Update README.md

README.md

@@ -16,7 +16,7 @@ pretty_name: genshin_voice_sovits
 推荐用**谷歌浏览器**，其他浏览器可能无法正确加载预览的音频。</br>
 正常说话的音色转换较为准确，歌曲包含较广的音域且bgm和声等难以去除干净，效果有所折扣。</br>
 有推荐的歌想要转换听听效果,或者其他内容建议，[**点我**](https://huggingface.co/datasets/jiaheillu/audio_preview/discussions/new)发起讨论</br>
-下面是预览音频，左右滑动可以看到全部
+下面是预览音频，**上下左右滑动**可以看到全部
 
 <style>
   .scrolling-container {
@@ -96,13 +96,22 @@ pretty_name: genshin_voice_sovits
         <td><a href="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/blob/main/刻晴_preview/README.md">刻晴</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_xiaoogong2keqing.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/%E6%B5%B7%E9%98%94%E5%A4%A9%E7%A9%BA2imallryt.WAV">海阔天空</a>,
+        </td>
+      </tr>
     </tbody>
   </table>
 </div>
@@ -123,7 +132,7 @@ step=segments*epoch/batch_size,即模型文件后面数字由来<br>
 损失函数图像：主要看step 与 loss5，比如：<br>
 给一个大致的参考，待转换音频都为高音女生，这是较为刁钻的测试：如图，10min纯净人声，<br>
 差不多2800epoch（10000step）就已经出结果了，实际使用的是5571epoch(19500step)的文件，被训练音色和原音色相差几<br>
-何，差不多有个概念。当然即使loss也不足以参考，唯一的衡量标准就是当事人的耳朵。当然，正常训练，10min还是有些少的。<br>
+何，请听上方预览音频。正常训练，10min是较为不足的训练集时长。<br>
 
 [点我查看相关文件](https://huggingface.co/datasets/jiaheillu/audio_preview/tree/main)<br>
 ![sanbing_loss](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/%E6%95%A3%E5%85%B5%E6%95%88%E6%9E%9C%E9%A2%84%E8%A7%88/%E8%AE%AD%E7%BB%83%E5%8F%82%E6%95%B0%E9%80%9F%E8%A7%88.assets/sanbing_loss.png)