Create stt_uz_structure.py

stt_uz_structure.py

@@ -0,0 +1,116 @@
+import os
+import tarfile
+import csv
+import datasets
+from datasets.utils.py_utils import size_str
+from tqdm import tqdm
+
+
+class STTUzbekConfig(datasets.BuilderConfig):
+    """BuilderConfig for the STT Uzbek Dataset."""
+
+    def __init__(self, **kwargs):
+        description = (
+            "Speech-to-Text dataset for the Uzbek language. "
+            "The dataset contains audio files stored in different folders: "
+            "`wavs`, `uz_other_dataset`, `uz_validated_dataset`, `uz_train_dataset`. "
+            "The corresponding transcriptions are provided in the `metadata.csv` file."
+        )
+        super(STTUzbekConfig, self).__init__(description=description, **kwargs)
+
+
+class STTUzbek(datasets.GeneratorBasedBuilder):
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    BUILDER_CONFIGS = [
+        STTUzbekConfig(
+            name="stt_uzbek",
+            version=datasets.Version("1.0.0"),
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "file_name": datasets.Value("string"),
+                "audio": datasets.features.Audio(sampling_rate=48_000),
+                "transcription": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=features,
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/Beehzod/STT_uz",
+            license="Apache License 2.0",
+            citation="""@misc{uzbek_stt_dataset,
+            author = {Beehzod},
+            title = {Uzbek Speech-to-Text Dataset},
+            year = {2024},
+            howpublished = {https://huggingface.co/datasets/Beehzod/STT_uz},
+            note = {Dataset for Uzbek language speech-to-text tasks.}
+                }""",
+            version=self.config.version,
+        )
+
+    def _split_generators(self, dl_manager):
+        # Adjust the paths according to your setup
+        wavs_dir = "audio/wavs"
+        uz_other_dir = "audio/uz_other_dataset"
+        uz_validated_dir = "audio/uz_validated_dataset"
+        uz_train_dir = "audio/uz_train_dataset"
+        metadata_file = "metadata.csv"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "wavs_dir": wavs_dir,
+                    "uz_other_dir": uz_other_dir,
+                    "uz_validated_dir": uz_validated_dir,
+                    "uz_train_dir": uz_train_dir,
+                    "metadata_file": metadata_file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, wavs_dir, uz_other_dir, uz_validated_dir, uz_train_dir, metadata_file):
+        with open(metadata_file, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in tqdm(reader, desc="Processing metadata..."):
+                file_name = row["file_name"]
+                transcription = row["transcription"]
+
+                # Determine the file's location based on the path prefix
+                if file_name.startswith("audio/wavs"):
+                    audio_path = os.path.join(wavs_dir, os.path.basename(file_name))
+                elif file_name.startswith("audio/uz_other_dataset"):
+                    audio_path = self._extract_from_tar(uz_other_dir, file_name)
+                elif file_name.startswith("audio/uz_validated_dataset"):
+                    audio_path = self._extract_from_tar(uz_validated_dir, file_name)
+                elif file_name.startswith("audio/uz_train_dataset"):
+                    audio_path = self._extract_from_tar(uz_train_dir, file_name)
+                else:
+                    raise ValueError(f"Unknown path prefix in file_name: {file_name}")
+
+                # Yield the example
+                yield file_name, {
+                    "file_name": file_name,
+                    "audio": audio_path,
+                    "transcription": transcription,
+                }
+
+    def _extract_from_tar(self, tar_dir, file_name):
+        # Extract the specific file from the tar archives
+        for tar_file in os.listdir(tar_dir):
+            tar_path = os.path.join(tar_dir, tar_file)
+            with tarfile.open(tar_path, "r") as tar:
+                try:
+                    file_path = file_name.split("/")[-1]
+                    extracted_file = tar.extractfile(file_path)
+                    if extracted_file:
+                        return {"path": file_path, "bytes": extracted_file.read()}
+                except KeyError:
+                    continue
+        raise FileNotFoundError(f"File {file_name} not found in any tar archives in {tar_dir}.")