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YAML Metadata Warning: The task_categories "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other

预览.

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本仓库用于预览so-vits-svc-4.0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。
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。
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。
有推荐的歌想要转换听听效果,或者其他内容建议,点我发起讨论
下面是预览音频,上下左右滑动可以看到全部

关键参数:

audio duration:训练集总时长
epoch: 轮数

其余:
batch_size = 一个step训练的片段数
segments = 音频被切分的片段
step=segments*epoch/batch_size,即模型文件后面数字由来

以散兵为例:
损失函数图像:主要看step 与 loss5,比如:
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, 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 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。

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sanbing_loss

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