.py

my_stt_dataset.py

@@ -1,120 +1,129 @@
 import os
-import tarfile
 import csv
-
 import datasets
 from datasets import Audio
 
-
+# Har xil config - 'sample' va 'full'
 class MySTTDatasetConfig(datasets.BuilderConfig):
-    """Config klass (kerak bo'lsa turli versiya yoki param qo'yish uchun)."""
+    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):
     BUILDER_CONFIGS = [
-        MySTTDatasetConfig(name="default", version=datasets.Version("1.0.0")),
+        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,
+        ),
     ]
-
+    
+    DEFAULT_CONFIG_NAME = "sample"
+    
     def _info(self):
         return datasets.DatasetInfo(
-            description="STT dataset .tar ichida audio, .tsv ichida transkript",
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "audio": Audio(sampling_rate=None),  # Audio turi
-                    "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="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"),
+            }),
             supervised_keys=None,
         )
-
+    
     def _split_generators(self, dl_manager):
-        """
-          dl_manager.download_and_extract() bilan remote fayllarni yuklaysiz
-          yoki local path bersangiz bo'ladi.
-          Masalan: 
-            https://huggingface.co/datasets/Elyordev/new_dataset_stt/resolve/main/Dataset_STT/audio/uz/train/train.tar
-            https://huggingface.co/datasets/Elyordev/new_dataset_stt/resolve/main/Dataset_STT/transcript/uz/train/train.tsv
-        """
-        train_tar = dl_manager.download_and_extract("URL_train_tar")  # masalan
-        train_tsv = dl_manager.download("URL_train_tsv")
-
-        val_tar = dl_manager.download_and_extract("URL_val_tar")
-        val_tsv = dl_manager.download("URL_val_tsv")
-
-        test_tar = dl_manager.download_and_extract("URL_test_tar")
-        test_tsv = dl_manager.download("URL_test_tsv")
-
+        # 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"
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "archive_path": train_tar,
+                    "tar_path": train_tar,
                     "tsv_path": train_tsv,
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "archive_path": val_tar,
+                    "tar_path": val_tar,
                     "tsv_path": val_tsv,
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "archive_path": test_tar,
+                    "tar_path": test_tar,
                     "tsv_path": test_tsv,
                 },
             ),
         ]
-
-    def _generate_examples(self, archive_path, tsv_path):
+    
+    def _generate_examples(self, tar_path, tsv_path):
         """
-         Har bir tar faylni ochib, TSV dagi id + sentence boshqariladi.
+        limit=10_000 bo'lsa, har bir splitdan 10 mingtagina qator qaytaradi.
+        Agar limit=None bo'lsa, hamma qatorni o'qiydi.
         """
-        # `archive_path` bu dl_manager.download_and_extract qilgan manzil
-        # lekin e’tibor bering: .extract automatik ravishda chiqarib yuboradi.
-        # Agar `.tar` ochilmasin desangiz, download() deyish kifoya, 
-        # lekin streaming qilish uchun biroz boshqacha yo'l tutish kerak.
-
-        # Keling, bu yerda tar fayl allaqachon extract qilingan deb faraz qilamiz:
-        #  train.tar -> train/ (ichida mp3 fayllar paydo bo'lgan bo'ladi).
-        #  Agar real .tar ichidan "on-the-fly" o'qimoqchi bo'lsak, 
-        #  dl_manager.download() + tarfile.open(...) da ishlash lozim.
-
-        # Shunchaki misol tariqasida:
-        audio_base_path = archive_path  # extract bo'lgach papka manzili
-        # Ehtimol, audio_base_path = os.path.join(archive_path, "train") bo'lishi ham mumkin
-        # chunki tar ichida "train/" deb nomlangan ichki papka bo'lishi mumkin.
-
+        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:
         with open(tsv_path, "r", encoding="utf-8") as f:
             reader = csv.DictReader(f, delimiter="\t")
-            for n, row in enumerate(reader):
-                audio_id = row["id"]  # masalan '009f0d56-c7db-4de3-bd3e-92a37d6f0cb9'
-                audio_file = audio_id + ".mp3"
+            for idx, row in enumerate(reader):
+                if limit is not None and idx >= limit:
+                    break  # 10k dan oshsa, to'xtaymiz
                 
-                # to'liq path
-                audio_path = os.path.join(audio_base_path, audio_file)
-
-                # Agar audio fayl topilsa:
-                if os.path.isfile(audio_path):
-                    yield n, {
-                        "id": audio_id,
-                        "audio": audio_path,  # Audio typeda faqat path bersak, HF o'zi o'qiydi
-                        "sentence": row["sentence"],
-                        "duration": float(row["duration"]),
-                        "age": row["age"],
-                        "gender": row["gender"],
-                        "accents": row["accents"],
-                        "locale": row["locale"],
-                    }
-                else:
-                    # Agar mp3 topilmasa, o'ziz xato signal qilishingiz mumkin
-                    # yoki continue qilishingiz mumkin
-                    pass
+                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"],
+                }