From 2fa0b710eac5d39088402e369965cad126575d71 Mon Sep 17 00:00:00 2001 From: Wenbing Li <10278425+wenbingl@users.noreply.github.com> Date: Thu, 4 May 2023 13:30:10 -0700 Subject: [PATCH] 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 | 2 +- onnxruntime_extensions/_ortapi2.py | 3 +- onnxruntime_extensions/cvt.py | 4 +- operators/audio/audio_decoder.hpp | 61 +++++++++++++-- operators/audio/sampling.h | 121 +++++++++++++++++++++++++++++ test/data/jfk.flac | Bin 0 -> 1152693 bytes test/test_audio_codec.py | 9 ++- tutorials/whisper_e2e.py | 19 +++-- 8 files changed, 199 insertions(+), 20 deletions(-) create mode 100644 operators/audio/sampling.h create mode 100644 test/data/jfk.flac diff --git a/includes/onnxruntime_customop.hpp b/includes/onnxruntime_customop.hpp index c2c29e42..4279dff7 100644 --- a/includes/onnxruntime_customop.hpp +++ b/includes/onnxruntime_customop.hpp @@ -249,7 +249,7 @@ inline void CustomOpApi::SetDimensions(OrtTensorTypeAndShapeInfo* info, _In_ con template inline T* CustomOpApi::GetTensorMutableData(_Inout_ OrtValue* value) const { - T* data; + T* data = nullptr; ThrowOnError(api_.GetTensorMutableData(value, reinterpret_cast(&data))); return data; } diff --git a/onnxruntime_extensions/_ortapi2.py b/onnxruntime_extensions/_ortapi2.py index 56f59aed..99643a4a 100644 --- a/onnxruntime_extensions/_ortapi2.py +++ b/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 diff --git a/onnxruntime_extensions/cvt.py b/onnxruntime_extensions/cvt.py index 9951479d..ba2d503b 100644 --- a/onnxruntime_extensions/cvt.py +++ b/onnxruntime_extensions/cvt.py @@ -52,7 +52,7 @@ class HFTokenizerConverter(CustomOpConverter): *sorted_merges[n_]) for n_ in range(len(sorted_merges))) attrs.update(**kwargs) return attrs - + def roberta_tokenizer(self, **kwargs): hf_roberta_tokenizer = self.tokenizer attrs = {'vocab': json.dumps( @@ -62,4 +62,4 @@ class HFTokenizerConverter(CustomOpConverter): attrs['merges'] = '\n'.join("{} {}".format( *sorted_merges[n_]) for n_ in range(len(sorted_merges))) attrs.update(**kwargs) - return attrs \ No newline at end of file + return attrs diff --git a/operators/audio/audio_decoder.hpp b/operators/audio/audio_decoder.hpp index 8b155688..bf8dbb7e 100644 --- a/operators/audio/audio_decoder.hpp +++ b/operators/audio/audio_decoder.hpp @@ -7,6 +7,7 @@ #include #include +#include #define DR_FLAC_IMPLEMENTATION #include "dr_flac.h" #define DR_MP3_IMPLEMENTATION 1 @@ -16,12 +17,17 @@ #include "dr_wav.h" #include +#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("downsampling_rate", 0)), + stereo_mixer_(TryToGetAttributeWithDefault("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> 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(); 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 dim_out = {1, total_buf_size}; - OrtValue* v = ort_.KernelContext_GetOutput(context, 0, dim_out.data(), dim_out.size()); - float* p_output = ort_.GetTensorMutableData(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 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 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 dim_out = {1, ort_extensions::narrow(buf.size())}; + OrtValue* v = ort_.KernelContext_GetOutput(context, 0, dim_out.data(), dim_out.size()); + float* p_output = ort_.GetTensorMutableData(v); + std::copy(buf.begin(), buf.end(), p_output); } + + private: + int64_t downsample_rate_ = 0; + int64_t stereo_mixer_ = 0; }; struct CustomOpAudioDecoder : OrtW::CustomOpBase { diff --git a/operators/audio/sampling.h b/operators/audio/sampling.h new file mode 100644 index 00000000..e6bb6387 --- /dev/null +++ b/operators/audio/sampling.h @@ -0,0 +1,121 @@ +// Copyright (c) Microsoft Corporation. +// Licensed under the MIT License. + +#pragma once + +#include +#include +#include +#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 Process(const std::vector& inputSignal) { + std::vector 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& input, std::vector& 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(std::ceil(static_cast(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(index); + float fractionalPart = index - integerPart; + + // Calculate the range of input samples for interpolation + int range = static_cast(std::ceil(kBeta / (2.0f * factor))); + int startSample = std::max(0, integerPart - range); + int endSample = std::min(static_cast(input.size()) - 1, integerPart + range); + + // Calculate the Kaiser window weights for the input samples + std::vector weights = KaiserWin(static_cast(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(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 KaiserWin(size_t window_length) { + std::vector 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; + } +}; diff --git a/test/data/jfk.flac b/test/data/jfk.flac new file mode 100644 index 0000000000000000000000000000000000000000..e44b7c13897eae7f78beb220c61fe77429a3961d GIT binary patch literal 1152693 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+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() diff --git a/tutorials/whisper_e2e.py b/tutorials/whisper_e2e.py index 1d9f9e1f..5852d00d 100644 --- a/tutorials/whisper_e2e.py +++ b/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) @@ -172,7 +172,7 @@ def preprocessing(audio_data): return pre_f(audio_data) else: pre_full = onnx.compose.merge_models( - decoder.onnx_model, + decoder.onnx_model, pre_model, io_map=[("floatPCM", "audio_pcm")]) pre_f = PyOrtFunction.from_model(pre_full, cpu_only=True) @@ -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...")

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