inference_code_whisper_example_with_Portuguese.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Inference code for Whisper (example with Whisper Medium in Portuguese)"
      ],
      "metadata": {
        "id": "lXj9fHZrwyAF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Autor: [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou)\n",
        "- Date: 09/12/2022\n",
        "- Credit: this notebook copy most of the code and text of the notebook [Whisper Large inference in 8-bit mode](https://colab.research.google.com/drive/1EMOwwfm1V1fHxH7eT1LLg7yBjhTooB6j?usp=sharing) from [Vaibhav Srivastav](https://www.linkedin.com/in/vaibhavs10/)\n",
        "- Blog post: [Speech-to-Text & IA | Transcreva qualquer áudio para o português com o Whisper (OpenAI)... sem nenhum custo!](https://medium.com/@pierre_guillou/speech-to-text-ia-transcreva-qualquer-%C3%A1udio-para-o-portugu%C3%AAs-com-o-whisper-openai-sem-ad0c17384681)"
      ],
      "metadata": {
        "id": "CWsqfbrN2_lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check GPU or CPU\n",
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "809bo1YO3UJd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Language & Whisper model"
      ],
      "metadata": {
        "id": "8HCp9Sd13NL2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# whisper model\n",
        "lang = \"pt\"\n",
        "model_name = \"pierreguillou/whisper-medium-portuguese\""
      ],
      "metadata": {
        "id": "FeT48mEB1_8H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll first install the necessary packages. We need ffmpeg to decode mp3 files from the CV11 dataset and transformers."
      ],
      "metadata": {
        "id": "zdjGedAL086A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!add-apt-repository -y ppa:jonathonf/ffmpeg-4 && apt update && apt install -y ffmpeg\n",
        "!pip install --quiet datasets git+https://github.com/huggingface/transformers evaluate huggingface_hub jiwer "
      ],
      "metadata": {
        "id": "K8kvTHa7w0zV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we will be running inference on CV11 dataset, we'd need to authenticate ourselves (since, CV11 requires accepting its Terms and Conditions)."
      ],
      "metadata": {
        "id": "SFg9vFTL1nmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store\n",
        "from huggingface_hub import login\n",
        "\n",
        "login()"
      ],
      "metadata": {
        "colab": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To reduce the memory and time overhead, we'll load the dataset in streaming fashion. During the time of inference we'll stream one data point at a time. This is specially useful for larger datasets."
      ],
      "metadata": {
        "id": "wyOJayaR11JD"
      }
    },
    {
      "cell_type": "code",
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        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\n",
        "    \"mozilla-foundation/common_voice_11_0\", lang, revision=\"streaming\", split=\"test\", streaming=True, use_auth_token=True\n",
        ")"
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      "execution_count": null,
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              "Downloading builder script:   0%|          | 0.00/8.30k [00:00<?, ?B/s]"
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      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import WhisperForConditionalGeneration, WhisperProcessor\n",
        "\n",
        "# load model and processor\n",
        "processor = WhisperProcessor.from_pretrained(model_name)\n",
        "model = WhisperForConditionalGeneration.from_pretrained(model_name)"
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    {
      "cell_type": "markdown",
      "source": [
        "Preprocess the dataset to be sampled at 16KHz, since Whisper expects 16KHz input."
      ],
      "metadata": {
        "id": "NogLfnzu2jVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Audio\n",
        "\n",
        "num_audios = 10\n",
        "dataset = dataset.take(num_audios)\n",
        "\n",
        "# resample to 16kHz\n",
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=16000))"
      ],
      "metadata": {
        "id": "aFIgxee02l7O"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Voila! Time to run inference loop!"
      ],
      "metadata": {
        "id": "D-UBMuHc2p1n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from time import perf_counter\n",
        "total = 0\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model = model.to(device)\n",
        "\n",
        "for data in dataset:\n",
        "  \n",
        "    inputs = processor.feature_extractor(data[\"audio\"][\"array\"], return_tensors=\"pt\", sampling_rate=16_000).input_features.to(device)\n",
        "    forced_decoder_ids = processor.get_decoder_prompt_ids(language=\"en\", task=\"transcribe\")\n",
        "\n",
        "    start = perf_counter()\n",
        "    predicted_ids = model.generate(inputs, forced_decoder_ids=forced_decoder_ids)\n",
        "    text = processor.tokenizer.batch_decode(predicted_ids, skip_special_tokens=True, normalize=False)[0]\n",
        "    end = perf_counter()\n",
        "\n",
        "    diff = end - start\n",
        "    total += diff\n",
        "    print(text)\n",
        "\n",
        "print(f\"\\ntotal prediction time for 10 audios: {round(total,2)}s\")\n",
        "print(f\"average prediction time: {round(total/num_audios,2)}s\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bm0Uu9dY2sNE",
        "outputId": "9e1f9190-ab80-412c-a177-4f3599f44835"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reading metadata...: 8693it [00:01, 7015.14it/s]\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1387: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 448 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` 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": "stream",
          "name": "stdout",
          "text": [
            "Cheque match\n",
            "É necessário fornecer, quando formulado, uma vazação.\n",
            "Buteá\n",
            "Se esta primeira condição for satisfeita, sensata, forte e ágil.\n",
            "Most of us tempos are pistolas.\n",
            "For more information, visit the site of the Fedora Project.\n",
            "Bem, digitalizar Don Quixote é um passo para levar a cultura a todos.\n",
            "Arivém\n",
            "Ele é advogado do comando vermelho.\n",
            "Curuçá\n",
            "\n",
            "total prediction time for 10 audios: 18.28s\n",
            "average prediction time: 1.83s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END"
      ],
      "metadata": {
        "id": "5qbi40uW44h1"
      }
    }
  ]
}