From 559d6f31e83b15a880fdb72fa80001e72045135a Mon Sep 17 00:00:00 2001
From: Mish Ushakov <mishushakov@users.noreply.github.com>
Date: Mon, 18 Jan 2021 19:19:58 +0100
Subject: [PATCH] Updated docs

---
 .gitignore                                   |   1 -
 README.md                                    |  68 ++++++++++++-------
 data/README.md                               |  19 +++++-
 data/{ts9_in.wav => ts9_test1_in_FP32.wav}   | Bin
 data/{ts9_out.wav => ts9_test1_out_FP32.wav} | Bin
 figures/example_plot.png                     | Bin 0 -> 67026 bytes
 notebooks/colab_GPU_playground.ipynb         |   2 +-
 7 files changed, 64 insertions(+), 26 deletions(-)
 rename data/{ts9_in.wav => ts9_test1_in_FP32.wav} (100%)
 rename data/{ts9_out.wav => ts9_test1_out_FP32.wav} (100%)
 create mode 100644 figures/example_plot.png

diff --git a/.gitignore b/.gitignore
index 4e002af..9420a15 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 lightning_logs/
 *.ckpt
 *.pickle
-*.wav
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/README.md b/README.md
index 5a6e907..fc8162c 100644
--- a/README.md
+++ b/README.md
@@ -35,21 +35,51 @@ For a great explanation of how it works, check out blog post from original pedal
 http://teddykoker.com/2020/05/deep-learning-for-guitar-effect-emulation/
 
 
+## Setup
+
+> Jupyter Notebooks for Google Colab are available in [notebooks](notebooks)
+
+1. Install [Python 3](https://www.python.org/downloads/) with pip package manager
+2. Install [git](https://git-scm.com/downloads)
+3. Create and enter virtual environment
+   ```sh
+   python -m venv .
+   ```
+
+   ```sh
+   . bin/activate
+   ```
+4. Clone the repository
+   ```sh
+   git clone https://github.com/GuitarML/PedalNetRT.git src/
+   ```
+5. Enter the directory and install the dependencies using `pip` package manager
+   ```sh
+   cd src
+   ```
+
+   ```sh
+   python -m pip install -r requirements.txt
+   ```
+
+   (Optional development dependencies)
+
+   ```sh
+   python -m pip install -r requirements-dev.txt
+   ```
+
 ## 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_epoch=1362.ckpt` - Pretrained model weights
-
+`models/pedalnet/pedalnet.ckpt` - Pretrained model weights
 
-## Usage
 
 **Run effect on .wav file**:
 Must be single channel, 44.1 kHz, FP32 wav data (not int16)
 ```bash
-# must be same data used to train
-python prepare_data.py data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav
+python train.py data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav
 
 # specify input file and desired output file
 python predict.py my_input_guitar.wav my_output.wav
@@ -60,21 +90,19 @@ python predict.py my_input_guitar.wav my_output.wav
 
 **Train**:
 ```bash
-python prepare_data.py data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav  # or use your own!
-python train.py
+python train.py data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav
 
-python train.py --resume_training=path_to_ckpt_file  # to continue training from .ckpt file
+python train.py --resume  # to resume training
 
 python train.py --gpus "0,1"  # for multiple gpus
-python train.py --cpu=1       # for cpu training
-python train.py -h  # help (see for other hyperparameters)
-
+python train.py --cpu # for cpu training
+python train.py -h # help (see for other hyperparameters)
 ```
 
 **Test**:
 ```bash
 python test.py # test pretrained model
-python test.py --model lightning_logs/version_{X}/epoch={EPOCH}.ckpt  # test trained model
+python test.py --model your_trained_model.ckpt  # test trained model
 ```
 Creates files `y_test.wav`, `y_pred.wav`, and `x_test.wav`, for the ground truth
 output, predicted output, and input signal respectively.
@@ -85,22 +113,21 @@ output, predicted output, and input signal respectively.
 The .ckpt model must be converted to a .json model to run in the plugin.
 Usage:
 
-	python convert_pedalnet_to_wavnetva.py --model=your_trained_model.ckpt
+	python export.py --model=your_trained_model.ckpt
 
 Generates a file named "converted_model.json" that can be loaded into the VST plugin.
 
 **Analysis**:
 
-You can also use "plot_wav.py" to evaluate the trained PedalNet model. By
+You can also use "plot.py" to evaluate the trained PedalNet model. By
 default, this will analyze the three .wav files from the test.py output. It
 will output analysis plots and calculate the error to signal ratio.
 
-Usage (after running "python test.py --model=your_model.ckpt"):
+Usage (after running "python test.py --model=your_trained_model.ckpt"):
 
-	python plot_wav.py
-
-![app](https://github.com/keyth72/pedalnet/blob/master/figures/example_plot.png)
+	python plot.py
 
+![plot.py output](figures/example_plot.png)
 
 Public spreadsheet for sharing analysis results (can request write access through google account):<br>
 https://docs.google.com/spreadsheets/d/1sIkhW3cdLkMc8bYrYspE8xzdCrJbyWXg_I0vAuJec88/edit?usp=sharing
@@ -137,8 +164,3 @@ Helpful tips on training models:
 6. This WaveNet model is effective at reproducing distortion/overdrive, but
    not reverb/delay effects (or other time-based effects). Mostly untested on
    compressor/limiter effects, but initial results seem promising.
-
-
- Note: Added an experimental Google Colab notebook to train pedalnet models on TPU's.
-       Upload "colab_TPU_training.ipynb" in Google Colab, and upload this pedalnet
-       repository to your Google Drive to use.
diff --git a/data/README.md b/data/README.md
index 7fe1038..cfa4e0a 100644
--- a/data/README.md
+++ b/data/README.md
@@ -1 +1,18 @@
-here is the training data
+# Training data
+
+1. Must be single channel, 44.1 kHz, FP32 wav data (not int16)
+2. 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.
+3. A buffer splitter was used with pedals to obtain a pure guitar signal
+   and post effect signal.
+4. 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.
+5. When recording samples, try to maximize the volume levels without clipping.
+   The levels you train the model at will be reproduced by the plugin. Also try
+   to make the pre effect and post effect wav samples equal in volume levels.
+   Even though the actual amp or effect may raise the level significantly, this isn't
+   necessarily desirable in the end plugin.
diff --git a/data/ts9_in.wav b/data/ts9_test1_in_FP32.wav
similarity index 100%
rename from data/ts9_in.wav
rename to data/ts9_test1_in_FP32.wav
diff --git a/data/ts9_out.wav b/data/ts9_test1_out_FP32.wav
similarity index 100%
rename from data/ts9_out.wav
rename to data/ts9_test1_out_FP32.wav
diff --git a/figures/example_plot.png b/figures/example_plot.png
new file mode 100644
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diff --git a/notebooks/colab_GPU_playground.ipynb b/notebooks/colab_GPU_playground.ipynb
index 7518285..64d125c 100644
--- a/notebooks/colab_GPU_playground.ipynb
+++ b/notebooks/colab_GPU_playground.ipynb
@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"colab_GPU_playground.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"a4W98Rf771lR","colab":{"base_uri":"https://localhost:8080/"},"outputId":"73781a99-1ee8-4f9f-92f5-3486f0eba18c"},"source":["!git clone https://github.com/GuitarML/PedalNetRT.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'PedalNetRT'...\n","remote: Enumerating objects: 10, done.\u001b[K\n","remote: Counting objects: 100% (10/10), done.\u001b[K\n","remote: Compressing objects: 100% (9/9), done.\u001b[K\n","remote: Total 226 (delta 0), reused 4 (delta 0), pack-reused 216\u001b[K\n","Receiving objects: 100% (226/226), 85.04 MiB | 27.51 MiB/s, done.\n","Resolving deltas: 100% (98/98), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WOVSyPgGMcYi","colab":{"base_uri":"https://localhost:8080/"},"outputId":"28907ebb-6e76-4571-b4b6-ae48e4fd4dae"},"source":["%cd PedalNetRT"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/PedalNetRT\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Vv3lwCpPudVb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b9883fe9-6f16-426e-982a-4e525f24dcf7"},"source":["!pip install -r requirements-colab.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting pytorch_lightning==0.7.3\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4e/53/0549dd9c44c90e96d217592e094e9c53ef39ae2fed0c5cdb7e57aca65af6/pytorch_lightning-0.7.3-py3-none-any.whl (203kB)\n","\u001b[K     |████████████████████████████████| 204kB 11.9MB/s \n","\u001b[?25hCollecting future>=0.17.1\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)\n","\u001b[K     |████████████████████████████████| 829kB 12.7MB/s \n","\u001b[?25hRequirement already satisfied: tensorboard>=1.14 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning==0.7.3) (2.3.0)\n","Requirement already satisfied: torch>=1.1 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning==0.7.3) (1.7.0+cu101)\n","Requirement already satisfied: numpy>=1.16.4 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning==0.7.3) (1.18.5)\n","Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.6/dist-packages (from pytorch_lightning==0.7.3) (4.41.1)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning==0.7.3) (1.17.2)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages 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tensorboard>=1.14->pytorch_lightning==0.7.3) (50.3.2)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning==0.7.3) (3.3.3)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning==0.7.3) (3.12.4)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning==0.7.3) (1.0.1)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard>=1.14->pytorch_lightning==0.7.3) (0.4.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch>=1.1->pytorch_lightning==0.7.3) (3.7.4.3)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch>=1.1->pytorch_lightning==0.7.3) (0.8)\n","Requirement already 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\"3.8\"->markdown>=2.6.8->tensorboard>=1.14->pytorch_lightning==0.7.3) (3.4.0)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=1.14->pytorch_lightning==0.7.3) (3.1.0)\n","Building wheels for collected packages: future\n","  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for future: filename=future-0.18.2-cp36-none-any.whl size=491057 sha256=745be7372a3c702a9eb466c1cb82625633700e071d9cd422b6f7da25da5d8cdf\n","  Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e\n","Successfully built future\n","Installing collected packages: future, pytorch-lightning\n","  Found existing installation: future 0.16.0\n","    Uninstalling future-0.16.0:\n","      Successfully uninstalled future-0.16.0\n","Successfully installed future-0.18.2 pytorch-lightning-0.7.3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XBb2THG2BtLm","colab":{"base_uri":"https://localhost:8080/"},"outputId":"17f2b4ec-51ba-487d-f745-9786de93df64"},"source":["!python3 train.py data/ts9_in.wav data/ts9_out.wav"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\rEpoch 1484:  75% 18/24 [00:02<00:00,  7.85it/s, loss=0.026, v_num=2]\n","\rValidating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1484:  83% 20/24 [00:02<00:00,  8.16it/s, loss=0.026, v_num=2]\n","Epoch 1484: 100% 24/24 [00:02<00:00,  8.97it/s, loss=0.026, v_num=2]\n","Epoch 1485:  75% 18/24 [00:02<00:00,  7.84it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1485:  83% 20/24 [00:02<00:00,  8.12it/s, loss=0.026, v_num=2]\n","Epoch 1485: 100% 24/24 [00:02<00:00,  9.02it/s, loss=0.026, v_num=2]\n","Epoch 1486:  75% 18/24 [00:02<00:00,  7.80it/s, loss=0.029, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1486:  83% 20/24 [00:02<00:00,  8.04it/s, loss=0.029, v_num=2]\n","Epoch 1486: 100% 24/24 [00:02<00:00,  8.88it/s, loss=0.029, v_num=2]\n","Epoch 1487:  75% 18/24 [00:02<00:00,  7.84it/s, loss=0.040, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1487:  83% 20/24 [00:02<00:00,  8.04it/s, loss=0.040, v_num=2]\n","Epoch 1487: 100% 24/24 [00:02<00:00,  8.91it/s, loss=0.040, v_num=2]\n","Epoch 1488:  75% 18/24 [00:02<00:00,  7.81it/s, loss=0.032, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1488:  83% 20/24 [00:02<00:00,  8.05it/s, loss=0.032, v_num=2]\n","Epoch 1488: 100% 24/24 [00:02<00:00,  8.92it/s, loss=0.032, v_num=2]\n","Epoch 1489:  75% 18/24 [00:02<00:00,  7.73it/s, loss=0.031, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1489:  83% 20/24 [00:02<00:00,  7.98it/s, loss=0.031, v_num=2]\n","Epoch 1489: 100% 24/24 [00:02<00:00,  8.86it/s, loss=0.031, v_num=2]\n","Epoch 1490:  75% 18/24 [00:02<00:00,  7.75it/s, loss=0.035, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1490:  83% 20/24 [00:02<00:00,  8.01it/s, loss=0.035, v_num=2]\n","Epoch 1490: 100% 24/24 [00:02<00:00,  8.91it/s, loss=0.035, v_num=2]\n","Epoch 1491:  75% 18/24 [00:02<00:00,  7.75it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1491:  83% 20/24 [00:02<00:00,  8.01it/s, loss=0.026, v_num=2]\n","Epoch 1491: 100% 24/24 [00:02<00:00,  8.88it/s, loss=0.026, v_num=2]\n","Epoch 1492:  75% 18/24 [00:02<00:00,  7.86it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1492:  83% 20/24 [00:02<00:00,  8.07it/s, loss=0.026, v_num=2]\n","Epoch 1492: 100% 24/24 [00:02<00:00,  8.88it/s, loss=0.026, v_num=2]\n","Epoch 1493:  75% 18/24 [00:02<00:00,  7.88it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1493:  83% 20/24 [00:02<00:00,  8.09it/s, loss=0.026, v_num=2]\n","Epoch 1493: 100% 24/24 [00:02<00:00,  8.96it/s, loss=0.026, v_num=2]\n","Epoch 1494:  75% 18/24 [00:02<00:00,  7.89it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1494:  83% 20/24 [00:02<00:00,  8.17it/s, loss=0.026, v_num=2]\n","Epoch 1494: 100% 24/24 [00:02<00:00,  9.02it/s, loss=0.026, v_num=2]\n","Epoch 1495:  75% 18/24 [00:02<00:00,  7.74it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1495:  83% 20/24 [00:02<00:00,  7.97it/s, loss=0.026, v_num=2]\n","Epoch 1495: 100% 24/24 [00:02<00:00,  8.82it/s, loss=0.026, v_num=2]\n","Epoch 1496:  75% 18/24 [00:02<00:00,  7.85it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1496:  83% 20/24 [00:02<00:00,  8.10it/s, loss=0.026, v_num=2]\n","Epoch 1496: 100% 24/24 [00:02<00:00,  8.91it/s, loss=0.026, v_num=2]\n","Epoch 1497:  75% 18/24 [00:02<00:00,  7.87it/s, loss=0.025, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1497:  83% 20/24 [00:02<00:00,  8.05it/s, loss=0.025, v_num=2]\n","Epoch 1497: 100% 24/24 [00:02<00:00,  8.90it/s, loss=0.025, v_num=2]\n","Epoch 1498:  75% 18/24 [00:02<00:00,  7.78it/s, loss=0.025, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1498:  83% 20/24 [00:02<00:00,  8.08it/s, loss=0.025, v_num=2]\n","Epoch 1498: 100% 24/24 [00:02<00:00,  8.91it/s, loss=0.025, v_num=2]\n","Epoch 1499:  75% 18/24 [00:02<00:00,  7.82it/s, loss=0.027, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1499:  83% 20/24 [00:02<00:00,  8.11it/s, loss=0.027, v_num=2]\n","Epoch 1499: 100% 24/24 [00:02<00:00,  8.92it/s, loss=0.027, v_num=2]\n","Epoch 1500:  75% 18/24 [00:02<00:00,  7.70it/s, loss=0.028, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1500:  83% 20/24 [00:02<00:00,  7.95it/s, loss=0.028, v_num=2]\n","Epoch 1500: 100% 24/24 [00:02<00:00,  8.78it/s, loss=0.028, v_num=2]\n","Epoch 1500: 100% 24/24 [00:02<00:00,  8.78it/s, loss=0.028, v_num=2]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KZ6wG3E0rmQO"},"source":["!python3 test.py"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m_KwfnpXryEY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"805f9260-0358-4906-85b2-2b6114ed2656"},"source":["!python3 plot.py"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Error to signal (with pre-emphasis filter):  0.014186971\n","Error to signal (no pre-emphasis filter):  0.014186973\n","Creating spectrogram data..\n","plot.py:127: RuntimeWarning: divide by zero encountered in log10\n","  plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram))\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vCJoHzyKDxBl"},"source":["!python3 export.py"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oIEkvTqVYKAX","outputId":"f7bf2f8a-9cd4-4e74-ba82-cea81487f3b0"},"source":["!python predict.py data/ts9_in.wav models/pedalnet/predict.wav"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\r  0% 0/7 [00:00<?, ?it/s]tcmalloc: large alloc 1507164160 bytes == 0xd8862000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f5469d53 0x7f24f4de454a 0x7f24f513ec0a 0x7f24f5166803 0x7f24f52ecb14 0x7f24f54294ee 0x7f24f4e82976 0x7f24f4e83b30 0x7f24f5140b09 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e58a5 0x7f24f4e8541b 0x7f24f53757d8 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f24f67b9997 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f2504fff30e 0x50a4a5 0x50cc96 0x507be4 0x508ec2 0x594a01\n","tcmalloc: large alloc 1507164160 bytes == 0x1325ba000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f50c0271 0x7f24f50b0489 0x7f24f50b0e30 0x7f24f50babda 0x7f24f50beb57 0x7f24f50b7cf6 0x7f24f50b877f 0x7f24f537d5b6 0x7f24f53c179c 0x7f24f4a35e53 0x7f24f52de4bb 0x7f24f51eebbb 0x7f24f6733e0d 0x7f24f4a35e53 0x7f24f52de4bb 0x7f24f51eebbb 0x7f24f4bbed00 0x7f24f5376803 0x7f24f53c0be3 0x7f24f4a35dba 0x7f24f52dca1c 0x7f24f51ed4c4 0x7f24f4bb5e16 0x7f24f537650d 0x7f24f53c0aa3 0x7f24f4a35f0f 0x7f24f52dbdc5\n"," 14% 1/7 [00:11<01:09, 11.57s/it]tcmalloc: large alloc 1507164160 bytes == 0xab372000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f5469d53 0x7f24f4de454a 0x7f24f513ec0a 0x7f24f5166803 0x7f24f52ecb14 0x7f24f54294ee 0x7f24f4e82976 0x7f24f4e83b30 0x7f24f5140b09 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e58a5 0x7f24f4e8541b 0x7f24f53757d8 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f24f67b9997 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f2504fff30e 0x50a4a5 0x50cc96 0x507be4 0x508ec2 0x594a01\n","100% 7/7 [01:07<00:00,  9.67s/it]\n"],"name":"stdout"}]}]}
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"colab_GPU_playground.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"a4W98Rf771lR","colab":{"base_uri":"https://localhost:8080/"},"outputId":"73781a99-1ee8-4f9f-92f5-3486f0eba18c"},"source":["!git clone https://github.com/GuitarML/PedalNetRT.git"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Cloning into 'PedalNetRT'...\n","remote: Enumerating objects: 10, done.\u001b[K\n","remote: Counting objects: 100% (10/10), done.\u001b[K\n","remote: Compressing objects: 100% (9/9), done.\u001b[K\n","remote: Total 226 (delta 0), reused 4 (delta 0), pack-reused 216\u001b[K\n","Receiving objects: 100% (226/226), 85.04 MiB | 27.51 MiB/s, done.\n","Resolving deltas: 100% (98/98), done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"WOVSyPgGMcYi","colab":{"base_uri":"https://localhost:8080/"},"outputId":"28907ebb-6e76-4571-b4b6-ae48e4fd4dae"},"source":["%cd PedalNetRT"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/content/PedalNetRT\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Vv3lwCpPudVb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b9883fe9-6f16-426e-982a-4e525f24dcf7"},"source":["!pip install -r requirements-colab.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting pytorch_lightning==0.7.3\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4e/53/0549dd9c44c90e96d217592e094e9c53ef39ae2fed0c5cdb7e57aca65af6/pytorch_lightning-0.7.3-py3-none-any.whl (203kB)\n","\u001b[K     |████████████████████████████████| 204kB 11.9MB/s \n","\u001b[?25hCollecting future>=0.17.1\n","\u001b[?25l  Downloading 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pytorch-lightning-0.7.3\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XBb2THG2BtLm","colab":{"base_uri":"https://localhost:8080/"},"outputId":"17f2b4ec-51ba-487d-f745-9786de93df64"},"source":["!python3 train.py data/ts9_test1_in_FP32.wav data/ts9_test1_out_FP32.wav"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\rEpoch 1484:  75% 18/24 [00:02<00:00,  7.85it/s, loss=0.026, v_num=2]\n","\rValidating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1484:  83% 20/24 [00:02<00:00,  8.16it/s, loss=0.026, v_num=2]\n","Epoch 1484: 100% 24/24 [00:02<00:00,  8.97it/s, loss=0.026, v_num=2]\n","Epoch 1485:  75% 18/24 [00:02<00:00,  7.84it/s, loss=0.026, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1485:  83% 20/24 [00:02<00:00,  8.12it/s, loss=0.026, v_num=2]\n","Epoch 1485: 100% 24/24 [00:02<00:00,  9.02it/s, loss=0.026, v_num=2]\n","Epoch 1486:  75% 18/24 [00:02<00:00,  7.80it/s, loss=0.029, v_num=2]\n","Validating:   0% 0/6 [00:00<?, 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83% 20/24 [00:02<00:00,  8.05it/s, loss=0.025, v_num=2]\n","Epoch 1497: 100% 24/24 [00:02<00:00,  8.90it/s, loss=0.025, v_num=2]\n","Epoch 1498:  75% 18/24 [00:02<00:00,  7.78it/s, loss=0.025, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1498:  83% 20/24 [00:02<00:00,  8.08it/s, loss=0.025, v_num=2]\n","Epoch 1498: 100% 24/24 [00:02<00:00,  8.91it/s, loss=0.025, v_num=2]\n","Epoch 1499:  75% 18/24 [00:02<00:00,  7.82it/s, loss=0.027, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1499:  83% 20/24 [00:02<00:00,  8.11it/s, loss=0.027, v_num=2]\n","Epoch 1499: 100% 24/24 [00:02<00:00,  8.92it/s, loss=0.027, v_num=2]\n","Epoch 1500:  75% 18/24 [00:02<00:00,  7.70it/s, loss=0.028, v_num=2]\n","Validating:   0% 0/6 [00:00<?, ?it/s]\u001b[A\n","Epoch 1500:  83% 20/24 [00:02<00:00,  7.95it/s, loss=0.028, v_num=2]\n","Epoch 1500: 100% 24/24 [00:02<00:00,  8.78it/s, loss=0.028, v_num=2]\n","Epoch 1500: 100% 24/24 [00:02<00:00,  8.78it/s, loss=0.028, v_num=2]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KZ6wG3E0rmQO"},"source":["!python3 test.py"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"m_KwfnpXryEY","colab":{"base_uri":"https://localhost:8080/"},"outputId":"805f9260-0358-4906-85b2-2b6114ed2656"},"source":["!python3 plot.py"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Error to signal (with pre-emphasis filter):  0.014186971\n","Error to signal (no pre-emphasis filter):  0.014186973\n","Creating spectrogram data..\n","plot.py:127: RuntimeWarning: divide by zero encountered in log10\n","  plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram))\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"vCJoHzyKDxBl"},"source":["!python3 export.py"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oIEkvTqVYKAX","outputId":"f7bf2f8a-9cd4-4e74-ba82-cea81487f3b0"},"source":["!python predict.py data/ts9_test1_in_FP32.wav models/pedalnet/predict.wav"],"execution_count":null,"outputs":[{"output_type":"stream","text":["\r  0% 0/7 [00:00<?, ?it/s]tcmalloc: large alloc 1507164160 bytes == 0xd8862000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f5469d53 0x7f24f4de454a 0x7f24f513ec0a 0x7f24f5166803 0x7f24f52ecb14 0x7f24f54294ee 0x7f24f4e82976 0x7f24f4e83b30 0x7f24f5140b09 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e58a5 0x7f24f4e8541b 0x7f24f53757d8 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f24f67b9997 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f2504fff30e 0x50a4a5 0x50cc96 0x507be4 0x508ec2 0x594a01\n","tcmalloc: large alloc 1507164160 bytes == 0x1325ba000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f50c0271 0x7f24f50b0489 0x7f24f50b0e30 0x7f24f50babda 0x7f24f50beb57 0x7f24f50b7cf6 0x7f24f50b877f 0x7f24f537d5b6 0x7f24f53c179c 0x7f24f4a35e53 0x7f24f52de4bb 0x7f24f51eebbb 0x7f24f6733e0d 0x7f24f4a35e53 0x7f24f52de4bb 0x7f24f51eebbb 0x7f24f4bbed00 0x7f24f5376803 0x7f24f53c0be3 0x7f24f4a35dba 0x7f24f52dca1c 0x7f24f51ed4c4 0x7f24f4bb5e16 0x7f24f537650d 0x7f24f53c0aa3 0x7f24f4a35f0f 0x7f24f52dbdc5\n"," 14% 1/7 [00:11<01:09, 11.57s/it]tcmalloc: large alloc 1507164160 bytes == 0xab372000 @  0x7f2509ad0b6b 0x7f2509af0379 0x7f24ba9fd74e 0x7f24ba9ff7b6 0x7f24f5469d53 0x7f24f4de454a 0x7f24f513ec0a 0x7f24f5166803 0x7f24f52ecb14 0x7f24f54294ee 0x7f24f4e82976 0x7f24f4e83b30 0x7f24f5140b09 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e58a5 0x7f24f4e8541b 0x7f24f53757d8 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f24f67b9997 0x7f24f49bf249 0x7f24f52d9ae8 0x7f24f51e59f5 0x7f2504fff30e 0x50a4a5 0x50cc96 0x507be4 0x508ec2 0x594a01\n","100% 7/7 [01:07<00:00,  9.67s/it]\n"],"name":"stdout"}]}]}