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"<a href=\"https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/train_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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"cell_type": "code",
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"try:\n",
" # are we running on Google Colab?\n",
" import google.colab\n",
" !git clone -q https://github.com/teticio/audio-diffusion.git\n",
" %cd audio-diffusion\n",
" %pip install -q -r requirements.txt .\n",
"except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2fc0e7a",
"metadata": {
"id": "c2fc0e7a"
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"source": [
"from IPython.display import Audio\n",
"from audiodiffusion import AudioDiffusion"
]
},
{
"cell_type": "markdown",
"id": "MqlpL75_mDVv",
"metadata": {
"id": "MqlpL75_mDVv"
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"source": [
"### Upload / specify audio files to train on\n",
"Provide some MP3 or WAV files that will be split into samples and converted to Mel spectrograms. For a resolution of 256, the samples will be about 5 seconds long."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "jg1zAHVsmCBG",
"metadata": {
"colab": {
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"height": 73
},
"id": "jg1zAHVsmCBG",
"outputId": "414244c9-02b6-4ccf-cbfd-83f9022a0fc1"
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"outputs": [],
"source": [
"try:\n",
" # are we running on Google Colab?\n",
" from google.colab import files\n",
" input_dir = '.'\n",
" files.upload();\n",
"except:\n",
" input_dir = \"/home/teticio/Music/liked\""
]
},
{
"cell_type": "markdown",
"id": "10v0RCSUu75P",
"metadata": {
"id": "10v0RCSUu75P"
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"source": [
"### Prepare dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "NJNeEU6ftaTM",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"source": [
"!python scripts/audio_to_images.py \\\n",
" --resolution 256,256 \\\n",
" --input_dir {input_dir} \\\n",
" --output_dir data"
]
},
{
"cell_type": "markdown",
"id": "5mGeXyJFvQCO",
"metadata": {
"id": "5mGeXyJFvQCO"
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"source": [
"### Train model\n",
"The DDIM scheduler generates samples much faster."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "JGnlePbLvTOH",
"metadata": {
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"!python scripts/train_unet.py \\\n",
" --dataset_name data \\\n",
" --output_dir model \\\n",
" --num_epochs 10 \\\n",
" --train_batch_size 2 \\\n",
" --eval_batch_size 2 \\\n",
" --gradient_accumulation_steps 8 \\\n",
" --save_images_epochs 100 \\\n",
" --save_model_epochs 1 \\\n",
" --scheduler ddim"
]
},
{
"cell_type": "markdown",
"id": "nTMAYEtMxtt0",
"metadata": {
"id": "nTMAYEtMxtt0"
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"source": [
"### Generate samples with model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b294a94a",
"metadata": {
"id": "b294a94a"
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"source": [
"audio_diffusion = AudioDiffusion('model')"
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