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       "'output.jams'"
      ]
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     "execution_count": 6,
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   "source": [
    "#!/usr/bin/env python\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from glob import glob\n",
    "import librosa\n",
    "import jams\n",
    "infile = 'experiment.wav'\n",
    "#infile = '05_SS1-68-E_solo_mic.wav'\n",
    "def beat_track(infile, outfile):\n",
    "\n",
    "    # Load the audio file\n",
    "    y, sr = librosa.load(infile)\n",
    "\n",
    "    # Compute the track duration\n",
    "    track_duration = librosa.get_duration(y=y, sr=sr)\n",
    "\n",
    "    # Extract tempo and beat estimates\n",
    "    tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)\n",
    "\n",
    "    # Convert beat frames to time\n",
    "    beat_times = librosa.frames_to_time(beat_frames, sr=sr)\n",
    "\n",
    "    # Construct a new JAMS object and annotation records\n",
    "    jam = jams.JAMS()\n",
    "\n",
    "    # Store the track duration\n",
    "    jam.file_metadata.duration = track_duration\n",
    "\n",
    "    beat_a = jams.Annotation(namespace='beat')\n",
    "    beat_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa beat tracker')\n",
    "\n",
    "    # Add beat timings to the annotation record.\n",
    "    # The beat namespace does not require value or confidence fields,\n",
    "    # so we can leave those blank.\n",
    "    for t in beat_times:\n",
    "        beat_a.append(time=t, duration=0.0)\n",
    "\n",
    "    # Store the new annotation in the jam\n",
    "    jam.annotations.append(beat_a)\n",
    "\n",
    "    # Add tempo estimation to the annotation.\n",
    "    tempo_a = jams.Annotation(namespace='tempo', time=0, duration=track_duration)\n",
    "    tempo_a.annotation_metadata = jams.AnnotationMetadata(data_source='librosa tempo estimator')\n",
    "\n",
    "    # The tempo estimate is global, so it should start at time=0 and cover the full\n",
    "    # track duration.\n",
    "    # If we had a likelihood score on the estimation, it could be stored in\n",
    "    # `confidence`.  Since we have no competing estimates, we'll set it to 1.0.\n",
    "    tempo_a.append(time=0.0,\n",
    "                   duration=track_duration,\n",
    "                   value=tempo,\n",
    "                   confidence=1.0)\n",
    "\n",
    "    # Store the new annotation in the jam\n",
    "    jam.annotations.append(tempo_a)\n",
    "\n",
    "    # Save to disk\n",
    "    jam.save(outfile)\n",
    "    return outfile\n",
    "\n",
    "beat_track(infile, 'output1.jams')\n",
    "'output.jams'\n",
    "#if __name__ == '__main__':\n",
    "\n",
    "#    infile = librosa.util.example_audio_file()\n",
    "#    beat_track(infile, 'output.jams')\n"
   ]
  },
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