notebook-examples/whisper_audio_to_text.ipynb

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   "metadata": {},
   "source": [
    "...original notebook from https://github.com/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "#  OpenAI's Whisper\n",
    "Speech to text...\n",
    "\n",
    "more information at\n",
    "- https://openai.com/blog/whisper\n",
    "- https://github.com/openai/whisper\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "%%capture\n",
    "# install dependencies\n",
    "\n",
    "! pip install git+https://github.com/openai/whisper.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "3CqtR2Fi5-vP"
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     "text": [
      "2023-10-13 09:14:42.948361: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "# use imports and select cuda\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "try:\n",
    "    import tensorflow \n",
    "except ImportError:\n",
    "    pass\n",
    "\n",
    "import torch\n",
    "import pandas as pd\n",
    "import whisper\n",
    "import torchaudio\n",
    "\n",
    "from ipywebrtc import AudioRecorder, CameraStream\n",
    "from IPython.display import Audio, display\n",
    "import ipywidgets as widgets\n",
    "\n",
    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\""
   ]
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       "AudioRecorder(audio=Audio(value=b'', format='webm'), stream=CameraStream(constraints={'audio': True, 'video': …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# manually record using webcam - if u want to use a custom audio file, skip this section\n",
    "camera = CameraStream(constraints={'audio': True,'video':False})\n",
    "recorder = AudioRecorder(stream=camera)\n",
    "recorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "EDDgAohMGrCR"
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   "source": [
    "# save recording as file and convert to wav\n",
    "with open('recording.webm', 'wb') as f:\n",
    "    f.write(recorder.audio.value)\n",
    "!ffmpeg -i recording.webm -ac 1 -f wav my_recording.wav -y -hide_banner -loglevel panic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "id": "U7p2AoJItnIM"
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    "# Whisper is capable of performing transcriptions for many languages (though it performs better for some languages and worse for others.) Whisper is also capable of detecting the input language. \n",
    "# However, to be on the safe side, we can explicitly tell Whisper which language to expect. \n",
    "language_options = whisper.tokenizer.TO_LANGUAGE_CODE \n",
    "language_list = list(language_options.keys())"
   ]
  },
  {
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   "execution_count": 8,
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       "Dropdown(options=('english', 'chinese', 'german', 'spanish', 'russian', 'korean', 'french', 'japanese', 'portu…"
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   "source": [
    "# Whisper is also capable of several tasks, including English-only transcription, \n",
    "# Any-to-English translation, and non-English transcription. \n",
    "lang_dropdown = widgets.Dropdown(options=language_list, value='english')\n",
    "output = widgets.Output()\n",
    "display(lang_dropdown)"
   ]
  },
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       "Dropdown(options=('transcribe', 'translate'), value='transcribe')"
      ]
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   ],
   "source": [
    "task_dropdown = widgets.Dropdown(options=['transcribe', 'translate'], value='transcribe')\n",
    "output = widgets.Output()\n",
    "display(task_dropdown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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     "output_type": "stream",
     "text": [
      "Model is English-only and has 71,825,408 parameters.\n"
     ]
    }
   ],
   "source": [
    "# load the model (takes some seconds)\n",
    "# hint: Whisper comes in five model sizes, \n",
    "# four of which also have an optimized English-only version. \n",
    "# This notebook loads \"base\"-sized models (bigger than \"tiny\" but smaller than the others), which require about 1GB of RAM.\n",
    "\n",
    "#If you selected English above, the cell below will load the optimized English-only version. Otherwise, it will load the multilingual model.\n",
    "\n",
    "if lang_dropdown.value == \"english\":\n",
    "    model = whisper.load_model(\"base.en\")\n",
    "else:\n",
    "    model = whisper.load_model(\"base\")\n",
    "print(\n",
    "    f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n",
    "    f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    },
    "id": "DwOUHau-dkUt",
    "outputId": "d16eef87-3af5-43e6-b54e-407a2e32f5d8"
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     "data": {
      "text/plain": [
       "DecodingOptions(task='transcribe', language='english', temperature=0.0, sample_len=None, best_of=None, beam_size=None, patience=None, length_penalty=None, prompt=None, prefix=None, suppress_tokens='-1', suppress_blank=True, without_timestamps=True, max_initial_timestamp=1.0, fp16=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set the options\n",
    "options = whisper.DecodingOptions(language=lang_dropdown.value, task=task_dropdown.value, without_timestamps=True)\n",
    "options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "q6S0VvoK0vfq"
   },
   "outputs": [],
   "source": [
    "# choose your audio file\n",
    "#audio = whisper.load_audio(\"my_recording.wav\")\n",
    "audio = whisper.load_audio(\"QA-01.mp3\")\n",
    "audio = whisper.pad_or_trim(audio)\n",
    "mel = whisper.log_mel_spectrogram(audio).to(model.device)\n",
    "result = model.decode(mel, options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "Mg64_MWW1uMb",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How many people are there in your family? There are five people in my family. My father, mother, brother, sister, and me. Does your family live in a house or an apartment? We live in a house in the countryside. What does your father do? My father is a doctor. He works at the local hospital. How old is your mother? She is 40 years old, one year younger than my father.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the text\n",
    "result.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# or write it into a text file\n",
    "\n",
    "text_file = open(\"output.txt\", \"w\")\n",
    "text_file.write(result.text)\n",
    " \n",
    "#close file\n",
    "text_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# close all widgets\n",
    "from ipywidgets import Widget\n",
    "Widget.close_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it takes around 2GB memory on GPU, so please clear it\n",
    "from numba import cuda\n",
    "device = cuda.get_current_device()\n",
    "device.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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