#!/usr/bin/env python3

"""
This file computes fbank features of the yesno dataset.
It looks for manifests in the directory data/manifests.

The generated fbank features are saved in data/fbank.
"""

import logging
import os
from pathlib import Path

import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
from lhotse.recipes.utils import read_manifests_if_cached

from icefall.utils import get_executor

# Torch's multithreaded behavior needs to be disabled or it wastes a
# lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)


def compute_fbank_yesno():
    src_dir = Path("data/manifests")
    output_dir = Path("data/fbank")

    # This dataset is rather small, so we use only one job
    num_jobs = min(1, os.cpu_count())
    num_mel_bins = 23

    dataset_parts = (
        "train",
        "test",
    )
    manifests = read_manifests_if_cached(
        dataset_parts=dataset_parts, output_dir=src_dir
    )
    assert manifests is not None

    extractor = Fbank(
        FbankConfig(sampling_rate=8000, num_mel_bins=num_mel_bins)
    )

    with get_executor() as ex:  # Initialize the executor only once.
        for partition, m in manifests.items():
            if (output_dir / f"cuts_{partition}.json.gz").is_file():
                logging.info(f"{partition} already exists - skipping.")
                continue
            logging.info(f"Processing {partition}")
            cut_set = CutSet.from_manifests(
                recordings=m["recordings"],
                supervisions=m["supervisions"],
            )
            if "train" in partition:
                cut_set = (
                    cut_set
                    + cut_set.perturb_speed(0.9)
                    + cut_set.perturb_speed(1.1)
                )
            cut_set = cut_set.compute_and_store_features(
                extractor=extractor,
                storage_path=f"{output_dir}/feats_{partition}",
                # when an executor is specified, make more partitions
                num_jobs=num_jobs if ex is None else 1,  # use one job
                executor=ex,
                storage_type=LilcomHdf5Writer,
            )
            cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")


if __name__ == "__main__":
    formatter = (
        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
    )

    logging.basicConfig(format=formatter, level=logging.INFO)

    compute_fbank_yesno()