commit 31b247704ec9b04cb2b3eda2ca92896cf66bb8ce
Author: Keith <kbloemer@aegistg.com>
Date: Fri, 4 Dec 2020 11:14:19 -0600
first commit
Diffstat:
11 files changed, 1385 insertions(+), 0 deletions(-)
diff --git a/LICENSE.txt b/LICENSE.txt
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+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ <one line to give the program's name and a brief idea of what it does.>
+ Copyright (C) <year> <name of author>
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ <program> Copyright (C) <year> <name of author>
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
diff --git a/ReadMe.txt b/ReadMe.txt
@@ -0,0 +1,75 @@
+# GuitarLSTM
+
+GuitarLSTM trains guitar effect/amp neural network models for processing
+on wav data. In comparison to the WaveNet model from PedalNetRT, this
+implementation is much faster and better suited for copying the sound
+of guitar amps and pedals.
+
+
+## Info
+Re-creation of LSTM model from [Real-Time Guitar Amplifier Emulation with Deep
+Learning](https://www.mdpi.com/2076-3417/10/3/766/htm)
+
+
+For a great explanation of how LSTMs works, check out this blog post:<br>
+https://colah.github.io/posts/2015-08-Understanding-LSTMs/
+
+
+## Data
+
+`data/ts9_test1_in_FP32.wav` - Playing from a Fender Telecaster, bridge pickup, max tone and volume<br>
+`data/ts9_test1_out_FP32.wav` - Split with JHS Buffer Splitter to Ibanez TS9 Tube Screamer
+(max drive, mid tone and volume).<br>
+`models/ts9_model.h5` - Pretrained model weights
+
+
+## Usage
+
+**Run effect on .wav file**:
+Must be single channel, 44.1 kHz, FP32 wav data (not int16)
+```bash
+# This will preprocess the input data, perform training, and generate test wavs and analysis plots.
+# Output will be saved to "models/out_model_name" folder.
+python train.py data\ts9_test1_in_FP32.wav data\ts9_test1_out_FP32.wav out_model_name
+
+# Run prediction on target wav file
+# Specify input file, desired output file, and model path
+predict.py data\ts9_test1_in_FP32.wav output models\ts9_model.h5
+```
+
+
+## Training Info
+
+Helpful tips on training models:
+1. Wav files should be 3 - 4 minutes long, and contain a variety of
+ chords, individual notes, and playing techniques to get a full spectrum
+ of data for the model to "learn" from.
+2. A buffer splitter was used with pedals to obtain a pure guitar signal
+ and post effect signal.
+3. Obtaining sample data from an amp can be done by splitting off the original
+ signal, with the post amp signal coming from a microphone (I used a SM57).
+ Keep in mind that this captures the dynamic response of the mic and cabinet.
+ In the original research the sound was captured directly from within the amp
+ circuit to have a "pure" amp signal.
+4. Generally speaking, the more distorted the effect/amp, the more difficult it
+ is to train.
+5. Requires float32 .wav files for training (as opposed to int16).
+
+
+## Future Work
+
+A real time implementation for use in a guitar plugin is
+currenty in work. This would theoretically perform much faster
+(less cpu usage) than the previous WaveNet model. If you want to
+use deep learning models through a real time guitar plugin,
+reference the following repositories:
+
+PedalNetRT<br>
+https://github.com/GuitarML/PedalNetRT<br>
+
+SmartGuitarAmp<br>
+https://github.com/GuitarML/SmartGuitarAmp<br>
+
+
+Note: See Google Colab notebook (guitar_lstm_colab.ipynb) for training
+ GuitarLSTM models in the cloud. See notebook comments for instructions.
+\ No newline at end of file
diff --git a/data/ts9_test1_in_FP32.wav b/data/ts9_test1_in_FP32.wav
Binary files differ.
diff --git a/data/ts9_test1_out_FP32.wav b/data/ts9_test1_out_FP32.wav
Binary files differ.
diff --git a/figures/signal_chain_amp.png b/figures/signal_chain_amp.png
Binary files differ.
diff --git a/figures/signal_chain_pedal.png b/figures/signal_chain_pedal.png
Binary files differ.
diff --git a/guitar_lstm_colab.ipynb b/guitar_lstm_colab.ipynb
@@ -0,0 +1,224 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "guitar_lstm_colab.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "RF2uyPfxgi8H"
+ },
+ "source": [
+ "# TO USE: \n",
+ "# 1. Upload your input and output wav files to the current directory in Colab\n",
+ "# 2. Edit the USER INPUTS section to point to your wav files, and choose a\n",
+ "# model name, and number of epochs for training. If you experience \n",
+ "# crashing due to low RAM, reduce the \"input_size\" parameter.\n",
+ "# 3. Run each section of code. The trained models and output wav files will be \n",
+ "# added to the \"models\" directory.\n",
+ "#\n",
+ "# Note: Tested on CPU and GPU runtimes.\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import Sequential\n",
+ "from tensorflow.keras.layers import LSTM, Conv1D, Dense\n",
+ "from tensorflow.keras.optimizers import Adam\n",
+ "from tensorflow.keras.backend import clear_session\n",
+ "from tensorflow.keras.activations import tanh, elu, relu\n",
+ "from tensorflow.keras.models import load_model\n",
+ "import tensorflow.keras.backend as K\n",
+ "from tensorflow.keras.utils import Sequence\n",
+ "\n",
+ "import os\n",
+ "from scipy import signal\n",
+ "from scipy.io import wavfile\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import math\n",
+ "import h5py\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U22mDBe4jaf2"
+ },
+ "source": [
+ "# EDIT THIS SECTION FOR USER INPUTS\n",
+ "#\n",
+ "name = 'test'\n",
+ "in_file = 'ts9_test1_in_FP32.wav'\n",
+ "out_file = 'ts9_test1_out_FP32.wav'\n",
+ "epochs = 1\n",
+ "\n",
+ "\n",
+ "train_mode = 0 # 0 = speed training, \n",
+ " # 1 = accuracy training \n",
+ " # 2 = extended training\n",
+ "\n",
+ "input_size = 75 # !!!IMPORTANT !!!: The input_size is set at 75 for Colab notebook. \n",
+ " # a higher setting may result in crashing due to\n",
+ " # memory limitation of 8GB for the free version\n",
+ " # of Colab. This setting limits the accuracy of\n",
+ " # the training, especially for complex guitar signals\n",
+ " # such as high distortion.\n",
+ " # \n",
+ " # !!!IMPORTANT!!!: You will most likely need to cycle the runtime to \n",
+ " # free up RAM between training sessions.\n",
+ " #\n",
+ " # Future dev note: Using a custom dataloader may be a good\n",
+ " # workaround for this limitation, at the cost\n",
+ " # of slower training.\n",
+ "\n",
+ "if not os.path.exists('models/'+name):\n",
+ " os.makedirs('models/'+name)\n",
+ "else:\n",
+ " print(\"A model with the same name already exists. Please choose a new name.\")\n",
+ " exit\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WqI-cGt1jaG2"
+ },
+ "source": [
+ "\n",
+ "def pre_emphasis_filter(x, coeff=0.95):\n",
+ " return tf.concat([x, x - coeff * x], 1)\n",
+ " \n",
+ "def error_to_signal(y_true, y_pred): \n",
+ " \"\"\"\n",
+ " Error to signal ratio with pre-emphasis filter:\n",
+ " \"\"\"\n",
+ " y_true, y_pred = pre_emphasis_filter(y_true), pre_emphasis_filter(y_pred)\n",
+ " return K.sum(tf.pow(y_true - y_pred, 2), axis=0) / K.sum(tf.pow(y_true, 2), axis=0) + 1e-10\n",
+ " \n",
+ "def save_wav(name, data):\n",
+ " wavfile.write(name, 44100, data.flatten().astype(np.float32))\n",
+ "\n",
+ "def normalize(data):\n",
+ " data_max = max(data)\n",
+ " data_min = min(data)\n",
+ " data_norm = max(data_max,abs(data_min))\n",
+ " return data / data_norm\n",
+ "\n",
+ "\n",
+ "'''This is a similar Tensorflow/Keras implementation of the LSTM model from the paper:\n",
+ " \"Real-Time Guitar Amplifier Emulation with Deep Learning\"\n",
+ " https://www.mdpi.com/2076-3417/10/3/766/htm\n",
+ "\n",
+ " Uses a stack of two 1-D Convolutional layers, followed by LSTM, followed by \n",
+ " a Dense (fully connected) layer. Three preset training modes are available, \n",
+ " with further customization by editing the code. A Sequential tf.keras model \n",
+ " is implemented here.\n",
+ "\n",
+ " Note: RAM may be a limiting factor for the parameter \"input_size\". The wav data\n",
+ " is preprocessed and stored in RAM, which improves training speed but quickly runs out\n",
+ " if using a large number for \"input_size\". Reduce this if you are experiencing\n",
+ " RAM issues. \n",
+ " \n",
+ " --training_mode=0 Speed training (default)\n",
+ " --training_mode=1 Accuracy training\n",
+ " --training_mode=2 Extended training (set max_epochs as desired, for example 50+)\n",
+ "'''\n",
+ "\n",
+ "batch_size = 4096 \n",
+ "test_size = 0.2\n",
+ "\n",
+ "if train_mode == 0: # Speed Training\n",
+ " learning_rate = 0.01 \n",
+ " conv1d_strides = 12 \n",
+ " conv1d_filters = 16\n",
+ " hidden_units = 36\n",
+ "elif train_mode == 1: # Accuracy Training (~10x longer than Speed Training)\n",
+ " learning_rate = 0.01 \n",
+ " conv1d_strides = 4\n",
+ " conv1d_filters = 36\n",
+ " hidden_units= 64\n",
+ "else: # Extended Training (~60x longer than Accuracy Training)\n",
+ " learning_rate = 0.0005 \n",
+ " conv1d_strides = 3\n",
+ " conv1d_filters = 36\n",
+ " hidden_units= 96\n",
+ "\n",
+ "\n",
+ "# Load and Preprocess Data ###########################################\n",
+ "in_rate, in_data = wavfile.read(in_file)\n",
+ "out_rate, out_data = wavfile.read(out_file)\n",
+ "\n",
+ "X = in_data.astype(np.float32).flatten() \n",
+ "X = normalize(X).reshape(len(X),1) \n",
+ "y = out_data.astype(np.float32).flatten() \n",
+ "y = normalize(y).reshape(len(y),1) \n",
+ "\n",
+ "y_ordered = y[input_size-1:] \n",
+ "\n",
+ "indices = np.arange(input_size) + np.arange(len(X)-input_size+1)[:,np.newaxis] \n",
+ "X_ordered = tf.gather(X,indices) \n",
+ "\n",
+ "shuffled_indices = np.random.permutation(len(X_ordered)) \n",
+ "X_random = tf.gather(X_ordered,shuffled_indices)\n",
+ "y_random = tf.gather(y_ordered, shuffled_indices)\n",
+ "\n",
+ "# Create Sequential Model ###########################################\n",
+ "clear_session()\n",
+ "model = Sequential()\n",
+ "model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same',input_shape=(input_size,1)))\n",
+ "model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same'))\n",
+ "model.add(LSTM(hidden_units))\n",
+ "model.add(Dense(1, activation=None))\n",
+ "model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])\n",
+ "print(model.summary())\n",
+ "\n",
+ "# Train Model ###################################################\n",
+ "model.fit(X_random,y_random, epochs=epochs, batch_size=batch_size, validation_split=test_size) \n",
+ "\n",
+ "model.save('models/'+name+'/'+name+'.h5')\n",
+ "\n",
+ "#model.save('model_data/')\n",
+ "#model = load_model('new_model_'+name+'.h5', custom_objects={'error_to_signal' : error_to_signal})\n",
+ "#learning_rate = 0.005\n",
+ "#model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])\n",
+ "\n",
+ "# Run Prediction #################################################\n",
+ "print(\"Running prediction..\")\n",
+ "y_the_rest, y_last_part = np.split(y_ordered, [int(len(y_ordered)*.8)])\n",
+ "x_the_rest, x_last_part = np.split(X, [int(len(X)*.8)])\n",
+ "\n",
+ "x_the_rest, x_ordered_last_part = np.split(X_ordered, [int(len(X_ordered)*.8)])\n",
+ "prediction = model.predict(x_ordered_last_part, batch_size=batch_size)\n",
+ "\n",
+ "save_wav('models/'+name+'/y_pred.wav', prediction)\n",
+ "save_wav('models/'+name+'/x_test.wav', x_last_part)\n",
+ "save_wav('models/'+name+'/y_test.wav', y_last_part)\n",
+ "\n",
+ "# Add additional data to the saved model (like input_size)\n",
+ "filename = 'models/'+name+'/'+name+'.h5'\n",
+ "f = h5py.File(filename, 'a')\n",
+ "grp = f.create_group(\"info\")\n",
+ "dset = grp.create_dataset(\"input_size\", (1,), dtype='int16')\n",
+ "dset[0] = input_size\n",
+ "f.close()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
+\ No newline at end of file
diff --git a/models/ts9_model.h5 b/models/ts9_model.h5
Binary files differ.
diff --git a/plot.py b/plot.py
@@ -0,0 +1,159 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+# import wave
+from scipy.io import wavfile
+import sys
+from scipy import signal
+import argparse
+
+import struct
+
+
+def error_to_signal(y, y_pred, use_filter=1):
+ """
+ Error to signal ratio with pre-emphasis filter:
+ https://www.mdpi.com/2076-3417/10/3/766/htm
+ """
+ if use_filter == 1:
+ y, y_pred = pre_emphasis_filter(y), pre_emphasis_filter(y_pred)
+ return np.sum(np.power(y - y_pred, 2)) / (np.sum(np.power(y, 2) + 1e-10))
+
+
+def pre_emphasis_filter(x, coeff=0.95):
+ return np.concatenate([x, np.subtract(x, np.multiply(x, coeff))])
+
+
+def read_wave(wav_file):
+ # Extract Audio and framerate from Wav File
+ fs, signal = wavfile.read(wav_file)
+ return signal, fs
+
+
+def analyze_pred_vs_actual(args):
+ """Generate plots to analyze the predicted signal vs the actual
+ signal.
+
+ Inputs:
+ output_wav : The actual signal, by default will use y_test.wav from the test.py output
+ pred_wav : The predicted signal, by default will use y_pred.wav from the test.py output
+ input_wav : The pre effect signal, by default will use x_test.wav from the test.py output
+ model_name : Used to add the model name to the plot .png filename
+ path : The save path for generated .png figures
+ show_plots : Default is 1 to show plots, 0 to only generate .png files and suppress plots
+
+ 1. Plots the two signals
+ 2. Calculates Error to signal ratio the same way Pedalnet evauluates the model for training
+ 3. Plots the absolute value of pred_signal - actual_signal (to visualize abs error over time)
+ 4. Plots the spectrogram of (pred_signal - actual signal)
+ The idea here is to show problem frequencies from the model training
+ """
+ try:
+ output_wav = args.path + '/' + args.output_wav
+ pred_wav = args.path + '/' + args.pred_wav
+ input_wav = args.path + '/' + args.input_wav
+ model_name = args.model_name
+ show_plots = args.show_plots
+ path = args.path
+ except:
+ output_wav = args['output_wav']
+ pred_wav = args['pred_wav']
+ input_wav = args['input_wav']
+ model_name = args['model_name']
+ show_plots = args['show_plots']
+ path = args['path']
+
+ # Read the input wav file
+ signal3, fs3 = read_wave(input_wav)
+
+ # Read the output wav file
+ signal1, fs = read_wave(output_wav)
+
+ Time = np.linspace(0, len(signal1) / fs, num=len(signal1))
+ fig, (ax3, ax1, ax2) = plt.subplots(3, sharex=True, figsize=(13, 8))
+ fig.suptitle("Predicted vs Actual Signal")
+ ax1.plot(Time, signal1, label=output_wav, color="red")
+
+ # Read the predicted wav file
+ signal2, fs2 = read_wave(pred_wav)
+
+ Time2 = np.linspace(0, len(signal2) / fs2, num=len(signal2))
+ ax1.plot(Time2, signal2, label=pred_wav, color="green")
+ ax1.legend(loc="upper right")
+ ax1.set_xlabel("Time (s)")
+ ax1.set_ylabel("Amplitude")
+ ax1.set_title("Wav File Comparison")
+ ax1.grid("on")
+
+ error_list = []
+ for s1, s2 in zip(signal1, signal2):
+ error_list.append(abs(s2 - s1))
+
+ # Calculate error to signal ratio with pre-emphasis filter as
+ # used to train the model
+ e2s = error_to_signal(signal1, signal2)
+ e2s_no_filter = error_to_signal(signal1, signal2, use_filter=0)
+ print("Error to signal (with pre-emphasis filter): ", e2s)
+ print("Error to signal (no pre-emphasis filter): ", e2s_no_filter)
+ fig.suptitle("Predicted vs Actual Signal (error to signal: " + str(round(e2s, 4)) + ")")
+ # Plot signal difference
+ signal_diff = signal2 - signal1
+ ax2.plot(Time2, error_list, label="signal diff", color="blue")
+ ax2.set_xlabel("Time (s)")
+ ax2.set_ylabel("Amplitude")
+ ax2.set_title("abs(pred_signal-actual_signal)")
+ ax2.grid("on")
+
+ # Plot the original signal
+ Time3 = np.linspace(0, len(signal3) / fs3, num=len(signal3))
+ ax3.plot(Time3, signal3, label=input_wav, color="purple")
+ ax3.legend(loc="upper right")
+ ax3.set_xlabel("Time (s)")
+ ax3.set_ylabel("Amplitude")
+ ax3.set_title("Original Input")
+ ax3.grid("on")
+
+ # Save the plot
+ plt.savefig(path+'/'+model_name + "_signal_comparison_e2s_" + str(round(e2s, 4)) + ".png", bbox_inches="tight")
+
+ # Create a zoomed in plot of 0.01 seconds centered at the max input signal value
+ sig_temp = signal1.tolist()
+ plt.axis(
+ [
+ Time3[sig_temp.index((max(sig_temp)))] - 0.005,
+ Time3[sig_temp.index((max(sig_temp)))] + 0.005,
+ min(signal2),
+ max(signal2),
+ ]
+ )
+ plt.savefig(path+'/'+model_name + "_Detail_signal_comparison_e2s_" + str(round(e2s, 4)) + ".png", bbox_inches="tight")
+
+ # Reset the axis
+ plt.axis([0, Time3[-1], min(signal2), max(signal2)])
+
+ # Plot spectrogram difference
+ # plt.figure(figsize=(12, 8))
+ # print("Creating spectrogram data..")
+ # frequencies, times, spectrogram = signal.spectrogram(signal_diff, 44100)
+ # plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram))
+ # plt.colorbar()
+ # plt.title("Diff Spectrogram")
+ # plt.ylabel("Frequency [Hz]")
+ # plt.xlabel("Time [sec]")
+ # plt.savefig(path+'/'+model_name + "_diff_spectrogram.png", bbox_inches="tight")
+
+ if show_plots == 1:
+ plt.show()
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ #parser.add_argument("--path", default=".")
+ parser.add_argument("--output_wav", default="y_test.wav")
+ parser.add_argument("--pred_wav", default="y_pred.wav")
+ parser.add_argument("--input_wav", default="x_test.wav")
+ parser.add_argument("--model_name", default="plot")
+ parser.add_argument("--path", default="")
+ parser.add_argument("--show_plots", default=1)
+ args = parser.parse_args()
+ analyze_pred_vs_actual(args)
diff --git a/predict.py b/predict.py
@@ -0,0 +1,76 @@
+
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import tensorflow.keras.backend as K
+from tensorflow.keras.optimizers import Adam
+
+import matplotlib.pyplot as plt
+import os
+from scipy import signal
+from scipy.io import wavfile
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import h5py
+import argparse
+
+
+def save_wav(name, data):
+ if name.endswith('.wav') == False:
+ name = name + '.wav'
+ wavfile.write(name, 44100, data.flatten().astype(np.float32))
+ print("Predicted wav file generated: "+name)
+
+def pre_emphasis_filter(x, coeff=0.95):
+ return tf.concat([x, x - coeff * x], 1)
+
+def error_to_signal(y_true, y_pred):
+ y_true, y_pred = pre_emphasis_filter(y_true), pre_emphasis_filter(y_pred)
+ return K.sum(tf.pow(y_true - y_pred, 2), axis=0) / K.sum(tf.pow(y_true, 2), axis=0) + 1e-10
+
+def normalize(data):
+ data_max = max(data)
+ data_min = min(data)
+ data_norm = max(data_max,abs(data_min))
+ return data / data_norm
+
+def predict(args):
+ '''
+ Predicts the output wav given an input wav file, trained GuitarLSTM model,
+ and output wav filename.
+ '''
+ # Read the input_size from the .h5 model file
+ f = h5py.File(args.model, 'a')
+ input_size = f["info"]["input_size"][0]
+ f.close()
+ input_size = 180
+ learning_rate = 0.01
+ # Load model from .h5 model file
+ name = args.out_filename
+ model = load_model(args.model, custom_objects={'error_to_signal' : error_to_signal})
+ model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])
+
+ # Load and Preprocess Data
+ print("Processing input wav..")
+ in_rate, in_data = wavfile.read(args.in_file)
+
+ X = in_data.astype(np.float32).flatten()
+ X = normalize(X).reshape(len(X),1)
+
+ indices = np.arange(input_size) + np.arange(len(X)-input_size+1)[:,np.newaxis]
+ X_ordered = tf.gather(X,indices)
+
+ # Run prediction and save output audio as a wav file
+ print("Running prediction..")
+ prediction = model.predict(X_ordered, batch_size=args.batch_size)
+ save_wav(name, prediction)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("in_file")
+ parser.add_argument("out_filename")
+ parser.add_argument("model")
+ parser.add_argument("--train_data", default="data.pickle")
+ parser.add_argument("--batch_size", type=int, default=4096)
+ args = parser.parse_args()
+ predict(args)
diff --git a/train.py b/train.py
@@ -0,0 +1,175 @@
+import tensorflow as tf
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers import LSTM, Conv1D, Dense
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.backend import clear_session
+from tensorflow.keras.activations import tanh, elu, relu
+from tensorflow.keras.models import load_model
+import tensorflow.keras.backend as K
+from tensorflow.keras.utils import Sequence
+
+import os
+from scipy import signal
+from scipy.io import wavfile
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import h5py
+import argparse
+
+
+def pre_emphasis_filter(x, coeff=0.95):
+ return tf.concat([x, x - coeff * x], 1)
+
+def error_to_signal(y_true, y_pred):
+ """
+ Error to signal ratio with pre-emphasis filter:
+ """
+ y_true, y_pred = pre_emphasis_filter(y_true), pre_emphasis_filter(y_pred)
+ return K.sum(tf.pow(y_true - y_pred, 2), axis=0) / K.sum(tf.pow(y_true, 2), axis=0) + 1e-10
+
+def save_wav(name, data):
+ wavfile.write(name, 44100, data.flatten().astype(np.float32))
+
+def normalize(data):
+ data_max = max(data)
+ data_min = min(data)
+ data_norm = max(data_max,abs(data_min))
+ return data / data_norm
+
+def main(args):
+ '''Ths is a similar Tensorflow/Keras implementation of the LSTM model from the paper:
+ "Real-Time Guitar Amplifier Emulation with Deep Learning"
+ https://www.mdpi.com/2076-3417/10/3/766/htm
+
+ Uses a stack of two 1-D Convolutional layers, followed by LSTM, followed by
+ a Dense (fully connected) layer. Three preset training modes are available,
+ with further customization by editing the code. A Sequential tf.keras model
+ is implemented here.
+
+ Note: RAM may be a limiting factor for the parameter "input_size". The wav data
+ is preprocessed and stored in RAM, which improves training speed but quickly runs out
+ if using a large number for "input_size". Reduce this if you are experiencing
+ RAM issues.
+
+ --training_mode=0 Speed training (default)
+ --training_mode=1 Accuracy training
+ --training_mode=2 Extended training (set max_epochs as desired, for example 50+)
+ '''
+
+ name = args.name
+ if not os.path.exists('models/'+name):
+ os.makedirs('models/'+name)
+ else:
+ print("A model with the same name already exists. Please choose a new name.")
+ return
+
+ train_mode = args.training_mode # 0 = speed training,
+ # 1 = accuracy training
+ # 2 = extended training
+ batch_size = args.batch_size
+ test_size = 0.2
+ epochs = args.max_epochs
+ if train_mode == 0: # Speed Training
+ learning_rate = 0.01
+ conv1d_strides = 12
+ conv1d_filters = 16
+ hidden_units = 36
+ input_size = 180
+ elif train_mode == 1: # Accuracy Training (~10x longer than Speed Training)
+ learning_rate = 0.01
+ conv1d_strides = 4
+ conv1d_filters = 36
+ hidden_units= 64
+ input_size = 300
+ else: # Extended Training (~60x longer than Accuracy Training)
+ learning_rate = 0.0005
+ conv1d_strides = 3
+ conv1d_filters = 36
+ hidden_units= 96
+ input_size = 500
+
+
+ # Load and Preprocess Data ###########################################
+ in_rate, in_data = wavfile.read(args.in_file)
+ out_rate, out_data = wavfile.read(args.out_file)
+
+ X = in_data.astype(np.float32).flatten()
+ X = normalize(X).reshape(len(X),1)
+ y = out_data.astype(np.float32).flatten()
+ y = normalize(y).reshape(len(y),1)
+
+ y_ordered = y[input_size-1:]
+
+ indices = np.arange(input_size) + np.arange(len(X)-input_size+1)[:,np.newaxis]
+ X_ordered = tf.gather(X,indices)
+
+ shuffled_indices = np.random.permutation(len(X_ordered))
+ X_random = tf.gather(X_ordered,shuffled_indices)
+ y_random = tf.gather(y_ordered, shuffled_indices)
+
+ # Create Sequential Model ###########################################
+ clear_session()
+ model = Sequential()
+ model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same',input_shape=(input_size,1)))
+ model.add(Conv1D(conv1d_filters, 12,strides=conv1d_strides, activation=None, padding='same'))
+ model.add(LSTM(hidden_units))
+ model.add(Dense(1, activation=None))
+ model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])
+ print(model.summary())
+
+ # Train Model ###################################################
+ model.fit(X_random,y_random, epochs=epochs, batch_size=batch_size, validation_split=test_size)
+
+ model.save('models/'+name+'/'+name+'.h5')
+
+ #model.save('model_data/')
+ #model = load_model('new_model_'+name+'.h5', custom_objects={'error_to_signal' : error_to_signal})
+ #learning_rate = 0.005
+ #model.compile(optimizer=Adam(learning_rate=learning_rate), loss=error_to_signal, metrics=[error_to_signal])
+
+ # Run Prediction #################################################
+ print("Running prediction..")
+ y_the_rest, y_last_part = np.split(y_ordered, [int(len(y_ordered)*.8)])
+ x_the_rest, x_last_part = np.split(X, [int(len(X)*.8)])
+
+ x_the_rest, x_ordered_last_part = np.split(X_ordered, [int(len(X_ordered)*.8)])
+ prediction = model.predict(x_ordered_last_part, batch_size=batch_size)
+
+ save_wav('models/'+name+'/y_pred.wav', prediction)
+ save_wav('models/'+name+'/x_test.wav', x_last_part)
+ save_wav('models/'+name+'/y_test.wav', y_last_part)
+
+ # Add additional data to the saved model (like input_size)
+ filename = 'models/'+name+'/'+name+'.h5'
+ f = h5py.File(filename, 'a')
+ grp = f.create_group("info")
+ dset = grp.create_dataset("input_size", (1,), dtype='int16')
+ dset[0] = input_size
+ f.close()
+
+ # Create Analysis Plots ###########################################
+ if args.create_plots == 1:
+ print("Plotting results..")
+ import plot
+
+ plot.analyze_pred_vs_actual({ 'output_wav':'models/'+name+'/y_test.wav',
+ 'pred_wav':'models/'+name+'/y_pred.wav',
+ 'input_wav':'models/'+name+'/x_test.wav',
+ 'model_name':name,
+ 'show_plots':1,
+ 'path':'models/'+name
+ })
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+
+ parser.add_argument("in_file")
+ parser.add_argument("out_file")
+ parser.add_argument("name")
+ parser.add_argument("--training_mode", type=int, default=0)
+ parser.add_argument("--batch_size", type=int, default=4096)
+ parser.add_argument("--max_epochs", type=int, default=1)
+ parser.add_argument("--create_plots", type=int, default=1)
+ args = parser.parse_args()
+ main(args)
