data_utils.py

from datasets import load_dataset, Audio
from normalizer import EnglishTextNormalizer, BasicMultilingualTextNormalizer

from .eval_utils import read_manifest, write_manifest


def is_target_text_in_range(ref):
    if ref.strip() == "ignore time segment in scoring":
        return False
    else:
        return ref.strip() != ""


def get_text(sample):
    if "text" in sample:
        return sample["text"]
    elif "sentence" in sample:
        return sample["sentence"]
    elif "normalized_text" in sample:
        return sample["normalized_text"]
    elif "transcript" in sample:
        return sample["transcript"]
    elif "transcription" in sample:
        return sample["transcription"]
    else:
        raise ValueError(
            f"Expected transcript column of either 'text', 'sentence', 'normalized_text' or 'transcript'. Got sample of "
            ".join{sample.keys()}. Ensure a text column name is present in the dataset."
        )

normalizer = EnglishTextNormalizer()

ml_normalizer = BasicMultilingualTextNormalizer()


def normalize(batch):
    batch["original_text"] = get_text(batch)
    batch["norm_text"] = normalizer(batch["original_text"])
    return batch


def load_data(args):
    dataset = load_dataset(
        args.dataset_path,
        args.dataset,
        split=args.split,
        streaming=args.streaming,
        token=True,
    )

    return dataset

def prepare_data(dataset, decode_audio=True):
    # Re-sample to 16kHz and normalise transcriptions
    if decode_audio:
        dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
    else:
        # Keep decode=False but ensure sampling_rate is set
        current_audio_feature = dataset.features["audio"]
        if hasattr(current_audio_feature, 'decode') and not current_audio_feature.decode:
            # Already set up with decode=False, don't change it
            pass
        else:
            dataset = dataset.cast_column("audio", Audio(sampling_rate=16000, decode=False))
    dataset = dataset.map(normalize)
    dataset = dataset.filter(is_target_text_in_range, input_columns=["norm_text"])

    return dataset