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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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| 角色名 | 角色原声A | 被转换人声B |A音色替换B|A音色翻唱(点击直接下载)| |
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| :------: | :----: | :----: | :----: |:----: | |
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| [散兵](https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/散兵效果预览/训练参数速览.md)| <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 src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/散兵效果预览/原声/shenli3.wav" controls="controls"> | <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">|[夢で会えたら](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)| |
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|[胡桃](https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/胡桃_preview/README.md)| <audio style="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/hutao.wav" controls="controls"> | .........| ......... |[moonlight shadow](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/moonlight_shadow2胡桃.WAV),[云烟成雨](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/云烟成雨2胡桃.WAV),[原点](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/原点2胡桃.WAV),[夢で逢えたら](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/夢だ会えたら2胡桃.WAV),[贝加尔湖畔](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/胡桃_preview/贝加尔湖畔2胡桃.WAV) | |
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|[神里绫华](https://huggingface.co/datasets/jiaheillu/audio_preview/blob/main/绫华_preview/README.md)| <audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/linghua428.wav" controls="controls"> | <audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/yelan.wav" controls="controls"> | <audio src="https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/yelan.wav_auto_linghua_0.5.flac" controls="controls"> |[アムリタ](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/アムリタ2绫华.WAV),[大鱼](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/大鱼2绫华.WAV),[遊園施設](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/遊園施設2绫华.WAV),[the day you want away](https://huggingface.co/datasets/jiaheillu/audio_preview/resolve/main/绫华_preview/the_day_you_want_away2绫华.WAV)| |
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关键参数： |
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音频时长：min<br> |
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epoch: 轮 <br> |
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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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相关文件全部在“散兵效果预览”文件夹中<br> |
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