Bump actions/upload-pages-artifact from 4 to 5 in the update-actions group by dependabot[bot] · Pull Request #121 · istupakov/onnx-asr

dependabot · 2026-05-01T09:58:19Z

Bumps the update-actions group with 1 update: actions/upload-pages-artifact.

Updates actions/upload-pages-artifact from 4 to 5

Release notes

Sourced from actions/upload-pages-artifact's releases.

v5.0.0

Changelog

Update upload-artifact action to version 7 @Tom-van-Woudenberg (#139)

feat: add include-hidden-files input @jonchurch (#137)

See details of all code changes since previous release.

Commits

fc324d3 Merge pull request #139 from Tom-van-Woudenberg/patch-1
fe9d4b7 Merge branch 'main' into patch-1
0ca1617 Merge pull request #137 from jonchurch/include-hidden-files
57f0e84 Update action.yml
4a90348 v7 --> hash
56f665a Update upload-artifact action to version 7
f7615f5 Add include-hidden-files input
See full diff in compare view

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Bumps the update-actions group with 1 update: [actions/upload-pages-artifact](https://github.com/actions/upload-pages-artifact). Updates `actions/upload-pages-artifact` from 4 to 5 - [Release notes](https://github.com/actions/upload-pages-artifact/releases) - [Commits](actions/upload-pages-artifact@v4...v5) --- updated-dependencies: - dependency-name: actions/upload-pages-artifact dependency-version: '5' dependency-type: direct:production update-type: version-update:semver-major dependency-group: update-actions ... Signed-off-by: dependabot[bot] <support@github.com>

codecov-commenter · 2026-05-01T10:03:56Z

❌ 7 Tests Failed:

Tests completed	Failed	Passed	Skipped
381	7	374	1

View the top 3 failed test(s) by shortest run time

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_empty_recognize[onnx-community/whisper-tiny]

Stack Traces | 0s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_recognize_batch[onnx-community/whisper-tiny]

Stack Traces | 0s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_recognize_with_timestamps[onnx-community/whisper-tiny]

Stack Traces | 0s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_supported_only_mono_audio_error[onnx-community/whisper-tiny]

Stack Traces | 0s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_wrong_sample_rate_error[onnx-community/whisper-tiny]

Stack Traces | 0s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_recognize[onnx-community/whisper-tiny]

Stack Traces | 0.001s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

tests\onnx_asr\test_recognize.py::onnx_asr::test_recognize::test_file_not_found_error[onnx-community/whisper-tiny]

Stack Traces | 1.68s run time

request = <SubRequest 'model' for <Function test_file_not_found_error[onnx-community/whisper-tiny]>>

    @pytest.fixture(scope="module", params=models)
    def model(request: pytest.FixtureRequest) -> TextResultsAsrAdapter:
        match request.param:
            case "t-tech/t-one":
                return onnx_asr.load_model(request.param)
            case "onnx-community/whisper-tiny":
>               return onnx_asr.load_model(request.param, quantization="uint8")
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

tests\onnx_asr\test_recognize.py:35: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
.venv\Lib\site-packages\onnx_asr\loader.py:347: in load_model
    return manager.create_asr(model, path, quantization=quantization, config=asr_config)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnx_asr\loader.py:248: in create_asr
    resolver.model_type(resolver.resolve_model(quantization=quantization), self._create_preprocessor, config)
.venv\Lib\site-packages\onnx_asr\models\whisper.py:157: in __init__
    self._decoder = rt.InferenceSession(model_files["decoder"], **onnx_options)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:529: in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

self = <onnxruntime.capi.onnxruntime_inference_collection.InferenceSession object at 0x00000242832627B0>
providers = ['CPUExecutionProvider'], provider_options = [{}]
disabled_optimizers = set()

    def _create_inference_session(self, providers, provider_options, disabled_optimizers=None):
        available_providers = C.get_available_providers()
    
        # Validate that TensorrtExecutionProvider and NvTensorRTRTXExecutionProvider are not both specified
        if providers:
            has_tensorrt = any(
                provider == "TensorrtExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                for provider in providers
            )
            has_tensorrt_rtx = any(
                provider == "NvTensorRTRTXExecutionProvider"
                or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                for provider in providers
            )
            if has_tensorrt and has_tensorrt_rtx:
                raise ValueError(
                    "Cannot enable both 'TensorrtExecutionProvider' and 'NvTensorRTRTXExecutionProvider' "
                    "in the same session."
                )
        # Tensorrt and TensorRT RTX can fall back to CUDA if it's explicitly assigned. All others fall back to CPU.
        if "NvTensorRTRTXExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "NvTensorRTRTXExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "NvTensorRTRTXExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        elif "TensorrtExecutionProvider" in available_providers:
            if (
                providers
                and any(
                    provider == "CUDAExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "CUDAExecutionProvider")
                    for provider in providers
                )
                and any(
                    provider == "TensorrtExecutionProvider"
                    or (isinstance(provider, tuple) and provider[0] == "TensorrtExecutionProvider")
                    for provider in providers
                )
            ):
                self._fallback_providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
            else:
                self._fallback_providers = ["CPUExecutionProvider"]
        else:
            self._fallback_providers = ["CPUExecutionProvider"]
    
        # validate providers and provider_options before other initialization
        providers, provider_options = check_and_normalize_provider_args(
            providers, provider_options, available_providers
        )
    
        # Print a warning if user passed providers to InferenceSession() but the SessionOptions instance
        # already has provider information (e.g., via add_provider_for_devices()). The providers specified
        # here will take precedence.
        if self._sess_options is not None and (providers or provider_options) and self._sess_options.has_providers():
            warnings.warn(
                "Specified 'providers'/'provider_options' when creating InferenceSession but SessionOptions has "
                "already been configured with providers. InferenceSession will only use the providers "
                "passed to InferenceSession()."
            )
    
        session_options = self._sess_options if self._sess_options else C.get_default_session_options()
    
        self._register_ep_custom_ops(session_options, providers, provider_options, available_providers)
    
        if self._model_path:
            sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
        else:
            sess = C.InferenceSession(session_options, self._model_bytes, False, self._read_config_from_model)
    
        if disabled_optimizers is None:
            disabled_optimizers = set()
        elif not isinstance(disabled_optimizers, set):
            # convert to set. assumes iterable
            disabled_optimizers = set(disabled_optimizers)
    
        # initialize the C++ InferenceSession
>       sess.initialize_session(providers, provider_options, disabled_optimizers)
E       onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : qdq_actions.cc:137 onnxruntime::QDQ::`anonymous-namespace'::TransposeDQWeightsForMatMulNBits Missing required scale: model.decoder.embed_tokens.weight_merged_0_scale for node: model.decoder.embed_tokens.weight_transposed_DequantizeLinear

.venv\Lib\site-packages\onnxruntime\capi\onnxruntime_inference_collection.py:635: Fail

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