# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Compute mean and std for feature normalizer, and save to file."""
import argparse
import functools

from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
from paddlespeech.s2t.frontend.featurizer.audio_featurizer import AudioFeaturizer
from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
from paddlespeech.utils.argparse import add_arguments
from paddlespeech.utils.argparse import print_arguments


def compute_cmvn(manifest_path="data/librispeech/manifest.train",
                 output_path="data/librispeech/mean_std.npz",
                 num_samples=2000,
                 num_workers=0,
                 spectrum_type="linear",
                 feat_dim=13,
                 delta_delta=False,
                 stride_ms=10,
                 window_ms=20,
                 sample_rate=16000,
                 use_dB_normalization=True,
                 target_dB=-20):

    augmentation_pipeline = AugmentationPipeline('{}')
    audio_featurizer = AudioFeaturizer(
        spectrum_type=spectrum_type,
        feat_dim=feat_dim,
        delta_delta=delta_delta,
        stride_ms=float(stride_ms),
        window_ms=float(window_ms),
        n_fft=None,
        max_freq=None,
        target_sample_rate=sample_rate,
        use_dB_normalization=use_dB_normalization,
        target_dB=target_dB,
        dither=0.0)

    def augment_and_featurize(audio_segment):
        augmentation_pipeline.transform_audio(audio_segment)
        return audio_featurizer.featurize(audio_segment)

    normalizer = FeatureNormalizer(
        mean_std_filepath=None,
        manifest_path=manifest_path,
        featurize_func=augment_and_featurize,
        num_samples=num_samples,
        num_workers=num_workers)
    normalizer.write_to_file(output_path)


def define_argparse():
    parser = argparse.ArgumentParser(description=__doc__)
    add_arg = functools.partial(add_arguments, argparser=parser)

    # yapf: disable
    add_arg('manifest_path', str,
            'data/librispeech/manifest.train',
            "Filepath of manifest to compute normalizer's mean and stddev.")

    add_arg('output_path', str,
            'data/librispeech/mean_std.npz',
            "Filepath of write mean and stddev to (.npz).")
    add_arg('num_samples',  int,    2000,    "# of samples to for statistics.")
    add_arg('num_workers',
                            default=0,
                            type=int,
                            help='num of subprocess workers for processing')


    add_arg('spectrum_type', str,
            'linear',
            "Audio feature type. Options: linear, mfcc, fbank.",
            choices=['linear', 'mfcc', 'fbank'])
    add_arg('feat_dim', int, 13, "Audio feature dim.")
    add_arg('delta_delta', bool,  False, "Audio feature with delta delta.")
    add_arg('stride_ms', int, 10,  "stride length in ms.")
    add_arg('window_ms', int, 20,  "stride length in ms.")
    add_arg('sample_rate',  int, 16000,  "target sample rate.")
    add_arg('use_dB_normalization', bool, True, "do dB normalization.")
    add_arg('target_dB',   int, -20,  "target dB.")
    # yapf: disable

    args = parser.parse_args()
    return args

def main():
    args = define_argparse()
    print_arguments(args, globals())
    compute_cmvn(**vars(args))

if __name__ == '__main__':
    main()