{
"cells": [
{
"cell_type": "code",
"execution_count": 79,
"id": "5998a3e5",
"metadata": {},
"outputs": [],
"source": [
"# watermark from here: https://github.com/facebookresearch/audioseal\n",
"# notes:\n",
"# works with high fidelity audio, robust against attacks\n",
"# out-of-the-box mono support only, applying channel wise to hack stereo\n",
"# todo:\n",
"# testing on robustness if applied to stereo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "59d20ccd",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from audioseal import AudioSeal\n",
"from suno_utils.audio import Audio\n",
"\n",
"# model name corresponds to the YAML card file name found in audioseal/cards\n",
"model = AudioSeal.load_generator(\"audioseal_wm_16bits\")\n",
"detector = AudioSeal.load_detector(\"audioseal_detector_16bits\")\n",
"\n",
"SECRET_MESSAGE = torch.tensor(\n",
" [int(e) for e in \"\".join([bin(ord(ee))[-4:] for ee in \"suno\"])], \n",
" dtype=torch.int32\n",
")[None]\n",
"# 0011010111101111"
]
},
{
"cell_type": "markdown",
"id": "c50b7712",
"metadata": {},
"source": [
"## do on mono file"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "b33496ab",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"torch.Size([1, 1440000])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torchaudio\n",
"\n",
"audio = Audio.from_file(\"../samples/halo.m4a\", sample_rate=48_000, n_channels=1).get_segment(to_s=30)\n",
"audio.play()\n",
"audio_arr = torch.from_numpy(audio.array_float)[None]\n",
"audio_arr.shape"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "cd1fdf6f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 1440000])"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watermarked_audio = model(audio_arr[None], message=SECRET_MESSAGE, alpha=1)[0]\n",
"watermarked_audio.shape"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "bc4cc439",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Watermark detected with 100.0% likelihood.\n"
]
}
],
"source": [
"result, message = detector.detect_watermark(watermarked_audio[None], message_threshold=0.5)\n",
"print(f\"Watermark detected with {result*100:.1f}% likelihood.\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "f977a746",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]], dtype=torch.int32)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"message"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "ee532c9c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Audio.from_array_float(watermarked_audio[0].detach().cpu().numpy(), sample_rate=48_000).play()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d7fdd76b",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "a1c6157f",
"metadata": {},
"source": [
"## test on stereo"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "69ccb463",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"torch.Size([1, 1, 1440000])"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torchaudio\n",
"\n",
"audio = Audio.from_file(\"../samples/halo.m4a\", sample_rate=48_000, n_channels=2).get_segment(to_s=30)\n",
"audio.play()\n",
"audio_arr = torch.from_numpy(audio.array_float).mean(axis=0, keepdims=True)[None]\n",
"audio_arr.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "35086e3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 1, 1440000])"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watermark = model.get_watermark(audio_arr)\n",
"watermark.shape"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "2015b9a6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"watermarked_audio = Audio.from_array_float(\n",
" torch.from_numpy(audio.array_float) + watermark[0].detach().cpu().numpy().repeat(2, axis=0),\n",
" sample_rate=48_000,\n",
")\n",
"watermarked_audio.play()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"id": "1fd163c6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Watermark detected with 100.0% likelihood.\n"
]
}
],
"source": [
"audio_arr = torch.from_numpy(watermarked_audio.array_float).mean(axis=0, keepdims=True)[None]\n",
"result, message = detector.detect_watermark(audio_arr, message_threshold=0.5)\n",
"print(f\"Watermark detected with {result*100:.1f}% likelihood.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "de412bda",
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
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"metadata": {},
"outputs": [],
"source": []
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"cell_type": "code",
"execution_count": null,
"id": "fc2cd3c2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "863412bb",
"metadata": {},
"outputs": [],
"source": []
}
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"toc": {
"base_numbering": 1,
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"title_cell": "Table of Contents",
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