├── .gitignore ├── neuralseed.jpg ├── .gitmodules ├── NeuralSeed ├── Makefile ├── README.md ├── neuralseed.cpp └── all_model_data.h ├── .github └── workflows │ └── cpp.yml ├── LICENSE ├── models ├── gru4_ts9_pytorch.json ├── lstm4_ts9_pytorch.json ├── ts9_gru7_gain10_loss003_TEST.json ├── gru8_ts9_pytorch.json └── lstm8_ts9_pytorch.json ├── scripts ├── Parameterization-Config-2-dist.json ├── convert_json_to_c_header_gru.py └── Parameterization-Config-3-mi.json └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | build 2 | *.bin 3 | -------------------------------------------------------------------------------- /neuralseed.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GuitarML/NeuralSeed/HEAD/neuralseed.jpg -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "libDaisy"] 2 | path = libDaisy 3 | url = https://github.com/electro-smith/libDaisy 4 | [submodule "DaisySP"] 5 | path = DaisySP 6 | url = https://github.com/electro-smith/DaisySP 7 | [submodule "Terrarium"] 8 | path = Terrarium 9 | url = https://github.com/PedalPCB/Terrarium 10 | [submodule "RTNeural"] 11 | path = RTNeural 12 | url = https://github.com/jatinchowdhury18/RTNeural.git 13 | -------------------------------------------------------------------------------- /NeuralSeed/Makefile: -------------------------------------------------------------------------------- 1 | # Project Name 2 | TARGET = neuralseed 3 | 4 | # APP_TYPE = BOOT_SRAM 5 | 6 | # Sources 7 | CPP_SOURCES = neuralseed.cpp 8 | 9 | # Library Locations 10 | LIBDAISY_DIR = ../libDaisy 11 | DAISYSP_DIR = ../DaisySP 12 | 13 | # Core location, and generic Makefile. 14 | SYSTEM_FILES_DIR = $(LIBDAISY_DIR)/core 15 | include $(SYSTEM_FILES_DIR)/Makefile 16 | 17 | # Include terrarium.h 18 | C_INCLUDES += -I../Terrarium -I../RTNeural 19 | CPPFLAGS += -DRTNEURAL_DEFAULT_ALIGNMENT=8 -DRTNEURAL_NO_DEBUG=1 20 | -------------------------------------------------------------------------------- /.github/workflows/cpp.yml: -------------------------------------------------------------------------------- 1 | name: CI 2 | 3 | on: 4 | push: 5 | branches: [ main ] 6 | pull_request: 7 | branches: [ main ] 8 | 9 | jobs: 10 | build: 11 | 12 | runs-on: ubuntu-latest 13 | env: 14 | PEDAL_NAME: your_pedal 15 | 16 | steps: 17 | - name: Checkout repository 18 | uses: actions/checkout@v2 19 | with: 20 | submodules: 'recursive' 21 | 22 | - name: Install arm-none-eabi-gcc toolchain 23 | uses: fiam/arm-none-eabi-gcc@v1 24 | with: 25 | release: '9-2019-q4' 26 | 27 | - name: Build Daisy libraries 28 | run: | 29 | make -C libDaisy 30 | make -C DaisySP 31 | 32 | - name: Build binary for pedal 33 | run: | 34 | make -C $PEDAL_NAME 35 | 36 | - name: Upload binary for terrarium 37 | uses: actions/upload-artifact@v2.2.1 38 | with: 39 | name: Binary for terrarium 40 | # A file, directory or wildcard pattern that describes what to upload 41 | path: ${{ env.PEDAL_NAME }}/build/${{ env.PEDAL_NAME }}.bin 42 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Felix Wiegand 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /NeuralSeed/README.md: -------------------------------------------------------------------------------- 1 | # NeuralSeed 2 | ## Author 3 | 4 | Keith Bloemer (GuitarML) 5 | 6 | ## Description 7 | 8 | A description for your pedal for the [terrarium](https://www.pedalpcb.com/product/pcb351/) from [PedalPCB](https://www.pedalpcb.com). 9 | 10 | # Control 11 | 12 | | Control | Description | Comment | 13 | | --- | --- | --- | 14 | | Ctrl 1 | Input Level | Adjusts the input level to the neural net model (0 to 3x volume) | 15 | | Ctrl 2 | Mix | Set the dry/wet amount (full left for dry, full right for only neural net output | 16 | | Ctrl 3 | Volume | Sets the output volume of the pedal | 17 | | Ctrl 4 | Neural Param 1 | Adjusts the first model parameter, such as gain/tone (only applies to 1-conditioned models) | 18 | | Ctrl 5 | Neural Param 2 | Adjusts the second model parameter, such as gain/tone (only applies to 2-conditioned models) | 19 | | Ctrl 6 | Neural Param 3 | Adjusts the third model parameter, such as gain/tone (only applies to 3-conditioned models) | 20 | | SW 1 - 4 | 4Band EQ Boost | Bass, Low Mid, High Mid, Treble Boost (Band Pass) | 21 | | FS 1 | Bypass/Active | Bypass / effect engaged | 22 | | FS 2 | Cycle Neural Model | Loads the next available Neural Model, starts at beginning after the last in the list (i.e. next pedal/amp) | 23 | | LED 1 | Bypass/Active Indicator |Illuminated when effect is set to Active | 24 | | LED 2 | Brightness based on selected model parameterization | Off = Snapshot, Dim = 1 Knob, Brighter = 2 Knobs, Brightest = 3 Knobs | 25 | | Audio In 1 | Audio input | Mono only for Terrarium | 26 | | Audio Out 1 | Mix Out | Mono only for Terrarium | -------------------------------------------------------------------------------- /models/gru4_ts9_pytorch.json: -------------------------------------------------------------------------------- 1 | {"model_data": {"model": "SimpleRNN", "input_size": 1, "skip": 1, "output_size": 1, "unit_type": "GRU", "num_layers": 1, "hidden_size": 4, "bias_fl": true}, "state_dict": {"rec.weight_ih_l0": [[-0.16722558438777924], [0.01975015178322792], [-0.13067777454853058], [-0.36868321895599365], [-0.14056400954723358], [-0.00028827268397435546], [0.09012904763221741], [-0.01467723399400711], [0.1328529715538025], [-1.7829099893569946], [1.2268108129501343], [-3.370378255844116]], "rec.weight_hh_l0": [[0.015003484673798084, -1.6720917224884033, -0.196049302816391, 0.897007167339325], [0.02133222483098507, -3.877852201461792, -2.629592180252075, -1.8613425493240356], [0.0575256422162056, 0.2898749113082886, 0.1637880951166153, 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0.15707488358020782, 0.4005063772201538, -0.024402067065238953, 0.17498262226581573], "rec.bias_hh_l0": [0.5256650447845459, 0.1058599129319191, -0.1444644331932068, 0.5203583240509033, 0.9612132906913757, -1.5589677095413208, 1.400840401649475, -1.3022371530532837, -0.02608598582446575, -0.3783565163612366, -0.08976533263921738, -0.29260292649269104], "lin.weight": [[0.3130643367767334, 0.2206755131483078, -1.0869636535644531, 0.298419326543808]], "lin.bias": [0.05116382986307144]}} -------------------------------------------------------------------------------- /models/lstm4_ts9_pytorch.json: -------------------------------------------------------------------------------- 1 | {"model_data": {"model": "SimpleRNN", "input_size": 1, "skip": 1, "output_size": 1, "unit_type": "LSTM", "num_layers": 1, "hidden_size": 4, "bias_fl": true}, "state_dict": {"rec.weight_ih_l0": [[-0.40859732031822205], [-0.2994958162307739], [-1.205723762512207], [0.004180729389190674], [0.3240227997303009], [-0.15424445271492004], [-1.603854775428772], [0.2632805109024048], [0.4495038688182831], [-1.1334184408187866], [-0.8224048018455505], [5.223653316497803], [1.6121869087219238], [-0.679532527923584], [-0.25211164355278015], [0.10690157115459442]], "rec.weight_hh_l0": [[0.04963681846857071, -0.25277504324913025, 0.10926760733127594, 0.7901617288589478], [-0.7565931081771851, -0.9312542676925659, -1.1180447340011597, 0.4560006558895111], [-0.9195056557655334, 0.7695092558860779, -0.8108106851577759, 1.443317174911499], [0.8282002806663513, 0.6504446864128113, -0.5704371929168701, -1.5406476259231567], [0.759588360786438, 0.3715253174304962, 0.5797180533409119, -0.23294642567634583], [1.0382938385009766, 0.3147668242454529, 0.440157026052475, -0.2740095853805542], [0.33401772379875183, 0.06152934208512306, -0.18892185389995575, -0.27787354588508606], [-1.0321886539459229, 0.3888491690158844, 0.1165250763297081, 0.41261026263237], [0.23236501216888428, 0.4998260736465454, 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"rec.bias_hh_l0": [-0.19129574298858643, -0.4464627206325531, 0.47541600465774536, 1.2613887786865234, 0.5516027808189392, 0.9437131881713867, 0.34322988986968994, -0.39146074652671814, -0.16890326142311096, 0.17396381497383118, 0.3726302683353424, -0.2608138918876648, 0.4486640393733978, 0.45024561882019043, 1.2887099981307983, 1.019371509552002], "lin.weight": [[-1.0720735788345337, 1.2480158805847168, 0.2723093628883362, -0.27655816078186035]], "lin.bias": [0.15313947200775146]}} -------------------------------------------------------------------------------- /scripts/Parameterization-Config-2-dist.json: -------------------------------------------------------------------------------- 1 | { 2 | "Number of Parameters":2, 3 | "Data Sets":[ 4 | { 5 | "Parameters": [ 0.0, 0.0 ], 6 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 7 | "TrainingTarget": "../d_t0_d0.wav" 8 | }, 9 | { 10 | "Parameters": [ 0.0, 0.25 ], 11 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 12 | "TrainingTarget": "../d_t25_d0.wav" 13 | }, 14 | { 15 | "Parameters": [ 0.0, 0.50 ], 16 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 17 | "TrainingTarget": "../d_t5_d0.wav" 18 | }, 19 | { 20 | "Parameters": [ 0.0, 0.75 ], 21 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 22 | "TrainingTarget": "../d_t75_d0.wav" 23 | }, 24 | { 25 | "Parameters": [ 0.0, 1.00 ], 26 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 27 | "TrainingTarget": "../d_t10_d0.wav" 28 | }, 29 | { 30 | "Parameters": [ 0.25, 0.0 ], 31 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 32 | "TrainingTarget": "../d_t0_d25.wav" 33 | }, 34 | { 35 | "Parameters": [ 0.25, 0.25 ], 36 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 37 | "TrainingTarget": "../d_t25_d25.wav" 38 | }, 39 | { 40 | "Parameters": [ 0.25, 0.50 ], 41 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 42 | "TrainingTarget": "../d_t5_d25.wav" 43 | }, 44 | { 45 | "Parameters": [ 0.25, 0.75 ], 46 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 47 | "TrainingTarget": "../d_t75_d25.wav" 48 | }, 49 | { 50 | "Parameters": [ 0.25, 1.00 ], 51 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 52 | "TrainingTarget": "../d_t10_d25.wav" 53 | }, 54 | { 55 | "Parameters": [ 0.50, 0.0 ], 56 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 57 | "TrainingTarget": "../d_t0_d5.wav" 58 | }, 59 | { 60 | "Parameters": [ 0.50, 0.25 ], 61 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 62 | "TrainingTarget": "../d_t25_d5.wav" 63 | }, 64 | { 65 | "Parameters": [ 0.50, 0.50 ], 66 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 67 | "TrainingTarget": "../d_t5_d5.wav" 68 | }, 69 | { 70 | "Parameters": [ 0.50, 0.75 ], 71 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 72 | "TrainingTarget": "../d_t75_d5.wav" 73 | }, 74 | { 75 | "Parameters": [ 0.50, 1.00 ], 76 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 77 | "TrainingTarget": "../d_t10_d5.wav" 78 | }, 79 | { 80 | "Parameters": [ 0.75, 0.0 ], 81 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 82 | "TrainingTarget": "../d_t0_d75.wav" 83 | }, 84 | { 85 | "Parameters": [ 0.75, 0.25 ], 86 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 87 | "TrainingTarget": "../d_t25_d75.wav" 88 | }, 89 | { 90 | "Parameters": [ 0.75, 0.50 ], 91 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 92 | "TrainingTarget": "../d_t5_d75.wav" 93 | }, 94 | { 95 | "Parameters": [ 0.75, 0.75 ], 96 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 97 | "TrainingTarget": "../d_t75_d75.wav" 98 | }, 99 | { 100 | "Parameters": [ 0.75, 1.00 ], 101 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 102 | "TrainingTarget": "../d_t10_d75.wav" 103 | }, 104 | { 105 | "Parameters": [ 1.00, 0.0 ], 106 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 107 | "TrainingTarget": "../d_t0_d10.wav" 108 | }, 109 | { 110 | "Parameters": [ 1.00, 0.25 ], 111 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 112 | "TrainingTarget": "../d_t25_d10.wav" 113 | }, 114 | { 115 | "Parameters": [ 1.00, 0.50 ], 116 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 117 | "TrainingTarget": "../d_t5_d10.wav" 118 | }, 119 | { 120 | "Parameters": [ 1.00, 0.75 ], 121 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 122 | "TrainingTarget": "../d_t75_d10.wav" 123 | }, 124 | { 125 | "Parameters": [ 1.00, 1.00 ], 126 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 127 | "TrainingTarget": "../d_t10_d10.wav" 128 | } 129 | ] 130 | } 131 | -------------------------------------------------------------------------------- /scripts/convert_json_to_c_header_gru.py: -------------------------------------------------------------------------------- 1 | #################################################################################### 2 | # This script converts a PyTorch json model (GRU) to a c style header file. 3 | # The purpose is to compress the model data to include in compiled c++ programs. 4 | # 5 | # Tested with GRU models trained from the Automated-GuitarAmpModelling tool. 6 | # 7 | # usage: python convert_json_to_c_header.py 8 | # 9 | # Generates a .h file with the same name as the json model. 10 | # 11 | #################################################################################### 12 | 13 | import json 14 | import argparse 15 | import sys 16 | import numpy as np 17 | 18 | def add_layer(layer_name, layer_data, header_data, size): 19 | """ 20 | Adds layer weights and biases to header_data 21 | Inputs: 22 | layer_name (string) : The name of the layer as labelled by Pytorch (i.e. "rec.weight_ih_l0") 23 | layer_data (list) : Either a 1D or 2D list of weights loaded from json file. 24 | header_data (list) : The cumulative list of lines to write to the header file. 25 | """ 26 | 27 | line = "" 28 | # Use appropriate declaration for 1D or 2D vector 29 | if layer_name == "rec.weight_ih_l0" or layer_name == "rec.weight_hh_l0" or layer_name == "lin.weight": 30 | var_declaration = " Model." + layer_name.replace(".", "_") + " = " 31 | else: 32 | var_declaration = " Model." + layer_name.replace(".", "_") + " = " 33 | line += var_declaration 34 | 35 | line += "{" 36 | c = 0 # Counter to determine last item in list 37 | 38 | # If reading a 2d array 39 | if len(np.asarray(layer_data).shape) > 1: 40 | for i in layer_data: 41 | c += 1 42 | if c == 1: 43 | line += "{" 44 | else: 45 | line += " "*len(var_declaration) + " { " 46 | c2 = 0 # 2nd counter to determine last item in list 47 | # For GRU's, swap rz (needed for RTNeural to convert from pytorch to tensorflow conventions) 48 | i_rzSwap = np.concatenate((i[size:size*2], i[0:size], i[size*2:])) 49 | for j in i_rzSwap: 50 | c2 += 1 51 | if c2 == len(i): 52 | line += str(j) 53 | else: 54 | line += str(j) + ", " 55 | if c == len(layer_data): 56 | line += "}}; " 57 | else: 58 | line += "}, " 59 | header_data.append(line) 60 | line = "" 61 | # Else if reading a 1d array 62 | else: 63 | for i in layer_data: 64 | c += 1 65 | line += str(i) 66 | if c == len(layer_data): 67 | line += "}; " 68 | else: 69 | line += ", " 70 | 71 | header_data.append(line) 72 | 73 | header_data.append("") 74 | 75 | if __name__ == "__main__": 76 | parser = argparse.ArgumentParser() 77 | parser.add_argument("json_model", type=str, default="", help="Path to json file, including filename") 78 | args = parser.parse_args() 79 | 80 | # Enter target json file here 81 | f = open(args.json_model) 82 | data = json.load(f) 83 | 84 | header_data = [] 85 | header_data.append("//========================================================================") 86 | header_data.append("//" + args.json_model.split(".json")[0]) 87 | # Read Model Data from Pytorch Json model to include in .h as comment 88 | header_data.append("/*") 89 | for item in data['model_data'].keys(): 90 | header_data.append(item + " : " + str(data['model_data'][item])) 91 | header_data.append("*/\n") 92 | size = int(data['model_data']['hidden_size']) 93 | 94 | # Read the state dict from Pytorch Json model and reorganize data into c header format 95 | # Sum the rec.bias layers, skip adding individually 96 | for layer_name in data['state_dict'].keys(): 97 | if layer_name.startswith("rec.bias_") == True: 98 | continue 99 | if layer_name == "rec.weight_ih_l0" or layer_name == "rec.weight_hh_l0": 100 | add_layer(layer_name, np.array(data['state_dict'][layer_name]).T, header_data, size) # Transpose 2D arrays (Pytorch->Tensorflow/Keras) 101 | else: 102 | add_layer(layer_name, data['state_dict'][layer_name], header_data, size) 103 | 104 | gru_bias = np.array([np.array(data['state_dict']['rec.bias_ih_l0']), np.array(data['state_dict']['rec.bias_hh_l0'])]) 105 | add_layer('rec_bias', gru_bias, header_data, size) 106 | 107 | # Write data to .h file 108 | new_filename = args.json_model.split(".json")[0] + '.h' 109 | file = open(new_filename,'w') 110 | for item in header_data: 111 | file.write(item+"\n") 112 | file.close() 113 | print("Finished generating header file: "+ new_filename) -------------------------------------------------------------------------------- /models/ts9_gru7_gain10_loss003_TEST.json: -------------------------------------------------------------------------------- 1 | {"model_data": {"model": "SimpleRNN", "input_size": 1, "skip": 1, "output_size": 1, "unit_type": "GRU", "num_layers": 1, "hidden_size": 7, "bias_fl": true}, "state_dict": {"rec.weight_ih_l0": [[-0.14920450747013092], [-0.25961729884147644], [0.12047041952610016], [-0.05256455019116402], [0.036633871495723724], [0.5456557273864746], [0.12307374179363251], [-0.016953421756625175], [0.2610606253147125], [0.1232185810804367], [0.020649343729019165], [-0.022517362609505653], [0.16897769272327423], [0.14818689227104187], [0.2134445309638977], [-0.286651074886322], [0.2537027597427368], [-0.011594315059483051], [1.343133568763733], [0.9051094055175781], [0.1152697205543518]], "rec.weight_hh_l0": [[0.05735047161579132, 0.271841824054718, -0.9227047562599182, 0.13665202260017395, 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[PedalPCB](https://www.pedalpcb.com/product/pcb351/). Models are trained on audio recordings from your amp or pedal, and can be uploaded to the Daisy Seed 5 | as firmware. NeuralSeed includes several built in amp/pedal models, as well as controls for input/output level, wet/dry mix, and up to 3 parameterized knobs 6 | for the selected neural model. Effective for Amps/PreAmps (direct out, no cab), Distortion/Overdrive/Boost pedals (non-time based, no Reverb/Delay/Flange/Phaser). 7 | 8 | View the Demo on [YouTube](https://youtu.be/c3zJRMWr_Ls) 9 | 10 | Read the tech article published in [Towards Data Science](https://medium.com/p/ea9cdad2a29c) 11 | 12 | Download the neuralseed.bin for Daisy Seed from the [Releases](https://github.com/GuitarML/NeuralSeed/releases) page. See release notes for a list of built in amp/pedal models. 13 | 14 | Start training models for NeuralSeed the easy way using the [Colab Script](https://colab.research.google.com/github/GuitarML/Automated-GuitarAmpModelling/blob/ns-capture/ProteusCapture.ipynb)! 15 | 16 | ![app](https://github.com/GuitarML/NeuralSeed/blob/main/neuralseed.jpg) 17 | 18 | The Daisy Seed is a powerful ARM Cortex-M7 powered board intended for audio effects. The Terrarium pcb provides input and output buffers, as well as connections 19 | to the Daisy Seed for up to 6 potentiometer knobs, 4 switches, in/out jacks, and 9v power supply. NeuralSeed is the software developed by GuitarML to run on this hardware. 20 | Total material cost was less than $100 to build a fully capable digital guitar multi-effect. 21 | 22 | For information on compiling NeuralSeed in the Daisy Ecosystem see the [Daisy Wiki](https://github.com/electro-smith/DaisyWiki/wiki). 23 | 24 | ## Technical Info 25 | In comparison to other GuitarML plugins, Neural Seed is very minimal, running only a GRU size 10 (by comparison 26 | [Proteus](https://github.com/GuitarML/Proteus) uses LSTM size 40). Using this size model, may not be able to accurately capture certain devices, especially 27 | high gain amps, but should work decently well for distortion/overdrive pedals and low gain amps (direct out, not 28 | from a microphone). This is due to limited processing power on the M7 microcontroller. The current code processes on 29 | float32 audio data, so this could be optimized for the M7 chip using quantized int16 data. The Daisy Seed 30 | supports the addition of a display so this could be a future modification, as well as an encoder knob to switch models. 31 | 32 | The compiled binary and model data fits into Flash Memory, which is limited to 128KB. The [RTNeural](https://github.com/jatinchowdhury18/RTNeural) 33 | engine is used for fast inferencing of the neural models with a very tiny footprint. It is possible to add more models utilizing other data storage 34 | areas on the Daisy Seed. 35 | 36 | Utilizing only the 128KB Flash memory, NeuralSeed can hold around 15 different models. The initial release includes 11 built in models, 37 | and there is room to add more, however the lack of a display means it's up to you to keep track of what model you're on! For live shows one 38 | could choose a subset of their favorite models by editing the ```NeuralSeed/all_model_data.h``` file to make this more manageable. 39 | 40 | ## Getting started 41 | Build the daisy libraries and NeuralSeed with (after installing the Daisy Toolchain): 42 | ``` 43 | make -C DaisySP 44 | make -C libDaisy 45 | cd NeuralSeed 46 | make 47 | ``` 48 | 49 | Then flash your terrarium with the following commands (or use the [Electrosmith Web Programmer](https://electro-smith.github.io/Programmer/)) 50 | ``` 51 | cd your_pedal 52 | # using USB (after entering bootloader mode) 53 | make program-dfu 54 | # using JTAG/SWD adaptor (like STLink) 55 | make program 56 | ``` 57 | 58 | # Control 59 | 60 | | Control | Description | Comment | 61 | | --- | --- | --- | 62 | | Ctrl 1 | Input Level | Adjusts the input level to the neural net model (0 to 3x volume) | 63 | | Ctrl 2 | Mix | Set the dry/wet amount (full left for dry, full right for only neural net output | 64 | | Ctrl 3 | Volume | Sets the output volume of the pedal | 65 | | Ctrl 4 | Neural Param 1 | Adjusts the first model parameter, such as gain/tone (only applies to 1-conditioned models) | 66 | | Ctrl 5 | Neural Param 2 | Adjusts the second model parameter, such as gain/tone (only applies to 2-conditioned models) | 67 | | Ctrl 6 | Neural Param 3 | Adjusts the third model parameter, such as gain/tone (only applies to 3-conditioned models) | 68 | | SW 1 - 4 | 4Band EQ Boost | Bass, Low Mid, High Mid, Treble Boost (Band Pass) | 69 | | FS 1 | Bypass/Active | Bypass / effect engaged | 70 | | FS 2 | Cycle Neural Model | Loads the next available Neural Model, starts at beginning after the last in the list (i.e. next pedal/amp) | 71 | | LED 1 | Bypass/Active Indicator |Illuminated when effect is set to Active | 72 | | LED 2 | Brightness based on selected model parameterization | Off = Snapshot, Dim = 1 Knob, Brighter = 2 Knobs, Brightest = 3 Knobs | 73 | | Audio In 1 | Audio input | Mono only for Terrarium | 74 | | Audio Out 1 | Mix Out | Mono only for Terrarium | 75 | 76 | ## Training your own Models (Amp/Pedal Capture) 77 | 78 | You can train models for Neural Seed using the GuitarML fork (ns-capture branch) of [Automated-GuitarAmpModelling](https://github.com/GuitarML/Automated-GuitarAmpModelling/tree/ns-capture) code. 79 | Run "scripts/convert_json_to_c_header.py" on the resulting JSON model to generate a .h file, which you can copy and paste the weights into the "all_model_data.h" header before 80 | compiling "neuralseed.bin". Only GRU size 10 models (snapshot, 1-param) or GRU size 8 (2-param, 3-param) models are currently compatible. Train models using 48kHz to match Daisy Seed processing 81 | (note that this is different from most other GuitarML plugins). A 48kHz input wav file based on the Proteus input wav is available [here](https://github.com/GuitarML/Automated-GuitarAmpModelling/blob/ns-capture/Data/Proteus_Capture_48k.wav). 82 | 83 | Keep in mind that capture with such small models is hit or miss, and may take some massaging to get a proper capture, or may not be captured at all. Loss values over 0.08 probably won't sound good, 84 | or may contain harmonic noise. Ensure to start with volume low when testing a new model on the pedal. 85 | 86 | ## Adding your model to NeuralSeed 87 | To add your models to a custom build of Neural Seed, run the converter script on your json model.
88 | ```python convert_json_to_c_header_gru.py ```
89 | Then copy and paste the information from the generated c-header into the ```all_model_data.h``` file 90 | before compiling. Ensure to modify the "Model" weights to a unique identifier, and add to the initialization list a the top and the "model_collection" vector at the bottom. 91 | 92 | Email either the .json or .h model file to smartguitarml@gmail.com and it may be included in the official repo. 93 | -------------------------------------------------------------------------------- /scripts/Parameterization-Config-3-mi.json: -------------------------------------------------------------------------------- 1 | { 2 | "Number of Parameters":3, 3 | "Data Sets":[ 4 | { 5 | "Parameters": [ 0.0, 0.0, 0.0 ], 6 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 7 | "TrainingTarget": "../g0_t0_p0.wav" 8 | }, 9 | { 10 | "Parameters": [ 0.0, 0.0, 0.5 ], 11 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 12 | "TrainingTarget": "../g0_t0_p5.wav" 13 | }, 14 | { 15 | "Parameters": [ 0.0, 0.0, 1.0 ], 16 | "TrainingClean": "Data/Proteus_Capture_48k.wav", 17 | "TrainingTarget": "../g0_t0_p10.wav" 18 | }, 19 | 20 | 21 | { 22 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-------------------------------------------------------------------------------- /NeuralSeed/neuralseed.cpp: -------------------------------------------------------------------------------- 1 | #include "daisy_petal.h" 2 | #include "daisysp.h" 3 | #include "terrarium.h" 4 | #include 5 | 6 | // Model Weights (edit this file to add model weights trained with Colab script) 7 | #include "all_model_data.h" 8 | 9 | using namespace daisy; 10 | using namespace daisysp; 11 | using namespace terrarium; // This is important for mapping the correct controls to the Daisy Seed on Terrarium PCB 12 | 13 | // Declare a local daisy_petal for hardware access 14 | DaisyPetal hw; 15 | Parameter inLevel, modelParam, modelParam2, modelParam3, outLevel, wetDryMix; 16 | bool bypass; 17 | int modelInSize; 18 | unsigned int modelIndex; 19 | 20 | Led led1, led2; 21 | 22 | static CrossFade cfade; // For blending the wet/dry signals while maintaining constant volume 23 | 24 | // Each EQ will be turned on/off independently 25 | bool eqOn[4]; 26 | int freqs[4]; 27 | 28 | // Our bandpass filter for each EQ boost switch 29 | struct Filter 30 | { 31 | Svf filt; 32 | float amp; 33 | 34 | void Init(float samplerate, float freq) 35 | { 36 | filt.Init(samplerate); 37 | filt.SetRes(0.6); 38 | filt.SetDrive(0.000); 39 | filt.SetFreq(freq); 40 | amp = .5f; 41 | } 42 | 43 | float Process(float in) 44 | { 45 | filt.Process(in); 46 | return filt.Band() * amp; 47 | } 48 | }; 49 | 50 | Filter filters[4]; 51 | 52 | // The center frequency of each filter (change as desired) 53 | // Currently set at the lower end of the spectrum 54 | void InitFreqs() 55 | { 56 | freqs[0] = 120; 57 | freqs[1] = 400; 58 | freqs[2] = 800; 59 | freqs[3] = 1600; 60 | } 61 | 62 | void InitFilters(float samplerate) 63 | { 64 | for(int i = 0; i < 4; i++) 65 | { 66 | filters[i].Init(samplerate, freqs[i]); 67 | } 68 | } 69 | 70 | 71 | // These are each of the Neural Model options. 72 | // GRU (Gated Recurrent Unit) networks with input sizes ranging 1 - 4. 73 | // 1 input for audio, up to 3 inputs for parameterized controls, such 74 | // as Gain/Drive or Tone. 75 | 76 | RTNeural::ModelT, 78 | RTNeural::DenseT> model; 79 | 80 | RTNeural::ModelT, 82 | RTNeural::DenseT> model2; 83 | 84 | RTNeural::ModelT, 86 | RTNeural::DenseT> model3; 87 | 88 | RTNeural::ModelT, 90 | RTNeural::DenseT> model4; 91 | 92 | // Notes: With default settings, GRU 10 is max size currently able to run on Daisy Seed 93 | // - Parameterized 1-knob GRU 10 is max 94 | // - Parameterized 2-knob/3-knob at GRU 8 is max 95 | 96 | // Loads a new model using the correct template and resets the right LED brightness 97 | void changeModel() 98 | { 99 | if (bypass) { 100 | return; 101 | } 102 | if (modelIndex == model_collection.size() - 1) { 103 | modelIndex = 0; 104 | } else { 105 | modelIndex += 1; 106 | } 107 | 108 | if (model_collection[modelIndex].rec_weight_ih_l0.size() == 2) { 109 | auto& gru = (model2).template get<0>(); 110 | auto& dense = (model2).template get<1>(); 111 | modelInSize = 2; 112 | gru.setWVals(model_collection[modelIndex].rec_weight_ih_l0); 113 | gru.setUVals(model_collection[modelIndex].rec_weight_hh_l0); 114 | gru.setBVals(model_collection[modelIndex].rec_bias); 115 | dense.setWeights(model_collection[modelIndex].lin_weight); 116 | dense.setBias(model_collection[modelIndex].lin_bias.data()); 117 | led2.Set(0.3f); 118 | 119 | } else if (model_collection[modelIndex].rec_weight_ih_l0.size() == 3) { 120 | auto& gru = (model3).template get<0>(); 121 | auto& dense = (model3).template get<1>(); 122 | modelInSize = 3; 123 | gru.setWVals(model_collection[modelIndex].rec_weight_ih_l0); 124 | gru.setUVals(model_collection[modelIndex].rec_weight_hh_l0); 125 | gru.setBVals(model_collection[modelIndex].rec_bias); 126 | dense.setWeights(model_collection[modelIndex].lin_weight); 127 | dense.setBias(model_collection[modelIndex].lin_bias.data()); 128 | led2.Set(0.65f); 129 | 130 | } else if (model_collection[modelIndex].rec_weight_ih_l0.size() == 4) { 131 | auto& gru = (model4).template get<0>(); 132 | auto& dense = (model4).template get<1>(); 133 | modelInSize = 4; 134 | gru.setWVals(model_collection[modelIndex].rec_weight_ih_l0); 135 | gru.setUVals(model_collection[modelIndex].rec_weight_hh_l0); 136 | gru.setBVals(model_collection[modelIndex].rec_bias); 137 | dense.setWeights(model_collection[modelIndex].lin_weight); 138 | dense.setBias(model_collection[modelIndex].lin_bias.data()); 139 | led2.Set(1.0f); 140 | 141 | } else { 142 | auto& gru = (model).template get<0>(); 143 | auto& dense = (model).template get<1>(); 144 | modelInSize = 1; 145 | gru.setWVals(model_collection[modelIndex].rec_weight_ih_l0); 146 | gru.setUVals(model_collection[modelIndex].rec_weight_hh_l0); 147 | gru.setBVals(model_collection[modelIndex].rec_bias); 148 | dense.setWeights(model_collection[modelIndex].lin_weight); 149 | dense.setBias(model_collection[modelIndex].lin_bias.data()); 150 | led2.Set(0.0f); 151 | } 152 | 153 | // Initialize Neural Net 154 | if (modelInSize == 1) { 155 | model.reset(); 156 | } else if (modelInSize == 2) { 157 | model2.reset(); 158 | } else if (modelInSize == 3) { 159 | model3.reset(); 160 | } else { 161 | model4.reset(); 162 | } 163 | } 164 | 165 | // This runs at a fixed rate, to prepare audio samples 166 | static void AudioCallback(AudioHandle::InputBuffer in, 167 | AudioHandle::OutputBuffer out, 168 | size_t size) 169 | { 170 | //hw.ProcessAllControls(); 171 | hw.ProcessAnalogControls(); 172 | hw.ProcessDigitalControls(); 173 | led1.Update(); 174 | led2.Update(); 175 | 176 | float in_level = inLevel.Process(); 177 | float model_param = modelParam.Process(); 178 | float model_param2 = modelParam2.Process(); 179 | float model_param3 = modelParam3.Process(); 180 | float out_level = outLevel.Process(); 181 | float wet_dry_mix = wetDryMix.Process(); 182 | float final_mix; 183 | 184 | cfade.SetPos(wet_dry_mix); 185 | 186 | float input_arr[4] = { 0.0, 0.0, 0.0, 0.0 }; 187 | 188 | // (De-)Activate bypass and toggle LED when left footswitch is pressed 189 | if(hw.switches[Terrarium::FOOTSWITCH_1].RisingEdge()) 190 | { 191 | bypass = !bypass; 192 | led1.Set(bypass ? 0.0f : 1.0f); 193 | } 194 | 195 | // Cycle available models 196 | if(hw.switches[Terrarium::FOOTSWITCH_2].RisingEdge()) 197 | { 198 | changeModel(); 199 | } 200 | 201 | // EQ Switches 202 | // - The .Pressed() function below counts an 'ON' switch as pressed. 203 | // - Each EQ boost is controlled by it's own switch 204 | int switches[4] = {Terrarium::SWITCH_1, Terrarium::SWITCH_2, Terrarium::SWITCH_3, Terrarium::SWITCH_4}; // Can this be moved elsewhere? 205 | for(int i=0; i<4; i++) 206 | eqOn[i] = hw.switches[switches[i]].Pressed(); 207 | 208 | for(size_t i = 0; i < size; i++) 209 | { 210 | float input = in[0][i]; 211 | float wet = input; 212 | 213 | // Process your signal here 214 | if(bypass) 215 | { 216 | out[0][i] = in[0][i]; 217 | } 218 | else 219 | { 220 | if (modelInSize == 2) { 221 | input_arr[0] = input * in_level; // Set input array with input level adjustment 222 | input_arr[1] = model_param; 223 | wet = model2.forward (input_arr) + input; // Run Parameterized Model and add Skip Connection 224 | 225 | } else if (modelInSize == 3) { 226 | input_arr[0] = input * in_level; 227 | input_arr[1] = model_param; 228 | input_arr[2] = model_param2; 229 | wet = model3.forward (input_arr) + input; // Run Parameterized Model and add Skip Connection 230 | 231 | } else if (modelInSize == 4) { 232 | input_arr[0] = input * in_level; 233 | input_arr[1] = model_param; 234 | input_arr[2] = model_param2; 235 | input_arr[3] = model_param3; 236 | wet = model4.forward (input_arr) + input; // Run Parameterized Model and add Skip Connection 237 | 238 | } else { 239 | input_arr[0] = input * in_level; 240 | wet = model.forward (input_arr) + input; // Run Model and add Skip Connection 241 | } 242 | 243 | //wet *= 0.6; // Level adjustment, models are too loud 244 | 245 | // wet = wet * wet_dry_mix * 0.2 + input * (1 - wet_dry_mix); // Set Wet/Dry Mix (and reduce model output) 246 | 247 | // Process EQ 248 | float sig = 0.f; 249 | bool noEQ = true; 250 | for(int j = 0; j < 4; j++) 251 | { 252 | if (eqOn[j]) { 253 | sig += filters[j].Process(wet); 254 | noEQ = false; 255 | } 256 | } 257 | sig *= .7; // EQ level adjust if needed 258 | 259 | if(!noEQ) { 260 | wet += sig; 261 | } 262 | 263 | final_mix = cfade.Process(input, wet); // crossfade wet/dry at constant power (consistent volume) 264 | 265 | out[0][i] = final_mix * out_level; // Set output level 266 | } 267 | 268 | // Copy left channel to right channel 269 | // Not needed for Terrarium, mono only (left channel) 270 | //for(size_t i = 0; i < size; i++) 271 | //{ 272 | // out[1][i] = out[0][i]; 273 | //} 274 | 275 | } 276 | } 277 | 278 | int main(void) 279 | { 280 | float samplerate; 281 | 282 | hw.Init(); 283 | samplerate = hw.AudioSampleRate(); 284 | 285 | setupWeights(); 286 | //hw.SetAudioBlockSize(4); 287 | 288 | inLevel.Init(hw.knob[Terrarium::KNOB_1], 0.0f, 3.0f, Parameter::LINEAR); 289 | wetDryMix.Init(hw.knob[Terrarium::KNOB_2], 0.0f, 1.0f, Parameter::LINEAR); 290 | outLevel.Init(hw.knob[Terrarium::KNOB_3], 0.0f, 1.0f, Parameter::LINEAR); 291 | modelParam.Init(hw.knob[Terrarium::KNOB_4], 0.0f, 1.0f, Parameter::LINEAR); 292 | modelParam2.Init(hw.knob[Terrarium::KNOB_5], 0.0f, 1.0f, Parameter::LINEAR); 293 | modelParam3.Init(hw.knob[Terrarium::KNOB_6], 0.0f, 1.0f, Parameter::LINEAR); 294 | 295 | // Initialize the correct model 296 | modelIndex = -1; 297 | changeModel(); 298 | 299 | // Initialize filters for 4 EQ boost switches 300 | InitFreqs(); 301 | InitFilters(samplerate); 302 | 303 | // Initialize & set params for CrossFade object 304 | cfade.Init(); 305 | cfade.SetCurve(CROSSFADE_CPOW); 306 | 307 | // Init the LEDs and set activate bypass 308 | led1.Init(hw.seed.GetPin(Terrarium::LED_1),false); 309 | led1.Update(); 310 | bypass = true; 311 | 312 | led2.Init(hw.seed.GetPin(Terrarium::LED_2),false); 313 | led2.Update(); 314 | 315 | hw.StartAdc(); 316 | hw.StartAudio(AudioCallback); 317 | while(1) 318 | { 319 | // Do Stuff Infinitely Here 320 | System::Delay(10); 321 | } 322 | } -------------------------------------------------------------------------------- /NeuralSeed/all_model_data.h: -------------------------------------------------------------------------------- 1 | 2 | 3 | struct modelData { 4 | std::vector> rec_weight_ih_l0; 5 | std::vector> rec_weight_hh_l0; 6 | std::vector> lin_weight; 7 | std::vector lin_bias; 8 | std::vector> rec_bias; 9 | }; 10 | 11 | // ADD YOUR MODEL IDENTIFIER HERE ////////////////////////////////// < ------------------- 12 | modelData Model1; 13 | modelData Model2; 14 | modelData Model3; 15 | modelData Model4; 16 | modelData Model5; 17 | modelData Model6; 18 | modelData Model7; 19 | modelData Model8; 20 | modelData Model9; 21 | modelData Model10; 22 | modelData Model11; 23 | modelData Model12; 24 | modelData Model13; 25 | ///////////////////////////////////////////////////////////////// 26 | 27 | std::vector model_collection; 28 | 29 | /*========================================================================*/ 30 | 31 | void setupWeights() { 32 | 33 | // COPY AND PASTE YOUR MODEL WEIGHTS BELOW (After converting .json to .h file) ////////////////////////////////// < ------------------- 34 | // ADD AND REMOVE MODELS AS DESIRED (CAN HOLD AROUND 15-16 MODELS IN FLASH MEMORY) 35 | //======================================================================== 36 | //engl_g25_p0056_GRU10 37 | /* 38 | model : SimpleRNN 39 | input_size : 1 40 | skip : 1 41 | output_size : 1 42 | unit_type : GRU 43 | num_layers : 1 44 | hidden_size : 10 45 | bias_fl : True 46 | */ 47 | 48 | Model1.rec_weight_ih_l0 = {{0.031066833063960075, -0.1917823851108551, 0.03541354089975357, 0.0601169690489769, -0.11907631903886795, -0.13151410222053528, 0.452418714761734, -0.14533299207687378, 0.1045185923576355, 0.6251623034477234, 0.16545456647872925, -0.061233844608068466, -0.13335895538330078, -0.043372590094804764, -0.19606295228004456, 0.03639846295118332, 0.031128054484725, 0.17975164949893951, -0.008440341800451279, -0.06337689608335495, 0.4222928583621979, 0.4281714856624603, -0.29407572746276855, 0.2906924784183502, 0.9929586052894592, -0.4552931487560272, 0.6429793834686279, 0.7638277411460876, 0.09207411855459213, -0.771666407585144}}; 49 | 50 | Model1.rec_weight_hh_l0 = {{0.20177601277828217, 0.004856780171394348, 0.10558751970529556, -0.05069819837808609, 0.24097707867622375, 0.0224301740527153, 0.2635056674480438, 0.18215304613113403, -0.2726777195930481, -0.12086351960897446, 0.0030990515369921923, -0.06408045440912247, -0.24003838002681732, -0.022579200565814972, 0.22238370776176453, 0.0594414658844471, -0.22309660911560059, 0.1887175440788269, 0.09307199716567993, 0.0217086523771286, 0.9018120765686035, -0.23720158636569977, -0.8546924591064453, -0.2431672066450119, 0.05906054750084877, -0.08291418850421906, -0.03726206347346306, 0.6491894125938416, -0.2550809979438782, 0.1458258181810379}, 51 | { -0.14263252913951874, 0.12943635880947113, 0.059299249202013016, 0.614011287689209, -0.05988861247897148, 0.015825903043150902, -0.1416185051202774, -0.013525145128369331, -0.41738468408584595, 0.2396647185087204, -0.44749101996421814, -0.32073771953582764, -0.23964092135429382, -0.2013629972934723, -0.12070386111736298, -0.3316197991371155, 0.0012333671329542994, 0.13529638946056366, 0.20257039368152618, 0.29221415519714355, -0.26530733704566956, 0.8802353143692017, -0.08827732503414154, -0.2863454222679138, -0.6485391855239868, -0.7028092741966248, 0.15552441775798798, -0.15084894001483917, -0.35654371976852417, 0.5806340575218201}, 52 | { 0.30833879113197327, -0.12045816332101822, -0.36025264859199524, 0.08327727019786835, 0.025447865948081017, -0.2382698506116867, 0.11285501718521118, 0.34369131922721863, 0.18814639747142792, 0.32229524850845337, -0.10971741378307343, 0.20609325170516968, 0.5340304970741272, 0.08227720111608505, 0.0394572839140892, -0.0023928615264594555, -0.09449804574251175, 0.15910612046718597, 0.2104734480381012, -0.2961612939834595, 1.1451689004898071, 0.04887771978974342, 1.478275179862976, -0.003794983262196183, 0.0346347913146019, -0.07387383282184601, -0.08336415886878967, -0.29823464155197144, -0.060548242181539536, -0.09568038582801819}, 53 | { 0.022263120859861374, 0.24196921288967133, -0.06115982308983803, -0.35412997007369995, -0.2903015613555908, -0.03440966084599495, 0.2049437165260315, -0.08510968834161758, -0.14888699352741241, -0.3051755428314209, -0.035428669303655624, 0.1607954353094101, -0.15244749188423157, 0.1855466663837433, 0.2733589708805084, 0.0054395608603954315, -0.022498294711112976, 0.704329788684845, -0.20961245894432068, 0.03021444007754326, -0.41936567425727844, -0.36085426807403564, -0.5756834745407104, 0.3555752635002136, 0.5825146436691284, 0.3097256124019623, 0.3787934184074402, 0.571614146232605, 0.02966705895960331, 0.27437856793403625}, 54 | { 0.13680455088615417, -0.07170388102531433, 0.0755503699183464, -0.01280447281897068, 0.10977186262607574, -0.06169876083731651, -0.6499229073524475, 0.01984235644340515, 0.22131016850471497, -0.423290491104126, -0.33004850149154663, 0.013850664719939232, -0.21517549455165863, 0.21374067664146423, -0.17674148082733154, 0.04937143996357918, 0.1345149725675583, 0.12632536888122559, -0.27245739102363586, 0.0037794685922563076, -0.11678510159254074, 0.5843338966369629, 0.09738250076770782, -0.7022457718849182, 0.8969604969024658, 1.2235277891159058, -0.20337456464767456, -0.8130390644073486, 0.6999301910400391, 0.18093225359916687}, 55 | { 0.05081959068775177, -0.19854243099689484, -0.0329849049448967, 0.06542101502418518, -0.092860646545887, -0.21766090393066406, 0.043591562658548355, -0.12422965466976166, -0.03134971857070923, 0.2864479124546051, 0.05135170370340347, 0.10002532601356506, 0.18362265825271606, 0.15763065218925476, 0.01706857606768608, 0.005822605919092894, 0.16486403346061707, 0.2717548906803131, -0.4674034118652344, 0.1356368064880371, 0.2724386155605316, 0.5549485683441162, 0.14812704920768738, 0.6708014607429504, -0.5247065424919128, 1.22848641872406, -0.22220540046691895, 0.7705738544464111, -0.2696042060852051, -0.3662813603878021}, 56 | { -0.0626228004693985, 0.028076142072677612, -0.054817378520965576, -0.08991778641939163, -0.18162737786769867, -0.12895995378494263, -0.0515238419175148, -0.1925976574420929, -0.09435547143220901, 0.052043307572603226, 0.005187171045690775, 0.2647310793399811, 0.16722701489925385, 0.11405277997255325, 0.18666867911815643, 0.05069455876946449, -0.19294089078903198, 0.13648545742034912, -0.171188622713089, -0.11880752444267273, -0.13137483596801758, -0.6156428456306458, 0.3693668246269226, 0.6653515100479126, -0.2690305709838867, 0.4424712061882019, 0.8490952253341675, -0.44096827507019043, -0.13054822385311127, 0.5315102338790894}, 57 | { 0.2584003210067749, 0.31204771995544434, -0.14979057013988495, -0.09369843453168869, 0.16633200645446777, -0.06808231770992279, -0.45215776562690735, 0.08520067483186722, 0.22829408943653107, -0.5599738359451294, 0.007758351508527994, -0.17358702421188354, 0.13154403865337372, -0.14272142946720123, -0.08979261666536331, 0.18386134505271912, 0.3110773265361786, 0.19182071089744568, 0.0521128885447979, -0.05353396758437157, -0.9411237239837646, -0.3278161585330963, 0.8964213132858276, -0.6993135809898376, 0.22330103814601898, -0.1119077056646347, -0.5022743940353394, 0.7259848713874817, -0.25079360604286194, 0.30639907717704773}, 58 | { 0.4383332133293152, -0.2809648811817169, -0.0712730810046196, 0.060503605753183365, 0.16629952192306519, -0.04366474971175194, 0.02063082717359066, -0.08167927712202072, 0.061922766268253326, -0.04578019678592682, -0.19152158498764038, 0.351031631231308, -0.08164817094802856, -0.06862080097198486, 0.018377725034952164, 0.09010710567235947, 0.1984410583972931, -0.4189642071723938, -0.025315746665000916, -0.3629223108291626, 0.198740154504776, -0.2515227496623993, -0.2069222778081894, -0.7412787675857544, -0.26687124371528625, -0.22615472972393036, -0.3784654140472412, 0.2990957498550415, 0.8432539105415344, 0.36018455028533936}, 59 | { 0.01465811301022768, 0.15410825610160828, -0.07933460921049118, 0.7714570164680481, -0.025397799909114838, -0.015135029330849648, -0.27442213892936707, -0.23515233397483826, -0.22950708866119385, 0.10126563906669617, -0.46131208539009094, 0.1292101889848709, -0.3699306845664978, -0.20560425519943237, -0.016732990741729736, -0.2626641094684601, -0.024794980883598328, -0.541941225528717, -0.24166247248649597, -0.17994984984397888, 0.24461498856544495, 0.24351564049720764, 0.18126176297664642, 0.2189396768808365, 0.7773070931434631, 0.05085135996341705, -0.019348619505763054, -0.06872287392616272, 0.04079936444759369, 1.108238697052002}}; 60 | 61 | Model1.lin_weight = {{-1.0221177339553833, 0.0016925361705943942, 0.22669070959091187, 0.7160730361938477, -0.398703932762146, 0.0344085656106472, -0.4923301041126251, -0.6397364139556885, 0.10984684526920319, -0.07459311932325363}}; 62 | 63 | Model1.lin_bias = {-0.0651477500796318}; 64 | 65 | Model1.rec_bias = {{-0.12936924397945404, 0.46621736884117126, -0.799197256565094, -0.19017978012561798, -0.5838021039962769, -0.5557725429534912, 1.7034634351730347, -0.39807337522506714, 0.6822990775108337, 0.33023783564567566, 0.3146650195121765, 0.2561561167240143, 0.4527322053909302, 0.4939012825489044, 0.38576656579971313, 0.628652036190033, 0.035285111516714096, 0.3752743601799011, 0.17872905731201172, 0.3187229335308075, -0.20034565031528473, 0.06142352521419525, -0.11195066571235657, 0.08379722386598587, 0.027981184422969818, -0.07469739764928818, 0.09411071985960007, -0.07896793633699417, 0.12720729410648346, 0.017595896497368813}, 66 | { -0.12935344874858856, 0.4693470299243927, -0.799197256565094, -0.19017989933490753, -0.5838021039962769, -0.5557725429534912, 1.704106092453003, -0.39812150597572327, 0.6822990775108337, 0.3302902281284332, 0.3164612352848053, 0.2457863837480545, 0.46679043769836426, 0.49349021911621094, 0.38703399896621704, 0.6285818219184875, -0.017987139523029327, 0.3912634253501892, 0.17977337539196014, 0.31603384017944336, 0.016099264845252037, -0.04874003678560257, 0.16898034512996674, -0.05830793455243111, -0.08926837891340256, 0.1342272013425827, -0.17292416095733643, 0.12401363998651505, -0.16338978707790375, -0.04292384535074234}}; 67 | 68 | //======================================================================== 69 | //klondc3_is2_gKnob_p024 70 | /* 71 | model : SimpleRNN 72 | input_size : 2 73 | skip : 1 74 | output_size : 1 75 | unit_type : GRU 76 | num_layers : 1 77 | hidden_size : 10 78 | bias_fl : True 79 | */ 80 | 81 | Model2.rec_weight_ih_l0 = {{0.40511399507522583, 0.013489293865859509, 0.037870414555072784, -0.04589049145579338, 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GRU 285 | num_layers : 1 286 | hidden_size : 10 287 | bias_fl : True 288 | */ 289 | 290 | Model8.rec_weight_ih_l0 = {{-0.038240402936935425, -0.12627609074115753, 0.04524319991469383, -0.20157793164253235, 0.019972339272499084, -0.030932258814573288, -0.5973972082138062, -0.014899694360792637, 0.27736589312553406, -0.051672209054231644, 0.03422516956925392, 0.12485488504171371, -0.4295569956302643, -0.10258268564939499, -0.12284930050373077, 0.16241466999053955, -1.3411877155303955, 0.021335305646061897, 0.17406094074249268, -0.08349363505840302, -0.42922085523605347, -0.37637901306152344, -0.0472559928894043, -0.2090030163526535, 1.1165282726287842, -0.4527968764305115, 1.2177175283432007, -0.03525995835661888, 0.010816176421940327, -0.0745701789855957}}; 291 | 292 | Model8.rec_weight_hh_l0 = {{-0.24508050084114075, 0.08836090564727783, 0.34679287672042847, -0.32514598965644836, 0.3086284399032593, -0.33168718218803406, 0.4045145511627197, 0.21326576173305511, 0.14174263179302216, 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//bassman_g25_p025_gru10 347 | /* 348 | model : SimpleRNN 349 | input_size : 1 350 | skip : 1 351 | output_size : 1 352 | unit_type : GRU 353 | num_layers : 1 354 | hidden_size : 10 355 | bias_fl : True 356 | */ 357 | 358 | Model10.rec_weight_ih_l0 = {{0.09695376455783844, 0.5007107257843018, 0.12475814670324326, 0.3805599808692932, 0.12975265085697174, 0.1879885345697403, 0.42077115178108215, 0.11486832797527313, 0.053640302270650864, 0.1155240535736084, -0.15351641178131104, 0.6600791811943054, -0.17813242971897125, 0.18772229552268982, -0.08457648754119873, 0.23264944553375244, -0.15173929929733276, -0.07730113714933395, -0.2988188564777374, -0.836515486240387, -0.41984468698501587, -0.6804504990577698, 0.04745206609368324, -0.028751706704497337, 0.33705249428749084, -0.4202679395675659, 0.4340899586677551, 0.2071204036474228, 0.8046587705612183, -0.3901323080062866}}; 359 | 360 | Model10.rec_weight_hh_l0 = {{-0.2048230767250061, 0.0790369063615799, -0.06234673783183098, 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//======================================================================== 380 | //fender57_g5_p008 381 | /* 382 | model : SimpleRNN 383 | input_size : 1 384 | skip : 1 385 | output_size : 1 386 | unit_type : GRU 387 | num_layers : 1 388 | hidden_size : 10 389 | bias_fl : True 390 | */ 391 | 392 | Model11.rec_weight_ih_l0 = {{0.017345795407891273, -0.020677218213677406, 0.1504078507423401, 0.7670391201972961, 0.37774088978767395, -0.08400850743055344, -0.41377174854278564, -0.3605259358882904, 0.38630104064941406, 0.3814362585544586, 0.03851919621229172, 0.2333492785692215, -0.008619832806289196, -0.013494011014699936, 0.5944750905036926, -0.37649044394493103, 0.07574236392974854, 0.10991287976503372, 0.2870367467403412, -0.04125448316335678, -0.35053518414497375, -0.5794589519500732, 0.1873023509979248, -0.07705070078372955, 1.3316806554794312, -0.14250995218753815, 0.48817482590675354, -0.02308153733611107, 1.1764395236968994, 1.3411662578582764}}; 393 | 394 | 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