Offline-Arabic_speech_to_text-jonatasgrosman-wav2vec2-large-xlsr-53-arabic-server.ipynb

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     "text": [
      "C:\\Users\\mozwa\\anaconda3\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "from huggingsound import SpeechRecognitionModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4d6926f",
   "metadata": {},
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      "07/09/2023 20:21:01 - INFO - huggingsound.speech_recognition.model - Loading model...\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "C:\\Users\\mozwa\\anaconda3\\lib\\site-packages\\transformers\\configuration_utils.py:380: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model = SpeechRecognitionModel(\"jonatasgrosman/wav2vec2-large-xlsr-53-arabic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75f52d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_paths = [\"A_audio.wav\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e356bf17",
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    {
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     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.81s/it]\n"
     ]
    }
   ],
   "source": [
    "transcriptions = model.transcribe(audio_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb859c09",
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     "name": "stdout",
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     "text": [
      "حرف الباء\n"
     ]
    }
   ],
   "source": [
    "print(((transcriptions[0])['transcription']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc28df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "waiting for a connection...\n"
     ]
    }
   ],
   "source": [
    "import socket\n",
    "\n",
    "# set up a TCP/IP socket\n",
    "sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n",
    "\n",
    "# set up the server address and port\n",
    "server_address = ('192.168.1.2', 9988)\n",
    "sock.bind(server_address)\n",
    "\n",
    "# listen for incoming connections\n",
    "sock.listen()\n",
    "\n",
    "while True:\n",
    "    # wait for a connection\n",
    "    print('waiting for a connection...')\n",
    "    connection, client_address = sock.accept()\n",
    "    print(f'connection from {client_address}')\n",
    "\n",
    "    # receive the audio data from the client\n",
    "    audio_data = connection.recv(1024*1024)\n",
    "    print(f'received {len(audio_data)} bytes of audio data')\n",
    "\n",
    "    # save the audio data as a file\n",
    "    with open('audio.wav', 'wb') as f:\n",
    "        f.write(audio_data)\n",
    "     \n",
    "    #########\n",
    "    audio_paths = [\"audio.wav\"]\n",
    "    transcriptions = model.transcribe(audio_paths)\n",
    "    print(((transcriptions[0])['transcription']))\n",
    "    #########\n",
    "    \n",
    "    # send a response back to the client\n",
    "    connection.sendall(((transcriptions[0])['transcription']).encode('utf-8'))\n",
    "\n",
    "    # close the connection\n",
    "    connection.close()"
   ]
  }
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