Opus

https://github.com/xiph/opus
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Opus is a codec for interactive speech and audio transmission designed to handle diverse audio...
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### Create Training Setup

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lpcnet/README.md

Generate a default setup file for training. Customize the path to your dataset.

```python
python make_default_setup.py my_setup.yml --path2dataset my_dataset_folder
```

--------------------------------

### Install Dependencies

Source: https://github.com/xiph/opus/blob/main/dnn/torch/testsuite/README.md

Install project dependencies using pip.

```bash
pip install -r requirements.txt
```

--------------------------------

### Create Default NoLACE Adversarial Setup

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Generates a default configuration file for NoLACE adversarial training. Specify the dataset path.

```bash
python make_default_setup.py nolace_adv.yml --model nolace --adversarial --path2dataset <path2dataset>
```

--------------------------------

### Example LPCNet Processing Configuration

Source: https://github.com/xiph/opus/blob/main/dnn/torch/testsuite/README.md

An example YAML configuration for the run_warpq_test.py script, defining a processing pipeline for LPCNet. This configuration specifies shell commands to convert input audio, extract features, synthesize audio, and resample the output.

```yaml
test: "LPCNet reference test"
processing:
  - "sox {INPUT} {INPUT}.raw"
  - "/local/code/LPCNet/lpcnet_demo -features {INPUT}.raw {INPUT}.features.f32"
  - "/local/code/LPCNet/lpcnet_demo -synthesis {INPUT}.features.f32 {INPUT}.decoded.raw"
  - "sox -r 16000 -L -e signed-integer -b 16 -c 1 {INPUT}.decoded.raw {OUTPUT}"
```

--------------------------------

### Train LPCNet Model

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lpcnet/README.md

Execute the training process using the generated setup file. The output will be saved to the specified directory.

```python
python train_lpcnet.py my_setup.yml my_output
```

--------------------------------

### Create Default LACE/NoLACE Setup

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Generates a default configuration file for LACE or NoLACE training. Specify the model type and dataset path.

```bash
python make_default_setup.py model.yml --model lace/nolace --path2dataset <path2dataset>
```

--------------------------------

### Install CMake Configuration Module

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Sets up CMake configuration files (OpusConfig.cmake and OpusConfigVersion.cmake) for installation. This enables CMake projects to find the Opus library using find_package(Opus).

```cmake
if(OPUS_INSTALL_CMAKE_CONFIG_MODULE)
  set(CPACK_GENERATOR TGZ)
  include(CPack)
  set(CMAKE_INSTALL_PACKAGEDIR ${CMAKE_INSTALL_LIBDIR}/cmake/${PROJECT_NAME})
  install(EXPORT OpusTargets
          NAMESPACE Opus::
          DESTINATION ${CMAKE_INSTALL_PACKAGEDIR})

  include(CMakePackageConfigHelpers)

  set(INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR})
  configure_package_config_file(${PROJECT_SOURCE_DIR}/cmake/OpusConfig.cmake.in
                                OpusConfig.cmake
                                INSTALL_DESTINATION
                                ${CMAKE_INSTALL_PACKAGEDIR}
                                PATH_VARS
                                INCLUDE_INSTALL_DIR
                                INSTALL_PREFIX
                                ${CMAKE_INSTALL_PREFIX})
  write_basic_package_version_file(OpusConfigVersion.cmake
                                   VERSION ${PROJECT_VERSION}
                                   COMPATIBILITY SameMajorVersion)
  install(FILES ${CMAKE_CURRENT_BINARY_DIR}/OpusConfig.cmake
                ${CMAKE_CURRENT_BINARY_DIR}/OpusConfigVersion.cmake
          DESTINATION ${CMAKE_INSTALL_PACKAGEDIR})
endif()
```

--------------------------------

### Install Opus Target

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Installs the Opus target, including libraries and executables, to the appropriate directories based on CMAKE_INSTALL_LIBDIR, CMAKE_INSTALL_BINDIR, and CMAKE_INSTALL_PREFIX.

```cmake
install(TARGETS opus
        EXPORT OpusTargets
        ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
        LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
        RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
        FRAMEWORK DESTINATION ${CMAKE_INSTALL_PREFIX})
```

--------------------------------

### LPCNet Feature Extraction Setup

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Instructions for setting up LPCNet feature extraction by adding source files and compiling. This involves modifying C files and Makefiles.

```bash
# Add lpcnet_extractor.c and relevant functions to lpcnet_enc.c
# Add source for headers/c files and Makefile.am
# Compile to generate ./lpcnet_xcorr_extractor object
```

--------------------------------

### Install Opus Package Configuration Files

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Configures and installs pkg-config and CMake configuration files for the Opus library. This allows other projects to easily find and link against Opus.

```cmake
if(OPUS_INSTALL_PKG_CONFIG_MODULE)
  set(prefix ${CMAKE_INSTALL_PREFIX})
  set(exec_prefix ${CMAKE_INSTALL_PREFIX})
  set(libdir ${CMAKE_INSTALL_FULL_LIBDIR})
  set(includedir ${CMAKE_INSTALL_FULL_INCLUDEDIR})
  set(VERSION ${PACKAGE_VERSION})
  if(HAVE_LIBM)
    set(LIBM "-lm")
  endif()
  configure_file(opus.pc.in opus.pc)
  install(FILES ${CMAKE_CURRENT_BINARY_DIR}/opus.pc
          DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
endif()
```

--------------------------------

### Evaluate DRED with opus_demo

Source: https://github.com/xiph/opus/blob/main/dnn/torch/rdovae/README.md

Demonstrates how to use the opus_demo executable to evaluate the DRED feature. This example encodes a 16 kHz audio file with specific bitrate, redundancy, and simulated loss settings.

```bash
./opus_demo voip 16000 1 64000 -loss 50 -dred 100 -sim_loss 50 input.pcm output.pcm
```

--------------------------------

### Import PyTorch Exchange Module

Source: https://github.com/xiph/opus/blob/main/dnn/torch/weight-exchange/README.md

Import the routines for loading and dumping PyTorch weights. Requires PyTorch to be installed.

```python
import exchange.torch
```

--------------------------------

### Download PTDB Dataset

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Downloads and unzips the PTDB dataset. Ensure you have wget and unzip installed.

```bash
wget https://www2.spsc.tugraz.at/databases/PTDB-TUG/SPEECH_DATA_ZIPPED.zip
unzip SPEECH_DATA_ZIPPED.zip
```

--------------------------------

### Import TensorFlow Exchange Module

Source: https://github.com/xiph/opus/blob/main/dnn/torch/weight-exchange/README.md

Import the routines for loading and dumping TensorFlow weights. Requires TensorFlow to be installed.

```python
import exchange.tf
```

--------------------------------

### Get and Message Library Version

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Retrieves the Opus library version and major version number, then logs them in debug mode.

```cmake
get_library_version(OPUS_LIBRARY_VERSION OPUS_LIBRARY_VERSION_MAJOR)
message(DEBUG "Opus library version: ${OPUS_LIBRARY_VERSION}")
```

--------------------------------

### Download Datasets

Source: https://github.com/xiph/opus/blob/main/dnn/torch/rdovae/README.md

Fetches all necessary data from the datasets.txt file. This is the first step in data preparation.

```bash
./download_datasets.sh
```

--------------------------------

### Create Conda Environment

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Sets up a Python 3.11 environment for the project. Activate it before proceeding.

```bash
conda create -n osce python=3.11
conda activate osce
python -m pip install -r requirements.txt
```

--------------------------------

### Configure LPCNet Build Options

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Options for the configure script to disable dot product instructions or compile on 32-bit ARM.

```bash
./configure --disable-dot-product
```

--------------------------------

### Prepare Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lpcnet/README.md

Use this script to convert raw data and feature files into a training dataset. Ensure data and feature files are in the specified folder.

```python
python add_dataset_config.py my_dataset_folder
```

--------------------------------

### Run Model Training

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Initiates the model training process using the augmented data. Refer to the script's arguments for configuration.

```python
python training.py
```

--------------------------------

### Prepare Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/rdovae/README.md

Prepares the training data by running the 'dump_data' executable with specified parameters. Requires Opus to be built with --enable-dred.

```bash
./dump_data -train all_speech.pcm all_features.f32 /dev/null
```

--------------------------------

### Generate Training Data using opus_demo

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Generates training data by running the opus_demo binary with specific parameters for VoIP, sample rate, and random switching.

```bash
cd dataset && <path_to_opus_demo_with_training_data>/opus_demo voip 16000 1 9000 -silk_random_switching 249 clean.s16 coded.s16
```

--------------------------------

### Build Opus with OSCE Training Data Support

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Configures and builds the Opus binary with support for writing decoder features to disk for training data generation.

```bash
./configure --disable-shared --enable-osce-training-data && make clean && make -j
```

--------------------------------

### Prepare Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Use the 'dump_data' executable to prepare raw PCM speech files into feature and speech files for training.

```bash
./dump_data -train in_speech.pcm out_features.f32 out_speech.pcm
```

--------------------------------

### Synthesize Speech with C (CPU)

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Prepares LPCNet model files (nnet_data.h/c) for C-based inference by rebuilding the software. C inference is significantly faster than Python/GPU.

```bash
./training_tf2/dump_lpcnet.py lpcnet_model_name.h5
```

--------------------------------

### Train Model (Foreground)

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Runs the model training script in the foreground. Output is directed to standard output.

```bash
python train_model.py model.yml <output folder> --no-redirect
```

--------------------------------

### Train Model (Background)

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Runs the model training script in the background. Output is saved to '<output folder>/out.txt'.

```bash
nohup python train_model.py model.yml <output folder> &
```

--------------------------------

### Process Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lossgen/README.md

Prepares the training data by sorting loss information. Ensure the path to the training data is correctly specified.

```bash
./process_data.sh /<path>/test_train/train/lossy_signals/
```

--------------------------------

### Build Opus Demo Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Builds the main 'opus_demo' executable. This executable is used for demonstrating Opus encoding and decoding functionalities.

```cmake
add_executable(opus_demo ${opus_demo_sources})
target_include_directories(opus_demo PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
target_include_directories(opus_demo PRIVATE silk) # debug.h
target_include_directories(opus_demo PRIVATE celt) # arch.h
target_include_directories(opus_demo PRIVATE dnn)
target_link_libraries(opus_demo PRIVATE opus ${OPUS_REQUIRED_LIBRARIES})
target_compile_definitions(opus_demo PRIVATE OPUS_BUILD)
```

--------------------------------

### Generate Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Generates training data from raw PCM audio. This program makes multiple passes with different filters to create a large dataset.

```bash
./dump_data -train input.s16 features.f32 data.s16
```

--------------------------------

### Generate Features for Inference

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Use the './fargan_demo' executable to generate audio features required for inference.

```bash
./fargan_demo -features test_speech.pcm test_features.f32
```

--------------------------------

### Load and preprocess audio data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb

Loads LPCNet features and audio data, then preprocesses the audio signal by applying pre-emphasis. This prepares the signal for linear prediction analysis.

```python
lpcnet_features = load_lpcnet_features('lp/features.f32')

features = lpcnet_features['features'].numpy()
periods = lpcnet_features['periods'].squeeze(-1).numpy()
lpcs = lpcnet_features['lpcs'].numpy()

x = np.fromfile('data/a3_short.pcm', dtype=np.int16).astype(np.float32) / 2**15
x = np.concatenate((np.zeros(80), x, np.zeros(320)))
x_preemph = x.copy()
x_preemph[1:] -= 0.85 * x_preemph[:-1]

num_frames = features.shape[0]
x = x[:160 * num_frames]
x_preemph = x_preemph[:160 * num_frames]
```

--------------------------------

### Build Opus Custom Demo Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Builds the 'opus_custom_demo' executable if custom modes are enabled. It links against the Opus library and includes necessary directories.

```cmake
if(OPUS_CUSTOM_MODES)
  add_executable(opus_custom_demo ${opus_custom_demo_sources})
  target_include_directories(opus_custom_demo
                               PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
  target_link_libraries(opus_custom_demo PRIVATE opus)
  target_compile_definitions(opus_custom_demo PRIVATE OPUS_BUILD)
endif()
```

--------------------------------

### Adversarial Train Model (Background)

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Runs the adversarial model training script in the background. Output is saved to '<output folder>/out.txt'.

```bash
nohup python adv_train_model.py nolace_adv.yml <output folder> &
```

--------------------------------

### Clone Opus Repository

Source: https://github.com/xiph/opus/blob/main/meson/README.md

Use Git to clone the Opus repository and navigate into the directory. This is the initial step before configuring the build.

```shell
git clone https://gitlab.xiph.org/xiph/opus
cd opus
```

--------------------------------

### Run Opus Tests with Meson

Source: https://github.com/xiph/opus/blob/main/meson/README.md

Execute the Opus test suite using Meson to verify the project's functionality and correctness. Ensure you are in the build directory.

```shell
meson test -C builddir
```

--------------------------------

### Load and Preprocess Audio Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/postfilter.ipynb

Loads LPCNet features and audio data, applies pre-emphasis to the audio signal, and prepares frames for analysis.

```python
lpcnet_features = load_lpcnet_features('lp/features.f32')

features = lpcnet_features['features'].numpy()
periods = lpcnet_features['periods'].squeeze(-1).numpy()
lpcs = lpcnet_features['lpcs'].numpy()

x = np.fromfile('data/critical_3.pcm', dtype=np.int16).astype(np.float32) / 2**15
x = np.concatenate((np.zeros(80), x, np.zeros(320)))
x_preemph = x.copy()
x_preemph[1:] -= 0.85 * x_preemph[:-1]

num_frames = features.shape[0]
x = x[:160 * num_frames]
x_preemph = x_preemph[:160 * num_frames]

```

--------------------------------

### Adversarial Train Model (Foreground)

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Runs the adversarial model training script in the foreground. Output is directed to standard output.

```bash
python adv_train_model.py nolace_adv.yml <output folder> --no-redirect
```

--------------------------------

### Configure Opus Build with Meson (Disable Tests)

Source: https://github.com/xiph/opus/blob/main/meson/README.md

Configure the Opus project using Meson, disabling tests. This command sets up the build directory with specified options.

```shell
meson setup builddir -Dtests=disabled
```

--------------------------------

### Build Opus Project with CMake

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Build the Opus project after configuration using the CMake build command.

```shell
cmake --build .
```

--------------------------------

### Download LPCNet PLC Model

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Downloads a pre-trained Packet Loss Concealment (PLC) model. Use 'plc_challenge' for PLC challenge submissions.

```bash
./download_model.sh plc-3b1eab4
```

```bash
./download_model.sh plc_challenge
```

--------------------------------

### Import Libraries for LACE Demo

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Imports necessary libraries including numpy, matplotlib, scipy, and torch for audio processing, plotting, and adaptive filtering.

```python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation
from scipy.io import wavfile
import scipy.signal
import torch

from playback import make_playback_animation
from endoscopy import read_data



```

--------------------------------

### Import necessary libraries

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb

Imports essential libraries for numerical operations, plotting, audio file handling, and PyTorch.

```python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation
from scipy.io import wavfile
import scipy.signal
import torch

from playback import make_playback_animation
```

--------------------------------

### Run Experiments

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Executes experimental runs, likely for evaluating the trained models. Consult the script's arguments for details.

```python
python experiments.py
```

--------------------------------

### Generate Spectrograms and Animations

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Iterates through specified audio file names, reads WAV files, generates spectrograms using plt.specgram, and creates playback animations for the spectrograms.

```python
# comparison opus@6kb/s vs. LACE@6kb/s
for name in ['opus_6kbps', 'lace_6kbps']:
    fs, y = wavfile.read(f'lace_demo/{name}.wav')
    spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)
    spec = 10*np.log10(spec)
    
    make_playback_animation(f'lace_demo/{name}_spec.mp4', spec, len(y)/16)

```

--------------------------------

### Generate Visual Studio Projects with Meson

Source: https://github.com/xiph/opus/blob/main/meson/README.md

Generate Visual Studio projects for the Opus build using Meson. This command is typically run from the Visual Studio Command Prompt.

```shell
meson setup builddir -Dtests=enabled --backend vs
```

--------------------------------

### Initialize Adaptive Filter Impulse Responses and Features

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Calculates the impulse responses for two adaptive comb filters and one adaptive convolutional filter using the previously defined functions. It also extracts hidden features.

```python
impz_comb1 = get_adacomb_impz(data, 'limited_adaptive_comb1d_1')
impz_comb2 = get_adacomb_impz(data, 'limited_adaptive_comb1d_2')
impz_conv1 = get_adaconv_impz(data, 'limited_adaptive_conv1d_1')

phi = data['hidden_features']['data'][0, 0]

```

--------------------------------

### Compile Opus Project with Meson

Source: https://github.com/xiph/opus/blob/main/meson/README.md

Compile the Opus project after configuration using the Meson compile command. The compiled files will be located in the specified build directory.

```shell
meson compile -C builddir
```

--------------------------------

### Train RDO-VAE Model

Source: https://github.com/xiph/opus/blob/main/dnn/torch/rdovae/README.md

Initiates the training process for the RDO-VAE model using specified hyperparameters and data file. The final model is saved in the output directory.

```python
python ./train_rdovae.py --sequence-length 400 --split-mode random_split --state-dim 80 --batch-size 512 --epochs 400 --lambda-max 0.04 --lr 0.003 --lr-decay-factor 0.0001 all_features.f32 output_dir
```

--------------------------------

### Pre-train FARGAN Model

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Initiate the pre-training phase for the FARGAN model using specified Python script and parameters.

```python
python ./train_fargan.py out_features.f32 out_speech.pcm output_dir --epochs 400 --batch-size 4096 --lr 0.002 --cuda-visible-devices 0
```

--------------------------------

### Import Libraries

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/postfilter.ipynb

Imports necessary libraries for numerical operations, plotting, audio processing, and deep learning.

```python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation
from scipy.io import wavfile
import scipy.signal
import torch

```

--------------------------------

### Generate Inference Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Prepares an audio item for inference by converting it to raw PCM and running it through opus_demo to generate features.

```bash
mkdir item1.se && sox item1.wav -r 16000 -e signed-integer -b 16 item1.raw && cd item1.se && <path_to_opus_demo_with_training_data>/opus_demo voip 16000 1 <bitrate> ../item1.raw noisy.s16
```

--------------------------------

### Build Padding Test Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Configures the 'test_opus_padding' executable, which tests Opus padding handling. It links against the Opus library.

```cmake
add_executable(test_opus_padding ${test_opus_padding_sources})
target_include_directories(test_opus_padding
                             PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
target_link_libraries(test_opus_padding PRIVATE opus)
add_test(NAME test_opus_padding COMMAND ${CMAKE_COMMAND}
         -DTEST_EXECUTABLE=$<TARGET_FILE:test_opus_padding>
         -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}
         -P "${PROJECT_SOURCE_DIR}/cmake/RunTest.cmake")
```

--------------------------------

### Convert FARGAN Weights to C

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Convert the trained FARGAN model weights into C source and header files for integration into C projects.

```python
python dump_fargan_weights.py output_dir/checkpoints/fargan_adv_50.pth fargan_c_dir
```

--------------------------------

### Download Demand Dataset

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Downloads and combines the Demand dataset. This is a simple script to fetch and merge the required data.

```bash
sh download_demand.sh
```

--------------------------------

### Set CFLAGS for x86 Optimization

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Recommended CFLAGS for enabling AVX and optimization on recent x86 CPUs. The -Ofast flag enables -ffast-math for better auto-vectorization.

```bash
export CFLAGS='-Ofast -g -march=native'
```

--------------------------------

### Create LACE Animation

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Sets up a Matplotlib figure and axes for visualizing LACE filter responses over time. It then generates frames for an animation by plotting impulse and frequency responses at different time steps and saves the animation to a video file.

```python
# LACE animation

fig, axs = plt.subplots(4, 2)
frame_duration=40
fig.set_size_inches(12, 18)
axs[0, 0].set_xlabel('Tap')
axs[0, 0].set_ylabel('Amplitude')
axs[0, 1].set_xlabel('Frequency (kHz)')
axs[0, 1].set_ylabel('Amplitude (dB)')
axs[1, 0].set_xlabel('Tap')
axs[1, 0].set_ylabel('Amplitude')
axs[1, 1].set_xlabel('Frequency (kHz)')
axs[1, 1].set_ylabel('Amplitude (dB)')
axs[2, 0].set_xlabel('Tap')
axs[2, 0].set_ylabel('Amplitude')
axs[2, 1].set_xlabel('Frequency (kHz)')
axs[2, 1].set_ylabel('Amplitude (dB)')
axs[3, 0].set_xlabel('Dim')
axs[3, 1].set_visible(False)
fig.tight_layout()

frames = []
for i in range(12800, 14000, 4):
    f = impz_comb1[i] 
    w, h = scipy.signal.freqz(f, fs=16000)
    frame = axs[0, 0].plot(f, 'b') + axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')
    
    f = impz_comb2[i]
    w, h = scipy.signal.freqz(f, fs=16000)
    frame += axs[1, 0].plot(f, 'b') + axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')
    
    f = impz_conv1[i]
    w, h = scipy.signal.freqz(f, fs=16000)
    frame += axs[2, 0].plot(f, 'b') + axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')
    
    frame += axs[3, 0].plot(phi[i//80], 'b')
    
    frames.append(frame)
    
ani = matplotlib.animation.ArtistAnimation(fig, frames, blit=True, interval=frame_duration)
ani.save('lace_demo/responses2.mp4', dpi=720)

```

--------------------------------

### Build LPCNet

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Standard build process for LPCNet. The autogen.sh script is used when building from Git.

```bash
./autogen.sh
./configure
make
```

--------------------------------

### Generate Spectrograms and Animations for Opus Audio

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/spectrogram.ipynb

This snippet iterates through various bitrates, reads corresponding WAV files, generates spectrograms, and creates playback animations. Ensure the 'playback' library and necessary audio files are available.

```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
import scipy.signal
from playback import make_playback_animation

# coding noise demonstration
for br in ["inf", 24000, 15000, 12000, 9000, 8000, 7000, 6000]:
    fs, x = wavfile.read(f'data/a3_short_opus_{br}bps.wav')
    spec, freqs, t, im = plt.specgram(x, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)
    spec = 10*np.log10(spec)
    
    make_playback_animation(f'animations/opus_{br}bps.mp4', spec, len(x)/16)
    

```

--------------------------------

### Test LPCNet Model with Python (GPU)

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Tests the trained LPCNet model for speech synthesis using Python and a GPU. Note that Python-based synthesis is very slow.

```bash
./dump_data -test test_input.s16 test_features.f32
./training_tf2/test_lpcnet.py lpcnet_model_name.h5 test_features.f32 test.s16
```

--------------------------------

### Set CFLAGS for ARM Neon Optimization

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Recommended CFLAGS for enabling Neon vectorization on ARM CPUs. The -Ofast flag enables -ffast-math for better auto-vectorization.

```bash
export CFLAGS='-Ofast -g -mfpu=neon'
```

--------------------------------

### Encode PCM File with LPCNet Demo

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Encodes a 16 kHz, 16-bit PCM input file into a compressed binary format. The output file contains 8 bytes per 40-ms packet.

```bash
./lpcnet_demo -encode input.pcm compressed.bin
```

--------------------------------

### Convert and Concatenate Training Data

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Converts audio files to 16 kHz, 16-bit PCM and concatenates them with optional random scaling. Requires a file list.

```bash
python scripts/concatenator.py filelist 16000 dataset/clean.s16 --db_min -40 --db_max 0
```

--------------------------------

### Generate and save spectrogram animations

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb

Generates spectrograms for each processed audio signal, converts them to a logarithmic scale, and creates MP4 animations of the spectral content over time using the `make_playback_animation` function.

```python
for y, name in [(x_in, 'x_in'), (x_stp, 'x_stp'), (x_ltp, 'x_ltp')]:
    spec, freqs, t, im = plt.specgram(y, NFFT=512, cmap='inferno', noverlap=256 + 128, pad_to=4096)
    spec = 10*np.log10(spec)
    
    make_playback_animation(f'lp/{name}_spec.mp4', spec, len(y)/16)
```

--------------------------------

### Enable AVX2 Presumption

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Sets compile definitions for AVX2 presumption, including SSE4.1, and applies AVX2 flags if not using MSVC.

```cmake
target_compile_definitions(opus PRIVATE OPUS_X86_PRESUME_AVX2)
target_compile_definitions(opus PRIVATE OPUS_X86_PRESUME_SSE4_1)
if(NOT MSVC)
  target_compile_options(opus PRIVATE -mavx2 -mfma -mavx)
endif()
```

--------------------------------

### Configure NEON Runtime Detection

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Enables runtime CPU capability detection for NEON support, adding OPUS_HAVE_RTCD and NEON-specific source groups.

```cmake
if(COMPILER_SUPPORT_NEON)
  if(OPUS_MAY_HAVE_NEON)
    if(RUNTIME_CPU_CAPABILITY_DETECTION)
      message(STATUS "OPUS_MAY_HAVE_NEON enabling runtime detection")
      target_compile_definitions(opus PRIVATE OPUS_HAVE_RTCD)
      add_sources_group(opus celt ${celt_sources_arm_rtcd})
      add_sources_group(opus silk ${silk_sources_arm_rtcd})
      add_sources_group(opus lpcnet ${dnn_sources_arm_rtcd})
    else()
      message(ERROR "Runtime cpu capability detection needed for MAY_HAVE_NEON")
    endif()
    # Do runtime check for NEON
    target_compile_definitions(opus
                               PRIVATE
                               OPUS_ARM_MAY_HAVE_NEON
                               OPUS_ARM_MAY_HAVE_NEON_INTR)
  endif()

  add_sources_group(opus celt ${celt_sources_arm_neon_intr})
  add_sources_group(opus silk ${silk_sources_arm_neon_intr})
  if (OPUS_DNN)
    add_sources_group(opus lpcnet ${dnn_sources_arm_neon})
  endif()

  # silk arm neon depends on main_Fix.h
  target_include_directories(opus PRIVATE silk/fixed)

  if(OPUS_FIXED_POINT)
    add_sources_group(opus silk ${silk_sources_fixed_arm_neon_intr})
  endif()

  if(OPUS_PRESUME_NEON)
    target_compile_definitions(opus
                               PRIVATE
                               OPUS_ARM_PRESUME_NEON
                               OPUS_ARM_PRESUME_NEON_INTR)
  endif()
endif()
```

--------------------------------

### Generate spectrogram of original signal

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/linear_prediction.ipynb

Creates and displays a spectrogram of the original audio signal using matplotlib. This visualization helps in analyzing the frequency content over time.

```python
spec_x, freqs, t, im = plt.specgram(x, NFFT=512, cmap='inferno')
```

--------------------------------

### Build API Test Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Configures the 'test_opus_api' executable to test the Opus API. It includes necessary directories and links against the Opus library.

```cmake
add_executable(test_opus_api ${test_opus_api_sources})
target_include_directories(test_opus_api
                           PRIVATE ${CMAKE_CURRENT_BINARY_DIR} celt)
target_link_libraries(test_opus_api PRIVATE opus)
target_compile_definitions(test_opus_api PRIVATE OPUS_BUILD)
if(OPUS_FIXED_POINT)
  target_compile_definitions(test_opus_api PRIVATEDISABLE_FLOAT_API)
endif()
add_test(NAME test_opus_api COMMAND ${CMAKE_COMMAND}
      -DTEST_EXECUTABLE=$<TARGET_FILE:test_opus_api>
      -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}
      -P "${PROJECT_SOURCE_DIR}/cmake/RunTest.cmake")
```

--------------------------------

### Set General Compile Definitions

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Sets compile definitions for HAVE_LRINT, HAVE_LRINTF, and HAVE_ELF_AUX_INFO based on their availability.

```cmake
target_compile_definitions(opus
                           PRIVATE
                           $<$<BOOL:${HAVE_LRINT}>:HAVE_LRINT>
                           $<$<BOOL:${HAVE_LRINTF}>:HAVE_LRINTF>
                           $<$<BOOL:${HAVE_ELF_AUX_INFO}>:HAVE_ELF_AUX_INFO>)
```

--------------------------------

### Run Data Augmentation

Source: https://github.com/xiph/opus/blob/main/dnn/torch/neural-pitch/README.md

Executes the data augmentation script. Check the script's arguments for customization options.

```python
python data_augmentation.py
```

--------------------------------

### Decode Compressed File with LPCNet Demo

Source: https://github.com/xiph/opus/blob/main/dnn/README.md

Decodes a compressed LPCNet binary file back into a 16 kHz, 16-bit PCM output file.

```bash
./lpcnet_demo -decode compressed.bin output.pcm
```

--------------------------------

### Set Include Directories for Opus Library

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Specifies include directories for the 'opus' library, differentiating between build-time, install-time, and private includes.

```cmake
target_include_directories(
  opus
  PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
         $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>
         $<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}/opus>
  PRIVATE ${CMAKE_CURRENT_BINARY_DIR}
          ${CMAKE_CURRENT_SOURCE_DIR}
          ${CMAKE_CURRENT_SOURCE_DIR}/dnn
          celt
          silk)
```

--------------------------------

### Configure Opus Framework Properties

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Sets properties for building Opus as a macOS framework, including versioning and identifier.

```cmake
if(OPUS_BUILD_FRAMEWORK)
  set_target_properties(opus PROPERTIES
                        FRAMEWORK TRUE
                        FRAMEWORK_VERSION ${PROJECT_VERSION}
                        MACOSX_FRAMEWORK_IDENTIFIER org.xiph.opus
                        MACOSX_FRAMEWORK_SHORT_VERSION_STRING ${PROJECT_VERSION}
                        MACOSX_FRAMEWORK_BUNDLE_VERSION ${PROJECT_VERSION}
                        XCODE_ATTRIBUTE_INSTALL_PATH "@rpath"
                        OUTPUT_NAME Opus)
endif()
```

--------------------------------

### Enable SSE4.1 Presumption

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Defines OPUS_X86_PRESUME_SSE4_1 and sets the -msse4.1 compile option if SSE4.1 is presumed and not using MSVC.

```cmake
target_compile_definitions(opus PRIVATE OPUS_X86_PRESUME_SSE4_1)
if(NOT MSVC OR MSVC_CLANG_CL)
  target_compile_options(opus PRIVATE -msse4.1)
endif()
```

--------------------------------

### Export RDO-VAE Weights to C

Source: https://github.com/xiph/opus/blob/main/dnn/torch/rdovae/README.md

Converts the trained RDO-VAE model weights into C source and header files. These files are intended to be copied into the opus/dnn/ directory.

```python
python export_rdovae_weights.py output_dir/checkpoints/chechpoint_400.pth dred_c_dir
```

--------------------------------

### Clone Opus Repository

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Use Git to clone the Opus project repository.

```shell
git clone https://gitlab.xiph.org/xiph/opus
```

--------------------------------

### Run Opus Tests with CTest

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Execute the Opus test suite using CTest from the build directory.

```shell
ctest
```

--------------------------------

### Build Opus Compare Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Builds the 'opus_compare' executable, used for comparing Opus encoder/decoder implementations. It links against the Opus library.

```cmake
add_executable(opus_compare ${opus_compare_sources})
target_include_directories(opus_compare PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
target_link_libraries(opus_compare PRIVATE opus ${OPUS_REQUIRED_LIBRARIES})
```

--------------------------------

### Add SSE4.1 Sources for Fixed Point

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Adds source files for fixed-point operations with SSE4.1 support. Applies the -msse4.1 flag if not using MSVC or if using clang-cl.

```cmake
add_sources_group(opus silk ${silk_sources_fixed_sse4_1})
if(NOT MSVC OR MSVC_CLANG_CL)
  set_source_files_properties(${silk_sources_fixed_sse4_1} PROPERTIES COMPILE_FLAGS -msse4.1)
endif()
```

--------------------------------

### Export Model Weights to C

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/README.md

Exports trained model weights to the opus/dnn directory for integration into Opus. Optionally quantizes the weights.

```bash
python export_model_weigths.py <path_to_checkpoint> ../../ [--quantize]
```

--------------------------------

### Build Encode Test Executable

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Configures the 'test_opus_encode' executable for testing Opus encoding. It includes directories for CELT and DNN, and links against the Opus library.

```cmake
add_executable(test_opus_encode ${test_opus_encode_sources})
target_include_directories(test_opus_encode
                          PRIVATE ${CMAKE_CURRENT_BINARY_DIR} celt dnn)
target_link_libraries(test_opus_encode PRIVATE opus)
target_compile_definitions(test_opus_encode PRIVATE OPUS_BUILD)
add_test(NAME test_opus_encode COMMAND ${CMAKE_COMMAND}
      -DTEST_EXECUTABLE=$<TARGET_FILE:test_opus_encode>
```

--------------------------------

### Cross-compile Opus for Android

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Configure the Opus build for Android cross-compilation using a specific toolchain file and ABI.

```shell
cmake .. -DCMAKE_TOOLCHAIN_FILE=${ANDROID_HOME}/ndk/27.2.12479018/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a
```

--------------------------------

### Configure Opus Build with CMake

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Configure the Opus build using CMake, enabling custom modes and building programs. Ensure you are in the 'build' directory within the 'opus' repository.

```shell
cmake .. -DOPUS_BUILD_PROGRAMS=ON -DOPUS_BUILD_TESTING=ON
```

--------------------------------

### Cross-Compile for iOS with CMake

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

Use this command to configure CMake for cross-compilation targeting iOS. Ensure you specify the correct architecture.

```shell
cmake .. -G "Unix Makefiles" -DCMAKE_SYSTEM_NAME=iOS -DCMAKE_OSX_ARCHITECTURES=arm64
```

--------------------------------

### Configure AVX2 Support

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Adds sources and sets compile flags for AVX2 support. Handles MSVC's /arch:AVX2 and other compilers' -mavx2 -mfma -mavx flags.

```cmake
add_sources_group(opus celt ${celt_sources_avx2})
add_sources_group(opus silk ${silk_sources_avx2})
if (NOT OPUS_FIXED_POINT)
  add_sources_group(opus silk ${silk_sources_float_avx2})
endif()
if (OPUS_DNN)
  add_sources_group(opus lpcnet ${dnn_sources_avx2})
endif()
target_compile_definitions(opus PRIVATE OPUS_X86_MAY_HAVE_AVX2)
if(MSVC)
  set(AVX2_FLAGS "${AVX2_FLAGS} /arch:AVX2")
else()
  set(AVX2_FLAGS "${AVX2_FLAGS} -mavx2 -mfma -mavx")
endif()
set_source_files_properties(${celt_sources_avx2} PROPERTIES COMPILE_FLAGS ${AVX2_FLAGS})
set_source_files_properties(${silk_sources_avx2} PROPERTIES COMPILE_FLAGS ${AVX2_FLAGS})
if (NOT OPUS_FIXED_POINT)
  set_source_files_properties(${silk_sources_float_avx2} PROPERTIES COMPILE_FLAGS ${AVX2_FLAGS})
endif()
set_source_files_properties(${dnn_sources_avx2} PROPERTIES COMPILE_FLAGS ${AVX2_FLAGS})
```

--------------------------------

### Perform LPCNet Inference

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lpcnet/README.md

Generate audio output from feature files using a trained model checkpoint. Inference is performed on the CPU.

```python
python test_lpcnet.py features.f32 my_output/checkpoints/checkpoint_ep_10.pth out.wav
```

--------------------------------

### Generate Packet Loss Sequence

Source: https://github.com/xiph/opus/blob/main/dnn/torch/lossgen/README.md

Generates a packet loss sequence using a trained model checkpoint. Specify the model checkpoint file, desired loss percentage, and output file name. The --length argument controls the sequence length.

```python
python3 ./test_lossgen.py <checkpoint> <percentage> output.txt --length 10000
```

--------------------------------

### Set Package Properties for Git

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Configures properties for the Git package, marking it as required and providing its type, description, URL, and purpose for version management.

```cmake
set_package_properties(Git
                       PROPERTIES
                       TYPE
                       REQUIRED
                       DESCRIPTION
                       "fast, scalable, distributed revision control system"
                       URL
                       "https://git-scm.com/"
                       PURPOSE
                       "required to set up package version")
```

--------------------------------

### Configure CMake for Windows Visual Studio

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

This command sets up CMake to use the Visual Studio generator for Windows. Specify the desired Visual Studio version and architecture.

```shell
cmake .. -G "Visual Studio 17 2022" -A x64
```

--------------------------------

### PyTorch FARGAN Inference

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Run inference using the PyTorch implementation of FARGAN to synthesize speech from generated features.

```python
python test_fargan.py output_dir/checkpoints/fargan_adv_50.pth test_features.f32 output_speech.pcm
```

--------------------------------

### Adversarial Training of FARGAN

Source: https://github.com/xiph/opus/blob/main/dnn/torch/fargan/README.md

Perform adversarial training for the FARGAN model, building upon the pre-trained checkpoint.

```python
python adv_train_fargan.py out_features.f32 out_speech.pcm output_dir --lr 0.000002 --reg-weight 5 --batch-size 160 --cuda-visible-devices 0 --initial-checkpoint output_dir/checkpoints/fargan_400.pth
```

--------------------------------

### Enable Testing and Build Decode Test

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Enables testing if building static libraries and configures the 'test_opus_decode' executable. This test verifies Opus decoding functionality.

```cmake
if(BUILD_TESTING AND NOT BUILD_SHARED_LIBS)
  enable_testing()

  # tests
  add_executable(test_opus_decode ${test_opus_decode_sources})
  target_include_directories(test_opus_decode
                             PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
  target_link_libraries(test_opus_decode PRIVATE opus)
  target_compile_definitions(test_opus_decode PRIVATE OPUS_BUILD)
  if(OPUS_FIXED_POINT)
    target_compile_definitions(test_opus_decode PRIVATE DISABLE_FLOAT_API)
  endif()
  add_test(NAME test_opus_decode COMMAND ${CMAKE_COMMAND}
           -DTEST_EXECUTABLE=$<TARGET_FILE:test_opus_decode>
           -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME}
           -P "${PROJECT_SOURCE_DIR}/cmake/RunTest.cmake")
endif()
```

--------------------------------

### Plotting LACE Filter Responses

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Generates and saves plots of impulse and frequency responses for LACE filter components. Requires matplotlib and scipy.signal.

```python
fig, axs = plt.subplots(4, 2)
frame_duration=40
fig.set_size_inches(12, 18)
axs[0, 0].set_xlabel('Tap')
axs[0, 0].set_ylabel('Amplitude')
axs[0, 1].set_xlabel('Frequency (kHz)')
axs[0, 1].set_ylabel('Amplitude (dB)')
axs[1, 0].set_xlabel('Tap')
axs[1, 0].set_ylabel('Amplitude')
axs[1, 1].set_xlabel('Frequency (kHz)')
axs[1, 1].set_ylabel('Amplitude (dB)')
axs[2, 0].set_xlabel('Tap')
axs[2, 0].set_ylabel('Amplitude')
axs[2, 1].set_xlabel('Frequency (kHz)')
axs[2, 1].set_ylabel('Amplitude (dB)')
axs[3, 0].set_xlabel('Tap')
axs[3, 0].set_ylabel('Amplitude')
axs[3, 1].set_xlabel('Frequency (kHz)')
axs[3, 1].set_ylabel('Amplitude (dB)')
fig.tight_layout()
i=10*80

f = impz_comb1[i]
w, h = scipy.signal.freqz(f, fs=16000)
axs[0, 0].plot(f, 'b')
axs[0, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')

f = impz_comb2[i]
w, h = scipy.signal.freqz(f, fs=16000)
axs[1, 0].plot(f, 'b')
axs[1, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')

f = impz_conv1[i]
w, h = scipy.signal.freqz(f, fs=16000)
axs[2, 0].plot(f, 'b')
axs[2, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')

f = np.convolve(np.convolve(impz_comb1[i], impz_comb2[i], mode='full'), impz_conv1[i])
w, h = scipy.signal.freqz(f, fs=16000)
axs[3, 0].plot(f, 'b')
axs[3, 1].plot(w/1000, 10 * np.log10(np.abs(h)), 'b')

fig.savefig('plots/lace_snapshot_unvoiced.png')
```

--------------------------------

### Configure Matplotlib

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/postfilter.ipynb

Sets Matplotlib parameters for using LaTeX for text rendering and specifies the font family and size for plots.

```python
plt.rcParams.update({
    "text.usetex": True,
    "font.family": "Helvetica",
    "font.size": 20
})

```

--------------------------------

### Link Libraries for Opus

Source: https://github.com/xiph/opus/blob/main/CMakeLists.txt

Links required libraries to the 'opus' target.

```cmake
target_link_libraries(opus PRIVATE ${OPUS_REQUIRED_LIBRARIES})
```

--------------------------------

### Run Opus Tests on Windows with CTest

Source: https://github.com/xiph/opus/blob/main/cmake/README.md

On Windows, specify the configuration (e.g., Debug) when running tests with CTest.

```shell
ctest -C Debug
```

--------------------------------

### Read Data for Adaptive Filters

Source: https://github.com/xiph/opus/blob/main/dnn/torch/osce/stndrd/presentation/lace_demo.ipynb

Reads data from a specified directory using the 'read_data' function, likely containing parameters for adaptive filters.

```python
data = read_data('lace_demo/endoscopy/')

```
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