ijik-loker
commited on
Commit
·
858758f
1
Parent(s):
dfc041a
Add README.md
Browse files
README.md
ADDED
@@ -0,0 +1,73 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
language:
|
3 |
+
- en
|
4 |
+
- ja
|
5 |
+
tags:
|
6 |
+
- rvc
|
7 |
+
- voice cloning
|
8 |
+
- The Amazing World of Gumball
|
9 |
+
- おかしなガムボール
|
10 |
+
- Gumball Watterson
|
11 |
+
- ガムボール
|
12 |
+
- Junko Takeuchi
|
13 |
+
- 竹内順子
|
14 |
+
---
|
15 |
+
|
16 |
+
## Model Details
|
17 |
+
|
18 |
+
### Model Description
|
19 |
+
|
20 |
+
<!-- Provide a longer summary of what this model is. -->
|
21 |
+
|
22 |
+
- **Developed by:** [ijik-loker](https://huggingface.co/ijik-loker)
|
23 |
+
- **Model type:** [Retrieval-based Voice Conversion (RVC)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)
|
24 |
+
- **Language(s):** Japanese
|
25 |
+
|
26 |
+
## Uses
|
27 |
+
|
28 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
29 |
+
|
30 |
+
Used in the popular Retrieval-based Voice Conversion WebUI via inference or real-time using [Voice Changer](https://github.com/w-okada/voice-changer).
|
31 |
+
|
32 |
+
## Training Details
|
33 |
+
|
34 |
+
### Training Data
|
35 |
+
|
36 |
+
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
37 |
+
|
38 |
+
Trained using [episode clips](https://www.youtube.com/playlist?list=PLWSC6OHatntSWjsbQWCSQn-MuwIscHL_8) uploaded by CartoonNetworkJP カートゥーン ネットワーク:
|
39 |
+
1. [The Watch](https://youtu.be/qn8M_KHPYno?si=6y02GSGaaY6zYTIa)
|
40 |
+
2. [The Void](https://youtu.be/Cxtg7LqmdkI?si=mGws0GXpV3K8yt6C)
|
41 |
+
3. [The Vegging](https://youtu.be/egUkeiy5Ujw?si=KoVwqrbduXTUdMfH)
|
42 |
+
4. [The Test](https://youtu.be/e3t0yldGmTw?si=DduDKGu1D1H39YCV)
|
43 |
+
5. [The Tape](https://youtu.be/3g6if7EhZNY?si=68lTnBIdYNH4n6fN)
|
44 |
+
6. [The Stories](https://youtu.be/c4yw042zJXA?si=yXs5vjyhHvgRkAjv)
|
45 |
+
7. [The Slide](https://youtu.be/KF-gZK8859Q?si=ls8KaPAhlYB4tGGo)
|
46 |
+
8. [The Sidekick](https://youtu.be/3vfZauRDqG4?si=yDHBpTF-7pm0x3gt)
|
47 |
+
9. [The Safety](https://youtu.be/hZT9I0TVpJk?si=eF2Xs8PT0xTSw1Oe)
|
48 |
+
10. [The Puppets](https://youtu.be/TH_JMIkCWTc?si=QaW3rmEJgWC_Msdq)
|
49 |
+
11. [The Procrastinators](https://youtu.be/FGH3-NR22YI?si=t7Ux_7ccgmqbixE7)
|
50 |
+
12. [The Pest](https://youtu.be/V1De2RI2q_E?si=i-GbCoy_eUxtdJbL)
|
51 |
+
13. [The Nobody](https://youtu.be/qC7Z1QigFLA?si=sO_WwgOGcf-krHI0)
|
52 |
+
14. [The Misunderstandings](https://youtu.be/7GOmjuB0aLk?si=5cb0XSKL3V3GwFEQ)
|
53 |
+
15. [The Matchmaker](https://youtu.be/_x-Czj3G8rc?si=rwAJC58492pDUR9P)
|
54 |
+
16. [The Burden](https://youtu.be/GN5c9FUbZMk?si=zZYkWAR8Z4GT0Ev_)
|
55 |
+
17. [The Best](https://youtu.be/LN2AyPry0hI?si=tdgIUw22f2o2kTv9)
|
56 |
+
|
57 |
+
[More Information Needed]
|
58 |
+
|
59 |
+
### Training Procedure
|
60 |
+
|
61 |
+
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
62 |
+
|
63 |
+
1. Remove noise using [Ultimate Vocal Remover 5](https://github.com/Anjok07/ultimatevocalremovergui) UVR-DeNoise.
|
64 |
+
2. Extract vocals using RVC Web UI [HP5-主旋律人声vocals+其他instrumentals.pth](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/uvr5_weights/HP5-%E4%B8%BB%E6%97%8B%E5%BE%8B%E4%BA%BA%E5%A3%B0vocals%2B%E5%85%B6%E4%BB%96instrumentals.pth).
|
65 |
+
3. Remove echo and reverb using Ultimate Vocal Remover 5 UVR-DeEcho-DeReverb.
|
66 |
+
4. Manually diarise voices in [Audacity](https://www.audacityteam.org/) using labels.
|
67 |
+
5. Export multiple to .wav by labels.
|
68 |
+
6. Train using RVC
|
69 |
+
* Target Sample Rate: 48k
|
70 |
+
* Version: v2
|
71 |
+
* Total training epochs: 200
|
72 |
+
* Base model G: f0G48k.pth
|
73 |
+
* Base model D: f0D48k.pth
|