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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Vaibhavs10/notebooks/blob/main/zero_to_asr_101.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Zero to Recognising Speech: Speed Run Edition ⚡️\n",
        "\n",
        "Welcome to a one size fits all notebook that takes you from finding a `transformers` compatible speech recognition checkpoint to building an easy to use demo with it. 🏃‍♂️"
      ],
      "metadata": {
        "id": "udNu4Qev9aAJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup environment\n",
        "\n",
        "🤗 `transformers` was made to get you up and running with as minimal friction. To be able to transcribe audio from *any* compatible checkpoint on the hub all you need to install is transformers. Later on we'll use `gradio` to build a neat demo that you can share with your friends, colleagues and network! 💥"
      ],
      "metadata": {
        "id": "-cR1yoJ295wg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "lClt9GWk83Qy"
      },
      "outputs": [],
      "source": [
        "!pip install --quiet transformers gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Transcription pipeline\n",
        "\n",
        "Pipelines within 🤗 `transformers` are a unified API for all supported architectures. This helps you to use the same codebase and play with different architectures."
      ],
      "metadata": {
        "id": "9bHVm9qI-ktt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's import pipeline from transformers."
      ],
      "metadata": {
        "id": "gsjsaaDB_a6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "7Ac_RtXC_aKi"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, let's define some helper variables to help us easily switch environments and models!"
      ],
      "metadata": {
        "id": "yNvZtKJz_48l"
      }
    },
    {
      "cell_type": "code",
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        "device = 0 if torch.cuda.is_available() else \"cpu\" # make sure to select GPU runtime\n",
        "MODEL_NAME = \"openai/whisper-small\""
      ],
      "metadata": {
        "id": "MULi9DseAB-b"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Last, we define our pipeline, this creates a wrapper around our model's generate function. All we need to define is the pipeline with an appropriate `task` and `model`."
      ],
      "metadata": {
        "id": "BFCBxdiuATa6"
      }
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        "pipe = pipeline(\n",
        "    task=\"automatic-speech-recognition\",\n",
        "    model=MODEL_NAME,\n",
        "    chunk_length_s=30,\n",
        "    device=device,\n",
        "    return_timestamps=True\n",
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        "id": "j_6iTtH39GP6",
        "outputId": "e9d007e1-2607-48aa-a8e5-d831d0393427"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.97k [00:00<?, ?B/s]"
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        },
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          "output_type": "display_data",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Awesome! Let's take this out for a spin!"
      ],
      "metadata": {
        "id": "uZeyxS9ZCDZz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe(\"sentence-1-1.wav\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQazs9I1A_VU",
        "outputId": "d918e377-fa41-4d3c-a1a4-7317bbf8bd04"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (448) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'text': ' This is kind of the conventional wisdom that they stand to gain or to from this environment right now.',\n",
              " 'chunks': [{'timestamp': (0.0, 5.84),\n",
              "   'text': ' This is kind of the conventional wisdom that they stand to gain or to from this environment right now.'}]}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Voila!\n",
        "\n",
        "Within seconds we have the transcriptions along with their associated timestamps 💥"
      ],
      "metadata": {
        "id": "Dt8vX1DvFDuY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's package it all up into an application now!"
      ],
      "metadata": {
        "id": "HYY56KZGF2S4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "import gradio as gr\n",
        "from transformers import pipeline\n",
        "\n",
        "MODEL_NAME = \"openai/whisper-small\" #this always needs to stay in line 8 :D sorry for the hackiness\n",
        "lang = \"en\"\n",
        "\n",
        "device = 0 if torch.cuda.is_available() else \"cpu\"\n",
        "pipe = pipeline(\n",
        "    task=\"automatic-speech-recognition\",\n",
        "    model=MODEL_NAME,\n",
        "    chunk_length_s=30,\n",
        "    device=device,\n",
        ")\n",
        "\n",
        "def transcribe(microphone, file_upload):\n",
        "    warn_output = \"\"\n",
        "    if (microphone is not None) and (file_upload is not None):\n",
        "        warn_output = (\n",
        "            \"WARNING: You've uploaded an audio file and used the microphone. \"\n",
        "            \"The recorded file from the microphone will be used and the uploaded audio will be discarded.\\n\"\n",
        "        )\n",
        "\n",
        "    elif (microphone is None) and (file_upload is None):\n",
        "        return \"ERROR: You have to either use the microphone or upload an audio file\"\n",
        "\n",
        "    file = microphone if microphone is not None else file_upload\n",
        "\n",
        "    text = pipe(file)[\"text\"]\n",
        "\n",
        "    return warn_output + text\n",
        "\n",
        "\n",
        "demo = gr.Blocks()\n",
        "\n",
        "mf_transcribe = gr.Interface(\n",
        "    fn=transcribe,\n",
        "    inputs=[\n",
        "        gr.inputs.Audio(source=\"microphone\", type=\"filepath\", optional=True),\n",
        "        gr.inputs.Audio(source=\"upload\", type=\"filepath\", optional=True),\n",
        "    ],\n",
        "    outputs=\"text\",\n",
        "    layout=\"horizontal\",\n",
        "    theme=\"huggingface\",\n",
        "    title=\"Whisper Demo: Transcribe Audio\",\n",
        "    description=(\n",
        "        \"Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the the fine-tuned\"\n",
        "        f\" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files\"\n",
        "        \" of arbitrary length.\"\n",
        "    ),\n",
        "    allow_flagging=\"never\",\n",
        ")\n",
        "\n",
        "\n",
        "with demo:\n",
        "    gr.TabbedInterface([mf_transcribe], [\"Transcribe Audio\"])\n",
        "\n",
        "demo.launch(enable_queue=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 779
        },
        "id": "-DfUpU25C8A1",
        "outputId": "01dd29ff-0012-41db-b7c9-9423bcdf81f2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:321: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/blocks.py:636: UserWarning: Cannot load huggingface. Caught Exception: The space huggingface does not exist\n",
            "  warnings.warn(f\"Cannot load {theme}. Caught Exception: {str(e)}\")\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `layout` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://49fce7475f25f3044d.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://49fce7475f25f3044d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That's it! You can now take this exact code snippet over to https://hf.co/spaces and share your own demos! \n",
        "\n",
        "Try it! and tweet your results with me [@reach_vb](https://twitter.com/reach_vb)"
      ],
      "metadata": {
        "id": "IFdxx452HCrs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interested in more?\n",
        "\n",
        "Checkout our 70x faster Whisper JAX implementation - https://huggingface.co/spaces/sanchit-gandhi/whisper-jax\n",
        "\n",
        "Fine-tune Whisper on your datasets - https://huggingface.co/blog/fine-tune-whisper"
      ],
      "metadata": {
        "id": "_HZ9XlSRKF29"
      }
    }
  ]
}