model card and meta info

README.md

@@ -1,5 +1,79 @@
 ---
 license: mit
 ---
+# Amphion Singing Voice Conversion Pretrained Models
+
+## Quick Start
+
+We provide a [DiffWaveNetSVC](https://github.com/open-mmlab/Amphion/tree/main/egs/svc/MultipleContentsSVC) pretrained checkpoint for you to play. Specially, it is trained under the  real-world vocalist data (total duration: 6.16 hours), including the following 15 professional singers:
+
+|       Singer        | Language | Training Duration (mins) |
+| :-----------------: | :------: | :----------------------: |
+|   David Tao 陶喆    | Chinese  |          45.51           |
+|  Eason Chan 陈奕迅  | Chinese  |          43.36           |
+|   Feng Wang 汪峰    | Chinese  |          41.08           |
+|    Jian Li 李健     | Chinese  |          38.90           |
+|     John Mayer      | English  |          30.83           |
+|        Adele        | English  |          27.23           |
+|    Ying Na 那英     | Chinese  |          27.02           |
+|  Yijie Shi 石倚洁   | Chinese  |          24.93           |
+| Jacky Cheung 张学友 | Chinese  |          18.31           |
+|    Taylor Swift     | English  |          18.31           |
+|   Faye Wong 王菲    | English  |          16.78           |
+|   Michael Jackson   | English  |          15.13           |
+|   Tsai Chin 蔡琴    | Chinese  |          10.12           |
+|     Bruno Mars      | English  |           6.29           |
+|       Beyonce       | English  |           6.06           |
+
+To make these singers sing the songs you want to listen to, just run the following commands:
+
+### Step1: Download the checkpoint
+```bash
+git lfs install
+git clone https://huggingface.co/amphion/singing_voice_conversion
+```
+
+### Step2: Clone the Amphion's Source Code of GitHub
+```bash
+git clone https://github.com/open-mmlab/Amphion.git
+```
+
+### Step3: Specify the checkpoint's path
+Use the soft link to specify the downloaded checkpoint in first step:
+
+```bash
+cd Amphion
+mkdir ckpts/svc
+ln -s ../singing_voice_conversion/vocalist_l1_contentvec+whisper ckpts/svc/vocalist_l1_contentvec+whisper
+```
+
+### Step4: Conversion
+
+You can follow [this recipe](https://github.com/open-mmlab/Amphion/tree/main/egs/svc/MultipleContentsSVC#4-inferenceconversion) to conduct the conversion. For example, if you want to make Taylor Swift sing the songs in the `[Your Audios Folder]``, just run:
+
+```bash
+sh egs/svc/MultipleContentsSVC/run.sh --stage 3 --gpu "0" \
+	--infer_expt_dir "ckpts/svc/vocalist_l1_contentvec+whisper" \
+	--infer_output_dir "ckpts/svc/vocalist_l1_contentvec+whisper/result" \
+	--infer_source_audio_dir [Your Audios Folder] \
+	--infer_target_speaker "vocalist_l1_TaylorSwift" \
+	--infer_key_shift "autoshift"
+```
+
+**Note**: The supported `infer_target_speaker` values can be seen [here](https://huggingface.co/amphion/singing_voice_conversion/blob/main/vocalist_l1_contentvec%2Bwhisper/singers.json).  
+
+## Citaions
+
+```bibtex
+@article{zhang2023leveraging,
+  title={Leveraging Content-based Features from Multiple Acoustic Models for Singing Voice Conversion},
+  author={Zhang, Xueyao and Gu, Yicheng and Chen, Haopeng and Fang, Zihao and Zou, Lexiao and Xue, Liumeng and Wu, Zhizheng},
+  journal={Machine Learning for Audio Worshop, NeurIPS 2023},
+  year={2023}
+}
+```
+
+
+
+
 
-# Amphion Singing Voice Conversion Pretrained Models

vocalist_l1_contentvec+whisper/data/vocalist_l1/meta_info.json

@@ -0,0 +1,31 @@
+{
+    "dataset": "vocalist_l1",
+    "train": {
+        "size": 3180,
+        "hours": 6.1643
+    },
+    "test": {
+        "size": 114,
+        "hours": 0.2224
+    },
+    "singers": {
+        "size": 15,
+        "training_minutes": {
+            "vocalist_l1_陶喆": 45.51,
+            "vocalist_l1_陈奕迅": 43.36,
+            "vocalist_l1_汪峰": 41.08,
+            "vocalist_l1_李健": 38.9,
+            "vocalist_l1_JohnMayer": 30.83,
+            "vocalist_l1_Adele": 27.23,
+            "vocalist_l1_那英": 27.02,
+            "vocalist_l1_石倚洁": 24.93,
+            "vocalist_l1_张学友": 18.31,
+            "vocalist_l1_TaylorSwift": 18.31,
+            "vocalist_l1_王菲": 16.78,
+            "vocalist_l1_MichaelJackson": 15.13,
+            "vocalist_l1_蔡琴": 10.12,
+            "vocalist_l1_BrunoMars": 6.29,
+            "vocalist_l1_Beyonce": 6.06
+        }
+    }
+}