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+This repository is used to preview the effects of various speech models trained by so-vits-svc-4.0. 
+**Click on the character name** to automatically jump to the corresponding training parameters.</br> 
+I recommend using **Google Chrome** as other browsers may not load the previewed audio correctly.</br>
+The conversion of normal speaking voices is relatively accurate, but songs with a wide range of sounds 
+and background music and other noises that are difficult to remove may result in a unstable effect.</br> 
+If you have recommended songs that you would like to try converting and listening to or any other suggestions,
+[**click here**](https://huggingface.co/datasets/jiaheillu/audio_preview/discussions/new) to give me advice.</br> 
+Below are preview audios. **Scroll up, down, left, and right** to see them all.
+
+<style>
+  .scrolling-container {
+    width: 100%;
+    max-width: 800px;
+    height: 300px;
+    overflow: auto;
+    margin: 0;
+  }
+  @media screen and (max-width: 768px) {
+    .scrolling-container {
+      width: 100%;
+      height: auto;
+      overflow: auto;
+    }
+  }
+</style>
+
+<div class="scrolling-container">
+  <table border="1" style="white-space: nowrap; text-align: center;">
+    <thead>
+      <tr>
+        <th>Character Name</th>
+		<th>Original Voice A</th>
+		<th>Converted Voice B</th>
+		<th>A Voice Replaced by B</th>
+		<th>Song Cover (Click to Download)</th>
+      </tr>
+    </thead>
+    <tbody>
+      <tr>
+        <td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/散兵效果预览/训练参数速览.md">Wanderer</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/部分训练集/真遗憾，小吉祥草王让他消除了那么多的切片，剥夺了我将他一片一片千刀万剐的快乐%E3%80%82.mp3" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/原声/shenli3.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/转换结果/shenli3mp3_auto_liulangzhe.wav" controls="controls"></audio></td>
+        <td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/转换结果/夢で逢えたら2liulangzhe_f.wav">夢で会えたら</a></td>
+      </tr>
+      <tr>
+        <td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/胡桃_preview/README.md">HuTao</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/%E8%83%A1%E6%A1%83_preview/hutao.wav" controls="controls"></audio></td>
+        <td>.........</td>
+        <td>.........</td>
+        <td>
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/moonlight_shadow2胡桃.WAV">moonlight shadow</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/云烟成雨2胡桃.WAV">云烟成雨</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/原点2胡桃.WAV">原点</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/夢だ会えたら2胡桃.WAV">夢で逢えたら</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/贝加尔湖畔2胡桃.WAV">贝加尔湖畔</a>
+        </td>
+      </tr>    
+      <tr>
+        <td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/绫华_preview/README.md">Kamisato Ayaka</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/linghua428.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/yelan.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/绫华_preview/yelan.wav_auto_linghua_0.5.wav" controls="controls"></audio></td>
+        <td>
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/アムリタ2绫华.WAV">アムリタ</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/大鱼2绫华.WAV">大鱼</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/遊園施設2绫华.WAV">遊園施設</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/the_day_you_want_away2绫华.WAV">the day you want away</a>
+        </td>
+      </tr>
+      <tr>  
+        <td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/宵宫_preview/README.md">yoimiya</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/xiaogong.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/hutao2.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/hutao2wav_0key_xiaogong_0.5-2.wav" controls="controls"></audio></td>
+        <td>
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/昨夜书2宵宫.WAV">昨夜书</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/lemon2宵宫.WAV">lemon</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/宵宫_preview/my_heart_will_go_no2宵宫.WAV">my heart will go on</a>,
+        </td>
+      </tr>
+      <tr>  
+        <td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/刻晴_preview/README.md">Keqing</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/原_keqing2.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/待_xiaogong3.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/已_xiaogong2keqing.wav" controls="controls"></audio></td>
+        <td>
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/嚣张2刻晴.WAV">嚣张</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/ファティマ2刻晴.WAV">ファティマ</a>,
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/刻晴_preview/hero2刻晴.WAV">hero</a>,
+        </td>
+      </tr>
+      <tr>  
+        <td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/imallryt_preview/README.md">imallryt</a></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%8E%9F_IVOL_1%20Care_DRY_120_Am_Main_Vocal.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%BE%85_Lead_A%20minor_DRY.wav" controls="controls"></audio></td>
+        <td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/%E5%B7%B2_Lead_A%20minor_DRYwav_0key_imallryt_0.5.wav" controls="controls"></audio></td>
+        <td>
+          <a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/imallryt_preview/海阔天空2imallryt.WAV">海阔天空</a>,
+        </td>
+      </tr>
+    </tbody>
+  </table>
+</div>
+
+Key Parameters:
+
+audio duration: total duration of the training dataset  </br>
+
+epoch: number of rounds of training</br>
+
+Others: </br>
+
+batch_size = number of audio segments trained in one step </br>
+
+segments = the number of segments that the audio is split into ,step = segments * epoch / batch_size, which is where the numbers in the model file name come from
+
+Using the example of "Wanderer" (a character name): Loss Function Graph: pay attention to step and loss5, 
+for example:<br> As a difficult test, all the original audios are high-pitched female voices, and this graph 
+shows the result of training on a 10-minute pure voice audio. The model achieved good performance at around 
+2800 epochs (10,000 steps), and the actual model used was trained for 5571 epochs (19,500 steps), with 
+slight differences between the trained voice and the original voice. Please refer to the preview audio above.
+In general, 10 minutes is not enough for a sufficient training dataset.
+[Click here to view related files](https://huggingface.co/datasets/jiaheillu/audio_preview/tree/main)<br>