DataPreprocessing_AudioSegmenting.ipynb

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   "execution_count": 1,
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     "text": [
      "Collecting pydub\n",
      "  Downloading pydub-0.25.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
      "Installing collected packages: pydub\n",
      "Successfully installed pydub-0.25.1\n",
      "Collecting pandas\n",
      "  Downloading pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
      "Collecting numpy>=1.26.0 (from pandas)\n",
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      "\u001b[?25hInstalling collected packages: tzdata, numpy, pandas\n",
      "Successfully installed numpy-1.26.4 pandas-2.2.2 tzdata-2024.1\n",
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     ]
    }
   ],
   "source": [
    "# !pip3 install pydub\n",
    "# !pip3 install pandas\n",
    "# !pip3 install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/restricted/projectnb/sparkpit/dlee5/.conda/envs/my_conda_env/lib/python3.12/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\n",
      "  warn(\"Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pydub import AudioSegment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_audio_segment(input_file, start_time, end_time, output_file):\n",
    "    \"\"\"\n",
    "    Extracts a segment from an audio file based on start and end timestamps.\n",
    "\n",
    "    Args:\n",
    "        input_file (str): Path to the input audio file.\n",
    "        start_time (int): Start time in milliseconds.\n",
    "        end_time (int): End time in milliseconds.\n",
    "        output_file (str): Path to the output audio file.\n",
    "    \"\"\"\n",
    "    audio = AudioSegment.from_wav(input_file) # Load the audio file\n",
    "    segment = audio[start_time:end_time] # Extract the segment\n",
    "    segment.export(output_file, format=\"wav\") # Export the segment as a new WAV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment participant's conversation audio files\n",
    "for i in range(1, 71):\n",
    "    number = str(i).zfill(2)\n",
    "    input_file = f'updated_annotation_deid_full/00{number}/D00{number}_A.txt'\n",
    "    df = pd.read_csv(input_file, sep='\\t', names=['Starting', 'Ending', 'Transcript'])\n",
    "    input_audio = f'audio_deid_full/00{number}/meeting_01.wav'\n",
    "    for j in range(df.shape[0]):\n",
    "        start_time = df['Starting'][j] * 1000\n",
    "        end_time = df['Ending'][j] * 1000\n",
    "        output_file = f'segmented_audio/conversationA/00{number}/conversationA-{number}_{start_time/1000}_{end_time/1000}.wav'\n",
    "        extract_audio_segment(input_audio, start_time, end_time, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment moderator's conversation audio files\n",
    "for i in range(1, 71):\n",
    "  number = str(i).zfill(2)\n",
    "  input_file = f'updated_annotation_deid_full/00{number}/D00{number}_B.txt'\n",
    "  df = pd.read_csv(input_file, sep='\\t', names=['Starting', 'Ending', 'Transcript'])\n",
    "  input_audio = f'audio_deid_full/00{number}/meeting_01.wav'\n",
    "  for j in range(df.shape[0]):\n",
    "    start_time = df['Starting'][j] * 1000\n",
    "    end_time = df['Ending'][j] * 1000\n",
    "    output_file = f'segmented_audio/conversationB/00{number}/conversationB-{number}_{start_time/1000}_{end_time/1000}.wav'\n",
    "    extract_audio_segment(input_audio, start_time, end_time, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# segment voice command audio files\n",
    "for i in range(1, 71):\n",
    "  number = str(i).zfill(2)\n",
    "  input_file = f'updated_annotation_deid_full/00{number}/P00{number}.txt'\n",
    "  df = pd.read_csv(input_file, sep='\\t', names=['Starting', 'Ending', 'Transcript'])\n",
    "  input_audio = f'audio_deid_full/00{number}/meeting_01.wav'\n",
    "  for j in range(df.shape[0]):\n",
    "    start_time = df['Starting'][j] * 1000\n",
    "    end_time = df['Ending'][j] * 1000\n",
    "    output_file = f'segmented_audio/voice_command/00{number}/command-{number}_{start_time/1000}_{end_time/1000}.wav'\n",
    "    extract_audio_segment(input_audio, start_time, end_time, output_file)"
   ]
  }
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