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--- |
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license: openrail |
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task_categories: |
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- conversational |
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language: |
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- aa |
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tags: |
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- music |
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size_categories: |
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- n<1K |
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pretty_name: genshin_voice_sovits |
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--- |
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# 效果预览 |
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本仓库用于预览训练出的各种语音模型的效果，点击角色名自动跳转对应训练参数。</br> |
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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> |
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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> |
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下面是预览音频，左右滑动可以看到全部 |
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<style> |
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.scrolling-container { |
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width: 100%; |
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max-width: 800px; |
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height: 300px; |
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overflow: auto; |
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margin: 0; |
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} |
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@media screen and (max-width: 768px) { |
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.scrolling-container { |
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width: 100%; |
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height: auto; |
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overflow: auto; |
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} |
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} |
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</style> |
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<div class="scrolling-container"> |
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<table border="1" style="white-space: nowrap; text-align: center;"> |
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<thead> |
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<tr> |
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<th>角色名</th> |
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<th>角色原声A</th> |
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<th>被转换人声B</th> |
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<th>A音色替换B</th> |
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<th>A音色翻唱(点击直接下载)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<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">散兵</a></td> |
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<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> |
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<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> |
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<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> |
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<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> |
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</tr> |
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<tr> |
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<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/胡桃_preview/README.md">胡桃</a></td> |
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<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> |
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<td>.........</td> |
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<td>.........</td> |
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<td> |
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<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/moonlight_shadow2胡桃.WAV">moonlight shadow</a>, |
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<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>, |
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<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/原点2胡桃.WAV">原点</a>, |
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<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>, |
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<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> |
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</td> |
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</tr> |
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<tr> |
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<td><a href="https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/绫华_preview/README.md">神里绫华</a></td> |
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<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/linghua428.wav" controls="controls"></audio></td> |
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<td><audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/yelan.wav" controls="controls"></audio></td> |
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<td><audio src="https://huggingface.co/datasets/jiaheillu/sovits_audio_preview/resolve/main/绫华_preview/yelan.wav_auto_linghua_0.5.flac" controls="controls"></audio></td> |
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<td> |
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<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>, |
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<a href="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/大鱼2绫华.WAV">大鱼</a>, |
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<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>, |
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<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> |
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</td> |
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</tr> |
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</tbody> |
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</table> |
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</div> |
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关键参数： |
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audio duration:训练集总时长 |
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epoch: 轮数 |
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其余： |
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batch_size = 一个step训练的片段数<br> |
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segments = 音频被切分的片段<br> |
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step=segments*epoch/batch_size,即模型文件后面数字由来<br> |
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以散兵为例： |
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损失函数图像：主要看step 与 loss5，比如：<br> |
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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> |
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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> |
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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> |
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[点我查看相关文件](https://huggingface.co/datasets/jiaheillu/audio_preview/tree/main)<br> |
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