Can't process audio files longer than a minute

When trying to process audio source files longer than a minute for Inference the webui just states 'Error' and there's no log in the cmd at all. It's the same for downloaded as well as for trained models. Strangely, sometimes the same file wont work that could be processed last time and vice versa. Shorten the files and converting to wav seems to increase chances of success but I did not find any 100% reproducable pattern so far. In addition, processing these files is possible when using batch mode and putting a file that probably works (a wav with < 1 min) at the top of the list. Anyone has an idea what's going on there? Thanks!

Running in an virtual environment on Windows, here's the pip list.

(myenv) C:\RVC-Project\Retrieval-based-Voice-Conversion-WebUI-main>pip list
Package Version

absl-py 2.1.0
aiofiles 24.1.0
aiohappyeyeballs 2.4.3
aiohttp 3.11.7
aiosignal 1.3.1
altair 5.5.0
antlr4-python3-runtime 4.8
anyio 4.6.2.post1
aria2 0.0.1b0
async-timeout 5.0.1
attrs 24.2.0
audioread 3.0.1
av 13.1.0
bitarray 3.0.0
certifi 2024.8.30
cffi 1.17.1
charset-normalizer 3.4.0
click 8.1.7
colorama 0.4.6
coloredlogs 15.0.1
contourpy 1.3.1
cycler 0.12.1
Cython 3.0.11
decorator 5.1.1
einops 0.8.0
exceptiongroup 1.2.2
fairseq 0.12.2
faiss-cpu 1.7.3
fastapi 0.88.0
ffmpeg-python 0.2.0
ffmpy 0.3.1
filelock 3.16.1
flatbuffers 24.3.25
fonttools 4.55.0
frozenlist 1.5.0
fsspec 2024.10.0
future 1.0.0
gradio 3.34.0
gradio_client 1.4.3
grpcio 1.68.0
h11 0.14.0
httpcore 1.0.7
httpx 0.27.2
huggingface-hub 0.26.2
humanfriendly 10.0
hydra-core 1.0.7
idna 3.10
Jinja2 3.1.4
joblib 1.4.2
json5 0.9.28
jsonschema 4.23.0
jsonschema-specifications 2024.10.1
kiwisolver 1.4.7
librosa 0.9.1
linkify-it-py 2.0.3
llvmlite 0.39.0
local-attention 1.9.15
lxml 5.3.0
Markdown 3.7
markdown-it-py 2.2.0
MarkupSafe 3.0.2
matplotlib 3.9.2
matplotlib-inline 0.1.7
mdit-py-plugins 0.3.3
mdurl 0.1.2
mpmath 1.3.0
multidict 6.1.0
narwhals 1.14.2
networkx 3.4.2
numba 0.56.4
numpy 1.23.5
omegaconf 2.0.6
onnxruntime-gpu 1.20.1
orjson 3.10.12
packaging 24.2
pandas 2.2.3
pillow 11.0.0
pip 22.2.1
platformdirs 4.3.6
pooch 1.8.2
portalocker 3.0.0
praat-parselmouth 0.4.5
propcache 0.2.0
protobuf 5.28.3
pyasn1 0.6.1
pyasn1_modules 0.4.1
pycparser 2.22
pydantic 1.10.19
pydub 0.25.1
Pygments 2.18.0
pyparsing 3.2.0
pyreadline3 3.5.4
python-dateutil 2.9.0.post0
python-dotenv 1.0.1
python-multipart 0.0.17
pytz 2024.2
pywin32 308
pyworld 0.3.2
PyYAML 6.0.2
referencing 0.35.1
regex 2024.11.6
requests 2.32.3
resampy 0.4.3
rpds-py 0.21.0
sacrebleu 2.4.3
scikit-learn 1.5.2
scipy 1.14.1
semantic-version 2.10.0
setuptools 63.2.0
six 1.16.0
sniffio 1.3.1
soundfile 0.12.1
starlette 0.22.0
sympy 1.13.1
tabulate 0.9.0
tensorboard 2.18.0
tensorboard-data-server 0.7.2
tensorboardX 2.6.2.2
threadpoolctl 3.5.0
torch 2.0.1+cu117
torchaudio 2.0.2+cu117
torchcrepe 0.0.20
torchfcpe 0.0.4
torchvision 0.15.2+cu117
tornado 6.4.2
tqdm 4.67.1
traitlets 5.14.3
typing_extensions 4.12.2
tzdata 2024.2
uc-micro-py 1.0.3
urllib3 2.2.3
uvicorn 0.32.1
websockets 12.0
Werkzeug 3.1.3
yarl 1.18.0

The report names only the webui inference flow and a Windows virtual environment, with no source file, test, or entry point. Start by reproducing the failure with long and short audio files in inference and batch mode, then identify where the silent Error occurs; done means the failure is consistently explained and reported with useful logs or corrected behavior.