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# Copyright (c) microsoft
#               2022 Chengdong Liang (liangchengdong@mail.nwpu.edu.cn)
#
# 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 gradio as gr
import torchaudio
import torchaudio.compliance.kaldi as kaldi
import torch
import onnxruntime as ort
from sklearn.metrics.pairwise import cosine_similarity

STYLE = """
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" integrity="sha256-YvdLHPgkqJ8DVUxjjnGVlMMJtNimJ6dYkowFFvp4kKs=" crossorigin="anonymous">
"""
OUTPUT_OK = (STYLE + """
    <div class="container">
        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
        <div class="row"><h1 class="display-1 text-success" style="text-align: center">{:.1f}%</h1></div>
        <div class="row"><h1 style="text-align: center">similar</h1></div>
        <div class="row"><h1 class="text-success" style="text-align: center">Welcome, human!</h1></div>
        <div class="row"><small style="text-align: center">(You must get at least 50% to be considered the same person)</small><div class="row">
    </div>
""")
OUTPUT_FAIL = (STYLE + """
    <div class="container">
        <div class="row"><h1 style="text-align: center">The speakers are</h1></div>
        <div class="row"><h1 class="display-1 text-danger" style="text-align: center">{:.1f}%</h1></div>
        <div class="row"><h1 style="text-align: center">similar</h1></div>
        <div class="row"><h1 class="text-danger" style="text-align: center">Warning! stranger!</h1></div>
        <div class="row"><small style="text-align: center">(You must get at least 50% to be considered the same person)</small><div class="row">
    </div>
""")

OUTPUT_ERROR = (STYLE + """
    <div class="container">
        <div class="row"><h1 style="text-align: center">Input Error</h1></div>
        <div class="row"><h1 class="text-danger" style="text-align: center">{}!</h1></div>
    </div>
""")


def compute_fbank(wav_path,
                  num_bel_bins=80,
                  frame_length=25,
                  frame_shift=10,
                  dither=0.0,
                  resample_rate=16000):
    """ Extract fbank, simlilar to the one in wespeaker.dataset.processor,
        While integrating the wave reading and CMN.
    """
    waveform, sample_rate = torchaudio.load(wav_path)
    # resample
    if sample_rate != resample_rate:
        waveform = torchaudio.transforms.Resample(
            orig_freq=sample_rate, new_freq=resample_rate)(waveform)
    waveform = waveform * (1 << 15)
    mat = kaldi.fbank(waveform,
                      num_mel_bins=num_bel_bins,
                      frame_length=frame_length,
                      frame_shift=frame_shift,
                      dither=dither,
                      sample_frequency=sample_rate,
                      window_type='hamming',
                      use_energy=False)
    # CMN, without CVN
    mat = mat - torch.mean(mat, dim=0)
    return mat


class OnnxModel(object):

    def __init__(self, model_path):
        so = ort.SessionOptions()
        so.inter_op_num_threads = 1
        so.intra_op_num_threads = 1
        self.session = ort.InferenceSession(model_path, sess_options=so)

    def extract_embedding(self, wav_path):
        feats = compute_fbank(wav_path)
        feats = feats.unsqueeze(0).numpy()

        embeddings = self.session.run(output_names=['embs'],
                                      input_feed={'feats': feats})
        return embeddings[0]


def speaker_verification(audio_path1, audio_path2, lang='CN'):
    if audio_path1 == None or audio_path2 == None:
        output = OUTPUT_ERROR.format('Please enter two audios')
        return output
    if lang == 'EN':
        model = OnnxModel('pre_model/voxceleb_resnet34_LM.onnx')
    elif lang == 'CN':
        model = OnnxModel('pre_model/cnceleb_resnet34_LM.onnx')
    else:
        output = OUTPUT_ERROR.format('Please select a language')
        return output
    emb1 = model.extract_embedding(audio_path1)
    emb2 = model.extract_embedding(audio_path2)
    cos_score = cosine_similarity(emb1.reshape(1, -1), emb2.reshape(1,
                                                                    -1))[0][0]

    if cos_score >= 0.5:
        output = OUTPUT_OK.format(cos_score * 100)
    else:
        output = OUTPUT_FAIL.format(cos_score * 100)

    return output


# input
inputs = [
    gr.inputs.Audio(source="microphone",
                    type="filepath",
                    optional=True,
                    label='Speaker#1'),
    gr.inputs.Audio(source="microphone",
                    type="filepath",
                    optional=True,
                    label='Speaker#2'),
    gr.Radio(['CN', 'EN'], label='Language'),
]

output = gr.outputs.HTML(label="")

# description
description = ("WeSpeaker Demo ! Try it with your own voice !")

article = (
    "<p style='text-align: center'>"
    "<a href='https://github.com/wenet-e2e/wespeaker' target='_blank'>Github: Learn more about WeSpeaker</a>"
    "</p>")

examples = [
    ['examples/BAC009S0764W0228.wav', 'examples/BAC009S0764W0328.wav', 'CN'],
    ['examples/BAC009S0913W0133.wav', 'examples/BAC009S0764W0228.wav', 'CN'],
    ['examples/00001_spk1.wav', 'examples/00003_spk2.wav', 'EN'],
    ['examples/00010_spk2.wav', 'examples/00024_spk1.wav', 'EN'],
    ['examples/00001_spk1.wav', 'examples/00024_spk1.wav', 'EN'],
    ['examples/00010_spk2.wav', 'examples/00003_spk2.wav', 'EN'],
]

interface = gr.Interface(
    fn=speaker_verification,
    inputs=inputs,
    outputs=output,
    title="Speaker verification in WeSpeaker : 基于 WeSpeaker 的说话人确认",
    description=description,
    article=article,
    examples=examples,
    theme="huggingface",
)

interface.launch(enable_queue=True)