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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "555ff0cb",
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   "source": [
    "import os\n",
    "# If running inside \"tests\" folder, move up one level\n",
    "pwd = os.getcwd()\n",
    "if pwd.endswith(\"tests\"):\n",
    "    os.chdir(os.path.dirname(pwd))\n",
    "\n",
    "import torch\n",
    "import torchaudio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.display import Audio\n",
    "from marble.utils.utils import id2chord_str\n",
    "\n",
    "def display_sample_audio_and_labels(dataset: torch.utils.data.Dataset, sample_idx: int):\n",
    "    \"\"\"\n",
    "    Visualizes an audio sample and its corresponding chord labels from the dataset.\n",
    "    Also supports audio playback in Jupyter Notebooks.\n",
    "\n",
    "    Args:\n",
    "        dataset (torch.utils.data.Dataset): The dataset to extract the sample from.\n",
    "        sample_idx (int): The index of the sample to visualize.\n",
    "    \"\"\"\n",
    "    waveform, targets, audio_path = dataset[sample_idx]\n",
    "    \n",
    "    # Plot the waveform with reduced opacity for clearer visibility of labels\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    plt.plot(np.linspace(0, len(waveform[0]) / dataset.sample_rate, len(waveform[0])), waveform[0].numpy(), color='gray', alpha=0.5)\n",
    "    plt.title(f\"Waveform of {audio_path}\")\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "    \n",
    "    # Plot the chord labels over the waveform\n",
    "    label_seq = targets.numpy()\n",
    "    time_axis = np.linspace(0, len(label_seq) / dataset.label_freq, len(label_seq))\n",
    "\n",
    "    # Merge consecutive segments with the same chord label\n",
    "    merged_segments = []\n",
    "    current_label = label_seq[0]\n",
    "    segment_start_time = time_axis[0]\n",
    "\n",
    "    for i in range(1, len(label_seq)):\n",
    "        if label_seq[i] != current_label:\n",
    "            segment_end_time = time_axis[i]\n",
    "            merged_segments.append((segment_start_time, segment_end_time, current_label))\n",
    "            current_label = label_seq[i]\n",
    "            segment_start_time = time_axis[i]\n",
    "    \n",
    "    # Add the last segment\n",
    "    merged_segments.append((segment_start_time, time_axis[-1], current_label))\n",
    "\n",
    "    # Display chord labels in the center of each segment\n",
    "    for start_time, end_time, chord_label in merged_segments:\n",
    "        # Assign color based on chord label (e.g., H chord gets a distinct color)\n",
    "        chord_color = \"blue\"  # Changed to blue for a neutral tone\n",
    "        # Display the chord label in the center of the segment\n",
    "        plt.text((start_time + end_time) / 2, 0.5, id2chord_str(chord_label), color=chord_color, fontsize=12, ha='center', va='center')\n",
    "\n",
    "        # Draw a vertical line at the segment boundary\n",
    "        plt.axvline(x=start_time, color='black', linestyle='-', linewidth=1)\n",
    "        plt.axvline(x=end_time, color='black', linestyle='-', linewidth=1)\n",
    "\n",
    "    # Tight layout to avoid label clipping\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Playback the audio in the notebook\n",
    "    audio_data = waveform.squeeze().numpy()  # Remove channel dimension if mono\n",
    "    return Audio(audio_data, rate=dataset.sample_rate)\n",
    "\n",
    "from marble.tasks.Chords1217.datamodule import Chords1217AudioTrain\n",
    "\n",
    "# Example usage:\n",
    "dataset = Chords1217AudioTrain(sample_rate=44100, channels=1, clip_seconds=5.0, jsonl=\"data/Chords1217/Chords1217.train.jsonl\", label_freq=10)\n",
    "sample_idx = 100  # Any index you'd like to inspect\n",
    "display_sample_audio_and_labels(dataset, sample_idx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81585aec",
   "metadata": {},
   "outputs": [],
   "source": []
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