Upload dataset_stt.py

dataset_stt.py

@@ -1,67 +1,49 @@
 import csv
+import os
 import tarfile
+
 import datasets
+from tqdm import tqdm
 
 _DESCRIPTION = """\
-This dataset is designed for speech-to-text tasks and contains audio files stored in tar archives along with corresponding transcript files in TSV format.
-The dataset is organized by splits (train, test, validation) for the Uzbek language.
+This dataset is designed for speech-to-text (STT) tasks. It contains audio files stored as tar archives along with their corresponding transcript files in TSV format. The data is for the Uzbek language.
 """
 
 _CITATION = """\
-@inproceedings{yourcitation2025,
-  title={Your Dataset Title},
+@misc{dataset_stt2025,
+  title={Dataset_STT},
   author={Your Name},
-  year={2025},
-  eprint={XXXX.XXXX},
-  archivePrefix={arXiv},
-  primaryClass={cs.CL}
+  year={2025}
 }
 """
 
-_LICENSE = "MIT"
-
 class DatasetSTT(datasets.GeneratorBasedBuilder):
     VERSION = datasets.Version("1.0.0")
-
+    
     def _info(self):
-        # Belgilangan feature'lar: audio field Audio tipida (sampling_rate ni moslashtiring, masalan 16000)
         features = datasets.Features({
             "id": datasets.Value("string"),
-            "audio": datasets.Audio(sampling_rate=16000),
+            "audio": datasets.Audio(sampling_rate=16000),  # Agar kerak bo'lsa, sampling_rate ni moslashtiring
             "sentence": datasets.Value("string"),
             "duration": datasets.Value("float"),
             "age": datasets.Value("string"),
             "gender": datasets.Value("string"),
             "accents": datasets.Value("string"),
-            "locale": datasets.Value("string"),
+            "locale": datasets.Value("string")
         })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=features,
             supervised_keys=None,
             homepage="https://huggingface.co/datasets/Elyordev/Dataset_STT",
-            license=_LICENSE,
             citation=_CITATION,
         )
-
+    
     def _split_generators(self, dl_manager):
         """
-        Biz kutilayotgan fayl strukturasini quyidagicha belgilaymiz:
-        
-        data_files = {
-            "train": {
-                "audio": "audio/uz/train/train.tar",
-                "transcript": "transcript/uz/train/train.tsv"
-            },
-            "test": {
-                "audio": "audio/uz/test/test.tar",
-                "transcript": "transcript/uz/test/test.tsv"
-            },
-            "validation": {
-                "audio": "audio/uz/validation/validation.tar",
-                "transcript": "transcript/uz/validation/validation.tsv"
-            }
-        }
+        _split_generators da har bir split uchun kerakli fayllarni belgilaymiz.
+        Biz quyidagi splitlarni qo'llaymiz: TRAIN, TEST va VALIDATION.
+        Data_files argumenti orqali audio arxiv va transcript TSV fayllarini olamiz.
         """
         data_files = self.config.data_files
         return [
@@ -87,42 +69,40 @@ class DatasetSTT(datasets.GeneratorBasedBuilder):
                 },
             ),
         ]
-
+    
     def _generate_examples(self, audio_archive, transcript_file):
         """
         Transcript TSV faylini o'qib, har bir yozuv uchun:
-         - tar arxivini ochib, "path" ustuni orqali audio faylni topamiz;
-         - Audio faylni baytlar shaklida o'qib, datasets.Audio feature'iga mos formatda qaytaramiz.
+          - Tar arxivni ochamiz va audio fayllarni indekslaymiz.
+          - Transcript faylida ko'rsatilgan "path" ustuni orqali mos audio faylni topamiz.
+          - Audio faylni butun baytlar shaklida o'qib, audio maydoni sifatida qaytaramiz.
         """
-        # Tar arxivini ochamiz
+        # Tar arxivni ochamiz
         with tarfile.open(audio_archive, "r:*") as tar:
-            # Tar arxividagi barcha fayllarni indekslaymiz (fayl nomi -> tarinfo)
-            tar_index = {member.name: member for member in tar.getmembers() if member.isfile()}
-
-            # Transcript TSV faylini o'qish (UTF-8 kodlashda)
+            # Arxiv ichidagi barcha fayllarni (fayl nomi -> tarinfo) indekslaymiz
+            tar_index = {os.path.basename(member.name): member for member in tar.getmembers() if member.isfile()}
+            
+            # Transcript TSV faylini ochamiz (UTF-8 kodlashda)
             with open(transcript_file, "r", encoding="utf-8") as f:
                 reader = csv.DictReader(f, delimiter="\t")
-                for row in reader:
-                    file_name = row["path"]
-                    # Agar arxivda fayl topilsa:
-                    if file_name in tar_index:
-                        # Faylni o'qib, butun baytlarni olamiz
-                        audio_member = tar.extractfile(tar_index[file_name])
-                        if audio_member is None:
-                            print(f"Warning: Could not extract {file_name} from {audio_archive}.")
-                            continue
-                        audio_bytes = audio_member.read()
-                    else:
-                        # Agar fayl topilmasa, ogohlantirish chiqaramiz va davom etamiz.
-                        print(f"Warning: File {file_name} not found in archive {audio_archive}.")
+                for row in tqdm(reader, desc="Processing transcripts"):
+                    file_name = row["path"]  # Masalan: "2cd08f62-aa25-4f5e-bb73-40cfc19a215e.mp3"
+                    if file_name not in tar_index:
+                        print(f"Warning: {file_name} not found in {audio_archive}")
                         continue
-
-                    # Yozuvni qaytaramiz, audio maydoni uchun file nomi va baytlarni dictionary ko'rinishida beramiz.
+                    
+                    audio_member = tar.extractfile(tar_index[file_name])
+                    if audio_member is None:
+                        print(f"Warning: Could not extract {file_name}")
+                        continue
+                    
+                    audio_bytes = audio_member.read()
+                    
                     yield row["id"], {
                         "id": row["id"],
                         "audio": {"path": file_name, "bytes": audio_bytes},
                         "sentence": row["sentence"],
-                        "duration": float(row["duration"]),
+                        "duration": float(row["duration"]) if row["duration"] else 0.0,
                         "age": row["age"],
                         "gender": row["gender"],
                         "accents": row["accents"],