Create dataset.py

dataset.py

@@ -0,0 +1,44 @@
+import datasets
+
+class AudioDataset(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "file_name": datasets.Value("string"),
+                    "transcription": datasets.Value("string"),
+                    "speaker_id": datasets.Value("int64"),
+                    "audio": datasets.Audio(sampling_rate=16_000),  # adjust the sampling rate if needed
+                }
+            ),
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = "data"  # Base directory containing both metadata and audio subdirectory
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": f"{data_dir}/train_metadata.csv", "audio_dir": f"{data_dir}/audio"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": f"{data_dir}/test_metadata.csv", "audio_dir": f"{data_dir}/audio"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath, audio_dir):
+        with open(filepath, "r", encoding="utf-8") as f:
+            for id_, line in enumerate(f):
+                if id_ == 0:
+                    continue  # skip header
+                parts = line.strip().split(",")
+                file_name = parts[0]
+                transcription = parts[1]
+                speaker_id = int(parts[2])
+                yield id_, {
+                    "file_name": file_name,
+                    "transcription": transcription,
+                    "speaker_id": speaker_id,
+                    "audio": f"{audio_dir}/{file_name.split('/')[-1]}",  # Extract the filename from the path and prepend the audio directory
+                }
+