{
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  {
   "cell_type": "markdown",
   "id": "eba9e610",
   "metadata": {},
   "source": [
    "A simple way to avoid being connected while transcribing is to first load the model version you want to use. See [here](https://github.com/openai/whisper/blob/main/README.md#available-models-and-languages) for more info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "85cd2d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Whisper(\n",
       "  (encoder): AudioEncoder(\n",
       "    (conv1): Conv1d(80, 1024, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "    (conv2): Conv1d(1024, 1024, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "    (blocks): ModuleList(\n",
       "      (0-23): 24 x ResidualAttentionBlock(\n",
       "        (attn): MultiHeadAttention(\n",
       "          (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (key): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "          (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (out): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        )\n",
       "        (attn_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): Sequential(\n",
       "          (0): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "          (1): GELU(approximate='none')\n",
       "          (2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "        )\n",
       "        (mlp_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "    )\n",
       "    (ln_post): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (decoder): TextDecoder(\n",
       "    (token_embedding): Embedding(51865, 1024)\n",
       "    (blocks): ModuleList(\n",
       "      (0-23): 24 x ResidualAttentionBlock(\n",
       "        (attn): MultiHeadAttention(\n",
       "          (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (key): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "          (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (out): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        )\n",
       "        (attn_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        (cross_attn): MultiHeadAttention(\n",
       "          (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (key): Linear(in_features=1024, out_features=1024, bias=False)\n",
       "          (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "          (out): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "        )\n",
       "        (cross_attn_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "        (mlp): Sequential(\n",
       "          (0): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "          (1): GELU(approximate='none')\n",
       "          (2): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "        )\n",
       "        (mlp_ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "      )\n",
       "    )\n",
       "    (ln): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import whisper\n",
    "#change to model size, bigger is more accurate but slower\n",
    "whisper.load_model(\"medium\") #base, small, medium, large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d2acd54",
   "metadata": {},
   "outputs": [],
   "source": [
    "#after it loads, you can disconnect from the internet and run the rest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2cd4050",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transcribe import transcribe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24e1d24e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function transcribe in module transcribe:\n",
      "\n",
      "transcribe(path, file_type, model=None, language=None, verbose=True)\n",
      "    Implementation of OpenAI's whisper model. Downloads model, transcribes audio files in a folder and returns the text files with transcriptions\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(transcribe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e52477fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "path='sample_audio/'#folder path\n",
    "file_type='ogg' #check your file for file type, will only transcribe those files\n",
    "model='medium' #'small', 'medium', 'large' (tradeoff between speed and accuracy)\n",
    "language= None #tries to auto-detect, other options include 'English', 'Spanish', etc...\n",
    "verbose = True # prints output while transcribing, False to deactivate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d66866af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using medium model, you can change this by specifying model=\"medium\" for example\n",
      "Only looking for file type ogg, you can change this by specifying file_type=\"mp3\"\n",
      "Expecting None language, you can change this by specifying language=\"English\". None will try to auto-detect\n",
      "Verbosity is True. If TRUE it will print out the text as it is transcribed, you can turn this off by setting verbose=False\n",
      "\n",
      "There are 2 ogg files in path: sample_audio/\n",
      "\n",
      "\n",
      "Loading model...\n",
      "Transcribing file number number 1: Armstrong_Small_Step\n",
      "Model and file loaded...\n",
      "Starting transcription...\n",
      "\n",
      "Detecting language using up to the first 30 seconds. Use `--language` to specify the language\n",
      "Detected language: English\n",
      "[00:00.000 --> 00:24.000]  That's one small step for man, one giant leap for mankind.\n",
      "\n",
      "Finished file number 1.\n",
      "\n",
      "\n",
      "\n",
      "Transcribing file number number 2: Axel_Pettersson_röstinspelning\n",
      "Model and file loaded...\n",
      "Starting transcription...\n",
      "\n",
      "Detecting language using up to the first 30 seconds. Use `--language` to specify the language\n",
      "Detected language: Swedish\n",
      "[00:00.000 --> 00:16.000]  Hej, jag heter Axel Pettersson, jag föddes i Örebro 1976. Jag har varit Wikipedia sen 2008 och jag har översatt röstintroduktionsprojektet till svenska.\n",
      "\n",
      "Finished file number 2.\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Finished transcription, files can be found in sample_audio/transcriptions'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transcribe(path, file_type, model, language, verbose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc67265",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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