Create whisper.py

whisper.py

@@ -0,0 +1,59 @@
+import csv
+import os
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = "Custom dataset for extracting audio files and matching sentences."
+
+_DATA_URL = "https://huggingface.co/datasets/ugshanyu/dataset/resolve/main"  # Replace with the URL of your data
+
+class CustomDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "audio": datasets.Audio(sampling_rate=16_000),
+                "sentence": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("audio", "sentence"),
+            homepage=None,
+            citation=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        audio_path = dl_manager.download_and_extract(_DATA_URL+"/audo.zip")
+        csv_path = dl_manager.download_and_extract(_DATA_URL+"/comma.csv")
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"audio_path": audio_path, "csv_path": csv_path},
+            )
+        ]
+
+    def _generate_examples(self, audio_path, csv_path):
+        print(audio_path)
+        print(csv_path)
+        key = 0
+        print(os.listdir(audio_path))
+        
+        with open(csv_path, encoding="utf-8") as csv_file:
+            csv_reader = csv.DictReader(csv_file)
+            for row in csv_reader:
+                original_sentence_id, sentence, locale = row.values()
+                audio_file = f"{original_sentence_id}.mp3"
+                audio_file_path = os.path.join(audio_path, audio_file)
+                yield key, {
+                    "audio": audio_file_path,
+                    "sentence": sentence,
+                }
+                key += 1