bark-with-voice-clone/bark/notebooks/fake_classifier.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e330c1de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchaudio\n",
    "from transformers import HubertModel\n",
    "from sklearn.metrics import PrecisionRecallDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ac3dd88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use hubert from HF for feature embedding\n",
    "model = HubertModel.from_pretrained(\"facebook/hubert-base-ls960\")\n",
    "arr, sr = torchaudio.load(\"my_audio.wav\")\n",
    "if sr != 16_000:\n",
    "    arr = torchaudio.functional.resample(arr, sr, 16_000)\n",
    "# use intermediate layer\n",
    "hidden_state = model(arr[None], output_hidden_states=True).hidden_states[6]\n",
    "# take mean over time\n",
    "feats = hidden_state.detach().cpu().numpy().squeeze().mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03a602e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load sk-learn classifier from here: http://s3.amazonaws.com/suno-public/bark/models/v0/classifier.pkl\n",
    "with open(\"classifier.pkl\", \"rb\") as f:\n",
    "    clf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e423794",
   "metadata": {},
   "source": [
    "### Precision-recall curve on test set"
   ]
  },
  {
   "attachments": {
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