scripts/prep_push_to_hf.py

#!/usr/bin/env python3
from os.path import basename, join
from pathlib import Path

import librosa
import numpy as np
import pandas as pd
from datasets import Audio, Dataset, DatasetDict

from leviticus import normalize

MAX_DURATION_IN_SECONDS = 10.0
MIN_DURATION_IN_SECONDS = 1.0
MAX_LEN = 50
MIN_LEN = 5


def duration_filter(item):
    return MIN_DURATION_IN_SECONDS < item < MAX_DURATION_IN_SECONDS


def text_filter(item):
    return MIN_LEN < len([i for i in item.split(" ") if len(i) > 0]) < MAX_LEN


def create_dataset(item):
    dataset = Dataset.from_pandas(item)
    dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
    dataset = dataset.filter(text_filter, input_columns=["text"])
    dataset = dataset.filter(duration_filter, input_columns=["duration"])
    return dataset


repo_dir = Path(__file__).resolve().parent.parent
data_dir = join(repo_dir, "data")
kendex_dir = join(data_dir, "Kendex")
audio_dir = join(kendex_dir, "wavs")

metadata = pd.read_csv(join(kendex_dir, "metadata.csv"), delimiter="|", header=None)
wavs = pd.Series([join(audio_dir, f"{f}.wav") for f in metadata[0]])
data = {
    "audio": wavs,
    "file": [basename(w) for w in wavs],
    "text": metadata[1],
    "norm": metadata[1].map(lambda x: normalize(x)),
    "duration": [librosa.get_duration(path=w) for w in wavs],
}

df = pd.DataFrame(data).sample(frac=1, random_state=666).reset_index(drop=True)

train, test = np.split(df, [int(0.9 * len(df))])

train_dataset = create_dataset(train)
test_dataset = create_dataset(test)

full_dataset = DatasetDict({"train": train_dataset, "test": test_dataset})
full_dataset.push_to_hub("michaelnetbiz/Kendex")