RVC-Project / RVC-Project/Retrieval-based-Voice-Conversion-WebUI
Segmentation fault: Failed to select voice
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- Python
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Description
Issue:
When I select an item in Inferencing voice it produces this output:
$python infer-web.py
2025-01-23 22:29:34 | INFO | configs.config | Found GPU Radeon RX 570 Series
2025-01-23 22:29:34 | INFO | configs.config | Half-precision floating-point: True, device: cuda:0
/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio_client/documentation.py:106: UserWarning: Could not get documentation group for <class 'gradio.mix.Parallel'>: No known documentation group for module 'gradio.mix'
warnings.warn(f"Could not get documentation group for {cls}: {exc}")
/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio_client/documentation.py:106: UserWarning: Could not get documentation group for <class 'gradio.mix.Series'>: No known documentation group for module 'gradio.mix'
warnings.warn(f"Could not get documentation group for {cls}: {exc}")
2025-01-23 22:29:36 | INFO | __main__ | Use Language: en_US
Running on local URL: http://0.0.0.0:7865
2025-01-23 22:29:48 | INFO | infer.modules.vc.modules | Get sid: mashima_himeko_580e_15660s.pth
2025-01-23 22:29:48 | INFO | infer.modules.vc.modules | Loading: assets/weights/mashima_himeko_580e_15660s.pth
Segmentation fault (core dumped)
Computer settings:
OS: Ubuntu 24.04.1 LTS
Processor: AMD Ryzen™ 5 3400G with Radeon™ Vega Graphics × 8
RAM: 32.0 GiB
GPU: Radeon™ RX 570 Series
ROCm Info:
ROCk module version 6.8.5 is loaded
=====================
HSA System Attributes
=====================
Runtime Version: 1.14
Runtime Ext Version: 1.6
System Timestamp Freq.: 1000.000000MHz
Sig. Max Wait Duration: 18446744073709551615 (0xFFFFFFFFFFFFFFFF) (timestamp count)
Machine Model: LARGE
System Endianness: LITTLE
Mwaitx: DISABLED
DMAbuf Support: YES
==========
HSA Agents
==========
*******
Agent 1
*******
Name: AMD Ryzen 5 3400G with Radeon Vega Graphics
Uuid: CPU-XX
Marketing Name: AMD Ryzen 5 3400G with Radeon Vega Graphics
Vendor Name: CPU
Feature: None specified
Profile: FULL_PROFILE
Float Round Mode: NEAR
Max Queue Number: 0(0x0)
Queue Min Size: 0(0x0)
Queue Max Size: 0(0x0)
Queue Type: MULTI
Node: 0
Device Type: CPU
Cache Info:
L1: 32768(0x8000) KB
Chip ID: 0(0x0)
ASIC Revision: 0(0x0)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 3700
BDFID: 0
Internal Node ID: 0
Compute Unit: 8
SIMDs per CU: 0
Shader Engines: 0
Shader Arrs. per Eng.: 0
WatchPts on Addr. Ranges:1
Memory Properties:
Features: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: FINE GRAINED
Size: 32787664(0x1f44cd0) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 2
Segment: GLOBAL; FLAGS: KERNARG, FINE GRAINED
Size: 32787664(0x1f44cd0) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
Pool 3
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 32787664(0x1f44cd0) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:4KB
Alloc Alignment: 4KB
Accessible by all: TRUE
ISA Info:
*******
Agent 2
*******
Name: gfx803
Uuid: GPU-XX
Marketing Name: Radeon RX 570 Series
Vendor Name: AMD
Feature: KERNEL_DISPATCH
Profile: BASE_PROFILE
Float Round Mode: NEAR
Max Queue Number: 128(0x80)
Queue Min Size: 64(0x40)
Queue Max Size: 131072(0x20000)
Queue Type: MULTI
Node: 1
Device Type: GPU
Cache Info:
L1: 16(0x10) KB
Chip ID: 26591(0x67df)
ASIC Revision: 1(0x1)
Cacheline Size: 64(0x40)
Max Clock Freq. (MHz): 1250
BDFID: 256
Internal Node ID: 1
Compute Unit: 32
SIMDs per CU: 4
Shader Engines: 4
Shader Arrs. per Eng.: 1
WatchPts on Addr. Ranges:4
Coherent Host Access: FALSE
Memory Properties:
Features: KERNEL_DISPATCH
Fast F16 Operation: TRUE
Wavefront Size: 64(0x40)
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Max Waves Per CU: 40(0x28)
Max Work-item Per CU: 2560(0xa00)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
Max fbarriers/Workgrp: 32
Packet Processor uCode:: 730
SDMA engine uCode:: 58
IOMMU Support:: None
Pool Info:
Pool 1
Segment: GLOBAL; FLAGS: COARSE GRAINED
Size: 4194304(0x400000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 2
Segment: GLOBAL; FLAGS: EXTENDED FINE GRAINED
Size: 4194304(0x400000) KB
Allocatable: TRUE
Alloc Granule: 4KB
Alloc Recommended Granule:2048KB
Alloc Alignment: 4KB
Accessible by all: FALSE
Pool 3
Segment: GROUP
Size: 64(0x40) KB
Allocatable: FALSE
Alloc Granule: 0KB
Alloc Recommended Granule:0KB
Alloc Alignment: 0KB
Accessible by all: FALSE
ISA Info:
ISA 1
Name: amdgcn-amd-amdhsa--gfx803
Machine Models: HSA_MACHINE_MODEL_LARGE
Profiles: HSA_PROFILE_BASE
Default Rounding Mode: NEAR
Default Rounding Mode: NEAR
Fast f16: TRUE
Workgroup Max Size: 1024(0x400)
Workgroup Max Size per Dimension:
x 1024(0x400)
y 1024(0x400)
z 1024(0x400)
Grid Max Size: 4294967295(0xffffffff)
Grid Max Size per Dimension:
x 4294967295(0xffffffff)
y 4294967295(0xffffffff)
z 4294967295(0xffffffff)
FBarrier Max Size: 32
Steps:
- Using ROCm 5.4.2:
- Created a virtual environment using venv using Python 3.10.16
- Run: pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/rocm5.4.2
- Run: pip install -r requirements-amd.txt
- Run: python tools/download_models.py
- Run: export ROCM_PATH=/opt/rocm
- Moved both *.index and *.pth files into weights/ folder.
- Run: python infer-web.py -
- Using ROCm 5.6:
- Created a virtual environment using venv using Python 3.10.16
- Run: pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/rocm5.6
- Run: pip install -r requirements-amd.txt
- Run: python tools/download_models.py
- export ROCM_PATH=/opt/rocm
- Moved both *.index and *.pth files into weights/ folder.
- Run: python infer-web.py
- Using ROCm 5.7
- Created a virtual environment using venv using Python 3.10.16
- Run: pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/rocm5.7
- Run: pip install -r requirements-amd.txt
- Run: python tools/download_models.py
- export ROCM_PATH=/opt/rocm
- Moved both *.index and *.pth files into weights/ folder.
- Run: python infer-web.py
ROCm 5.7 Output
python infer-web.py
2025-01-23 23:29:59 | INFO | configs.config | Found GPU Radeon RX 570 Series
2025-01-23 23:29:59 | INFO | configs.config | Half-precision floating-point: True, device: cuda:0
/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio_client/documentation.py:106: UserWarning: Could not get documentation group for <class 'gradio.mix.Parallel'>: No known documentation group for module 'gradio.mix'
warnings.warn(f"Could not get documentation group for {cls}: {exc}")
/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio_client/documentation.py:106: UserWarning: Could not get documentation group for <class 'gradio.mix.Series'>: No known documentation group for module 'gradio.mix'
warnings.warn(f"Could not get documentation group for {cls}: {exc}")
2025-01-23 23:30:01 | INFO | __main__ | Use Language: en_US
Running on local URL: http://0.0.0.0:7865
2025-01-23 23:30:06 | INFO | infer.modules.vc.modules | Get sid: mashima_himeko_580e_15660s.pth
2025-01-23 23:30:06 | INFO | infer.modules.vc.modules | Loading: assets/weights/mashima_himeko_580e_15660s.pth
Traceback (most recent call last):
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio/routes.py", line 437, in run_predict
output = await app.get_blocks().process_api(
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio/blocks.py", line 1346, in process_api
result = await self.call_function(
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/gradio/blocks.py", line 1074, in call_function
prediction = await anyio.to_thread.run_sync(
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2461, in run_sync_in_worker_thread
return await future
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 962, in run
result = context.run(func, *args)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/infer/modules/vc/modules.py", line 125, in get_vc
self.net_g = self.net_g.half()
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1007, in half
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
module._apply(fn)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
module._apply(fn)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
module._apply(fn)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 825, in _apply
param_applied = fn(param)
File "/home/leejun/pessoais/programs/Retrieval-based-Voice-Conversion-WebUI/rvcenv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1007, in <lambda>
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
RuntimeError: HIP error: invalid device function
HIP kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing HIP_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_HIP_DSA` to enable device-side assertions.
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-web.py and the voice-model loading path reached after selecting a voice; reproduce the crash using the supplied commands and compare the listed Python, PyTorch, and ROCm setups. Done means identifying a project-side failure or documenting that the segmentation fault is environment-specific with a reproducible result.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- audio-video-rtc, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100