RVC-Project / RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Feature File Path and Database File paths not being written after training
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Beschreibung
Using current version of RVC (pulled the latest to verify just before writing this report),
When Training, it generates the weights but does not generate the feature file or database file required for inference.
*** No Index File is created**
I'm using an Ubuntu system, I installed RVC via a venv to ensure no conflicts. This is the tail end of the training excerpt and crash log:
INFO:model3:Saving model and optimizer state at epoch 200 to ./logs/model3/G_200.pth
INFO:model3:Saving model and optimizer state at epoch 200 to ./logs/model3/D_200.pth
INFO:model3:====> Epoch: 200
INFO:model3:Training is done. The program is closed.
INFO:model3:saving final ckpt:Success.
Traceback (most recent call last):
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/train_nsf_sim_cache_sid_load_pretrain.py", line 534, in
main()
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/train_nsf_sim_cache_sid_load_pretrain.py", line 50, in main
mp.spawn(
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 239, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 197, in start_processes
while not context.join():
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 149, in join
raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with exit code 149
Traceback (most recent call last):
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/gradio/routes.py", line 401, in run_predict
output = await app.get_blocks().process_api(
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/gradio/blocks.py", line 1302, in process_api
result = await self.call_function(
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/gradio/blocks.py", line 1039, in call_function
prediction = await anyio.to_thread.run_sync(
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/anyio/to_thread.py", line 31, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 937, in run_sync_in_worker_thread
return await future
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 867, in run
result = context.run(func, *args)
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/lib/python3.10/site-packages/gradio/utils.py", line 491, in async_iteration
return next(iterator)
File "/home/user/Retrieval-based-Voice-Conversion-WebUI/infer-web.py", line 844, in train1key
big_npy = np.concatenate(npys, 0)
File "<array_function internals>", line 180, in concatenate
ValueError: need at least one array to concatenate
/usr/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 20 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
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Rechercherichtung
Beginne damit, den Ubuntu-Trainingsablauf zu reproduzieren, und untersuche train_nsf_sim_cache_sid_load_pretrain.py im Bereich von main() sowie infer-web.py im Bereich von train1key() in Zeile 844. Verfolge die generierten Pfade für die Feature-, Index- und Datenbankdateien zusammen mit der protokollierten ProcessExitedException und dem concatenate-Fehler. Als abgeschlossen gilt die Aufgabe, wenn das Training die erforderlichen Dateien erzeugt und das resultierende Modell die Inferenz ohne diesen Fehler durchlaufen kann.
Vom Indexierungsmodell aus dem Issue-Text verfasst.
Bewertung
- Tech-Stack
- numpy, python, pytorch
- Bereich
- backend, machine-learning
- Issue-Typ
- Bug
- Schwierigkeit
- 4/5
- Geschätzter Aufwand
- 3-5 Tage
- Aktivitätsstatus
- Veraltet
- Klarheit
- Muss geklärt werden
- Anfängerfreundlichkeit
- 25/100