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   "source": [
    "# HuggingFace Repository"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "This tutorial is available as an IPython notebook at [malaya-speech/example/huggingface-repository](https://github.com/huseinzol05/malaya-speech/tree/master/example/huggingface-repository).\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting Malaya-Speech 1.2.6, you can load Malaya-Speech models from https://huggingface.co/huseinzol05 to get better download speed, and by default Malaya-Speech will use HuggingFace as backend repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import malaya_speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.2.6.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya_speech.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load model from Backblaze\n",
    "\n",
    "If you found some models not exist in HuggingFace, you can try to use BackBlaze as alternative,\n",
    "\n",
    "1. First, set global `MALAYA_USE_HUGGINGFACE` = `false`,\n",
    "\n",
    "```python\n",
    "os.environ['MALAYA_USE_HUGGINGFACE'] = 'false'\n",
    "import malaya_speech\n",
    "```\n",
    "\n",
    "2. Simply pass `use_huggingface=False` in load model function parameter, for an example,\n",
    "\n",
    "```python\n",
    "malaya_speech.stt.deep_ctc(model = 'hubert-conformer-tiny', use_huggingface = False)\n",
    "```"
   ]
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     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<malaya_speech.model.wav2vec.Wav2Vec2_CTC at 0x10c539b50>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "malaya_speech.stt.deep_ctc(model = 'hubert-conformer-tiny', use_huggingface = False)"
   ]
  }
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