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
                }