From b4b77084ae4fbdf829b3a66844ed57f27c477de2 Mon Sep 17 00:00:00 2001 From: Brian Plancher Date: Fri, 5 Feb 2021 16:14:17 -0500 Subject: [PATCH] adding micro speech with modifications --- .../micro_speech/arduino_audio_provider.cpp | 126 ++ .../arduino_command_responder.cpp | 93 + examples/micro_speech/arduino_main.cpp | 20 + examples/micro_speech/audio_provider.h | 46 + examples/micro_speech/command_responder.h | 32 + examples/micro_speech/feature_provider.cpp | 120 ++ examples/micro_speech/feature_provider.h | 52 + examples/micro_speech/main_functions.h | 37 + ...icro_features_micro_features_generator.cpp | 116 ++ .../micro_features_micro_features_generator.h | 32 + .../micro_features_micro_model_settings.cpp | 23 + .../micro_features_micro_model_settings.h | 43 + .../micro_speech/micro_features_model.cpp | 1596 +++++++++++++++++ examples/micro_speech/micro_features_model.h | 27 + .../micro_features_no_micro_features_data.cpp | 188 ++ .../micro_features_no_micro_features_data.h | 23 + ...micro_features_yes_micro_features_data.cpp | 188 ++ .../micro_features_yes_micro_features_data.h | 23 + examples/micro_speech/micro_speech.ino | 177 ++ examples/micro_speech/recognize_commands.cpp | 142 ++ examples/micro_speech/recognize_commands.h | 159 ++ .../sparkfun_edge_audio_provider.cpp | 377 ++++ .../sparkfun_edge_command_responder.cpp | 63 + 23 files changed, 3703 insertions(+) create mode 100644 examples/micro_speech/arduino_audio_provider.cpp create mode 100644 examples/micro_speech/arduino_command_responder.cpp create mode 100644 examples/micro_speech/arduino_main.cpp create mode 100644 examples/micro_speech/audio_provider.h create mode 100644 examples/micro_speech/command_responder.h create mode 100644 examples/micro_speech/feature_provider.cpp create mode 100644 examples/micro_speech/feature_provider.h create mode 100644 examples/micro_speech/main_functions.h create mode 100644 examples/micro_speech/micro_features_micro_features_generator.cpp create mode 100644 examples/micro_speech/micro_features_micro_features_generator.h create mode 100644 examples/micro_speech/micro_features_micro_model_settings.cpp create mode 100644 examples/micro_speech/micro_features_micro_model_settings.h create mode 100644 examples/micro_speech/micro_features_model.cpp create mode 100644 examples/micro_speech/micro_features_model.h create mode 100644 examples/micro_speech/micro_features_no_micro_features_data.cpp create mode 100644 examples/micro_speech/micro_features_no_micro_features_data.h create mode 100644 examples/micro_speech/micro_features_yes_micro_features_data.cpp create mode 100644 examples/micro_speech/micro_features_yes_micro_features_data.h create mode 100644 examples/micro_speech/micro_speech.ino create mode 100644 examples/micro_speech/recognize_commands.cpp create mode 100644 examples/micro_speech/recognize_commands.h create mode 100644 examples/micro_speech/sparkfun_edge_audio_provider.cpp create mode 100644 examples/micro_speech/sparkfun_edge_command_responder.cpp diff --git a/examples/micro_speech/arduino_audio_provider.cpp b/examples/micro_speech/arduino_audio_provider.cpp new file mode 100644 index 0000000..fb19815 --- /dev/null +++ b/examples/micro_speech/arduino_audio_provider.cpp @@ -0,0 +1,126 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if defined(ARDUINO) && !defined(ARDUINO_ARDUINO_NANO33BLE) +#define ARDUINO_EXCLUDE_CODE +#endif // defined(ARDUINO) && !defined(ARDUINO_ARDUINO_NANO33BLE) + +#ifndef ARDUINO_EXCLUDE_CODE + +#include "audio_provider.h" + +#include "PDM.h" +#include "micro_features_micro_model_settings.h" + +namespace { +bool g_is_audio_initialized = false; +// An internal buffer able to fit 16x our sample size +constexpr int kAudioCaptureBufferSize = DEFAULT_PDM_BUFFER_SIZE * 16; +int16_t g_audio_capture_buffer[kAudioCaptureBufferSize]; +// A buffer that holds our output +int16_t g_audio_output_buffer[kMaxAudioSampleSize]; +// Mark as volatile so we can check in a while loop to see if +// any samples have arrived yet. +volatile int32_t g_latest_audio_timestamp = 0; +} // namespace + +void CaptureSamples() { + // This is how many bytes of new data we have each time this is called + const int number_of_samples = DEFAULT_PDM_BUFFER_SIZE/2; + // Calculate what timestamp the last audio sample represents + const int32_t time_in_ms = + g_latest_audio_timestamp + + (number_of_samples / (kAudioSampleFrequency / 1000)); + // Determine the index, in the history of all samples, of the last sample + const int32_t start_sample_offset = + g_latest_audio_timestamp * (kAudioSampleFrequency / 1000); + // Determine the index of this sample in our ring buffer + const int capture_index = start_sample_offset % kAudioCaptureBufferSize; + // Read the data to the correct place in our buffer + PDM.read(g_audio_capture_buffer + capture_index, DEFAULT_PDM_BUFFER_SIZE); + // This is how we let the outside world know that new audio data has arrived. + g_latest_audio_timestamp = time_in_ms; +} + +TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) { + // Hook up the callback that will be called with each sample + PDM.onReceive(CaptureSamples); + // Start listening for audio: MONO @ 16KHz with gain at 20 + PDM.begin(1, kAudioSampleFrequency); + PDM.setGain(20); + // Block until we have our first audio sample + while (!g_latest_audio_timestamp) { + } + + return kTfLiteOk; +} + +TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter, + int start_ms, int duration_ms, + int* audio_samples_size, int16_t** audio_samples) { + // Set everything up to start receiving audio + if (!g_is_audio_initialized) { + TfLiteStatus init_status = InitAudioRecording(error_reporter); + if (init_status != kTfLiteOk) { + return init_status; + } + g_is_audio_initialized = true; + } + // This next part should only be called when the main thread notices that the + // latest audio sample data timestamp has changed, so that there's new data + // in the capture ring buffer. The ring buffer will eventually wrap around and + // overwrite the data, but the assumption is that the main thread is checking + // often enough and the buffer is large enough that this call will be made + // before that happens. + + // Determine the index, in the history of all samples, of the first + // sample we want + const int start_offset = start_ms * (kAudioSampleFrequency / 1000); + // Determine how many samples we want in total + const int duration_sample_count = + duration_ms * (kAudioSampleFrequency / 1000); + for (int i = 0; i < duration_sample_count; ++i) { + // For each sample, transform its index in the history of all samples into + // its index in g_audio_capture_buffer + const int capture_index = (start_offset + i) % kAudioCaptureBufferSize; + // Write the sample to the output buffer + g_audio_output_buffer[i] = g_audio_capture_buffer[capture_index]; + } + + // Set pointers to provide access to the audio + *audio_samples_size = kMaxAudioSampleSize; + *audio_samples = g_audio_output_buffer; + + return kTfLiteOk; +} + +int32_t LatestAudioTimestamp() { return g_latest_audio_timestamp; } + +#endif // ARDUINO_EXCLUDE_CODE diff --git a/examples/micro_speech/arduino_command_responder.cpp b/examples/micro_speech/arduino_command_responder.cpp new file mode 100644 index 0000000..8395f6d --- /dev/null +++ b/examples/micro_speech/arduino_command_responder.cpp @@ -0,0 +1,93 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if defined(ARDUINO) && !defined(ARDUINO_ARDUINO_NANO33BLE) +#define ARDUINO_EXCLUDE_CODE +#endif // defined(ARDUINO) && !defined(ARDUINO_ARDUINO_NANO33BLE) + +#ifndef ARDUINO_EXCLUDE_CODE + +#include "command_responder.h" + +#include "Arduino.h" + +// Toggles the built-in LED every inference, and lights a colored LED depending +// on which word was detected. +void RespondToCommand(tflite::ErrorReporter* error_reporter, + int32_t current_time, const char* found_command, + uint8_t score, bool is_new_command) { + static bool is_initialized = false; + if (!is_initialized) { + pinMode(LED_BUILTIN, OUTPUT); + // Pins for the built-in RGB LEDs on the Arduino Nano 33 BLE Sense + pinMode(LEDR, OUTPUT); + pinMode(LEDG, OUTPUT); + pinMode(LEDB, OUTPUT); + // Ensure the LED is off by default. + // Note: The RGB LEDs on the Arduino Nano 33 BLE + // Sense are on when the pin is LOW, off when HIGH. + digitalWrite(LEDR, HIGH); + digitalWrite(LEDG, HIGH); + digitalWrite(LEDB, HIGH); + is_initialized = true; + } + static int32_t last_command_time = 0; + static int count = 0; + static int certainty = 220; + + if (is_new_command) { + TF_LITE_REPORT_ERROR(error_reporter, "Heard %s (%d) @%dms", found_command, + score, current_time); + // If we hear a command, light up the appropriate LED + if (found_command[0] == 'y') { + last_command_time = current_time; + digitalWrite(LEDG, LOW); // Green for yes + } + + if (found_command[0] == 'n') { + last_command_time = current_time; + digitalWrite(LEDR, LOW); // Red for no + } + + if (found_command[0] == 'u') { + last_command_time = current_time; + digitalWrite(LEDB, LOW); // Blue for unknown + } + } + + // If last_command_time is non-zero but was >3 seconds ago, zero it + // and switch off the LED. + if (last_command_time != 0) { + if (last_command_time < (current_time - 3000)) { + last_command_time = 0; + digitalWrite(LED_BUILTIN, LOW); + digitalWrite(LEDR, HIGH); + digitalWrite(LEDG, HIGH); + digitalWrite(LEDB, HIGH); + } + // If it is non-zero but <3 seconds ago, do nothing. + return; + } + + // Otherwise, toggle the LED every time an inference is performed. + ++count; + if (count & 1) { + digitalWrite(LED_BUILTIN, HIGH); + } else { + digitalWrite(LED_BUILTIN, LOW); + } +} + +#endif // ARDUINO_EXCLUDE_CODE diff --git a/examples/micro_speech/arduino_main.cpp b/examples/micro_speech/arduino_main.cpp new file mode 100644 index 0000000..c70a2bc --- /dev/null +++ b/examples/micro_speech/arduino_main.cpp @@ -0,0 +1,20 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "main_functions.h" + +// Arduino automatically calls the setup() and loop() functions in a sketch, so +// where other systems need their own main routine in this file, it can be left +// empty. diff --git a/examples/micro_speech/audio_provider.h b/examples/micro_speech/audio_provider.h new file mode 100644 index 0000000..c51cc5f --- /dev/null +++ b/examples/micro_speech/audio_provider.h @@ -0,0 +1,46 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" + +// This is an abstraction around an audio source like a microphone, and is +// expected to return 16-bit PCM sample data for a given point in time. The +// sample data itself should be used as quickly as possible by the caller, since +// to allow memory optimizations there are no guarantees that the samples won't +// be overwritten by new data in the future. In practice, implementations should +// ensure that there's a reasonable time allowed for clients to access the data +// before any reuse. +// The reference implementation can have no platform-specific dependencies, so +// it just returns an array filled with zeros. For real applications, you should +// ensure there's a specialized implementation that accesses hardware APIs. +TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter, + int start_ms, int duration_ms, + int* audio_samples_size, int16_t** audio_samples); + +// Returns the time that audio data was last captured in milliseconds. There's +// no contract about what time zero represents, the accuracy, or the granularity +// of the result. Subsequent calls will generally not return a lower value, but +// even that's not guaranteed if there's an overflow wraparound. +// The reference implementation of this function just returns a constantly +// incrementing value for each call, since it would need a non-portable platform +// call to access time information. For real applications, you'll need to write +// your own platform-specific implementation. +int32_t LatestAudioTimestamp(); + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_AUDIO_PROVIDER_H_ diff --git a/examples/micro_speech/command_responder.h b/examples/micro_speech/command_responder.h new file mode 100644 index 0000000..ac3f448 --- /dev/null +++ b/examples/micro_speech/command_responder.h @@ -0,0 +1,32 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// Provides an interface to take an action based on an audio command. + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" + +// Called every time the results of an audio recognition run are available. The +// human-readable name of any recognized command is in the `found_command` +// argument, `score` has the numerical confidence, and `is_new_command` is set +// if the previous command was different to this one. +void RespondToCommand(tflite::ErrorReporter* error_reporter, + int32_t current_time, const char* found_command, + uint8_t score, bool is_new_command); + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_COMMAND_RESPONDER_H_ diff --git a/examples/micro_speech/feature_provider.cpp b/examples/micro_speech/feature_provider.cpp new file mode 100644 index 0000000..77779ca --- /dev/null +++ b/examples/micro_speech/feature_provider.cpp @@ -0,0 +1,120 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "feature_provider.h" + +#include "audio_provider.h" +#include "micro_features_micro_features_generator.h" +#include "micro_features_micro_model_settings.h" + +FeatureProvider::FeatureProvider(int feature_size, int8_t* feature_data) + : feature_size_(feature_size), + feature_data_(feature_data), + is_first_run_(true) { + // Initialize the feature data to default values. + for (int n = 0; n < feature_size_; ++n) { + feature_data_[n] = 0; + } +} + +FeatureProvider::~FeatureProvider() {} + +TfLiteStatus FeatureProvider::PopulateFeatureData( + tflite::ErrorReporter* error_reporter, int32_t last_time_in_ms, + int32_t time_in_ms, int* how_many_new_slices) { + if (feature_size_ != kFeatureElementCount) { + TF_LITE_REPORT_ERROR(error_reporter, + "Requested feature_data_ size %d doesn't match %d", + feature_size_, kFeatureElementCount); + return kTfLiteError; + } + + // Quantize the time into steps as long as each window stride, so we can + // figure out which audio data we need to fetch. + const int last_step = (last_time_in_ms / kFeatureSliceStrideMs); + const int current_step = (time_in_ms / kFeatureSliceStrideMs); + + int slices_needed = current_step - last_step; + // If this is the first call, make sure we don't use any cached information. + if (is_first_run_) { + TfLiteStatus init_status = InitializeMicroFeatures(error_reporter); + if (init_status != kTfLiteOk) { + return init_status; + } + is_first_run_ = false; + slices_needed = kFeatureSliceCount; + } + if (slices_needed > kFeatureSliceCount) { + slices_needed = kFeatureSliceCount; + } + *how_many_new_slices = slices_needed; + + const int slices_to_keep = kFeatureSliceCount - slices_needed; + const int slices_to_drop = kFeatureSliceCount - slices_to_keep; + // If we can avoid recalculating some slices, just move the existing data + // up in the spectrogram, to perform something like this: + // last time = 80ms current time = 120ms + // +-----------+ +-----------+ + // | data@20ms | --> | data@60ms | + // +-----------+ -- +-----------+ + // | data@40ms | -- --> | data@80ms | + // +-----------+ -- -- +-----------+ + // | data@60ms | -- -- | | + // +-----------+ -- +-----------+ + // | data@80ms | -- | | + // +-----------+ +-----------+ + if (slices_to_keep > 0) { + for (int dest_slice = 0; dest_slice < slices_to_keep; ++dest_slice) { + int8_t* dest_slice_data = + feature_data_ + (dest_slice * kFeatureSliceSize); + const int src_slice = dest_slice + slices_to_drop; + const int8_t* src_slice_data = + feature_data_ + (src_slice * kFeatureSliceSize); + for (int i = 0; i < kFeatureSliceSize; ++i) { + dest_slice_data[i] = src_slice_data[i]; + } + } + } + // Any slices that need to be filled in with feature data have their + // appropriate audio data pulled, and features calculated for that slice. + if (slices_needed > 0) { + for (int new_slice = slices_to_keep; new_slice < kFeatureSliceCount; + ++new_slice) { + const int new_step = (current_step - kFeatureSliceCount + 1) + new_slice; + const int32_t slice_start_ms = (new_step * kFeatureSliceStrideMs); + int16_t* audio_samples = nullptr; + int audio_samples_size = 0; + // TODO(petewarden): Fix bug that leads to non-zero slice_start_ms + GetAudioSamples(error_reporter, (slice_start_ms > 0 ? slice_start_ms : 0), + kFeatureSliceDurationMs, &audio_samples_size, + &audio_samples); + if (audio_samples_size < kMaxAudioSampleSize) { + TF_LITE_REPORT_ERROR(error_reporter, + "Audio data size %d too small, want %d", + audio_samples_size, kMaxAudioSampleSize); + return kTfLiteError; + } + int8_t* new_slice_data = feature_data_ + (new_slice * kFeatureSliceSize); + size_t num_samples_read; + TfLiteStatus generate_status = GenerateMicroFeatures( + error_reporter, audio_samples, audio_samples_size, kFeatureSliceSize, + new_slice_data, &num_samples_read); + if (generate_status != kTfLiteOk) { + return generate_status; + } + } + } + return kTfLiteOk; +} diff --git a/examples/micro_speech/feature_provider.h b/examples/micro_speech/feature_provider.h new file mode 100644 index 0000000..d086e01 --- /dev/null +++ b/examples/micro_speech/feature_provider.h @@ -0,0 +1,52 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" + +// Binds itself to an area of memory intended to hold the input features for an +// audio-recognition neural network model, and fills that data area with the +// features representing the current audio input, for example from a microphone. +// The audio features themselves are a two-dimensional array, made up of +// horizontal slices representing the frequencies at one point in time, stacked +// on top of each other to form a spectrogram showing how those frequencies +// changed over time. +class FeatureProvider { + public: + // Create the provider, and bind it to an area of memory. This memory should + // remain accessible for the lifetime of the provider object, since subsequent + // calls will fill it with feature data. The provider does no memory + // management of this data. + FeatureProvider(int feature_size, int8_t* feature_data); + ~FeatureProvider(); + + // Fills the feature data with information from audio inputs, and returns how + // many feature slices were updated. + TfLiteStatus PopulateFeatureData(tflite::ErrorReporter* error_reporter, + int32_t last_time_in_ms, int32_t time_in_ms, + int* how_many_new_slices); + + private: + int feature_size_; + int8_t* feature_data_; + // Make sure we don't try to use cached information if this is the first call + // into the provider. + bool is_first_run_; +}; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_FEATURE_PROVIDER_H_ diff --git a/examples/micro_speech/main_functions.h b/examples/micro_speech/main_functions.h new file mode 100644 index 0000000..0ac0677 --- /dev/null +++ b/examples/micro_speech/main_functions.h @@ -0,0 +1,37 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_ + +// Expose a C friendly interface for main functions. +#ifdef __cplusplus +extern "C" { +#endif + +// Initializes all data needed for the example. The name is important, and needs +// to be setup() for Arduino compatibility. +void setup(); + +// Runs one iteration of data gathering and inference. This should be called +// repeatedly from the application code. The name needs to be loop() for Arduino +// compatibility. +void loop(); + +#ifdef __cplusplus +} +#endif + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MAIN_FUNCTIONS_H_ diff --git a/examples/micro_speech/micro_features_micro_features_generator.cpp b/examples/micro_speech/micro_features_micro_features_generator.cpp new file mode 100644 index 0000000..f3857be --- /dev/null +++ b/examples/micro_speech/micro_features_micro_features_generator.cpp @@ -0,0 +1,116 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "micro_features_micro_features_generator.h" + +#include +#include + +#include "tensorflow/lite/experimental/microfrontend/lib/frontend.h" +#include "tensorflow/lite/experimental/microfrontend/lib/frontend_util.h" +#include "micro_features_micro_model_settings.h" + +// Configure FFT to output 16 bit fixed point. +#define FIXED_POINT 16 + +namespace { + +FrontendState g_micro_features_state; +bool g_is_first_time = true; + +} // namespace + +TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter) { + FrontendConfig config; + config.window.size_ms = kFeatureSliceDurationMs; + config.window.step_size_ms = kFeatureSliceStrideMs; + config.noise_reduction.smoothing_bits = 10; + config.filterbank.num_channels = kFeatureSliceSize; + config.filterbank.lower_band_limit = 125.0; + config.filterbank.upper_band_limit = 7500.0; + config.noise_reduction.smoothing_bits = 10; + config.noise_reduction.even_smoothing = 0.025; + config.noise_reduction.odd_smoothing = 0.06; + config.noise_reduction.min_signal_remaining = 0.05; + config.pcan_gain_control.enable_pcan = 1; + config.pcan_gain_control.strength = 0.95; + config.pcan_gain_control.offset = 80.0; + config.pcan_gain_control.gain_bits = 21; + config.log_scale.enable_log = 1; + config.log_scale.scale_shift = 6; + if (!FrontendPopulateState(&config, &g_micro_features_state, + kAudioSampleFrequency)) { + TF_LITE_REPORT_ERROR(error_reporter, "FrontendPopulateState() failed"); + return kTfLiteError; + } + g_is_first_time = true; + return kTfLiteOk; +} + +// This is not exposed in any header, and is only used for testing, to ensure +// that the state is correctly set up before generating results. +void SetMicroFeaturesNoiseEstimates(const uint32_t* estimate_presets) { + for (int i = 0; i < g_micro_features_state.filterbank.num_channels; ++i) { + g_micro_features_state.noise_reduction.estimate[i] = estimate_presets[i]; + } +} + +TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter, + const int16_t* input, int input_size, + int output_size, int8_t* output, + size_t* num_samples_read) { + const int16_t* frontend_input; + if (g_is_first_time) { + frontend_input = input; + g_is_first_time = false; + } else { + frontend_input = input + 160; + } + FrontendOutput frontend_output = FrontendProcessSamples( + &g_micro_features_state, frontend_input, input_size, num_samples_read); + + for (size_t i = 0; i < frontend_output.size; ++i) { + // These scaling values are derived from those used in input_data.py in the + // training pipeline. + // The feature pipeline outputs 16-bit signed integers in roughly a 0 to 670 + // range. In training, these are then arbitrarily divided by 25.6 to get + // float values in the rough range of 0.0 to 26.0. This scaling is performed + // for historical reasons, to match up with the output of other feature + // generators. + // The process is then further complicated when we quantize the model. This + // means we have to scale the 0.0 to 26.0 real values to the -128 to 127 + // signed integer numbers. + // All this means that to get matching values from our integer feature + // output into the tensor input, we have to perform: + // input = (((feature / 25.6) / 26.0) * 256) - 128 + // To simplify this and perform it in 32-bit integer math, we rearrange to: + // input = (feature * 256) / (25.6 * 26.0) - 128 + constexpr int32_t value_scale = 256; + constexpr int32_t value_div = static_cast((25.6f * 26.0f) + 0.5f); + int32_t value = + ((frontend_output.values[i] * value_scale) + (value_div / 2)) / + value_div; + value -= 128; + if (value < -128) { + value = -128; + } + if (value > 127) { + value = 127; + } + output[i] = value; + } + + return kTfLiteOk; +} diff --git a/examples/micro_speech/micro_features_micro_features_generator.h b/examples/micro_speech/micro_features_micro_features_generator.h new file mode 100644 index 0000000..2930423 --- /dev/null +++ b/examples/micro_speech/micro_features_micro_features_generator.h @@ -0,0 +1,32 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_ + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" + +// Sets up any resources needed for the feature generation pipeline. +TfLiteStatus InitializeMicroFeatures(tflite::ErrorReporter* error_reporter); + +// Converts audio sample data into a more compact form that's appropriate for +// feeding into a neural network. +TfLiteStatus GenerateMicroFeatures(tflite::ErrorReporter* error_reporter, + const int16_t* input, int input_size, + int output_size, int8_t* output, + size_t* num_samples_read); + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_FEATURES_GENERATOR_H_ diff --git a/examples/micro_speech/micro_features_micro_model_settings.cpp b/examples/micro_speech/micro_features_micro_model_settings.cpp new file mode 100644 index 0000000..f772cef --- /dev/null +++ b/examples/micro_speech/micro_features_micro_model_settings.cpp @@ -0,0 +1,23 @@ +/* Copyright 2018 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "micro_features_micro_model_settings.h" + +const char* kCategoryLabels[kCategoryCount] = { + "silence", + "unknown", + "yes", + "no", +}; diff --git a/examples/micro_speech/micro_features_micro_model_settings.h b/examples/micro_speech/micro_features_micro_model_settings.h new file mode 100644 index 0000000..e542213 --- /dev/null +++ b/examples/micro_speech/micro_features_micro_model_settings.h @@ -0,0 +1,43 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_ + +// Keeping these as constant expressions allow us to allocate fixed-sized arrays +// on the stack for our working memory. + +// The size of the input time series data we pass to the FFT to produce the +// frequency information. This has to be a power of two, and since we're dealing +// with 30ms of 16KHz inputs, which means 480 samples, this is the next value. +constexpr int kMaxAudioSampleSize = 512; +constexpr int kAudioSampleFrequency = 16000; + +// The following values are derived from values used during model training. +// If you change the way you preprocess the input, update all these constants. +constexpr int kFeatureSliceSize = 40; +constexpr int kFeatureSliceCount = 49; +constexpr int kFeatureElementCount = (kFeatureSliceSize * kFeatureSliceCount); +constexpr int kFeatureSliceStrideMs = 20; +constexpr int kFeatureSliceDurationMs = 30; + +// Variables for the model's output categories. +constexpr int kSilenceIndex = 0; +constexpr int kUnknownIndex = 1; +// If you modify the output categories, you need to update the following values. +constexpr int kCategoryCount = 4; +extern const char* kCategoryLabels[kCategoryCount]; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MICRO_MODEL_SETTINGS_H_ diff --git a/examples/micro_speech/micro_features_model.cpp b/examples/micro_speech/micro_features_model.cpp new file mode 100644 index 0000000..428616e --- /dev/null +++ b/examples/micro_speech/micro_features_model.cpp @@ -0,0 +1,1596 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This is a standard TensorFlow Lite FlatBuffer model file that has been +// converted into a C data array, so it can be easily compiled into a binary +// for devices that don't have a file system. It was created using the command: +// xxd -i model.tflite > model.cc + +#include "micro_features_model.h" + +// We need to keep the data array aligned on some architectures. +#ifdef __has_attribute +#define HAVE_ATTRIBUTE(x) __has_attribute(x) +#else +#define HAVE_ATTRIBUTE(x) 0 +#endif +#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) +#define DATA_ALIGN_ATTRIBUTE __attribute__((aligned(4))) +#else +#define DATA_ALIGN_ATTRIBUTE +#endif + +const unsigned char g_model[] DATA_ALIGN_ATTRIBUTE = { + 0x20, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x12, 0x00, 0x1c, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, + 0x10, 0x00, 0x14, 0x00, 0x00, 0x00, 0x18, 0x00, 0x12, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x00, 0x00, 0x94, 0x48, 0x00, 0x00, 0x34, 0x42, 0x00, 0x00, + 0x1c, 0x42, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, + 0x04, 0x00, 0x08, 0x00, 0x08, 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0x04, + 0x03, 0x00, 0x00, 0x00}; +const int g_model_len = 18712; diff --git a/examples/micro_speech/micro_features_model.h b/examples/micro_speech/micro_features_model.h new file mode 100644 index 0000000..deec2d6 --- /dev/null +++ b/examples/micro_speech/micro_features_model.h @@ -0,0 +1,27 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +// This is a standard TensorFlow Lite FlatBuffer model file that has been +// converted into a C data array, so it can be easily compiled into a binary +// for devices that don't have a file system. It was created using the command: +// xxd -i model.tflite > model.cc + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_ + +extern const unsigned char g_model[]; +extern const int g_model_len; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_MODEL_H_ diff --git a/examples/micro_speech/micro_features_no_micro_features_data.cpp b/examples/micro_speech/micro_features_no_micro_features_data.cpp new file mode 100644 index 0000000..500cfbd --- /dev/null +++ b/examples/micro_speech/micro_features_no_micro_features_data.cpp @@ -0,0 +1,188 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "micro_features_no_micro_features_data.h" + +// Golden test values for the expected spectrogram from a "no" sample file +// speech_commands_test_set_v0.02/no/f9643d42_nohash_4.wav. + +const int g_no_micro_f9643d42_nohash_4_width = 40; +const int g_no_micro_f9643d42_nohash_4_height = 49; +const signed char g_no_micro_f9643d42_nohash_4_data[] = { + 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a/examples/micro_speech/micro_features_no_micro_features_data.h b/examples/micro_speech/micro_features_no_micro_features_data.h new file mode 100644 index 0000000..8c1b6d5 --- /dev/null +++ b/examples/micro_speech/micro_features_no_micro_features_data.h @@ -0,0 +1,23 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_ + +extern const int g_no_micro_f9643d42_nohash_4_width; +extern const int g_no_micro_f9643d42_nohash_4_height; +extern const signed char g_no_micro_f9643d42_nohash_4_data[]; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_NO_MICRO_FEATURES_DATA_H_ diff --git a/examples/micro_speech/micro_features_yes_micro_features_data.cpp b/examples/micro_speech/micro_features_yes_micro_features_data.cpp new file mode 100644 index 0000000..6665031 --- /dev/null +++ b/examples/micro_speech/micro_features_yes_micro_features_data.cpp @@ -0,0 +1,188 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "micro_features_yes_micro_features_data.h" + +// Golden test values for the expected spectrogram from a "yes" sample file +// speech_commands_test_set_v0.02/yes/f2e59fea_nohash_1.wav. + +const int g_yes_micro_f2e59fea_nohash_1_width = 40; +const int g_yes_micro_f2e59fea_nohash_1_height = 49; +const signed char g_yes_micro_f2e59fea_nohash_1_data[] = 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-128, -128, -128, -128, + -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, + -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, + -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, + -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, + -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, -128, + -128, -128, -128, -128, +}; diff --git a/examples/micro_speech/micro_features_yes_micro_features_data.h b/examples/micro_speech/micro_features_yes_micro_features_data.h new file mode 100644 index 0000000..cd1ad10 --- /dev/null +++ b/examples/micro_speech/micro_features_yes_micro_features_data.h @@ -0,0 +1,23 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_ + +extern const int g_yes_micro_f2e59fea_nohash_1_width; +extern const int g_yes_micro_f2e59fea_nohash_1_height; +extern const signed char g_yes_micro_f2e59fea_nohash_1_data[]; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_MICRO_FEATURES_YES_MICRO_FEATURES_DATA_H_ diff --git a/examples/micro_speech/micro_speech.ino b/examples/micro_speech/micro_speech.ino new file mode 100644 index 0000000..088a00c --- /dev/null +++ b/examples/micro_speech/micro_speech.ino @@ -0,0 +1,177 @@ +/* Copyright 2020 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include + +#include "main_functions.h" + +#include "audio_provider.h" +#include "command_responder.h" +#include "feature_provider.h" +#include "micro_features_micro_model_settings.h" +#include "micro_features_model.h" +#include "recognize_commands.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" +#include "tensorflow/lite/micro/micro_interpreter.h" +#include "tensorflow/lite/micro/micro_mutable_op_resolver.h" +#include "tensorflow/lite/schema/schema_generated.h" +#include "tensorflow/lite/version.h" + +// Globals, used for compatibility with Arduino-style sketches. +namespace { +tflite::ErrorReporter* error_reporter = nullptr; +const tflite::Model* model = nullptr; +tflite::MicroInterpreter* interpreter = nullptr; +TfLiteTensor* model_input = nullptr; +FeatureProvider* feature_provider = nullptr; +RecognizeCommands* recognizer = nullptr; +int32_t previous_time = 0; + +// Create an area of memory to use for input, output, and intermediate arrays. +// The size of this will depend on the model you're using, and may need to be +// determined by experimentation. +constexpr int kTensorArenaSize = 10 * 1024; +uint8_t tensor_arena[kTensorArenaSize]; +int8_t feature_buffer[kFeatureElementCount]; +int8_t* model_input_buffer = nullptr; +} // namespace + +// The name of this function is important for Arduino compatibility. +void setup() { + // Set up logging. Google style is to avoid globals or statics because of + // lifetime uncertainty, but since this has a trivial destructor it's okay. + // NOLINTNEXTLINE(runtime-global-variables) + static tflite::MicroErrorReporter micro_error_reporter; + error_reporter = µ_error_reporter; + + // Map the model into a usable data structure. This doesn't involve any + // copying or parsing, it's a very lightweight operation. + model = tflite::GetModel(g_model); + if (model->version() != TFLITE_SCHEMA_VERSION) { + TF_LITE_REPORT_ERROR(error_reporter, + "Model provided is schema version %d not equal " + "to supported version %d.", + model->version(), TFLITE_SCHEMA_VERSION); + return; + } + + // Pull in only the operation implementations we need. + // This relies on a complete list of all the ops needed by this graph. + // An easier approach is to just use the AllOpsResolver, but this will + // incur some penalty in code space for op implementations that are not + // needed by this graph. + // + // tflite::AllOpsResolver resolver; + // NOLINTNEXTLINE(runtime-global-variables) + static tflite::MicroMutableOpResolver<4> micro_op_resolver(error_reporter); + if (micro_op_resolver.AddDepthwiseConv2D() != kTfLiteOk) { + return; + } + if (micro_op_resolver.AddFullyConnected() != kTfLiteOk) { + return; + } + if (micro_op_resolver.AddSoftmax() != kTfLiteOk) { + return; + } + if (micro_op_resolver.AddReshape() != kTfLiteOk) { + return; + } + + // Build an interpreter to run the model with. + static tflite::MicroInterpreter static_interpreter( + model, micro_op_resolver, tensor_arena, kTensorArenaSize, error_reporter); + interpreter = &static_interpreter; + + // Allocate memory from the tensor_arena for the model's tensors. + TfLiteStatus allocate_status = interpreter->AllocateTensors(); + if (allocate_status != kTfLiteOk) { + TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed"); + return; + } + + // Get information about the memory area to use for the model's input. + model_input = interpreter->input(0); + if ((model_input->dims->size != 2) || (model_input->dims->data[0] != 1) || + (model_input->dims->data[1] != + (kFeatureSliceCount * kFeatureSliceSize)) || + (model_input->type != kTfLiteInt8)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Bad input tensor parameters in model"); + return; + } + model_input_buffer = model_input->data.int8; + + // Prepare to access the audio spectrograms from a microphone or other source + // that will provide the inputs to the neural network. + // NOLINTNEXTLINE(runtime-global-variables) + static FeatureProvider static_feature_provider(kFeatureElementCount, + feature_buffer); + feature_provider = &static_feature_provider; + + static RecognizeCommands static_recognizer(error_reporter); + recognizer = &static_recognizer; + + previous_time = 0; +} + +// The name of this function is important for Arduino compatibility. +void loop() { + // Fetch the spectrogram for the current time. + const int32_t current_time = LatestAudioTimestamp(); + int how_many_new_slices = 0; + TfLiteStatus feature_status = feature_provider->PopulateFeatureData( + error_reporter, previous_time, current_time, &how_many_new_slices); + if (feature_status != kTfLiteOk) { + TF_LITE_REPORT_ERROR(error_reporter, "Feature generation failed"); + return; + } + previous_time = current_time; + // If no new audio samples have been received since last time, don't bother + // running the network model. + if (how_many_new_slices == 0) { + return; + } + + // Copy feature buffer to input tensor + for (int i = 0; i < kFeatureElementCount; i++) { + model_input_buffer[i] = feature_buffer[i]; + } + + // Run the model on the spectrogram input and make sure it succeeds. + TfLiteStatus invoke_status = interpreter->Invoke(); + if (invoke_status != kTfLiteOk) { + TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed"); + return; + } + + // Obtain a pointer to the output tensor + TfLiteTensor* output = interpreter->output(0); + // Determine whether a command was recognized based on the output of inference + const char* found_command = nullptr; + uint8_t score = 0; + bool is_new_command = false; + TfLiteStatus process_status = recognizer->ProcessLatestResults( + output, current_time, &found_command, &score, &is_new_command); + if (process_status != kTfLiteOk) { + TF_LITE_REPORT_ERROR(error_reporter, + "RecognizeCommands::ProcessLatestResults() failed"); + return; + } + // Do something based on the recognized command. The default implementation + // just prints to the error console, but you should replace this with your + // own function for a real application. + RespondToCommand(error_reporter, current_time, found_command, score, + is_new_command); +} diff --git a/examples/micro_speech/recognize_commands.cpp b/examples/micro_speech/recognize_commands.cpp new file mode 100644 index 0000000..37e8063 --- /dev/null +++ b/examples/micro_speech/recognize_commands.cpp @@ -0,0 +1,142 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include "recognize_commands.h" + +#include + +RecognizeCommands::RecognizeCommands(tflite::ErrorReporter* error_reporter, + int32_t average_window_duration_ms, + uint8_t detection_threshold, + int32_t suppression_ms, + int32_t minimum_count) + : error_reporter_(error_reporter), + average_window_duration_ms_(average_window_duration_ms), + detection_threshold_(detection_threshold), + suppression_ms_(suppression_ms), + minimum_count_(minimum_count), + previous_results_(error_reporter) { + previous_top_label_ = "silence"; + previous_top_label_time_ = std::numeric_limits::min(); +} + +TfLiteStatus RecognizeCommands::ProcessLatestResults( + const TfLiteTensor* latest_results, const int32_t current_time_ms, + const char** found_command, uint8_t* score, bool* is_new_command) { + if ((latest_results->dims->size != 2) || + (latest_results->dims->data[0] != 1) || + (latest_results->dims->data[1] != kCategoryCount)) { + TF_LITE_REPORT_ERROR( + error_reporter_, + "The results for recognition should contain %d elements, but there are " + "%d in an %d-dimensional shape", + kCategoryCount, latest_results->dims->data[1], + latest_results->dims->size); + return kTfLiteError; + } + + if (latest_results->type != kTfLiteInt8) { + TF_LITE_REPORT_ERROR( + error_reporter_, + "The results for recognition should be int8_t elements, but are %d", + latest_results->type); + return kTfLiteError; + } + + if ((!previous_results_.empty()) && + (current_time_ms < previous_results_.front().time_)) { + TF_LITE_REPORT_ERROR( + error_reporter_, + "Results must be fed in increasing time order, but received a " + "timestamp of %d that was earlier than the previous one of %d", + current_time_ms, previous_results_.front().time_); + return kTfLiteError; + } + + // Add the latest results to the head of the queue. + previous_results_.push_back({current_time_ms, latest_results->data.int8}); + + // Prune any earlier results that are too old for the averaging window. + const int64_t time_limit = current_time_ms - average_window_duration_ms_; + while ((!previous_results_.empty()) && + previous_results_.front().time_ < time_limit) { + previous_results_.pop_front(); + } + + // If there are too few results, assume the result will be unreliable and + // bail. + const int64_t how_many_results = previous_results_.size(); + const int64_t earliest_time = previous_results_.front().time_; + const int64_t samples_duration = current_time_ms - earliest_time; + if ((how_many_results < minimum_count_) || + (samples_duration < (average_window_duration_ms_ / 4))) { + *found_command = previous_top_label_; + *score = 0; + *is_new_command = false; + return kTfLiteOk; + } + + // Calculate the average score across all the results in the window. + int32_t average_scores[kCategoryCount]; + for (int offset = 0; offset < previous_results_.size(); ++offset) { + PreviousResultsQueue::Result previous_result = + previous_results_.from_front(offset); + const int8_t* scores = previous_result.scores; + for (int i = 0; i < kCategoryCount; ++i) { + if (offset == 0) { + average_scores[i] = scores[i] + 128; + } else { + average_scores[i] += scores[i] + 128; + } + } + } + for (int i = 0; i < kCategoryCount; ++i) { + average_scores[i] /= how_many_results; + } + + // Find the current highest scoring category. + int current_top_index = 0; + int32_t current_top_score = 0; + for (int i = 0; i < kCategoryCount; ++i) { + if (average_scores[i] > current_top_score) { + current_top_score = average_scores[i]; + current_top_index = i; + } + } + const char* current_top_label = kCategoryLabels[current_top_index]; + + // If we've recently had another label trigger, assume one that occurs too + // soon afterwards is a bad result. + int64_t time_since_last_top; + if ((previous_top_label_ == kCategoryLabels[0]) || + (previous_top_label_time_ == std::numeric_limits::min())) { + time_since_last_top = std::numeric_limits::max(); + } else { + time_since_last_top = current_time_ms - previous_top_label_time_; + } + if ((current_top_score > detection_threshold_) && + ((current_top_label != previous_top_label_) || + (time_since_last_top > suppression_ms_))) { + previous_top_label_ = current_top_label; + previous_top_label_time_ = current_time_ms; + *is_new_command = true; + } else { + *is_new_command = false; + } + *found_command = current_top_label; + *score = current_top_score; + + return kTfLiteOk; +} diff --git a/examples/micro_speech/recognize_commands.h b/examples/micro_speech/recognize_commands.h new file mode 100644 index 0000000..378b138 --- /dev/null +++ b/examples/micro_speech/recognize_commands.h @@ -0,0 +1,159 @@ +/* Copyright 2017 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_ +#define TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_ + +#include + +#include "tensorflow/lite/c/common.h" +#include "micro_features_micro_model_settings.h" +#include "tensorflow/lite/micro/micro_error_reporter.h" + +// Partial implementation of std::dequeue, just providing the functionality +// that's needed to keep a record of previous neural network results over a +// short time period, so they can be averaged together to produce a more +// accurate overall prediction. This doesn't use any dynamic memory allocation +// so it's a better fit for microcontroller applications, but this does mean +// there are hard limits on the number of results it can store. +class PreviousResultsQueue { + public: + PreviousResultsQueue(tflite::ErrorReporter* error_reporter) + : error_reporter_(error_reporter), front_index_(0), size_(0) {} + + // Data structure that holds an inference result, and the time when it + // was recorded. + struct Result { + Result() : time_(0), scores() {} + Result(int32_t time, int8_t* input_scores) : time_(time) { + for (int i = 0; i < kCategoryCount; ++i) { + scores[i] = input_scores[i]; + } + } + int32_t time_; + int8_t scores[kCategoryCount]; + }; + + int size() { return size_; } + bool empty() { return size_ == 0; } + Result& front() { return results_[front_index_]; } + Result& back() { + int back_index = front_index_ + (size_ - 1); + if (back_index >= kMaxResults) { + back_index -= kMaxResults; + } + return results_[back_index]; + } + + void push_back(const Result& entry) { + if (size() >= kMaxResults) { + TF_LITE_REPORT_ERROR( + error_reporter_, + "Couldn't push_back latest result, too many already!"); + return; + } + size_ += 1; + back() = entry; + } + + Result pop_front() { + if (size() <= 0) { + TF_LITE_REPORT_ERROR(error_reporter_, + "Couldn't pop_front result, none present!"); + return Result(); + } + Result result = front(); + front_index_ += 1; + if (front_index_ >= kMaxResults) { + front_index_ = 0; + } + size_ -= 1; + return result; + } + + // Most of the functions are duplicates of dequeue containers, but this + // is a helper that makes it easy to iterate through the contents of the + // queue. + Result& from_front(int offset) { + if ((offset < 0) || (offset >= size_)) { + TF_LITE_REPORT_ERROR(error_reporter_, + "Attempt to read beyond the end of the queue!"); + offset = size_ - 1; + } + int index = front_index_ + offset; + if (index >= kMaxResults) { + index -= kMaxResults; + } + return results_[index]; + } + + private: + tflite::ErrorReporter* error_reporter_; + static constexpr int kMaxResults = 50; + Result results_[kMaxResults]; + + int front_index_; + int size_; +}; + +// This class is designed to apply a very primitive decoding model on top of the +// instantaneous results from running an audio recognition model on a single +// window of samples. It applies smoothing over time so that noisy individual +// label scores are averaged, increasing the confidence that apparent matches +// are real. +// To use it, you should create a class object with the configuration you +// want, and then feed results from running a TensorFlow model into the +// processing method. The timestamp for each subsequent call should be +// increasing from the previous, since the class is designed to process a stream +// of data over time. +class RecognizeCommands { + public: + // labels should be a list of the strings associated with each one-hot score. + // The window duration controls the smoothing. Longer durations will give a + // higher confidence that the results are correct, but may miss some commands. + // The detection threshold has a similar effect, with high values increasing + // the precision at the cost of recall. The minimum count controls how many + // results need to be in the averaging window before it's seen as a reliable + // average. This prevents erroneous results when the averaging window is + // initially being populated for example. The suppression argument disables + // further recognitions for a set time after one has been triggered, which can + // help reduce spurious recognitions. + explicit RecognizeCommands(tflite::ErrorReporter* error_reporter, + int32_t average_window_duration_ms = 1000, + uint8_t detection_threshold = 200, + int32_t suppression_ms = 1500, + int32_t minimum_count = 3); + + // Call this with the results of running a model on sample data. + TfLiteStatus ProcessLatestResults(const TfLiteTensor* latest_results, + const int32_t current_time_ms, + const char** found_command, uint8_t* score, + bool* is_new_command); + + private: + // Configuration + tflite::ErrorReporter* error_reporter_; + int32_t average_window_duration_ms_; + uint8_t detection_threshold_; + int32_t suppression_ms_; + int32_t minimum_count_; + + // Working variables + PreviousResultsQueue previous_results_; + const char* previous_top_label_; + int32_t previous_top_label_time_; +}; + +#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_MICRO_SPEECH_RECOGNIZE_COMMANDS_H_ diff --git a/examples/micro_speech/sparkfun_edge_audio_provider.cpp b/examples/micro_speech/sparkfun_edge_audio_provider.cpp new file mode 100644 index 0000000..a02eb06 --- /dev/null +++ b/examples/micro_speech/sparkfun_edge_audio_provider.cpp @@ -0,0 +1,377 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if defined(ARDUINO) && !defined(ARDUINO_SFE_EDGE) +#define ARDUINO_EXCLUDE_CODE +#endif // defined(ARDUINO) && !defined(ARDUINO_SFE_EDGE) + +#ifndef ARDUINO_EXCLUDE_CODE + +#include "audio_provider.h" + +#include + +// These are headers from Ambiq's Apollo3 SDK. +#include "am_bsp.h" +#include "am_mcu_apollo.h" +#include "am_util.h" +#include "micro_features_micro_model_settings.h" + +namespace { + +// These are the raw buffers that are filled by the ADC during DMA +constexpr int kAdcNumSlots = 2; +constexpr int kAdcSamplesPerSlot = 1024; +constexpr int kAdcSampleBufferSize = (kAdcNumSlots * kAdcSamplesPerSlot); +uint32_t g_ui32ADCSampleBuffer0[kAdcSampleBufferSize]; +uint32_t g_ui32ADCSampleBuffer1[kAdcSampleBufferSize]; +// Controls the double buffering between the two DMA buffers. +int g_dma_destination_index = 0; +// ADC Device Handle. +static void* g_adc_handle; +// ADC DMA error flag. +volatile bool g_adc_dma_error; +// So the interrupt can use the passed-in error handler to report issues. +tflite::ErrorReporter* g_adc_dma_error_reporter = nullptr; + +// Holds a longer history of audio samples in a ring buffer. +constexpr int kAudioCaptureBufferSize = 16000; +int16_t g_audio_capture_buffer[kAudioCaptureBufferSize] = {}; +int g_audio_capture_buffer_start = 0; +int64_t g_total_samples_captured = 0; +int32_t g_latest_audio_timestamp = 0; + +// Copy of audio samples returned to the caller. +int16_t g_audio_output_buffer[kMaxAudioSampleSize]; +bool g_is_audio_initialized = false; + +// Start the DMA fetch of ADC samples. +void adc_start_dma(tflite::ErrorReporter* error_reporter) { + am_hal_adc_dma_config_t ADCDMAConfig; + + // Configure the ADC to use DMA for the sample transfer. + ADCDMAConfig.bDynamicPriority = true; + ADCDMAConfig.ePriority = AM_HAL_ADC_PRIOR_SERVICE_IMMED; + ADCDMAConfig.bDMAEnable = true; + ADCDMAConfig.ui32SampleCount = kAdcSampleBufferSize; + if (g_dma_destination_index == 0) { + ADCDMAConfig.ui32TargetAddress = (uint32_t)g_ui32ADCSampleBuffer0; + } else { + ADCDMAConfig.ui32TargetAddress = (uint32_t)g_ui32ADCSampleBuffer1; + } + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_configure_dma(g_adc_handle, &ADCDMAConfig)) { + TF_LITE_REPORT_ERROR(error_reporter, "Error - configuring ADC DMA failed."); + } + + // Reset the ADC DMA flags. + g_adc_dma_error = false; + g_adc_dma_error_reporter = error_reporter; +} + +// Configure the ADC. +void adc_config0(tflite::ErrorReporter* error_reporter) { + am_hal_adc_config_t ADCConfig; + am_hal_adc_slot_config_t ADCSlotConfig; + + // Initialize the ADC and get the handle. + if (AM_HAL_STATUS_SUCCESS != am_hal_adc_initialize(0, &g_adc_handle)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - reservation of the ADC0 instance failed."); + } + + // Power on the ADC. + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_power_control(g_adc_handle, AM_HAL_SYSCTRL_WAKE, false)) { + TF_LITE_REPORT_ERROR(error_reporter, "Error - ADC0 power on failed."); + } + + // Set up the ADC configuration parameters. These settings are reasonable + // for accurate measurements at a low sample rate. + ADCConfig.eClock = AM_HAL_ADC_CLKSEL_HFRC_DIV2; + ADCConfig.ePolarity = AM_HAL_ADC_TRIGPOL_RISING; + ADCConfig.eTrigger = AM_HAL_ADC_TRIGSEL_SOFTWARE; + ADCConfig.eReference = + AM_HAL_ADC_REFSEL_INT_2P0; // AM_HAL_ADC_REFSEL_INT_1P5; + ADCConfig.eClockMode = AM_HAL_ADC_CLKMODE_LOW_LATENCY; + ADCConfig.ePowerMode = AM_HAL_ADC_LPMODE0; + ADCConfig.eRepeat = AM_HAL_ADC_REPEATING_SCAN; + if (AM_HAL_STATUS_SUCCESS != am_hal_adc_configure(g_adc_handle, &ADCConfig)) { + TF_LITE_REPORT_ERROR(error_reporter, "Error - configuring ADC0 failed."); + } + + // Set up an ADC slot (2) + ADCSlotConfig.eMeasToAvg = AM_HAL_ADC_SLOT_AVG_1; + ADCSlotConfig.ePrecisionMode = AM_HAL_ADC_SLOT_14BIT; + ADCSlotConfig.eChannel = AM_HAL_ADC_SLOT_CHSEL_SE2; + ADCSlotConfig.bWindowCompare = false; + ADCSlotConfig.bEnabled = true; + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_configure_slot(g_adc_handle, 2, &ADCSlotConfig)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - configuring ADC Slot 2 failed."); + } + + // Set up an ADC slot (1) + ADCSlotConfig.eMeasToAvg = AM_HAL_ADC_SLOT_AVG_1; + ADCSlotConfig.ePrecisionMode = AM_HAL_ADC_SLOT_14BIT; + ADCSlotConfig.eChannel = AM_HAL_ADC_SLOT_CHSEL_SE1; + ADCSlotConfig.bWindowCompare = false; + ADCSlotConfig.bEnabled = true; + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_configure_slot(g_adc_handle, 1, &ADCSlotConfig)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - configuring ADC Slot 1 failed."); + } + + // Configure the ADC to use DMA for the sample transfer. + adc_start_dma(error_reporter); + + // For this example, the samples will be coming in slowly. This means we + // can afford to wake up for every conversion. + am_hal_adc_interrupt_enable(g_adc_handle, + AM_HAL_ADC_INT_DERR | AM_HAL_ADC_INT_DCMP); + + // Enable the ADC. + if (AM_HAL_STATUS_SUCCESS != am_hal_adc_enable(g_adc_handle)) { + TF_LITE_REPORT_ERROR(error_reporter, "Error - enabling ADC0 failed."); + } +} + +// Initialize the ADC repetitive sample timer A3. +void init_timerA3_for_ADC() { + // Start a timer to trigger the ADC periodically (1 second). + am_hal_ctimer_config_single(3, AM_HAL_CTIMER_TIMERA, + AM_HAL_CTIMER_HFRC_12MHZ | + AM_HAL_CTIMER_FN_REPEAT | + AM_HAL_CTIMER_INT_ENABLE); + + am_hal_ctimer_int_enable(AM_HAL_CTIMER_INT_TIMERA3); + + // 750 = 12,000,000 (clock rate) / 16,000 (desired sample rate). + am_hal_ctimer_period_set(3, AM_HAL_CTIMER_TIMERA, 750, 374); + + // Enable the timer A3 to trigger the ADC directly + am_hal_ctimer_adc_trigger_enable(); + + // Start the timer. + am_hal_ctimer_start(3, AM_HAL_CTIMER_TIMERA); +} + +// Make sure the CPU is running as fast as possible. +void enable_burst_mode(tflite::ErrorReporter* error_reporter) { + am_hal_burst_avail_e eBurstModeAvailable; + am_hal_burst_mode_e eBurstMode; + + // Check that the Burst Feature is available. + if (AM_HAL_STATUS_SUCCESS == + am_hal_burst_mode_initialize(&eBurstModeAvailable)) { + if (AM_HAL_BURST_AVAIL == eBurstModeAvailable) { + TF_LITE_REPORT_ERROR(error_reporter, "Apollo3 Burst Mode is Available\n"); + } else { + TF_LITE_REPORT_ERROR(error_reporter, + "Apollo3 Burst Mode is Not Available\n"); + } + } else { + TF_LITE_REPORT_ERROR(error_reporter, + "Failed to Initialize for Burst Mode operation\n"); + } + + // Put the MCU into "Burst" mode. + if (AM_HAL_STATUS_SUCCESS == am_hal_burst_mode_enable(&eBurstMode)) { + if (AM_HAL_BURST_MODE == eBurstMode) { + TF_LITE_REPORT_ERROR(error_reporter, + "Apollo3 operating in Burst Mode (96MHz)\n"); + } + } else { + TF_LITE_REPORT_ERROR(error_reporter, + "Failed to Enable Burst Mode operation\n"); + } +} + +} // namespace + +// Interrupt handler for the ADC. +extern "C" void am_adc_isr(void) { + uint32_t ui32IntMask; + + // Read the interrupt status. + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_interrupt_status(g_adc_handle, &ui32IntMask, false)) { + TF_LITE_REPORT_ERROR(g_adc_dma_error_reporter, + "Error reading ADC0 interrupt status."); + } + + // Clear the ADC interrupt. + if (AM_HAL_STATUS_SUCCESS != + am_hal_adc_interrupt_clear(g_adc_handle, ui32IntMask)) { + TF_LITE_REPORT_ERROR(g_adc_dma_error_reporter, + "Error clearing ADC0 interrupt status."); + } + + // If we got a DMA complete, set the flag. + if (ui32IntMask & AM_HAL_ADC_INT_DCMP) { + uint32_t* source_buffer; + if (g_dma_destination_index == 0) { + source_buffer = g_ui32ADCSampleBuffer0; + g_dma_destination_index = 1; + } else { + source_buffer = g_ui32ADCSampleBuffer1; + g_dma_destination_index = 0; + } + adc_start_dma(g_adc_dma_error_reporter); + + // For slot 1: + uint32_t slotCount = 0; + for (uint32_t indi = 0; indi < kAdcSampleBufferSize; indi++) { + am_hal_adc_sample_t temp; + + temp.ui32Slot = AM_HAL_ADC_FIFO_SLOT(source_buffer[indi]); + temp.ui32Sample = AM_HAL_ADC_FIFO_SAMPLE(source_buffer[indi]); + + if (temp.ui32Slot == 1) { + g_audio_capture_buffer[g_audio_capture_buffer_start] = temp.ui32Sample; + g_audio_capture_buffer_start = + (g_audio_capture_buffer_start + 1) % kAudioCaptureBufferSize; + slotCount++; + } + } + + g_total_samples_captured += slotCount; + g_latest_audio_timestamp = + (g_total_samples_captured / (kAudioSampleFrequency / 1000)); + } + + // If we got a DMA error, set the flag. + if (ui32IntMask & AM_HAL_ADC_INT_DERR) { + g_adc_dma_error = true; + } +} + +TfLiteStatus InitAudioRecording(tflite::ErrorReporter* error_reporter) { + // Set the clock frequency. + if (AM_HAL_STATUS_SUCCESS != + am_hal_clkgen_control(AM_HAL_CLKGEN_CONTROL_SYSCLK_MAX, 0)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - configuring the system clock failed."); + return kTfLiteError; + } + + // Set the default cache configuration and enable it. + if (AM_HAL_STATUS_SUCCESS != + am_hal_cachectrl_config(&am_hal_cachectrl_defaults)) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - configuring the system cache failed."); + return kTfLiteError; + } + if (AM_HAL_STATUS_SUCCESS != am_hal_cachectrl_enable()) { + TF_LITE_REPORT_ERROR(error_reporter, + "Error - enabling the system cache failed."); + return kTfLiteError; + } + + // Ensure the CPU is running as fast as possible. + enable_burst_mode(error_reporter); + + // Start the CTIMER A3 for timer-based ADC measurements. + init_timerA3_for_ADC(); + + // Enable interrupts. + NVIC_EnableIRQ(ADC_IRQn); + am_hal_interrupt_master_enable(); + + // Edge Board Pin Definitions + constexpr int kSfEdgePinMic0 = 11; + const am_hal_gpio_pincfg_t g_sf_edge_pin_mic0 = { + .uFuncSel = AM_HAL_PIN_11_ADCSE2, + }; + constexpr int kSfEdgePinMic1 = 29; + const am_hal_gpio_pincfg_t g_sf_edge_pin_mic1 = { + .uFuncSel = AM_HAL_PIN_29_ADCSE1, + }; + + // Set pins to act as our ADC input + am_hal_gpio_pinconfig(kSfEdgePinMic0, g_sf_edge_pin_mic0); + am_hal_gpio_pinconfig(kSfEdgePinMic1, g_sf_edge_pin_mic1); + + // Configure the ADC + adc_config0(error_reporter); + + // Trigger the ADC sampling for the first time manually. + if (AM_HAL_STATUS_SUCCESS != am_hal_adc_sw_trigger(g_adc_handle)) { + TF_LITE_REPORT_ERROR(error_reporter, "Error - triggering the ADC0 failed."); + return kTfLiteError; + } + + // Enable the LED outputs. + am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_RED, g_AM_HAL_GPIO_OUTPUT_12); + am_hal_gpio_pinconfig(AM_BSP_GPIO_LED_YELLOW, g_AM_HAL_GPIO_OUTPUT_12); + + am_hal_gpio_output_set(AM_BSP_GPIO_LED_RED); + am_hal_gpio_output_set(AM_BSP_GPIO_LED_YELLOW); + + return kTfLiteOk; +} + +TfLiteStatus GetAudioSamples(tflite::ErrorReporter* error_reporter, + int start_ms, int duration_ms, + int* audio_samples_size, int16_t** audio_samples) { + if (!g_is_audio_initialized) { + TfLiteStatus init_status = InitAudioRecording(error_reporter); + if (init_status != kTfLiteOk) { + return init_status; + } + g_is_audio_initialized = true; + } + + // This is the 'zero' level of the microphone when no audio is present, and + // should be recalibrated if the hardware configuration ever changes. It was + // generated experimentally by averaging some samples captured on a board. + const int16_t kAdcSampleDC = 6003; + + // Temporary gain emulation to deal with too-quiet audio on prototype boards. + const int16_t kAdcSampleGain = 10; + + // This should only be called when the main thread notices that the latest + // audio sample data timestamp has changed, so that there's new data in the + // capture ring buffer. The ring buffer will eventually wrap around and + // overwrite the data, but the assumption is that the main thread is checking + // often enough and the buffer is large enough that this call will be made + // before that happens. + const int start_offset = start_ms * (kAudioSampleFrequency / 1000); + const int duration_sample_count = + duration_ms * (kAudioSampleFrequency / 1000); + for (int i = 0; i < duration_sample_count; ++i) { + const int capture_index = (start_offset + i) % kAudioCaptureBufferSize; + const int32_t capture_value = g_audio_capture_buffer[capture_index]; + int32_t output_value = capture_value - kAdcSampleDC; + output_value *= kAdcSampleGain; + if (output_value < std::numeric_limits::min()) { + output_value = std::numeric_limits::min(); + } + if (output_value > std::numeric_limits::max()) { + output_value = std::numeric_limits::max(); + } + g_audio_output_buffer[i] = output_value; + } + + *audio_samples_size = kMaxAudioSampleSize; + *audio_samples = g_audio_output_buffer; + return kTfLiteOk; +} + +int32_t LatestAudioTimestamp() { return g_latest_audio_timestamp; } + +#endif // ARDUINO_EXCLUDE_CODE diff --git a/examples/micro_speech/sparkfun_edge_command_responder.cpp b/examples/micro_speech/sparkfun_edge_command_responder.cpp new file mode 100644 index 0000000..781be23 --- /dev/null +++ b/examples/micro_speech/sparkfun_edge_command_responder.cpp @@ -0,0 +1,63 @@ +/* Copyright 2019 The TensorFlow Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#if defined(ARDUINO) && !defined(ARDUINO_SFE_EDGE) +#define ARDUINO_EXCLUDE_CODE +#endif // defined(ARDUINO) && !defined(ARDUINO_SFE_EDGE) + +#ifndef ARDUINO_EXCLUDE_CODE + +#include "command_responder.h" + +#include "am_bsp.h" + +// This implementation will light up the LEDs on the board in response to +// different commands. +void RespondToCommand(tflite::ErrorReporter* error_reporter, + int32_t current_time, const char* found_command, + uint8_t score, bool is_new_command) { + static bool is_initialized = false; + if (!is_initialized) { + // Setup LED's as outputs +#ifdef AM_BSP_NUM_LEDS + am_devices_led_array_init(am_bsp_psLEDs, AM_BSP_NUM_LEDS); + am_devices_led_array_out(am_bsp_psLEDs, AM_BSP_NUM_LEDS, 0x00000000); +#endif + is_initialized = true; + } + + // Toggle the blue LED every time an inference is performed. + am_devices_led_toggle(am_bsp_psLEDs, AM_BSP_LED_BLUE); + + // Turn on LEDs corresponding to the detection for the cycle + am_devices_led_off(am_bsp_psLEDs, AM_BSP_LED_RED); + am_devices_led_off(am_bsp_psLEDs, AM_BSP_LED_YELLOW); + am_devices_led_off(am_bsp_psLEDs, AM_BSP_LED_GREEN); + if (is_new_command) { + TF_LITE_REPORT_ERROR(error_reporter, "Heard %s (%d) @%dms", found_command, + score, current_time); + if (found_command[0] == 'y') { + am_devices_led_on(am_bsp_psLEDs, AM_BSP_LED_YELLOW); + } + if (found_command[0] == 'n') { + am_devices_led_on(am_bsp_psLEDs, AM_BSP_LED_RED); + } + if (found_command[0] == 'u') { + am_devices_led_on(am_bsp_psLEDs, AM_BSP_LED_GREEN); + } + } +} + +#endif // ARDUINO_EXCLUDE_CODE