Bug report - [Onnx Runtime 1.16 incompatible]

[sorgfresser]

🐛 Bug

Onnxruntime version 1.16 has been released yesterday. If I use it to load silero-vad using onnx=True, i get

ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled. Since ORT 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example, onnxruntime.InferenceSession(..., providers=['AzureExecutionProvider', 'CPUExecutionProvider'], ...)

Oddly enough, it works if I downgrade to 1.15 even if it's telling me this has been a thing since ORT 1.9.

To Reproduce

Steps to reproduce the behavior:

pip install onnxruntime==1.16.0

    model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                                  model="silero_vad",
                                  onnx=True,
                                  force_reload=False)

Full stack trace:

  File "/home/simon/PycharmProjects/ttsdata/src/vad.py", line 127, in yield_audio
    model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/PycharmProjects/ttsdata/venv/lib/python3.11/site-packages/torch/hub.py", line 558, in load
    model = _load_local(repo_or_dir, model, *args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/PycharmProjects/ttsdata/venv/lib/python3.11/site-packages/torch/hub.py", line 587, in _load_local
    model = entry(*args, **kwargs)
            ^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/.cache/torch/hub/snakers4_silero-vad_master/hubconf.py", line 44, in silero_vad
    model = OnnxWrapper(os.path.join(model_dir, 'silero_vad.onnx'), force_onnx_cpu)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/.cache/torch/hub/snakers4_silero-vad_master/utils_vad.py", line 24, in __init__
    self.session = onnxruntime.InferenceSession(path, sess_options=opts)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/simon/PycharmProjects/ttsdata/venv/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 432, in __init__
    raise e
  File "/home/simon/PycharmProjects/ttsdata/venv/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 419, in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
  File "/home/simon/PycharmProjects/ttsdata/venv/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 451, in _create_inference_session
    raise ValueError(
ValueError: This ORT build has ['AzureExecutionProvider', 'CPUExecutionProvider'] enabled. Since ORT 1.9, you are required to explicitly set the providers parameter when instantiating InferenceSession. For example, onnxruntime.InferenceSession(..., providers=['AzureExecutionProvider', 'CPUExecutionProvider'], ...)

Expected behavior

Environment

Collecting environment information...
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Manjaro Linux (x86_64)
GCC version: (GCC) 13.2.1 20230801
Clang version: 16.0.6
CMake version: version 3.27.5
Libc version: glibc-2.38

Python version: 3.11.5 (main, Aug 28 2023, 20:02:58) [GCC 13.2.1 20230801] (64-bit runtime)
Python platform: Linux-6.1.51-1-MANJARO-x86_64-with-glibc2.38
Is CUDA available: True
CUDA runtime version: 12.2.91
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1060 6GB
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/libcudnn.so.8.9.2
/usr/lib/libcudnn_adv_infer.so.8.9.2
/usr/lib/libcudnn_adv_train.so.8.9.2
/usr/lib/libcudnn_cnn_infer.so.8.9.2
/usr/lib/libcudnn_cnn_train.so.8.9.2
/usr/lib/libcudnn_ops_infer.so.8.9.2
/usr/lib/libcudnn_ops_train.so.8.9.2
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architektur: x86_64
CPU Operationsmodus: 32-bit, 64-bit
Adressgrößen: 48 bits physical, 48 bits virtual
Byte-Reihenfolge: Little Endian
CPU(s): 8
Liste der Online-CPU(s): 0-7
Anbieterkennung: AuthenticAMD
Modellname: AMD FX(tm)-8350 Eight-Core Processor
Prozessorfamilie: 21
Modell: 2
Thread(s) pro Kern: 2
Kern(e) pro Sockel: 4
Sockel: 1
Stepping: 0
Übertaktung: aktiviert
Skalierung der CPU(s): 69%
Maximale Taktfrequenz der CPU: 4000,0000
Minimale Taktfrequenz der CPU: 1400,0000
BogoMIPS: 8002,06
Markierungen: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 popcnt aes xsave avx f16c lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs xop skinit wdt fma4 tce nodeid_msr tbm topoext perfctr_core perfctr_nb cpb hw_pstate ssbd ibpb vmmcall bmi1 arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold
Virtualisierung: AMD-V
L1d Cache: 128 KiB (8 Instanzen)
L1i Cache: 256 KiB (4 Instanzen)
L2 Cache: 8 MiB (4 Instanzen)
L3 Cache: 8 MiB (1 Instanz)
NUMA-Knoten: 1
NUMA-Knoten0 CPU(s): 0-7

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] pytorch-lightning==2.0.9
[pip3] pytorch-metric-learning==2.3.0
[pip3] torch==2.0.1
[pip3] torch-audiomentations==0.11.0
[pip3] torch-pitch-shift==1.2.4
[pip3] torchaudio==2.0.2
[pip3] torchmetrics==1.1.2
[pip3] triton==2.0.0
[conda] Could not collect

[snakers4]

To be solved with a V5 release, most likely just by using latest ONNX compatibility level when exporting the model.

[ozancaglayan]

This is probably related to a bug in onnxruntime 1.16.0 which they fixed in 1.16.1. I'm using VAD with 1.16.1 without an issue.