pyannote

test_diarisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22292baf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyannote.core import notebook, Segment\n",
    "from pyannote.audio import Pipeline\n",
    "from pyannote.core import Annotation\n",
    "from os import path, getcwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8212d0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_local = Pipeline.from_pretrained(\"config.yaml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d2a0555",
   "metadata": {},
   "outputs": [],
   "source": [
    "cwd = getcwd()\n",
    "audio_source = f\"{cwd}/whisper_stereo_test_16b.wav\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bd3b97a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This takes quite some time... \n",
    "audio_recognised = pipeline_local(audio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25151f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sanity-check, visualise results\n",
    "assert isinstance(audio_recognised, Annotation)\n",
    "audio_recognised"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ede4809f",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = path.basename(audio_source).split(\".\")[0] + \"_labels.txt\"\n",
    "with open(labels, 'w+') as spk_file:\n",
    "    lines = []\n",
    "    for speech_turn, track, speaker in dia.itertracks(yield_label=True):\n",
    "        lines.append(f\"{speech_turn.start:4.1f} {speech_turn.end:4.1f} {speaker}\\n\")\n",
    "    spk_file.writelines(lines)"
   ]
  }
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