Merge branch 'svc-develop-team:4.0' into 4.0-768
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@ -205,7 +205,9 @@ Introduction: The mean filtering of F0 can effectively reduce the hoarse sound c
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### [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kv-3y2DmZo0uya8pEr1xk7cSB-4e_Pct?usp=sharing) [sovits4_for_colab.ipynb](https://colab.research.google.com/drive/1kv-3y2DmZo0uya8pEr1xk7cSB-4e_Pct?usp=sharing)
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#### [23/03/16] No longer need to download hubert manually
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**[23/03/16] No longer need to download hubert manually**
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**[23/04/14] Support NSF_HIFIGAN enhancer**
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## 📤 Exporting to Onnx
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@ -205,7 +205,9 @@ python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "
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### [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kv-3y2DmZo0uya8pEr1xk7cSB-4e_Pct?usp=sharing) [sovits4_for_colab.ipynb](https://colab.research.google.com/drive/1kv-3y2DmZo0uya8pEr1xk7cSB-4e_Pct?usp=sharing)
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#### [23/03/16] 不再需要手动下载hubert
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**[23/03/16] 不再需要手动下载hubert**
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**[23/04/14] 支持NSF_HIFIGAN增强器**
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## 📤 Onnx导出
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@ -31,7 +31,7 @@ def main():
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parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
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# 可选项部分
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parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,help='语音转换自动预测音高,转换歌声时不要打开这个会严重跑调')
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parser.add_argument('-a', '--auto_predict_f0', type=bool, default=False, help='语音转换自动预测音高,转换歌声时不要打开这个会严重跑调')
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parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径,如果没有训练聚类则随便填')
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parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比,范围0-1,若没有训练聚类模型则默认0即可')
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parser.add_argument('-lg', '--linear_gradient', type=float, default=0, help='两段音频切片的交叉淡入长度,如果强制切片后出现人声不连贯可调整该数值,如果连贯建议采用默认值0,单位为秒')
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@ -324,4 +324,8 @@ class CrepePitchExtractor(BasePitchExtractor):
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f0 = torch.where(torch.isnan(f0), torch.full_like(f0, 0), f0)[0]
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if torch.all(f0 == 0):
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rtn = f0.cpu().numpy() if pad_to==None else np.zeros(pad_to)
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return rtn,rtn
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return self.post_process(x, sampling_rate, f0, pad_to)
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