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  <body class="u-body"><header class="u-clearfix u-header u-sticky u-white u-header" id="sec-e669"><div class="u-clearfix u-sheet u-sheet-1">
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        <h1 class="u-align-center u-text u-text-1">An Open Source Implementation of Neural Voice Cloning with Few Samples</h1>
        <ul class="u-align-left u-text u-text-2">
          <li>We are trying to clone voices for speakers which is content independent. This means that we have to encapture the identity of the speaker rather than the content they speak. We try to do this by making a speaker embedding space for different speakers.</li>
          <li>The speaker embeddings tries to represent the identity of the speaker(various aspects of the voice like pitch, accent, etc of the speaker), you can consider this as the voice fingerprint of the speaker.&nbsp;</li>
        </ul>
        <a href="https://github.com/VisionBrain" class="u-border-2 u-border-black u-btn u-button-style u-hover-black u-none u-text-black u-text-hover-white u-btn-1">github</a>
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          <li>
            <font style="font-size: 1rem;">We propose a speaker encoding method to directly estimate the speaker embedding from audio
samples of an unseen speaker. Such a model does not require any fine-tuning during voice cloning.
Thus, the same model can be used for all unseen speakers.<br>
            </font>
          </li>
          <li>
            <font style="font-size: 1rem;">Speaker classifier determines which speaker an audio sample belongs to. For voice cloning evaluation,
a speaker classifier is trained with the set of speakers used for cloning. High-quality voice cloning
would result in high classification accuracy. The architecture is composed of similar spectral and
temporal processing layers and an additional embedding layer before the softmax function.</font>
          </li>
          <li>
            <font style="font-size: 1rem;">Speaker verification is the task of authenticating the claimed identity of a speaker, based on a test
audio and enrolled audios from the speaker. In particular, it performs binary classification to identify
whether the test audio and enrolled audios are from the same speaker &amp; we consider an end-to-end text-independent speaker verification model .</font>
            <br>
          </li>
        </ul>
        <h1 class="u-align-left u-text u-title u-text-4">Voice Cloning Process</h1>
        <a href="https://arxiv.org/pdf/1802.06006.pdf" class="u-border-2 u-border-black u-btn u-button-style u-hover-black u-none u-text-black u-text-hover-white u-btn-2" target="_blank">BAIDU RESEARCH PAPER</a>
        <h1 class="u-align-left u-heading-font u-text u-title u-text-5">Speaker Adaption</h1>
        <p class="u-align-left u-text u-text-6">
          <font style="font-size: 1.25rem;"><b>Predicted Voices for Speaker 84<br></b>
          </font>
          <br>
        </p>
        <img src="images/Screenshot90.png" alt="" class="u-expanded-width-xs u-image u-image-default u-image-3" data-image-width="625" data-image-height="427">
        <p class="u-align-left u-text u-text-default u-text-7">But don't expect anything right.</p>
        <img src="images/Screenshot91.png" alt="" class="u-expanded-width-xs u-image u-image-default u-image-4" data-image-width="619" data-image-height="433">
        <p class="u-align-left u-text u-text-8">I won't make an official complaint.</p>
        <img src="images/Screenshot92.png" alt="" class="u-expanded-width-xs u-image u-image-default u-image-5" data-image-width="617" data-image-height="425">
        <p class="u-align-left u-text u-text-9">They make a selective perception process.</p>
        <p class="u-align-left u-large-text u-text u-text-font u-text-variant u-text-10">Predicted Voices forSpeaker 85</p>
        <img src="images/Screenshot96.png" alt="" class="u-image u-image-default u-image-6" data-image-width="730" data-image-height="512">
        <p class="u-align-left u-text u-text-11">Players are fighting to stay in the team.</p>
        <img src="images/Screenshot98.png" alt="" class="u-image u-image-default u-image-7" data-image-width="723" data-image-height="512">
        <p class="u-align-left u-text u-text-12">The difference in rainbow depends considerably on the size of the drops and the width of the coloured band increases as the size of the drops increase..</p>
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