RVC-Project / RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Unable to seperate vocal and train models
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 38.4k
- Forks
- 5.3k
- PR merge metrics
- No merged PRs in 30d
Description
Hello,
Whenever I try to seperate vocal this message appeared.
Traceback (most recent call last):
File "C:\Users\user\Downloads\RVC1006Nvidia\infer\modules\uvr5\modules.py", line 41, in uvr
paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
NotADirectoryError: [WinError 267] The directory name is invalid: 'C:\Users\user\Downloads\VID 1.mp3'
Similarly, when I try to train a model, step 2A yield
Traceback (most recent call last):
File "C:\Users\user\Downloads\RVC1006Nvidia\infer\modules\train\preprocess.py", line 11, in
sr = int(sys.argv[2])
ValueError: invalid literal for int() with base 10: 'folder\VID'
2024-11-24 21:09:59 | INFO | main |
If I ignore all the error and proceed with the actual training process, it failed of course and upon insepecting the logs, seems like a lot of file location error is going on. " The system cannot find the path specified: 'C:\Users\user\Downloads\RVC1006Nvidia/logs/mi-test/1_16k_wavs'"
Should I put my RVC1006Nvidia foldier else where or I should place the audio file in a speific location for it to work?
Contributor guide
No contributing guide indexed for this repository
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with infer/modules/uvr5/modules.py and infer/modules/train/preprocess.py, then reproduce the reported Windows path errors using the vocal-separation and step 2A workflows. Determine whether the input paths are expected to be files or directories and whether the reported failures indicate a product bug or an undocumented setup requirement.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100