pyannote-audio-legacy/notebook/sharing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyannote.database import get_protocol, FileFinder\n",
    "protocol = get_protocol('Debug.SpeakerDiarization.Debug', \n",
    "                        preprocessors={\"audio\": FileFinder()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyannote.audio.tasks import VoiceActivityDetection\n",
    "from pyannote.audio.models.segmentation.debug import SimpleSegmentationModel\n",
    "import pytorch_lightning as pl\n",
    "\n",
    "vad = VoiceActivityDetection(protocol, duration=2., batch_size=32, num_workers=4)\n",
    "model = SimpleSegmentationModel(task=vad)\n",
    "trainer = pl.Trainer(max_epochs=1, default_root_dir='sharing/')\n",
    "_ = trainer.fit(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a model without knowing its class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyannote.audio import Model\n",
    "model = Model.from_pretrained('sharing/lightning_logs/version_0/checkpoints/epoch=0-step=3.ckpt')\n",
    "assert isinstance(model, SimpleSegmentationModel)\n",
    "\n",
    "# checkpoint should work with a URL as well (it relies on pl_load)"
   ]
  }
 ],
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