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.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
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+data/* filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text

README.md

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+# hy_wiki_openai_tts
+Huggingface dataset containing transcripts from Armenian wikipedia pages generated by OpenAI TTS

hy_wiki_openai_tts.py

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+##
+import os
+import pandas as pd
+from datasets import GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split, Features, Value, Audio, Version
+
+_URLS = {"train": "https://huggingface.co/datasets/aburnazy/hy_wiki_openai_tts/resolve/main/data/dataset.tar.gz"}
+
+class HyAsrGrqaser(GeneratorBasedBuilder):
+    """Armenian Audio-Transcription Dataset"""
+
+    VERSION = Version("1.0.0")
+
+    def _info(self):
+        return DatasetInfo(
+            description="This dataset contains Armenian speech and transcriptions.",
+            features=Features({
+                'audio': Audio(sampling_rate=16_000),  # Adjust the sampling rate as needed
+                'sentence': Value('string')
+            }),
+            supervised_keys=("audio", "sentence"),
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Assuming the script is in the root of the project structure
+        # metadata_path = os.path.join(os.path.dirname(__file__), "metadata.csv")
+        metadata_path = dl_manager.download_and_extract(
+            "https://huggingface.co/datasets/aburnazy/hy_wiki_openai_tts/resolve/main/metadata.csv")
+        data_dir = dl_manager.download_and_extract(_URLS)
+        print(f"----------data_dir: {data_dir}, \n----------metadata_path: {metadata_path}")
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir['train'], "metadata_path": metadata_path}
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, metadata_path):
+        print(f"data_dir: {data_dir}, metadata_path: {metadata_path}")
+        """Yields examples."""
+        # Load metadata.csv
+        metadata = pd.read_csv(metadata_path)
+
+        # Generate examples
+        for idx, row in metadata.iterrows():
+            file_path = os.path.join(data_dir, row['file_name'])
+            transcription = row['transcription']
+            yield idx, {
+                'audio': {'path': file_path},
+                'sentence': transcription
+            }
+
+# Testing the dataset locally
+# if __name__ == "__main__":
+#     from datasets import load_dataset
+#     dataset = load_dataset("C:\\Projects\\aeneas\\hy_asr_grqaser\\hy_asr_grqaser.py")
+#     print(dataset["train"][0])
+##
+# from datasets import load_dataset
+#
+# dataset = load_dataset("aburnazy/hy_asr_grqaser")
+# print('------------------')
+# print(dataset["train"][0])

requirements.txt

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+pandas
+librosa
+datasets