#!/usr/bin/env python3
# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


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

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

import logging
from pathlib import Path

import torch
from lhotse import CutSet, LilcomChunkyWriter, combine
from lhotse.features.kaldifeat import (
    KaldifeatFbank,
    KaldifeatFbankConfig,
    KaldifeatFrameOptions,
    KaldifeatMelOptions,
)
from lhotse.recipes.utils import read_manifests_if_cached

# 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_musan():
    src_dir = Path("data/manifests")
    output_dir = Path("data/fbank")

    sampling_rate = 16000
    num_mel_bins = 80

    dataset_parts = (
        "music",
        "speech",
        "noise",
    )
    prefix = "musan"
    suffix = "jsonl.gz"
    manifests = read_manifests_if_cached(
        dataset_parts=dataset_parts,
        output_dir=src_dir,
        prefix=prefix,
        suffix=suffix,
    )
    assert manifests is not None

    assert len(manifests) == len(dataset_parts), (
        len(manifests),
        len(dataset_parts),
        list(manifests.keys()),
        dataset_parts,
    )

    musan_cuts_path = src_dir / "musan_cuts.jsonl.gz"

    if musan_cuts_path.is_file():
        logging.info(f"{musan_cuts_path} already exists - skipping")
        return

    logging.info("Extracting features for Musan")

    extractor = KaldifeatFbank(
        KaldifeatFbankConfig(
            frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
            mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
            device="cuda",
        )
    )

    # create chunks of Musan with duration 5 - 10 seconds
    _ = (
        CutSet.from_manifests(
            recordings=combine(part["recordings"] for part in manifests.values())
        )
        .cut_into_windows(10.0)
        .filter(lambda c: c.duration > 5)
        .compute_and_store_features_batch(
            extractor=extractor,
            storage_path=output_dir / "musan_feats",
            manifest_path=musan_cuts_path,
            batch_duration=500,
            num_workers=4,
            storage_type=LilcomChunkyWriter,
        )
    )


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

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