feat: loading script

filimo2024asr.py

@@ -0,0 +1,97 @@
+import os
+import tarfile
+import datasets
+import pandas as pd
+from typing import Dict, List
+import io
+from tqdm import tqdm
+import csv
+import os
+
+_DESCRIPTION = """
+This dataset consists of about 400 hours of audio extracted from various Filimo videos in the Persian language.
+Note: This dataset contains raw, unvalidated transcriptions. Users are advised to:
+1. Perform their own quality assessment
+2. Create their own train/validation/test splits based on their specific needs
+3. Validate a subset of the data if needed for their use case
+"""
+
+_CITATION = """
+Use this repo info/link for citation.
+"""
+
+_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"
+
+_HOMEPAGE = "https://huggingface.co/datasets/PerSets/filimo2024asr"
+
+_BASE_URL = "https://huggingface.co/datasets/PerSets/filimo2024asr/resolve/main/"
+
+_AUDIO_URL = _BASE_URL + "data/unvalidated_{shard_idx:03d}.tar"
+
+class FilimoASRDataset(datasets.GeneratorBasedBuilder):
+    
+    DEFAULT_WRITER_BATCH_SIZE = 1000
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features({
+                "audio": datasets.Audio(sampling_rate=44_000),  # Adjust sampling rate as needed
+                "text": datasets.Value("string"),
+                "file_name": datasets.Value("string"),
+            }),
+            supervised_keys=None,
+            license=_LICENSE,
+            citation=_CITATION,
+            version=self.VERSION,
+            description=_DESCRIPTION
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        
+        archive_paths = [_AUDIO_URL.format(shard_idx=i) for i in range(1, 34)]
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
+
+        return [
+            datasets.SplitGenerator(
+                name="unvalidated",  # Or adjust splits as needed
+                gen_kwargs={
+                    #"tar_dir": tar_dir,
+                    #"metadata_path": metadata_path,
+                    "local_extracted_archive_paths": local_extracted_archive_paths,
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths],
+                    "meta_path": _BASE_URL + "unvalidated.csv",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
+        """Yields examples."""
+        # Load TSV metadata
+        data_fields = list(self._info().features.keys())
+        metadata = {}
+        with open(meta_path, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for row in tqdm(reader, desc="Reading metadata..."):
+                if not row["file_name"].endswith(".mp3"):
+                    row["file_name"] += ".mp3"
+                if "sentence" in row:
+                    row['text'] = row['sentence']
+                    del row['sentence']
+                for field in data_fields:
+                    if field not in row:
+                        row[field] = ""
+                metadata[row["file_name"]] = row
+
+        for i, audio_archive in enumerate(archives):
+            for path, file in audio_archive:
+                _, filename = os.path.split(path)
+                if filename in metadata:
+                    result = dict(metadata[filename])
+                    # set the audio feature and the path to the extracted file
+                    path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
+                    result["audio"] = {"path": path, "bytes": file.read()}
+                    result["file_name"] = path
+                    yield path, result