#!/usr/bin/env python3
# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)

"""
This script computes speaker similarity score in the range [0-1]
of two wave files using a speaker embedding model.
"""
import argparse
import wave
from pathlib import Path

import kaldi_native_fbank as knf
import numpy as np
import onnxruntime as ort
from numpy.linalg import norm


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model",
        type=str,
        required=True,
        help="Path to the input onnx model. Example value: model.onnx",
    )

    parser.add_argument(
        "--file1",
        type=str,
        required=True,
        help="Input wave 1",
    )

    parser.add_argument(
        "--file2",
        type=str,
        required=True,
        help="Input wave 2",
    )

    return parser.parse_args()


def read_wavefile(filename, expected_sample_rate: int = 16000) -> np.ndarray:
    """
    Args:
      filename:
        Path to a wave file, which must be of 16-bit and 16kHz.
     expected_sample_rate:
       Expected sample rate of the wave file.
    Returns:
      Return a 1-D float32 array containing audio samples. Each sample is in
      the range [-1, 1].
    """
    filename = str(filename)
    with wave.open(filename) as f:
        wave_file_sample_rate = f.getframerate()
        assert wave_file_sample_rate == expected_sample_rate, (
            wave_file_sample_rate,
            expected_sample_rate,
        )

        num_channels = f.getnchannels()
        assert f.getsampwidth() == 2, f.getsampwidth()  # it is in bytes
        num_samples = f.getnframes()
        samples = f.readframes(num_samples)
        samples_int16 = np.frombuffer(samples, dtype=np.int16)
        samples_int16 = samples_int16.reshape(-1, num_channels)[:, 0]
        samples_float32 = samples_int16.astype(np.float32)

        samples_float32 = samples_float32 / 32768

        return samples_float32


def compute_features(samples: np.ndarray, sample_rate: int) -> np.ndarray:
    opts = knf.FbankOptions()
    opts.frame_opts.dither = 0
    opts.frame_opts.samp_freq = sample_rate
    opts.frame_opts.snip_edges = False

    opts.mel_opts.num_bins = 80
    opts.mel_opts.debug_mel = False

    fbank = knf.OnlineFbank(opts)
    fbank.accept_waveform(sample_rate, samples)
    fbank.input_finished()

    features = []
    for i in range(fbank.num_frames_ready):
        f = fbank.get_frame(i)
        features.append(f)
    features = np.stack(features, axis=0)

    return features


class OnnxModel:
    def __init__(
        self,
        filename: str,
    ):
        session_opts = ort.SessionOptions()
        session_opts.inter_op_num_threads = 1
        session_opts.intra_op_num_threads = 1

        self.session_opts = session_opts

        self.model = ort.InferenceSession(
            filename,
            sess_options=self.session_opts,
        )

        meta = self.model.get_modelmeta().custom_metadata_map
        self.normalize_samples = int(meta["normalize_samples"])
        self.sample_rate = int(meta["sample_rate"])
        self.output_dim = int(meta["output_dim"])

    def __call__(self, x: np.ndarray) -> np.ndarray:
        """
        Args:
          x:
            A 2-D float32 tensor of shape (T, C).
          y:
            A 1-D float32 tensor containing model output.
        """
        x = np.expand_dims(x, axis=0)

        return self.model.run(
            [
                self.model.get_outputs()[0].name,
            ],
            {
                self.model.get_inputs()[0].name: x,
            },
        )[0][0]


def main():
    args = get_args()
    filename = Path(args.model)
    file1 = Path(args.file1)
    file2 = Path(args.file2)
    assert filename.is_file(), filename
    assert file1.is_file(), file1
    assert file2.is_file(), file2

    model = OnnxModel(filename)
    wave1 = read_wavefile(file1, model.sample_rate)
    wave2 = read_wavefile(file2, model.sample_rate)

    if not model.normalize_samples:
        wave1 = wave1 * 32768
        wave2 = wave2 * 32768

    features1 = compute_features(wave1, model.sample_rate)
    features2 = compute_features(wave2, model.sample_rate)

    output1 = model(features1)
    output2 = model(features2)

    similarity = np.dot(output1, output2) / (norm(output1) * norm(output2))
    print(f"similarity in the range [0-1]: {similarity}")


if __name__ == "__main__":
    main()