Feature extraction C API for whipser model

CMakeLists.txt

@@ -171,6 +171,10 @@ if (MSVC)
   endif()
   message(STATUS "_STATIC_MSVC_RUNTIME_LIBRARY: ${_STATIC_MSVC_RUNTIME_LIBRARY}")
 
+  # DLL initialization errors due to old conda msvcp140.dll dll are a result of the new MSVC compiler
+  # See https://developercommunity.visualstudio.com/t/Access-violation-with-std::mutex::lock-a/10664660#T-N10668856
+  # Remove this definition once the conda msvcp140.dll dll is updated.
+  add_compile_definitions(_DISABLE_CONSTEXPR_MUTEX_CONSTRUCTOR)
 endif()
 
 if(NOT OCOS_BUILD_PYTHON AND OCOS_ENABLE_PYTHON)
@@ -442,7 +446,9 @@ endif()
 if(OCOS_ENABLE_BERT_TOKENIZER)
   # Bert
   set(_HAS_TOKENIZER ON)
-  file(GLOB bert_TARGET_SRC "operators/tokenizer/basic_tokenizer.*" "operators/tokenizer/bert_tokenizer.*" "operators/tokenizer/bert_tokenizer_decoder.*")
+  file(GLOB bert_TARGET_SRC "operators/tokenizer/basic_tokenizer.*"
+                            "operators/tokenizer/bert_tokenizer.*"
+                            "operators/tokenizer/bert_tokenizer_decoder.*")
   list(APPEND TARGET_SRC ${bert_TARGET_SRC})
 endif()
 
@@ -820,7 +826,9 @@ if(OCOS_ENABLE_AZURE)
 endif()
 
 target_compile_definitions(ortcustomops PUBLIC ${OCOS_COMPILE_DEFINITIONS})
+target_include_directories(ortcustomops PUBLIC "$<TARGET_PROPERTY:noexcep_operators,INTERFACE_INCLUDE_DIRECTORIES>")
 target_include_directories(ortcustomops PUBLIC "$<TARGET_PROPERTY:ocos_operators,INTERFACE_INCLUDE_DIRECTORIES>")
+
 target_link_libraries(ortcustomops PUBLIC ocos_operators)
 
 if(_BUILD_SHARED_LIBRARY)
@@ -840,7 +848,8 @@ if(_BUILD_SHARED_LIBRARY)
   standardize_output_folder(extensions_shared)
 
   if(LINUX OR ANDROID)
-    set_property(TARGET extensions_shared APPEND_STRING PROPERTY LINK_FLAGS " -Wl,--version-script -Wl,${PROJECT_SOURCE_DIR}/shared/ortcustomops.ver")
+    set_property(TARGET extensions_shared APPEND_STRING PROPERTY LINK_FLAGS
+      " -Wl,--version-script -Wl,${PROJECT_SOURCE_DIR}/shared/ortcustomops.ver")
     # strip if not a debug build
     if(NOT CMAKE_BUILD_TYPE STREQUAL "Debug")
       set_property(TARGET extensions_shared APPEND_STRING PROPERTY LINK_FLAGS " -Wl,-s")

docs/c_api.md

@@ -0,0 +1,19 @@
+# ONNXRuntime Extensions C ABI
+
+ONNXRuntime Extensions provides a C-style ABI for pre-processing. It offers support for tokenization, image processing, speech feature extraction, and more. You can compile the ONNXRuntime Extensions as either a static library or a dynamic library to access these APIs.
+
+The C ABI header files are named `ortx_*.h` and can be found in the include folder. There are three types of data processing APIs available:
+
+- [`ortx_tokenizer.h`](../include/ortx_tokenizer.h): Provides tokenization for LLM models.
+- [`ortx_processor.h`](../include/ortx_processor.h): Offers image processing APIs for multimodels.
+- [`ortx_extraction.h`](../include/ortx_extractor.h): Provides speech feature extraction for audio data processing to assist the Whisper model.
+
+## ABI QuickStart
+
+Most APIs accept raw data inputs such as audio, image compressed binary formats, or UTF-8 encoded text for tokenization.
+
+**Tokenization:** You can create a tokenizer object using `OrtxCreateTokenizer` and then use the object to tokenize a text or decode the token ID into the text. A C-style code snippet is available [here](../test/pp_api_test/c_only_test.c).
+
+**Image processing:** `OrtxCreateProcessor` can create an image processor object from a pre-defined workflow in JSON format to process image files into a tensor-like data type. An example code snippet can be found [here](../test/pp_api_test/test_processor.cc#L75).
+
+**Audio feature extraction:** `OrtxCreateSpeechFeatureExtractor` creates a speech feature extractor to obtain log mel spectrum data as input for the Whisper model. An example code snippet can be found [here](../test/pp_api_test/test_feature_extractor.cc#L16).

include/ortx_extractor.h

@@ -0,0 +1,75 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+// C ABI header file for the onnxruntime-extensions tokenization module
+
+#pragma once
+
+#include "ortx_utils.h"
+
+typedef OrtxObject OrtxFeatureExtractor;
+typedef OrtxObject OrtxRawAudios;
+typedef OrtxObject OrtxTensorResult;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/**
+ * @brief Creates a feature extractor object.
+ *
+ * This function creates a feature extractor object based on the provided feature definition.
+ *
+ * @param[out] extractor Pointer to a pointer to the created feature extractor object.
+ * @param[in] fe_def The feature definition used to create the feature extractor.
+ *
+ * @return An error code indicating the result of the operation.
+ */
+extError_t ORTX_API_CALL OrtxCreateSpeechFeatureExtractor(OrtxFeatureExtractor** extractor, const char* fe_def);
+
+/**
+ * Loads a collection of audio files into memory.
+ *
+ * This function loads a collection of audio files specified by the `audio_paths` array
+ * into memory and returns a pointer to the loaded audio data in the `audios` parameter.
+ *
+ * @param audios A pointer to a pointer that will be updated with the loaded audio data.
+ *               The caller is responsible for freeing the memory allocated for the audio data.
+ * @param audio_paths An array of strings representing the paths to the audio files to be loaded.
+ * @param num_audios The number of audio files to be loaded.
+ * 
+ * @return An `extError_t` value indicating the success or failure of the operation.
+ */
+extError_t ORTX_API_CALL OrtxLoadAudios(OrtxRawAudios** audios, const char* const* audio_paths, size_t num_audios);
+
+/**
+ * @brief Creates an array of raw audio objects.
+ *
+ * This function creates an array of raw audio objects based on the provided data and sizes.
+ *
+ * @param audios Pointer to the variable that will hold the created raw audio objects.
+ * @param data Array of pointers to the audio data.
+ * @param sizes Array of pointers to the sizes of the audio data.
+ * @param num_audios Number of audio objects to create.
+ *
+ * @return extError_t Error code indicating the success or failure of the operation.
+ */
+extError_t ORTX_API_CALL OrtxCreateRawAudios(OrtxRawAudios** audios, const void* data[], const int64_t* sizes[], size_t num_audios);
+
+/**
+ * @brief Calculates the log mel spectrogram for a given audio using the specified feature extractor.
+ *
+ * This function takes an instance of the OrtxFeatureExtractor struct, an instance of the OrtxRawAudios struct,
+ * and a pointer to a OrtxTensorResult pointer. It calculates the log mel spectrogram for the given audio using
+ * the specified feature extractor and stores the result in the provided log_mel pointer.
+ *
+ * @param extractor The feature extractor to use for calculating the log mel spectrogram.
+ * @param audio The raw audio data to process.
+ * @param log_mel A pointer to a OrtxTensorResult pointer where the result will be stored.
+ * @return An extError_t value indicating the success or failure of the operation.
+ */
+extError_t ORTX_API_CALL OrtxSpeechLogMel(OrtxFeatureExtractor* extractor, OrtxRawAudios* audio, OrtxTensorResult** log_mel);
+
+#ifdef __cplusplus
+}
+#endif

include/ortx_processor.h

@@ -10,7 +10,6 @@
 // typedefs to create/dispose function flood, and to make the API more C++ friendly with less casting
 typedef OrtxObject OrtxProcessor;
 typedef OrtxObject OrtxRawImages;
-typedef OrtxObject OrtxImageProcessorResult;
 
 #ifdef __cplusplus
 extern "C" {
@@ -40,8 +39,22 @@ extError_t ORTX_API_CALL OrtxCreateProcessor(OrtxProcessor** processor, const ch
 extError_t ORTX_API_CALL OrtxLoadImages(OrtxRawImages** images, const char** image_paths, size_t num_images,
                                         size_t* num_images_loaded);
 
+
 /**
- * @brief Preprocesses the given raw images using the specified processor.
+ * @brief Creates raw images from the provided data.
+ *
+ * This function creates raw images from the provided data. The raw images are stored in the `images` parameter.
+ *
+ * @param images Pointer to a pointer to the `OrtxRawImages` structure that will hold the created raw images.
+ * @param data Array of pointers to the data for each image.
+ * @param sizes Array of pointers to the sizes of each image.
+ * @param num_images Number of images to create.
+ * @return An `extError_t` value indicating the success or failure of the operation.
+ */
+extError_t ORTX_API_CALL OrtxCreateRawImages(OrtxRawImages** images, const void* data[], const int64_t* sizes[], size_t num_images);
+
+/**
+ * @brief Pre-processes the given raw images using the specified processor.
  *
  * This function applies preprocessing operations on the raw images using the provided processor.
  * The result of the preprocessing is stored in the `OrtxImageProcessorResult` object.
@@ -52,24 +65,7 @@ extError_t ORTX_API_CALL OrtxLoadImages(OrtxRawImages** images, const char** ima
  * @return An `extError_t` value indicating the success or failure of the preprocessing operation.
  */
 extError_t ORTX_API_CALL OrtxImagePreProcess(OrtxProcessor* processor, OrtxRawImages* images,
-                                             OrtxImageProcessorResult** result);
-
-/**
- * @brief Retrieves the image processor result at the specified index.
- *
- * @param result Pointer to the OrtxImageProcessorResult structure to store the result.
- * @param index The index of the result to retrieve.
- * @return extError_t The error code indicating the success or failure of the operation.
- */
-extError_t ORTX_API_CALL OrtxImageGetTensorResult(OrtxImageProcessorResult* result, size_t index, OrtxTensor** tensor);
-
-/** \brief Clear the outputs of the processor
- *
- * \param processor The processor object
- * \param result The result object to clear
- * \return Error code indicating the success or failure of the operation
- */
-extError_t ORTX_API_CALL OrtxClearOutputs(OrtxProcessor* processor, OrtxImageProcessorResult* result);
+                                             OrtxTensorResult** result);
 
 #ifdef __cplusplus
 }

include/ortx_utils.h

@@ -17,19 +17,22 @@ typedef enum {
   kOrtxKindDetokenizerCache = 0x778B,
   kOrtxKindProcessor = 0x778C,
   kOrtxKindRawImages = 0x778D,
-  kOrtxKindImageProcessorResult = 0x778E,
+  kOrtxKindTensorResult = 0x778E,
   kOrtxKindProcessorResult = 0x778F,
   kOrtxKindTensor = 0x7790,
+  kOrtxKindFeatureExtractor = 0x7791,
+  kOrtxKindRawAudios = 0x7792,
   kOrtxKindEnd = 0x7999
 } extObjectKind_t;
 
 // all object managed by the library should be 'derived' from this struct
 // which eventually will be released by TfmDispose if C++, or TFM_DISPOSE if C
 typedef struct {
-  int ext_kind_;
+  extObjectKind_t ext_kind_;
 } OrtxObject;
 
 typedef OrtxObject OrtxTensor;
+typedef OrtxObject OrtxTensorResult;
 
 // C, instead of C++ doesn't cast automatically,
 // so we need to use a macro to cast the object to the correct type
@@ -77,6 +80,18 @@ extError_t ORTX_API_CALL OrtxDispose(OrtxObject** object);
  */
 extError_t ORTX_API_CALL OrtxDisposeOnly(OrtxObject* object);
 
+/**
+ * @brief Retrieves the tensor at the specified index from the given tensor result.
+ *
+ * This function allows you to access a specific tensor from a tensor result object.
+ *
+ * @param result The tensor result object from which to retrieve the tensor.
+ * @param index The index of the tensor to retrieve.
+ * @param tensor A pointer to a variable that will hold the retrieved tensor.
+ * @return An error code indicating the success or failure of the operation.
+ */
+extError_t ORTX_API_CALL OrtxTensorResultGetAt(OrtxTensorResult* result, size_t index, OrtxTensor** tensor);
+
 /** \brief Get the data from the tensor
  *
  * \param tensor The tensor object

onnxruntime_extensions/_torch_cvt.py

@@ -17,7 +17,7 @@
 from ._ortapi2 import make_onnx_model
 from ._cuops import SingleOpGraph
 from ._hf_cvt import HFTokenizerConverter
-from .util import remove_unused_initializers
+from .util import remove_unused_initializers, mel_filterbank
 
 
 class _WhisperHParams:
@@ -30,53 +30,15 @@ class _WhisperHParams:
     N_FRAMES = N_SAMPLES // HOP_LENGTH
 
 
-def _mel_filterbank(
-        n_fft: int, n_mels: int = 80, sr=16000, min_mel=0, max_mel=45.245640471924965, dtype=np.float32):
-    """
-    Compute a Mel-filterbank. The filters are stored in the rows, the columns,
-    and it is Slaney normalized mel-scale filterbank.
-    """
-    fbank = np.zeros((n_mels, n_fft // 2 + 1), dtype=dtype)
-
-    # the centers of the frequency bins for the DFT
-    freq_bins = np.fft.rfftfreq(n=n_fft, d=1.0 / sr)
-
-    mel = np.linspace(min_mel, max_mel, n_mels + 2)
-    # Fill in the linear scale
-    f_min = 0.0
-    f_sp = 200.0 / 3
-    freqs = f_min + f_sp * mel
-
-    # And now the nonlinear scale
-    min_log_hz = 1000.0  # beginning of log region (Hz)
-    min_log_mel = (min_log_hz - f_min) / f_sp  # same (Mels)
-    logstep = np.log(6.4) / 27.0  # step size for log region
-
-    log_t = mel >= min_log_mel
-    freqs[log_t] = min_log_hz * np.exp(logstep * (mel[log_t] - min_log_mel))
-    mel_bins = freqs
-
-    mel_spacing = np.diff(mel_bins)
-
-    ramps = mel_bins.reshape(-1, 1) - freq_bins.reshape(1, -1)
-    for i in range(n_mels):
-        left = -ramps[i] / mel_spacing[i]
-        right = ramps[i + 2] / mel_spacing[i + 1]
-
-        # intersect them with each other and zero
-        fbank[i] = np.maximum(0, np.minimum(left, right))
-
-    energy_norm = 2.0 / (mel_bins[2: n_mels + 2] - mel_bins[:n_mels])
-    fbank *= energy_norm[:, np.newaxis]
-    return fbank
-
-
 class CustomOpStftNorm(torch.autograd.Function):
     @staticmethod
     def symbolic(g, self, n_fft, hop_length, window):
-        t_n_fft = g.op('Constant', value_t=torch.tensor(n_fft, dtype=torch.int64))
-        t_hop_length = g.op('Constant', value_t=torch.tensor(hop_length, dtype=torch.int64))
-        t_frame_size = g.op('Constant', value_t=torch.tensor(n_fft, dtype=torch.int64))
+        t_n_fft = g.op('Constant', value_t=torch.tensor(
+            n_fft, dtype=torch.int64))
+        t_hop_length = g.op('Constant', value_t=torch.tensor(
+            hop_length, dtype=torch.int64))
+        t_frame_size = g.op(
+            'Constant', value_t=torch.tensor(n_fft, dtype=torch.int64))
         return g.op("ai.onnx.contrib::StftNorm", self, t_n_fft, t_hop_length, window, t_frame_size)
 
     @staticmethod
@@ -97,7 +59,7 @@ def __init__(self, sr=_WhisperHParams.SAMPLE_RATE, n_fft=_WhisperHParams.N_FFT,
         self.n_fft = n_fft
         self.window = torch.hann_window(n_fft)
         self.mel_filters = torch.from_numpy(
-            _mel_filterbank(sr=sr, n_fft=n_fft, n_mels=n_mels))
+            mel_filterbank(sr=sr, n_fft=n_fft, n_mels=n_mels))
 
     def forward(self, audio_pcm: torch.Tensor):
         stft_norm = CustomOpStftNorm.apply(audio_pcm,
@@ -112,7 +74,8 @@ def forward(self, audio_pcm: torch.Tensor):
         spec_shape = log_spec.shape
         padding_spec = torch.ones(spec_shape[0],
                                   spec_shape[1],
-                                  self.n_samples // self.hop_length - spec_shape[2],
+                                  self.n_samples // self.hop_length -
+                                  spec_shape[2],
                                   dtype=torch.float)
         padding_spec *= spec_min
         log_spec = torch.cat((log_spec, padding_spec), dim=2)
@@ -165,15 +128,20 @@ def _to_onnx_stft(onnx_model, n_fft):
         make_node('Slice', inputs=['transpose_1_output_0', 'const_18_output_0', 'const_minus_1_output_0',
                                    'const_17_output_0', 'const_20_output_0'], outputs=['slice_1_output_0'],
                   name='slice_1'),
-        make_node('Constant', inputs=[], outputs=['const0_output_0'], name='const0', value_int=0),
-        make_node('Constant', inputs=[], outputs=['const1_output_0'], name='const1', value_int=1),
+        make_node('Constant', inputs=[], outputs=[
+                  'const0_output_0'], name='const0', value_int=0),
+        make_node('Constant', inputs=[], outputs=[
+                  'const1_output_0'], name='const1', value_int=1),
         make_node('Gather', inputs=['slice_1_output_0', 'const0_output_0'], outputs=['gather_4_output_0'],
                   name='gather_4', axis=3),
         make_node('Gather', inputs=['slice_1_output_0', 'const1_output_0'], outputs=['gather_5_output_0'],
                   name='gather_5', axis=3),
-        make_node('Mul', inputs=['gather_4_output_0', 'gather_4_output_0'], outputs=['mul_output_0'], name='mul0'),
-        make_node('Mul', inputs=['gather_5_output_0', 'gather_5_output_0'], outputs=['mul_1_output_0'], name='mul1'),
-        make_node('Add', inputs=['mul_output_0', 'mul_1_output_0'], outputs=[stft_norm_node.output[0]], name='add0'),
+        make_node('Mul', inputs=['gather_4_output_0', 'gather_4_output_0'], outputs=[
+                  'mul_output_0'], name='mul0'),
+        make_node('Mul', inputs=['gather_5_output_0', 'gather_5_output_0'], outputs=[
+                  'mul_1_output_0'], name='mul1'),
+        make_node('Add', inputs=['mul_output_0', 'mul_1_output_0'], outputs=[
+                  stft_norm_node.output[0]], name='add0'),
     ]
     new_stft_nodes.extend(onnx_model.graph.node[:node_idx])
     new_stft_nodes.extend(replaced_nodes)
@@ -253,9 +221,11 @@ def post_processing(self, **kwargs):
         del g.node[:]
         g.node.extend(nodes)
 
-        inputs = [onnx.helper.make_tensor_value_info("sequences", onnx.TensorProto.INT32, ['N', 'seq_len', 'ids'])]
+        inputs = [onnx.helper.make_tensor_value_info(
+            "sequences", onnx.TensorProto.INT32, ['N', 'seq_len', 'ids'])]
         del g.input[:]
         g.input.extend(inputs)
-        g.output[0].type.CopyFrom(onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, ['N', 'text']))
+        g.output[0].type.CopyFrom(onnx.helper.make_tensor_type_proto(
+            onnx.TensorProto.STRING, ['N', 'text']))
 
         return make_onnx_model(g, opset_version=self.opset_version)

operators/audio/audio.cc

@@ -3,17 +3,15 @@
 
 #include "ocos.h"
 #ifdef ENABLE_DR_LIBS
-#include "audio_decoder.hpp"
+#include "audio_decoder.h"
 #endif  // ENABLE_DR_LIBS
 
-FxLoadCustomOpFactory LoadCustomOpClasses_Audio = []()-> CustomOpArray& {
+FxLoadCustomOpFactory LoadCustomOpClasses_Audio = []() -> CustomOpArray& {
   static OrtOpLoader op_loader(
-    []() { return nullptr; }
 #ifdef ENABLE_DR_LIBS
-    ,
-    CustomCpuStructV2("AudioDecoder", AudioDecoder)
+      CustomCpuStructV2("AudioDecoder", AudioDecoder),
 #endif
-  );
+      []() { return nullptr; });
 
   return op_loader.GetCustomOps();
 };