# Copyright (c) 2021 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.
import argparse
import os
import time

import paddle
from paddleaudio.backends import soundfile_load as load_audio
from paddleaudio.compliance.librosa import melspectrogram
from yacs.config import CfgNode

from paddlespeech.s2t.utils.log import Log
from paddlespeech.vector.io.batch import feature_normalize
from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
from paddlespeech.vector.modules.sid_model import SpeakerIdetification
from paddlespeech.vector.training.seeding import seed_everything

logger = Log(__name__).getlog()


def extract_audio_embedding(args, config):
    # stage 0: set the training device, cpu or gpu
    paddle.set_device(args.device)
    # set the random seed, it is a must for multiprocess training
    seed_everything(config.seed)

    # stage 1: build the dnn backbone model network
    ecapa_tdnn = EcapaTdnn(**config.model)

    # stage4: build the speaker verification train instance with backbone model
    model = SpeakerIdetification(
        backbone=ecapa_tdnn, num_class=config.num_speakers)
    # stage 2: load the pre-trained model
    args.load_checkpoint = os.path.abspath(
        os.path.expanduser(args.load_checkpoint))

    # load model checkpoint to sid model
    state_dict = paddle.load(
        os.path.join(args.load_checkpoint, 'model.pdparams'))
    model.set_state_dict(state_dict)
    logger.info(f'Checkpoint loaded from {args.load_checkpoint}')

    # stage 3: we must set the model to eval mode
    model.eval()

    # stage 4: read the audio data and extract the embedding
    # wavform is one dimension numpy array 
    waveform, sr = load_audio(args.audio_path)

    # feat type is numpy array, whose shape is [dim, time]
    # we need convert the audio feat to one-batch shape [batch, dim, time], where the batch is one
    # so the final shape is [1, dim, time]
    start_time = time.time()
    feat = melspectrogram(
        x=waveform,
        sr=config.sr,
        n_mels=config.n_mels,
        window_size=config.window_size,
        hop_length=config.hop_size)
    feat = paddle.to_tensor(feat).unsqueeze(0)

    # in inference period, the lengths is all one without padding
    lengths = paddle.ones([1])
    feat = feature_normalize(feat, mean_norm=True, std_norm=False)

    # model backbone network forward the feats and get the embedding
    embedding = model.backbone(
        feat, lengths).squeeze().numpy()  # (1, emb_size, 1) -> (emb_size)
    elapsed_time = time.time() - start_time
    audio_length = waveform.shape[0] / sr

    # stage 5: do global norm with external mean and std
    rtf = elapsed_time / audio_length
    logger.info(f"{args.device} rft={rtf}")

    return embedding


if __name__ == "__main__":
    # yapf: disable
    parser = argparse.ArgumentParser(__doc__)
    parser.add_argument('--device',
                        choices=['cpu', 'gpu'],
                        default="cpu",
                        help="Select which device to train model, defaults to gpu.")
    parser.add_argument("--config",
                        default=None,
                        type=str,
                        help="configuration file")
    parser.add_argument("--load-checkpoint",
                        type=str,
                        default='',
                        help="Directory to load model checkpoint to contiune trainning.")
    parser.add_argument("--audio-path",
                        default="./data/demo.wav",
                        type=str,
                        help="Single audio file path")
    args = parser.parse_args()
    # yapf: enable
    # https://yaml.org/type/float.html
    config = CfgNode(new_allowed=True)
    if args.config:
        config.merge_from_file(args.config)

    config.freeze()
    print(config)

    extract_audio_embedding(args, config)
