Azure_Microsoft.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install azure-cognitiveservices-speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import azure.cognitiveservices.speech as speechsdk\n",
    "import pandas as pd\n",
    "\n",
    "# speech_config = speechsdk.SpeechConfig(subscription=os.environ.get('SPEECH_KEY'), region=os.environ.get('SPEECH_REGION'))\n",
    "speech_config = speechsdk.SpeechConfig(subscription='271cb26fab6e41c3a57ac3a93b354e03', region='eastus')\n",
    "speech_config.speech_recognition_language=\"zh-CN\"\n",
    "\n",
    "def transcribe_Azure(filename):\n",
    "    audio_config = speechsdk.audio.AudioConfig(filename=filename)\n",
    "    speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_config)\n",
    "    speech_recognition_result = speech_recognizer.recognize_once_async().get()\n",
    "    return speech_recognition_result.text\n",
    "\n",
    "df_azure = pd.DataFrame({\n",
    "    'Filename': [],\n",
    "    'Start_time': [],\n",
    "    'End_time': [],\n",
    "    'Azure': []\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1, 71):\n",
    "    number = str(i).zfill(2)\n",
    "    input_files = [f for f in os.listdir(f\"segmented_audio/conversationA/00{number}\")]\n",
    "    for input_file in input_files:\n",
    "        predicted = transcribe_Azure(f'segmented_audio/conversationA/00{number}/{input_file}')\n",
    "        match = re.search(r'(\\d+\\.\\d+)_(\\d+\\.\\d+)\\.wav$', input_file)\n",
    "        start_time = match.group(1)\n",
    "        end_time = match.group(2)\n",
    "        df_azure.loc[len(df_azure)] = {'Filename': f'D00{number}_A','Start_time': start_time,'End_time': end_time,'Azure': predicted}\n",
    "\n",
    "for i in range(1, 71):\n",
    "    number = str(i).zfill(2)\n",
    "    input_files = [f for f in os.listdir(f\"segmented_audio/conversationB/00{number}\")]\n",
    "    for input_file in input_files:\n",
    "        predicted = transcribe_Azure(f'segmented_audio/conversationB/00{number}/{input_file}')\n",
    "        match = re.search(r'(\\d+\\.\\d+)_(\\d+\\.\\d+)\\.wav$', input_file)\n",
    "        start_time = match.group(1)\n",
    "        end_time = match.group(2)\n",
    "        df_azure.loc[len(df_azure)] = {'Filename': f'D00{number}_B','Start_time': start_time,'End_time': end_time,'Azure': predicted}\n",
    "\n",
    "for i in range(1, 71):\n",
    "    number = str(i).zfill(2)\n",
    "    input_files = [f for f in os.listdir(f\"segmented_audio/voice_command/00{number}\")]\n",
    "    for input_file in input_files:\n",
    "        predicted = transcribe_Azure(f'segmented_audio/voice_command/00{number}/{input_file}')\n",
    "        match = re.search(r'(\\d+\\.\\d+)_(\\d+\\.\\d+)\\.wav$', input_file)\n",
    "        start_time = match.group(1)\n",
    "        end_time = match.group(2)\n",
    "        df_azure.loc[len(df_azure)] = {'Filename': f'P00{number}','Start_time': start_time,'End_time': end_time,'Azure': predicted}\n",
    "\n",
    "df_azure.to_csv('predicted_transcription/Azure.csv')"
   ]
  }
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