Adding down-sampling and stereo mixing features for AudioDecoder (#420)

* initial draft

* second

* third

* polishing

* fix the M_PI name in LINUX platform

* fix bessel function issue

* add a unit test case

* fix the unit test name

includes/onnxruntime_customop.hpp

@@ -249,7 +249,7 @@ inline void CustomOpApi::SetDimensions(OrtTensorTypeAndShapeInfo* info, _In_ con
 
 template <typename T>
 inline T* CustomOpApi::GetTensorMutableData(_Inout_ OrtValue* value) const {
-  T* data;
+  T* data = nullptr;
   ThrowOnError(api_.GetTensorMutableData(value, reinterpret_cast<void**>(&data)));
   return data;
 }

onnxruntime_extensions/_ortapi2.py

@@ -157,8 +157,7 @@ class OrtPyFunction:
 
             x = args[idx]
             ts_x = np.array(x) if isinstance(x, (int, float, bool)) else x
-            # an annoying bug is numpy by default is int32, while pytorch is int64.
-            # so cast the input here automatically.
+            # numpy by default is int32 in some platforms, sometimes it is int64.
             feed[i_.name] = \
                 ts_x.astype(
                     np.int64) if i_.type.tensor_type.elem_type == onnx_proto.TensorProto.INT64 else ts_x

operators/audio/audio_decoder.hpp

@@ -7,6 +7,7 @@
 
 #include <list>
 #include <map>
+#include <memory>
 #define DR_FLAC_IMPLEMENTATION
 #include "dr_flac.h"
 #define DR_MP3_IMPLEMENTATION 1
@@ -16,12 +17,17 @@
 #include "dr_wav.h"
 
 #include <gsl/util>
+#include "narrow.h"
 #include "string_utils.h"
 #include "string_tensor.h"
+#include "sampling.h"
 
 struct KernelAudioDecoder : public BaseKernel {
  public:
-  KernelAudioDecoder(const OrtApi& api, const OrtKernelInfo& info) : BaseKernel(api, info) {
+  KernelAudioDecoder(const OrtApi& api, const OrtKernelInfo& info)
+      : BaseKernel(api, info),
+        downsample_rate_(TryToGetAttributeWithDefault<int64_t>("downsampling_rate", 0)),
+        stereo_mixer_(TryToGetAttributeWithDefault<int64_t>("stereo_to_mono", 0)) {
   }
 
   enum class AudioStreamType {
@@ -47,7 +53,8 @@ struct KernelAudioDecoder : public BaseKernel {
       if (pos == format_mapping.end()) {
         ORTX_CXX_API_THROW(MakeString(
                                "[AudioDecoder]: Unknown audio stream format: ", str_format),
-                           ORT_INVALID_ARGUMENT); }
+                           ORT_INVALID_ARGUMENT);
+      }
       stream_format = pos->second;
     }
 
@@ -104,12 +111,16 @@ struct KernelAudioDecoder : public BaseKernel {
 
     int64_t total_buf_size = 0;
     std::list<std::vector<float>> lst_frames;
+    int64_t orig_sample_rate = 0;
+    int64_t orig_channels = 0;
 
     if (stream_format == AudioStreamType::kMP3) {
       auto mp3_obj_ptr = std::make_unique<drmp3>();
       if (!drmp3_init_memory(mp3_obj_ptr.get(), p_data, input_dim.Size(), nullptr)) {
         ORTX_CXX_API_THROW("[AudioDecoder]: unexpected error on MP3 stream.", ORT_RUNTIME_EXCEPTION);
       }
+      orig_sample_rate = mp3_obj_ptr->sampleRate;
+      orig_channels = mp3_obj_ptr->channels;
       total_buf_size = DrReadFrames(lst_frames, drmp3_read_pcm_frames_f32, *mp3_obj_ptr);
 
     } else if (stream_format == AudioStreamType::kFLAC) {
@@ -118,6 +129,8 @@ struct KernelAudioDecoder : public BaseKernel {
       if (flac_obj == nullptr) {
         ORTX_CXX_API_THROW("[AudioDecoder]: unexpected error on FLAC stream.", ORT_RUNTIME_EXCEPTION);
       }
+      orig_sample_rate = flac_obj->sampleRate;
+      orig_channels = flac_obj->channels;
       total_buf_size = DrReadFrames(lst_frames, drflac_read_pcm_frames_f32, *flac_obj);
 
     } else {
@@ -125,18 +138,54 @@ struct KernelAudioDecoder : public BaseKernel {
       if (!drwav_init_memory(&wav_obj, p_data, input_dim.Size(), nullptr)) {
         ORTX_CXX_API_THROW("[AudioDecoder]: unexpected error on WAV stream.", ORT_RUNTIME_EXCEPTION);
       }
+      orig_sample_rate = wav_obj.sampleRate;
+      orig_channels = wav_obj.channels;
       total_buf_size = DrReadFrames(lst_frames, drwav_read_pcm_frames_f32, wav_obj);
     }
 
-    std::vector<int64_t> dim_out = {1, total_buf_size};
-    OrtValue* v = ort_.KernelContext_GetOutput(context, 0, dim_out.data(), dim_out.size());
-    float* p_output = ort_.GetTensorMutableData<float>(v);
+    if (downsample_rate_ != 0 &&
+      orig_sample_rate < downsample_rate_) {
+      ORTX_CXX_API_THROW("[AudioDecoder]: only down sampling supported.", ORT_INVALID_ARGUMENT);
+    }
+
+    // join all frames
+    std::vector<float> buf;
+    buf.resize(total_buf_size);
     int64_t offset = 0;
     for (auto& _b : lst_frames) {
-      std::copy(_b.begin(), _b.end(), p_output + offset);
+      std::copy(_b.begin(), _b.end(), buf.begin() + offset);
       offset += _b.size();
     }
+
+    // mix the stereo channels into mono channel
+    if (stereo_mixer_ && orig_channels > 1) {
+      if (buf.size() > 1) {
+        for (size_t i = 0; i < buf.size() / 2; ++i) {
+          buf[i] = (buf[i * 2] + buf[i * 2 + 1]) / 2;
+        }
+        buf.resize(buf.size() / 2);
+      }
+    }
+
+    if (downsample_rate_ != 0 &&
+        downsample_rate_ != orig_sample_rate) {
+      // A lowpass filter on buf audio data to remove high frequency noise
+      ButterworthLowpass filter(1.0f * orig_sample_rate, 0.5f * downsample_rate_);
+      std::vector<float> filtered_buf = filter.Process(buf);
+      // downsample the audio data
+      KaiserWindowInterpolation::Process(filtered_buf, buf,
+                                         1.0f * orig_sample_rate, 1.0f * downsample_rate_);
+    }
+
+    std::vector<int64_t> dim_out = {1, ort_extensions::narrow<int64_t>(buf.size())};
+    OrtValue* v = ort_.KernelContext_GetOutput(context, 0, dim_out.data(), dim_out.size());
+    float* p_output = ort_.GetTensorMutableData<float>(v);
+    std::copy(buf.begin(), buf.end(), p_output);
   }
+
+ private:
+  int64_t downsample_rate_ = 0;
+  int64_t stereo_mixer_ = 0;
 };
 
 struct CustomOpAudioDecoder : OrtW::CustomOpBase<CustomOpAudioDecoder, KernelAudioDecoder> {

operators/audio/sampling.h

@@ -0,0 +1,121 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
+#pragma once
+
+#include <vector>
+#include <cmath>
+#include <complex>
+#include "narrow.h"
+
+// https://en.wikipedia.org/wiki/Butterworth_filter
+class ButterworthLowpass {
+ public:
+  ButterworthLowpass(float sample_rate, float cutoff_frequency)
+      : x_prev_(0.0f), y_prev_(0.0f) {
+    float RC = 1.0f / (2.0f * 3.14159265359f * cutoff_frequency);
+    float dt = 1.0f / sample_rate;
+    float alpha = dt / (RC + dt);
+    a0_ = alpha;
+    a1_ = alpha;
+    b1_ = 1 - alpha;
+  }
+
+  float Process(float input) {
+    float output = a0_ * input + a1_ * x_prev_ - b1_ * y_prev_;
+    x_prev_ = input;
+    y_prev_ = output;
+    return output;
+  }
+
+  std::vector<float> Process(const std::vector<float>& inputSignal) {
+    std::vector<float> outputSignal(inputSignal.size());
+    for (size_t i = 0; i < inputSignal.size(); ++i) {
+      outputSignal[i] = Process(inputSignal[i]);
+    }
+    return outputSignal;
+  }
+
+ private:
+  float x_prev_, y_prev_;
+  float a0_, a1_, b1_;
+};
+
+// https://ccrma.stanford.edu/~jos/sasp/Kaiser_Window.html
+class KaiserWindowInterpolation {
+ private:
+  // Kaiser window parameters, empirically
+  constexpr static double kBeta = 6.0; // Beta controls the width of the transition band
+
+ public:
+  static void Process(const std::vector<float>& input, std::vector<float>& output, float inputSampleRate, float outputSampleRate) {
+    // Downsampling factor
+    float factor = outputSampleRate / inputSampleRate;
+    const double MY_PI = 3.14159265359;
+
+    // Calculate the number of output samples
+    int outputSize = static_cast<int>(std::ceil(static_cast<float>(input.size()) * factor));
+    output.resize(outputSize);
+
+    for (int i = 0; i < outputSize; i++) {
+      float index = i / factor;  // Fractional index for interpolation
+
+      // Calculate the integer and fractional parts of the index
+      int integerPart = static_cast<int>(index);
+      float fractionalPart = index - integerPart;
+
+      // Calculate the range of input samples for interpolation
+      int range = static_cast<int>(std::ceil(kBeta / (2.0f * factor)));
+      int startSample = std::max(0, integerPart - range);
+      int endSample = std::min(static_cast<int>(input.size()) - 1, integerPart + range);
+
+      // Calculate the Kaiser window weights for the input samples
+      std::vector<double> weights = KaiserWin(static_cast<size_t>(endSample - startSample + 1));
+      for (int j = startSample; j <= endSample; j++) {
+        double distance = std::abs(j - index);
+        double sincValue = (distance < 1e-6f) ? 1.0f : std::sin(MY_PI * distance) / (MY_PI * distance);
+        weights[j - startSample] *= sincValue;
+      }
+
+      // Perform the interpolation
+      double interpolatedValue = 0.0f;
+      for (int j = startSample; j <= endSample; j++) {
+        interpolatedValue += input[j] * weights[j - startSample];
+      }
+
+      output[i] = static_cast<float>(interpolatedValue);
+    }
+  }
+
+ private:
+  // std::cyl_bessel_i is not available for every platform.
+  static double cyl_bessel_i0(double x) {
+    double sum = 0.0;
+    double term = 1.0;
+    double x_squared = x * x / 4.0;
+    int n = 0;
+    double tolerance = 1e-8;
+
+    while (term > tolerance * sum) {
+      sum += term;
+      n += 1;
+      term *= x_squared / (n * n);
+    }
+
+    return sum;
+  }
+
+  // Kaiser Window function
+  static std::vector<double> KaiserWin(size_t window_length) {
+    std::vector<double> window(window_length);
+    static const double i0_beta = cyl_bessel_i0(kBeta);
+
+    for (size_t i = 0; i < window_length; i++) {
+      double x = 2.0 * i / (window_length - 1.0) - 1.0;
+      double bessel_value = cyl_bessel_i0(kBeta * std::sqrt(1 - x * x));
+      window[i] = bessel_value / i0_beta;
+    }
+
+    return window;
+  }
+};

test/test_audio_codec.py

@@ -7,7 +7,7 @@ from onnx import checker, helper, onnx_pb as onnx_proto
 from onnxruntime_extensions import PyOrtFunction, util
 
 
-class TestBpeTokenizer(unittest.TestCase):
+class TestAudioCodec(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         cls.test_mp3_file = util.get_test_data_file('data', '1272-141231-0002.mp3')
@@ -47,6 +47,13 @@ class TestBpeTokenizer(unittest.TestCase):
             np.asarray([np.max(pcm_tensor), np.average(pcm_tensor), np.min(pcm_tensor)]),
             np.asarray([np.max(self.raw_data), np.average(self.raw_data), np.min(self.raw_data)]), atol=1e-01)
 
+    def test_decoder_resampling(self):
+        test_file = util.get_test_data_file('data', 'jfk.flac')
+        blob = bytearray(util.read_file(test_file, mode='rb'))
+        decoder = PyOrtFunction.from_customop('AudioDecoder', cpu_only=True, downsampling_rate=16000, stereo_to_mono=1)
+        pcm_tensor = decoder(np.expand_dims(np.asarray(blob), axis=(0,)))
+        self.assertEqual(pcm_tensor.shape, (1, 176000))
+
 
 if __name__ == "__main__":
     unittest.main()

tutorials/whisper_e2e.py

@@ -8,7 +8,6 @@ import re
 import torch
 import numpy as np
 
-from pathlib import Path
 from onnx import numpy_helper
 from transformers import WhisperProcessor
 
@@ -142,7 +141,8 @@ def _torch_export(*arg, **kwargs):
 
 def preprocessing(audio_data):
     if USE_AUDIO_DECODER:
-        decoder = PyOrtFunction.from_customop("AudioDecoder", cpu_only=True)
+        decoder = PyOrtFunction.from_customop(
+            "AudioDecoder", cpu_only=True, downsampling_rate=SAMPLE_RATE, stereo_to_mono=1)
         audio_pcm = torch.from_numpy(decoder(audio_data))
     else:
         audio_pcm = torch.from_numpy(audio_data)
@@ -198,11 +198,14 @@ def merge_models(core: str, output_model: str, audio_data):
         make_node('Cast', ['sequences'], ["generated_ids"], to=onnx.TensorProto.INT64),
         bpe_decoder_node
         ])
-    onnx.save_model(m_all, output_model,
-                    save_as_external_data=True,
-                    all_tensors_to_one_file=True,
-                    location=f"{os.path.basename(output_model)}.data",
-                    convert_attribute=True)
+    try:
+        onnx.save_model(m_all, output_model)
+    except ValueError:
+        onnx.save_model(m_all, output_model,
+                        save_as_external_data=True,
+                        all_tensors_to_one_file=True,
+                        location=f"{os.path.basename(output_model)}.data",
+                        convert_attribute=True)
     print(f"The final merged model was saved as: {output_model}")
 
     print("Verify the final model...")