Google_Longaudio_API_without_speaker_diarization.ipynb

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   "source": [
    "filepath = \"~/audio_wav/\"\n",
    "output_filepath = \"~/Transcripts/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "from pydub import AudioSegment\n",
    "import io\n",
    "import os\n",
    "from google.cloud import speech\n",
    "from google.cloud.speech import enums\n",
    "from google.cloud.speech import types\n",
    "import wave\n",
    "from google.cloud import storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "def mp3_to_wav(audio_file_name):\n",
    "    if audio_file_name.split('.')[1] == 'mp3':    \n",
    "        sound = AudioSegment.from_mp3(audio_file_name)\n",
    "        audio_file_name = audio_file_name.split('.')[0] + '.wav'\n",
    "        sound.export(audio_file_name, format=\"wav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def frame_rate_channel(audio_file_name):\n",
    "    with wave.open(audio_file_name, \"rb\") as wave_file:\n",
    "        frame_rate = wave_file.getframerate()\n",
    "        channels = wave_file.getnchannels()\n",
    "        return frame_rate,channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "def stereo_to_mono(audio_file_name):\n",
    "    sound = AudioSegment.from_wav(audio_file_name)\n",
    "    sound = sound.set_channels(1)\n",
    "    sound.export(audio_file_name, format=\"wav\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "def upload_blob(bucket_name, source_file_name, destination_blob_name):\n",
    "    \"\"\"Uploads a file to the bucket.\"\"\"\n",
    "    storage_client = storage.Client()\n",
    "    bucket = storage_client.get_bucket(bucket_name)\n",
    "    blob = bucket.blob(destination_blob_name)\n",
    "\n",
    "    blob.upload_from_filename(source_file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def delete_blob(bucket_name, blob_name):\n",
    "    \"\"\"Deletes a blob from the bucket.\"\"\"\n",
    "    storage_client = storage.Client()\n",
    "    bucket = storage_client.get_bucket(bucket_name)\n",
    "    blob = bucket.blob(blob_name)\n",
    "\n",
    "    blob.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def google_transcribe(audio_file_name):\n",
    "    \n",
    "    file_name = filepath + audio_file_name\n",
    "    mp3_to_wav(file_name)\n",
    "\n",
    "    # The name of the audio file to transcribe\n",
    "    \n",
    "    frame_rate, channels = frame_rate_channel(file_name)\n",
    "    \n",
    "    if channels > 1:\n",
    "        stereo_to_mono(file_name)\n",
    "    \n",
    "    bucket_name = 'callsaudiofiles'\n",
    "    source_file_name = filepath + audio_file_name\n",
    "    destination_blob_name = audio_file_name\n",
    "    \n",
    "    upload_blob(bucket_name, source_file_name, destination_blob_name)\n",
    "    \n",
    "    gcs_uri = 'gs://callsaudiofiles/' + audio_file_name\n",
    "    transcript = ''\n",
    "    \n",
    "    client = speech.SpeechClient()\n",
    "    audio = types.RecognitionAudio(uri=gcs_uri)\n",
    "\n",
    "    config = types.RecognitionConfig(\n",
    "    encoding=enums.RecognitionConfig.AudioEncoding.LINEAR16,\n",
    "    sample_rate_hertz=frame_rate,\n",
    "    language_code='en-US')\n",
    "\n",
    "    # Detects speech in the audio file\n",
    "    operation = client.long_running_recognize(config, audio)\n",
    "    response = operation.result(timeout=10000)\n",
    "\n",
    "    for result in response.results:\n",
    "        transcript += result.alternatives[0].transcript\n",
    "    \n",
    "    delete_blob(bucket_name, destination_blob_name)\n",
    "    return transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def write_transcripts(transcript_filename,transcript):\n",
    "    f= open(output_filepath + transcript_filename,\"w+\")\n",
    "    f.write(transcript)\n",
    "    f.close() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    for audio_file_name in os.listdir(filepath):\n",
    "        transcript = google_transcribe(audio_file_name)\n",
    "        transcript_filename = audio_file_name.split('.')[0] + '.txt'\n",
    "        write_transcripts(transcript_filename,transcript)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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