whisper_more_efficient_encoding.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ig33N7IHYTKV"
      },
      "source": [
        "# Early-stop encoding\n",
        "\n",
        "The first part of this notebook is based on the [build notebook](https://github.com/futo-org/whisper-acft/blob/main/finetune.ipynb) for `whisper-acft`.\n",
        "\n",
        "This notebook does the following:\n",
        "- Downloads a Whisper model.\n",
        "- Fine-tunes it to support processing shorter audio segments with less zero-padding.\n",
        "- Adjusts the model's vocabulary, to make it less likely to hallucinate profanity.\n",
        "- Packages the model in a format supported by Joplin.\n",
        "\n",
        "The `whisper-acft` build notebook is licensed under the MIT license:\n",
        "<details><summary>MIT License</summary>\n",
        "\n",
        "MIT License\n",
        "\n",
        "Copyright (c) 2024 FUTO Organization\n",
        "\n",
        "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
        "\n",
        "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
        "\n",
        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
        "\n",
        "</details>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_RV_029B512y",
        "outputId": "25e5b795-e0be-4c4b-bd3f-f33891022d40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2024.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-3.4.1 dill-0.3.8 fsspec-2024.12.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers torch datasets"
      ]
    },
    {
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        },
        "id": "ylm1ZBQNZH8_",
        "outputId": "19de1f92-353d-44c9-b15a-bdebe6d60fd0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "410eaf91da6b4ba695a4a592bb74adf0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/13.3k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f793a05069a74f19a22a40c5c729eec8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "fleurs.py:   0%|          | 0.00/12.5k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for google/fleurs contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/google/fleurs.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "16069ec19b864f67a19d5158a794c020",
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              "version_minor": 0
            },
            "text/plain": [
              "train.tar.gz:   0%|          | 0.00/1.73G [00:00<?, ?B/s]"
            ]
          },
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          "output_type": "display_data"
        },
        {
          "data": {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "dev.tar.gz:   0%|          | 0.00/143M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "996a358a4215449998e7a538b89c3243",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "test.tar.gz:   0%|          | 0.00/349M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "269cc5846c8042bba6fc34066c5e0b8a",
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              "version_minor": 0
            },
            "text/plain": [
              "train.tsv:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5199471f3024891aaeb7cb420fd279c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "dev.tsv:   0%|          | 0.00/181k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7b642af9aad1498398c67533248695e4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "test.tsv:   0%|          | 0.00/457k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5f50c53037f4a4385e714c2a9184b08",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d99a52a6273f4c61b82af140c3e5bacc",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7cb03f29842a4306a998a023a6a01568",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import load_dataset, Audio\n",
        "\n",
        "# Change these to fine-tune on some other language\n",
        "transcribe_langauge = 'french'\n",
        "transcribe_language_code = 'fr_fr'\n",
        "\n",
        "dataset = load_dataset('google/fleurs', transcribe_language_code, split='train')\n",
        "\n",
        "audio_feature = Audio(sampling_rate=16_000)\n",
        "dataset = dataset.cast_column('audio', audio_feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Otmxn5yWaRKw"
      },
      "source": [
        "## Model setup\n",
        "\n",
        "Next, create two models: One to be trained and one to use as a refrerence. To try to keep the output as consistent as possible, we'll use the output of `model_base` as the expected output of `model_train`."
      ]
    },
    {
      "cell_type": "code",
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            "92fc26f675274b748715d9b897169128",
            "9fa963b472ff4cf79331a596917d9749",
            "5231684f0d8e468291b5c6b9d74e61f9",
            "df33097a61a044f2aeb574fcca1a9933",
            "f824c50bd5154f7d80a3cd6ba6523e92",
            "938301a45a534fa394574765d25d9406",
            "5ce158b08659409795daa27b8b56116a",
            "38d6e075d06c4d0b8253f9211331ba59",
            "8a1b82c01a6442b9b35edf0d62136ff4",
            "39f97eb9ebe64f81ba491fe2851e983d",
            "2b5ac09d264f4cebb8836518c8db0b74",
            "9758d78a083e4d2aad5f357e5e99e9a7",
            "be0c2044548e47ccb2b57537f8dc9273",
            "e52b0b0c2844458cac702dbe6b24726b"
          ]
        },
        "id": "pNeijGwl6Ldz",
        "outputId": "bed8c3b8-daa4-4bfa-f332-3f186ad1640e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5a80f26be46b4898a2f5a43a11cc51fe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.98k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25c6333a29114f29a55bd188e486c4ad",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/151M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "112e34418956440a98affc393ef142b4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/185k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8fb5f24b93064135ae1095f4493925c3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/283k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e3e71f8161348faa7ea8633a1f48cbe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/836k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90241101bacd465d9e289413fe00918b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.48M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "98fee9fdeef74ef490418f124bf0d3e8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/494k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f5e24fafd47b4f9790ac4ec4cb64dc30",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "normalizer.json:   0%|          | 0.00/52.7k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "68b5278fbdc94161a0a1941f922cdb4b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/34.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df33097a61a044f2aeb574fcca1a9933",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.19k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import WhisperModel, WhisperTokenizer, WhisperProcessor\n",
        "\n",
        "# TODO: Change model_name to match the name of the model to update (e.g. to personalizedrefrigerator/whisper-base-fr)\n",
        "whisper_mode = 'tiny'\n",
        "model_name = f'openai/whisper-{whisper_mode}'\n",
        "model_train = WhisperModel.from_pretrained(model_name).cuda().train()\n",
        "model_base = WhisperModel.from_pretrained(model_name).cuda().eval()\n",
        "processor = WhisperProcessor.from_pretrained(model_name, language=transcribe_langauge, task='transcribe')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gF18nNlcAIy"
      },
      "source": [
        "## Fine-tune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "vfUBTmVRgNfr"
      },
      "outputs": [],
      "source": [
        "ds = dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GzqOsTXCaOdh",
        "outputId": "c4a89e63-c01e-4f97-8eeb-8ff78f62ab3f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['<|startoftranscript|>',\n",
              " '<|fr|>',\n",
              " '<|transcribe|>',\n",
              " '<|notimestamps|>',\n",
              " 'Il',\n",
              " ' s',\n",
              " '�',\n",
              " '�',\n",
              " 'agit',\n",
              " ' d',\n",
              " '�',\n",
              " '�',\n",
              " 'une',\n",
              " ' ent',\n",
              " 'ité',\n",
              " ' très',\n",
              " ' complex',\n",
              " 'e',\n",
              " ' qui',\n",
              " ' consiste',\n",
              " ',',\n",
              " ' selon',\n",
              " ' un',\n",
              " ' modèle',\n",
              " ' de',\n",
              " ' Boh',\n",
              " 'r',\n",
              " ' simpl',\n",
              " 'ifi',\n",
              " 'é',\n",
              " ',',\n",
              " ' en',\n",
              " ' un',\n",
              " ' no',\n",
              " 'y',\n",
              " 'au',\n",
              " ' central',\n",
              " ' orb',\n",
              " 'ité',\n",
              " ' par',\n",
              " ' des',\n",
              " ' élect',\n",
              " 'rons',\n",
              " ',',\n",
              " ' un',\n",
              " ' peu',\n",
              " ' comme',\n",
              " ' les',\n",
              " ' plan',\n",
              " 'è',\n",
              " 'tes',\n",
              " ' en',\n",
              " ' orb',\n",
              " 'ite',\n",
              " ' autour',\n",
              " ' du',\n",
              " ' sole',\n",
              " 'il',\n",
              " ' —',\n",
              " ' c',\n",
              " 'f',\n",
              " '.',\n",
              " ' illustration',\n",
              " '�',\n",
              " '�',\n",
              " '1',\n",
              " '.',\n",
              " '1',\n",
              " '.',\n",
              " '<|endoftext|>']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_sample(example):\n",
        "    waveform = example['audio']['array']\n",
        "    sampling_rate = example['audio']['sampling_rate']\n",
        "    assert sampling_rate == 16_000\n",
        "\n",
        "    input_features = processor(\n",
        "        waveform, sampling_rate=sampling_rate, return_tensors='pt'\n",
        "    ).input_features\n",
        "    input_ids = processor.tokenizer.encode(example['raw_transcription'])\n",
        "    return {\n",
        "        'length': len(waveform) / sampling_rate,\n",
        "        'input_features': input_features,\n",
        "        'input_ids': input_ids\n",
        "    }\n",
        "\n",
        "# Test\n",
        "[processor.tokenizer.decode(i) for i in get_sample(ds[1])['input_ids']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "5BYxybfAaipJ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from tqdm import tqdm\n",
        "from torch import nn\n",
        "\n",
        "# Note: Mostly copied from https://github.com/futo-org/whisper-acft/blob/main/finetune.ipynb\n",
        "#       See above for license and other information.\n",
        "\n",
        "def compute_partially_encoder(model, data, n_audio_ctx):\n",
        "    \"\"\"\n",
        "        Computes hidden states for the given model with only a partial run of the encoder.\n",
        "\n",
        "        Parameters:\n",
        "        - model: The model.\n",
        "        - data: Input features to the model.\n",
        "        - n_audio_ctx: Constant slightly larger than the recording length (in 1 unit / 50s). Set to 1500 to use the full recording. See https://github.com/futo-org/whisper-acft/issues/6#issuecomment-2290093422.\n",
        "    \"\"\"\n",
        "    diffy = 2 * n_audio_ctx - data.shape[2]\n",
        "    if diffy > 0:\n",
        "        data = nn.functional.pad(data, [0, diffy, 0, 0, 0, 0], 'constant', 0.0)\n",
        "    elif diffy < 0:\n",
        "        data = data[:,:,:diffy]\n",
        "\n",
        "    # Default encoding -- the full audio\n",
        "    if n_audio_ctx == 1500:\n",
        "        return model.encoder(data).last_hidden_state\n",
        "\n",
        "    input_embeds = nn.functional.gelu(model.encoder.conv1(data))\n",
        "    input_embeds = nn.functional.gelu(model.encoder.conv2(input_embeds))\n",
        "    input_embeds = input_embeds.permute(0, 2, 1)\n",
        "\n",
        "    embed_pos = model.encoder.embed_positions.weight[:n_audio_ctx]\n",
        "\n",
        "    hidden_states = input_embeds + embed_pos\n",
        "    hidden_states = nn.functional.dropout(hidden_states, p=model.encoder.dropout, training=model.encoder.training)\n",
        "\n",
        "    for idx, encoder_layer in enumerate(model.encoder.layers):\n",
        "        to_drop = False\n",
        "        if model.encoder.training:\n",
        "            dropout_probability = torch.rand([])\n",
        "            if dropout_probability < model.encoder.layerdrop:\n",
        "                to_drop = True\n",
        "\n",
        "        if to_drop:\n",
        "            layer_outputs = (None, None)\n",
        "        else:\n",
        "            if model.encoder.gradient_checkpointing and model.encoder.training:\n",
        "                layer_outputs = model.encoder._gradient_checkpointing_func(\n",
        "                    encoder_layer.__call__,\n",
        "                    hidden_states,\n",
        "                    None,\n",
        "                    None,\n",
        "                    False,\n",
        "                )\n",
        "            else:\n",
        "                layer_outputs = encoder_layer(\n",
        "                    hidden_states,\n",
        "                    None,\n",
        "                    layer_head_mask=None,\n",
        "                    output_attentions=False,\n",
        "                )\n",
        "\n",
        "            hidden_states = layer_outputs[0]\n",
        "\n",
        "    hidden_states = model.encoder.layer_norm(hidden_states)\n",
        "    return hidden_states\n",
        "\n",
        "\n",
        "def compute_hidden_state_loss(model_train, model_base, optimizer, criterion, example):\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    n_ctx = int(round((1500.0 / 30.0) * example[\"length\"] ))\n",
        "\n",
        "    extra_ctx = torch.randint(-min(64, n_ctx // 3), min(64, n_ctx // 3), (1,)).item()\n",
        "    n_ctx += extra_ctx\n",
        "\n",
        "    input_features = example[\"input_features\"].cuda()\n",
        "    input_ids = torch.tensor([example[\"input_ids\"]], dtype=torch.long).cuda()\n",
        "\n",
        "    encoder_hidden_states_partial = compute_partially_encoder(model_train, input_features, n_ctx)\n",
        "    output_partial = model_train.decoder(\n",
        "        input_ids=input_ids,\n",
        "        encoder_hidden_states=encoder_hidden_states_partial,\n",
        "        output_hidden_states=True\n",
        "    )\n",
        "\n",
        "    with torch.no_grad():\n",
        "        encoder_hidden_states_full = compute_partially_encoder(model_base, input_features, 1500)\n",
        "        output_full = model_base.decoder(\n",
        "            input_ids=input_ids,\n",
        "            encoder_hidden_states=encoder_hidden_states_full,\n",
        "            output_hidden_states=True\n",
        "        )\n",
        "\n",
        "    loss = criterion(\n",
        "        #output_partial.hidden_states[-1],\n",
        "        #output_full.hidden_states[-1]\n",
        "        torch.cat(output_partial.hidden_states, 0),\n",
        "        torch.cat(output_full.hidden_states, 0)\n",
        "    )\n",
        "\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5MXMoMKbf35D"
      },
      "source": [
        "Next, enter the training loop:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4RCVEdHcjUX",
        "outputId": "cd7e261b-40aa-4bdb-a6d7-dec18178be1c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/3193 [00:00<?, ?it/s]Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.43.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n",
            "Epoch 0, Loss: 0.21972252428531647: 100%|██████████| 3193/3193 [03:59<00:00, 13.33it/s]\n",
            "Epoch 1, Loss: 0.1410713791847229: 100%|██████████| 3193/3193 [03:48<00:00, 13.98it/s]\n",
            "Epoch 2, Loss: 0.062303878366947174: 100%|██████████| 3193/3193 [03:50<00:00, 13.87it/s]\n",
            "Epoch 3, Loss: 0.11009455472230911: 100%|██████████| 3193/3193 [03:57<00:00, 13.42it/s]\n",
            "Epoch 4, Loss: 0.04652934893965721: 100%|██████████| 3193/3193 [03:51<00:00, 13.80it/s]\n",
            "Epoch 5, Loss: 0.09415868669748306: 100%|██████████| 3193/3193 [03:55<00:00, 13.58it/s]\n",
            "Epoch 6, Loss: 0.06815165281295776: 100%|██████████| 3193/3193 [03:56<00:00, 13.49it/s]\n",
            "Epoch 7, Loss: 0.053578853607177734: 100%|██████████| 3193/3193 [03:58<00:00, 13.38it/s]\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.tensorboard import SummaryWriter\n",
        "\n",
        "# Note: Mostly copied from https://github.com/futo-org/whisper-acft/blob/main/finetune.ipynb\n",
        "#       See above for license and other information.\n",
        "\n",
        "criterion = torch.nn.MSELoss()\n",
        "optimizer = torch.optim.Adam(model_train.parameters(), lr=1e-7)\n",
        "\n",
        "\n",
        "writer = SummaryWriter()\n",
        "writer.add_text(\"name\", f\"{model_name} v3\")\n",
        "\n",
        "num_length = 0\n",
        "step = 0\n",
        "for epoch in range(8):\n",
        "  pbar = tqdm(ds.shuffle(seed=epoch))\n",
        "  for example in pbar:\n",
        "    example = get_sample(example)\n",
        "    if example[\"length\"] > 29.0: continue\n",
        "\n",
        "    loss = compute_hidden_state_loss(model_train, model_base, optimizer, criterion, example)\n",
        "    step += 1\n",
        "    num_length += example[\"length\"]\n",
        "\n",
        "    writer.add_scalar(\"loss/train\", loss.item(), step)\n",
        "    writer.add_scalar(\"length/train\", num_length, step)\n",
        "    writer.add_scalar(\"epoch/train\", epoch, step)\n",
        "\n",
        "    pbar.set_description(f\"Epoch {epoch}, Loss: {loss.item()}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212,
          "referenced_widgets": [
            "9c12f167d1354d71bdcb50d5b0e2b0ef",
            "dd557791a698434986455ded05a978e5",
            "87e73f387deb4ad79bfd01a6de78fcf3",
            "b21177dff2044028a362d7940a49e1cf",
            "9fbf7740013846749d8fdc61a04930dc",
            "a8653eb1610e4bbf888f233625482af0",
            "9bdb6f66273f45d28da851a3a41723d0",
            "eff638b5dea64c9e9c4e7eb0bc9a9a0d",
            "8f42b1a10a8243cb9b2c43ed02929fc2",
            "7eef2eb47bd5418ea5722502cd0a8dac",
            "3ef080cc70284fa08e0cc694be3f4c2d"
          ]
        },
        "id": "dP_9YLfyhMk_",
        "outputId": "1fac1ef8-88e3-4d30-933d-21765633ee33"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9c12f167d1354d71bdcb50d5b0e2b0ef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/3.75k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py:3353: UserWarning: Moving the following attributes in the config to the generation config: {'max_length': 448, 'suppress_tokens': [1, 2, 7, 8, 9, 10, 14, 25, 26, 27, 28, 29, 31, 58, 59, 60, 61, 62, 63, 90, 91, 92, 93, 359, 503, 522, 542, 873, 893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627, 3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647, 7273, 9061, 9383, 10428, 10929, 11938, 12033, 12331, 12562, 13793, 14157, 14635, 15265, 15618, 16553, 16604, 18362, 18956, 20075, 21675, 22520, 26130, 26161, 26435, 28279, 29464, 31650, 32302, 32470, 36865, 42863, 47425, 49870, 50254, 50258, 50358, 50359, 50360, 50361, 50362], 'begin_suppress_tokens': [220, 50257]}. You are seeing this warning because you've set generation parameters in the model config, as opposed to in the generation config.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "('final-model/tokenizer_config.json',\n",
              " 'final-model/special_tokens_map.json',\n",
              " 'final-model/vocab.json',\n",
              " 'final-model/merges.txt',\n",
              " 'final-model/normalizer.json',\n",
              " 'final-model/added_tokens.json')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import WhisperForConditionalGeneration\n",
        "model = WhisperForConditionalGeneration.from_pretrained(model_name).eval().cpu()\n",
        "model.model = model_train.eval().cpu()\n",
        "\n",
        "model.save_pretrained('final-model')\n",
        "processor.tokenizer.save_pretrained('final-model')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ev5gYR3rgoWw"
      },
      "source": [
        "## Testing it!\n",
        "\n",
        "To verify that the model still works, log the model's output on the first sample in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHDnsucigpeH",
        "outputId": "2c84d2e7-63d2-4311-98eb-a87a058a7663"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "It is strongly recommended to pass the `sampling_rate` argument to `WhisperFeatureExtractor()`. Failing to do so can result in silent errors that might be hard to debug.\n",
            "/usr/local/lib/python3.11/dist-packages/transformers/models/whisper/generation_whisper.py:573: FutureWarning: The input name `inputs` is deprecated. Please make sure to use `input_features` instead.\n",
            "  warnings.warn(\n",
            "Due to a bug fix in https://github.com/huggingface/transformers/pull/28687 transcription using a multilingual Whisper will default to language detection followed by transcription instead of translation to English.This might be a breaking change for your use case. If you want to instead always translate your audio to English, make sure to pass `language='en'`.\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[\" Quand la capture entroira dans la demose fère de terrestre, vers 5 heures du matin, vers de l'est, elle offrira un spectacle humil, une spectaculaire, vous habitante d'une orbe la quel formie, de l'horégomme, du névada et de l'Uta.\"]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample_data = next(iter(dataset))\n",
        "\n",
        "input_ids = processor(\n",
        "    sample_data['audio']['array'], return_tensors='pt'\n",
        ").input_features\n",
        "output_ids = model.generate(inputs=input_ids)\n",
        "processor.batch_decode(output_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oW2jOvmsLsAm"
      },
      "source": [
        "# Vocabulary adjustements and packaging\n",
        "\n",
        "This section:\n",
        "1. Updates the model vocabulary.\n",
        "2. Builds a Joplin-compatible model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "FPGYcxa7zmO5"
      },
      "outputs": [],
      "source": [
        "from transformers import WhisperFeatureExtractor, WhisperTokenizer\n",
        "\n",
        "feature_extractor = processor.feature_extractor\n",
        "tokenizer_original = processor.tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAjHVQPCueTO"
      },
      "source": [
        "We'll create a customized tokenizer based on `tokenizer_original` in the next section."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2-3B1TW3Tc1"
      },
      "source": [
        "## Vocabulary adjustements\n",
        "\n",
        "Next, we remove several unwanted tokens from the vocabulary. This section is originally from `whisper_vocab_cleanup.ipynb`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWsP8YfrcmUC",
        "outputId": "d68abc23-b177-4464-a621-444d2d3ab4b6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('whisper-default-tokenizer/tokenizer_config.json',\n",
              " 'whisper-default-tokenizer/special_tokens_map.json',\n",
              " 'whisper-default-tokenizer/vocab.json',\n",
              " 'whisper-default-tokenizer/merges.txt',\n",
              " 'whisper-default-tokenizer/normalizer.json',\n",
              " 'whisper-default-tokenizer/added_tokens.json')"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from pathlib import Path\n",
        "\n",
        "# Step 1: Save the vocabulary to a file\n",
        "tokenizer_directory = Path('whisper-default-tokenizer')\n",
        "tokenizer_original.save_pretrained(tokenizer_directory)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ES0enoY-ezxm"
      },
      "source": [
        "Now that the tokenizer is saved in `tokenizer_directory`, we can load `tokenizer_directory/vocab.json` and modify it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "jtHNBGVPewT2"
      },
      "outputs": [],
      "source": [
        "# Step 2: Get vocab.json\n",
        "import json\n",
        "\n",
        "def json_from_path(path: Path):\n",
        "    with open(path, 'r', encoding='utf-8') as f:\n",
        "        return json.loads(f.read())\n",
        "\n",
        "vocab = json_from_path(tokenizer_directory / 'vocab.json')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "TBqPdNpLkSYg"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "NONWORD_REGEX = re.compile(r'[ \\t?.,;!()/\\-«»]+')\n",
        "def split_by_word(text: str):\n",
        "    \"\"\" Splits the given `text` into words. Returns a list of those words. \"\"\"\n",
        "    return NONWORD_REGEX.split(text)\n",
        "\n",
        "\n",
        "# This character marks the beginning of a word in vocab.json\n",
        "word_start_char = 'Ġ'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "PPBQrfHmsEdz"
      },
      "outputs": [],
      "source": [
        "# Step 3: Replace!\n",
        "next_replacement_idx = 0\n",
        "new_vocab = {}\n",
        "\n",
        "# Token IDs can be found by inspecting the original vocab.json. These token IDs\n",
        "# are specific to the multilingual whisper-tiny, but may also work for whisper-base. Each remapping should be unique.\n",
        "token_id_remappings = {\n",
        "    19186: \"[swearS1]\", # s***\n",
        "    30748: word_start_char + \"[swearS2]\",\n",
        "    4611: word_start_char + \"[swearS3]\",\n",
        "    19593: word_start_char + \"[swearS4]\", # S***\n",
        "    10965: word_start_char + \"[swearF1]\", # F***\n",
        "    26154: word_start_char + \"[swearF2]\", # F***\n",
        "    33342: word_start_char + \"[swearF3]\",\n",
        "    47069: word_start_char + \"[swearF4]\", # f****\n",
        "    3275: word_start_char + \"[swearF5]\",\n",
        "    22518: word_start_char + \"[swearF6]\",\n",
        "    20022: word_start_char + \"[swearF7]\",\n",
        "    5546: word_start_char + \"[swearF8]\",\n",
        "    47069: word_start_char + \"[swearM1]\",\n",
        "    29537: word_start_char + \"[swearM2]\",\n",
        "    22676: word_start_char + \"[swearB1]\", # bull****\n",
        "    11960: word_start_char + \"[swearB2]\",\n",
        "    42094: word_start_char + \"[swearB3]\",\n",
        "    40678: word_start_char + \"[swearB4]\"\n",
        "}\n",
        "replaced_keys = set()\n",
        "\n",
        "for key in vocab:\n",
        "    token_id = vocab[key]\n",
        "    if token_id in token_id_remappings:\n",
        "        new_key = token_id_remappings[token_id]\n",
        "        new_vocab[new_key] = token_id\n",
        "        replaced_keys.add(key)\n",
        "    else:\n",
        "        new_vocab[key] = token_id\n",
        "\n",
        "new_merges = []\n",
        "with open(tokenizer_directory / 'merges.txt', 'r', encoding='utf-8') as merges:\n",
        "    for line in merges.readlines():\n",
        "        if len(line) == 0:\n",
        "            continue\n",
        "        words = split_by_word(line)\n",
        "        if not (words[0] in replaced_keys):\n",
        "            new_merges.append(line.strip())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKCuKjtPIRaR"
      },
      "source": [
        "To check for other indexes to replace (keeping in mind that the output should still be multi-lingual), we could do something like this:\n",
        "```python\n",
        "!pip install better_profanity==0.7.0\n",
        "\n",
        "from better_profanity import profanity\n",
        "\n",
        "profanity.load_censor_words()\n",
        "for key in new_vocab:\n",
        "    word = key\n",
        "    if key.startswith(word_start_char):\n",
        "        word = key[1:]\n",
        "    if profanity.contains_profanity(word):\n",
        "        print(\"Consider replacing\", key, new_vocab[key])\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "mhNhzp98FjFR"
      },
      "outputs": [],
      "source": [
        "# !pip install better_profanity==0.7.0\n",
        "\n",
        "# from better_profanity import profanity\n",
        "\n",
        "# profanity.load_censor_words()\n",
        "# for key in new_vocab:\n",
        "#     word = key\n",
        "#     if key.startswith(word_start_char):\n",
        "#         word = key[1:]\n",
        "#     if profanity.contains_profanity(word):\n",
        "#         print(\"Consider replacing\", key, new_vocab[key])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_lDgRsIuLRY"
      },
      "source": [
        "Great! We now have an updated vocab file!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "xa4M3M_02GwI"
      },
      "outputs": [],
      "source": [
        "import shutil\n",
        "\n",
        "# Write to a file\n",
        "tokenizer_fr_directory = Path('updated-tokenizer')\n",
        "if tokenizer_fr_directory.exists():\n",
        "    shutil.rmtree(tokenizer_fr_directory)\n",
        "shutil.copytree(tokenizer_directory, tokenizer_fr_directory)\n",
        "with open(tokenizer_fr_directory / 'vocab.json', 'w', encoding='utf-8') as f:\n",
        "    json.dump(new_vocab, f, ensure_ascii=False)\n",
        "\n",
        "\n",
        "with open(tokenizer_fr_directory / 'merges.txt', 'w', encoding='utf-8') as f:\n",
        "    f.write('\\n'.join(new_merges))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2oSULQLZz5rQ",
        "outputId": "a8cf49fd-9eb0-4a9b-8518-d280582f6a32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "105"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import WhisperTokenizer\n",
        "\n",
        "# Use a normal WhisperTokenizer -- WhisperTokenizerFast has trouble with the updated\n",
        "# vocabulary.\n",
        "tokenizer = WhisperTokenizer(\n",
        "    tokenizer_fr_directory / 'vocab.json',\n",
        "    tokenizer_fr_directory / 'merges.txt',\n",
        "    tokenizer_fr_directory / 'normalizer.json',\n",
        "    bos_token='<|startoftranscript|>',\n",
        "    unk_token='',\n",
        "    pad_token='<|endoftext|>',\n",
        ")\n",
        "\n",
        "# See https://discuss.huggingface.co/t/fine-tuning-whisper-on-my-own-dataset-with-a-customized-tokenizer/25903\n",
        "tokenizer.add_special_tokens(tokenizer_original.special_tokens_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wl_cU43c7IHR",
        "outputId": "7bc7fdab-a6fb-4f96-fc53-7863925f67f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('updated-tokenizer/tokenizer_config.json',\n",
              " 'updated-tokenizer/special_tokens_map.json',\n",
              " 'updated-tokenizer/vocab.json',\n",
              " 'updated-tokenizer/merges.txt',\n",
              " 'updated-tokenizer/normalizer.json',\n",
              " 'updated-tokenizer/added_tokens.json')"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# For debugging, update the output directory\n",
        "shutil.rmtree(tokenizer_fr_directory)\n",
        "tokenizer.save_pretrained(tokenizer_fr_directory)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMpuESBPSRs6"
      },
      "source": [
        "Next, save the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvM1Tu2JOFEG",
        "outputId": "33a83405-79d1-4604-b7f0-a33be54028dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('/content/final-model/tokenizer_config.json',\n",
              " '/content/final-model/special_tokens_map.json',\n",
              " '/content/final-model/vocab.json',\n",
              " '/content/final-model/merges.txt',\n",
              " '/content/final-model/normalizer.json',\n",
              " '/content/final-model/added_tokens.json')"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_output_dir = Path('./final-model').resolve()\n",
        "model.save_pretrained(model_output_dir)\n",
        "tokenizer.save_pretrained(model_output_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0e9GSFSfDsr"
      },
      "source": [
        "## Model conversion\n",
        "\n",
        "Next, we need to convert the model into a format usable by Joplin. This next step converts the model from PyTorch to GGML. Note that this section has been copied and modified from the Joplin `whisper_vocab_cleanup.ipynb` notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sz5y6PdM5Qei",
        "outputId": "43e5c050-f463-4083-9f76-047e4a5acdc5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'whisper-github'...\n",
            "remote: Enumerating objects: 828, done.\u001b[K\n",
            "remote: Counting objects: 100% (370/370), done.\u001b[K\n",
            "remote: Compressing objects: 100% (69/69), done.\u001b[K\n",
            "remote: Total 828 (delta 333), reused 301 (delta 301), pack-reused 458 (from 2)\u001b[K\n",
            "Receiving objects: 100% (828/828), 8.26 MiB | 22.67 MiB/s, done.\n",
            "Resolving deltas: 100% (496/496), done.\n",
            "Cloning into 'whisper.cpp'...\n",
            "remote: Enumerating objects: 16341, done.\u001b[K\n",
            "remote: Counting objects: 100% (13/13), done.\u001b[K\n",
            "remote: Compressing objects: 100% (7/7), done.\u001b[K\n",
            "remote: Total 16341 (delta 7), reused 6 (delta 6), pack-reused 16328 (from 2)\u001b[K\n",
            "Receiving objects: 100% (16341/16341), 19.39 MiB | 23.27 MiB/s, done.\n",
            "Resolving deltas: 100% (11348/11348), done.\n",
            "Note: switching to 'v1.7.4'.\n",
            "\n",
            "You are in 'detached HEAD' state. You can look around, make experimental\n",
            "changes and commit them, and you can discard any commits you make in this\n",
            "state without impacting any branches by switching back to a branch.\n",
            "\n",
            "If you want to create a new branch to retain commits you create, you may\n",
            "do so (now or later) by using -c with the switch command. Example:\n",
            "\n",
            "  git switch -c <new-branch-name>\n",
            "\n",
            "Or undo this operation with:\n",
            "\n",
            "  git switch -\n",
            "\n",
            "Turn off this advice by setting config variable advice.detachedHead to false\n",
            "\n",
            "HEAD is now at 8a9ad78 release : v1.7.4\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/openai/whisper whisper-github\n",
        "!git clone https://github.com/ggerganov/whisper.cpp\n",
        "!cd whisper.cpp && git checkout v1.7.4"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvWDA0rdPAIK"
      },
      "source": [
        "Build it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YJADST9WPB9e",
        "outputId": "88c6123a-0337-47d2-a218-3f70a15c0c9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[0mCMake Deprecation Warning at CMakeLists.txt:1 (cmake_minimum_required):\n",
            "  Compatibility with CMake < 3.10 will be removed from a future version of\n",
            "  CMake.\n",
            "\n",
            "  Update the VERSION argument <min> value.  Or, use the <min>...<max> syntax\n",
            "  to tell CMake that the project requires at least <min> but has been updated\n",
            "  to work with policies introduced by <max> or earlier.\n",
            "\n",
            "\u001b[0m\n",
            "-- The C compiler identification is GNU 11.4.0\n",
            "-- The CXX compiler identification is GNU 11.4.0\n",
            "-- Detecting C compiler ABI info\n",
            "-- Detecting C compiler ABI info - done\n",
            "-- Check for working C compiler: /usr/bin/cc - skipped\n",
            "-- Detecting C compile features\n",
            "-- Detecting C compile features - done\n",
            "-- Detecting CXX compiler ABI info\n",
            "-- Detecting CXX compiler ABI info - done\n",
            "-- Check for working CXX compiler: /usr/bin/c++ - skipped\n",
            "-- Detecting CXX compile features\n",
            "-- Detecting CXX compile features - done\n",
            "-- Found Git: /usr/bin/git (found version \"2.34.1\")\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD\n",
            "-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success\n",
            "-- Found Threads: TRUE\n",
            "-- Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF\n",
            "-- CMAKE_SYSTEM_PROCESSOR: x86_64\n",
            "-- Including CPU backend\n",
            "-- Found OpenMP_C: -fopenmp (found version \"4.5\")\n",
            "-- Found OpenMP_CXX: -fopenmp (found version \"4.5\")\n",
            "-- Found OpenMP: TRUE (found version \"4.5\")\n",
            "-- x86 detected\n",
            "-- Adding CPU backend variant ggml-cpu: -march=native \n",
            "-- Configuring done (1.8s)\n",
            "-- Generating done (0.0s)\n",
            "-- Build files have been written to: /content/whisper.cpp/build\n",
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            "[ 57%] \u001b[32mBuilding CXX object examples/CMakeFiles/common.dir/grammar-parser.cpp.o\u001b[0m\n",
            "[ 60%] \u001b[32m\u001b[1mLinking CXX static library libcommon.a\u001b[0m\n",
            "[ 60%] Built target common\n",
            "[ 62%] \u001b[32mBuilding CXX object examples/cli/CMakeFiles/whisper-cli.dir/cli.cpp.o\u001b[0m\n",
            "[ 65%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/whisper-cli\u001b[0m\n",
            "[ 65%] Built target whisper-cli\n",
            "[ 67%] \u001b[32mBuilding CXX object examples/bench/CMakeFiles/whisper-bench.dir/bench.cpp.o\u001b[0m\n",
            "[ 70%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/whisper-bench\u001b[0m\n",
            "[ 70%] Built target whisper-bench\n",
            "[ 72%] \u001b[32mBuilding CXX object examples/server/CMakeFiles/whisper-server.dir/server.cpp.o\u001b[0m\n",
            "[ 75%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/whisper-server\u001b[0m\n",
            "[ 75%] Built target whisper-server\n",
            "[ 77%] \u001b[32mBuilding CXX object examples/quantize/CMakeFiles/quantize.dir/quantize.cpp.o\u001b[0m\n",
            "[ 80%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/quantize\u001b[0m\n",
            "[ 80%] Built target quantize\n",
            "[ 82%] \u001b[32mBuilding CXX object examples/deprecation-warning/CMakeFiles/main.dir/deprecation-warning.cpp.o\u001b[0m\n",
            "[ 85%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/main\u001b[0m\n",
            "[ 85%] Built target main\n",
            "[ 87%] \u001b[32mBuilding CXX object examples/deprecation-warning/CMakeFiles/bench.dir/deprecation-warning.cpp.o\u001b[0m\n",
            "[ 90%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/bench\u001b[0m\n",
            "[ 90%] Built target bench\n",
            "[ 92%] \u001b[32mBuilding CXX object examples/deprecation-warning/CMakeFiles/stream.dir/deprecation-warning.cpp.o\u001b[0m\n",
            "[ 95%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/stream\u001b[0m\n",
            "[ 95%] Built target stream\n",
            "[ 97%] \u001b[32mBuilding CXX object examples/deprecation-warning/CMakeFiles/command.dir/deprecation-warning.cpp.o\u001b[0m\n",
            "[100%] \u001b[32m\u001b[1mLinking CXX executable ../../bin/command\u001b[0m\n",
            "[100%] Built target command\n"
          ]
        },
        {
          "data": {
            "text/plain": []
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%shell\n",
        "cd whisper.cpp/\n",
        "cmake -B build && cmake --build build --config Release"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7JlQjQV6BGkY"
      },
      "source": [
        "`whisper.cpp` needs a patch in order to successfully convert the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "MUMzKNCmJfq0"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "# Patch convert-h5-to-ggml to work with more recent model versions\n",
        "conversion_script_path = Path('whisper.cpp/models/convert-h5-to-ggml.py')\n",
        "conversion_script_content = conversion_script_path.read_text()\n",
        "with open(conversion_script_path, 'w') as conversion_script:\n",
        "    bad_if_statement = 'if \"max_length\" not in hparams:'\n",
        "    replaced_if_statement = 'if \"max_length\" not in hparams or hparams[\"max_length\"] == None:'\n",
        "    conversion_script.write(conversion_script_content.replace(bad_if_statement, replaced_if_statement))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6lMlvbnZBGkY"
      },
      "source": [
        "Now that the patch is applied, the model can be converted:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CINj6diwNUg3"
      },
      "source": [
        "We can now convert the model to GGML, then [quantize](https://github.com/ggerganov/whisper.cpp/discussions/838) it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSpI8PlvNqhd",
        "outputId": "f33aefa5-7f75-4f41-890d-04d8a8cc0804"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2025-03-25 22:29:49.597031: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "E0000 00:00:1742941789.618819   10587 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "E0000 00:00:1742941789.625447   10587 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "model.encoder.conv1.weight  ->  encoder.conv1.weight\n",
            "encoder.conv1.weight 3 (384, 80, 3)\n",
            "model.encoder.conv1.bias  ->  encoder.conv1.bias\n",
            "  Reshaped variable:  encoder.conv1.bias  to shape:  (384, 1)\n",
            "encoder.conv1.bias 2 (384, 1)\n",
            "  Converting to float32\n",
            "model.encoder.conv2.weight  ->  encoder.conv2.weight\n",
            "encoder.conv2.weight 3 (384, 384, 3)\n",
            "model.encoder.conv2.bias  ->  encoder.conv2.bias\n",
            "  Reshaped variable:  encoder.conv2.bias  to shape:  (384, 1)\n",
            "encoder.conv2.bias 2 (384, 1)\n",
            "  Converting to float32\n",
            "model.encoder.embed_positions.weight  ->  encoder.positional_embedding\n",
            "encoder.positional_embedding 2 (1500, 384)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.self_attn.k_proj.weight  ->  encoder.blocks.0.attn.key.weight\n",
            "encoder.blocks.0.attn.key.weight 2 (384, 384)\n",
            "model.encoder.layers.0.self_attn.v_proj.weight  ->  encoder.blocks.0.attn.value.weight\n",
            "encoder.blocks.0.attn.value.weight 2 (384, 384)\n",
            "model.encoder.layers.0.self_attn.v_proj.bias  ->  encoder.blocks.0.attn.value.bias\n",
            "encoder.blocks.0.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.self_attn.q_proj.weight  ->  encoder.blocks.0.attn.query.weight\n",
            "encoder.blocks.0.attn.query.weight 2 (384, 384)\n",
            "model.encoder.layers.0.self_attn.q_proj.bias  ->  encoder.blocks.0.attn.query.bias\n",
            "encoder.blocks.0.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.self_attn.out_proj.weight  ->  encoder.blocks.0.attn.out.weight\n",
            "encoder.blocks.0.attn.out.weight 2 (384, 384)\n",
            "model.encoder.layers.0.self_attn.out_proj.bias  ->  encoder.blocks.0.attn.out.bias\n",
            "encoder.blocks.0.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.self_attn_layer_norm.weight  ->  encoder.blocks.0.attn_ln.weight\n",
            "encoder.blocks.0.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.self_attn_layer_norm.bias  ->  encoder.blocks.0.attn_ln.bias\n",
            "encoder.blocks.0.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.fc1.weight  ->  encoder.blocks.0.mlp.0.weight\n",
            "encoder.blocks.0.mlp.0.weight 2 (1536, 384)\n",
            "model.encoder.layers.0.fc1.bias  ->  encoder.blocks.0.mlp.0.bias\n",
            "encoder.blocks.0.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.fc2.weight  ->  encoder.blocks.0.mlp.2.weight\n",
            "encoder.blocks.0.mlp.2.weight 2 (384, 1536)\n",
            "model.encoder.layers.0.fc2.bias  ->  encoder.blocks.0.mlp.2.bias\n",
            "encoder.blocks.0.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.final_layer_norm.weight  ->  encoder.blocks.0.mlp_ln.weight\n",
            "encoder.blocks.0.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.0.final_layer_norm.bias  ->  encoder.blocks.0.mlp_ln.bias\n",
            "encoder.blocks.0.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.self_attn.k_proj.weight  ->  encoder.blocks.1.attn.key.weight\n",
            "encoder.blocks.1.attn.key.weight 2 (384, 384)\n",
            "model.encoder.layers.1.self_attn.v_proj.weight  ->  encoder.blocks.1.attn.value.weight\n",
            "encoder.blocks.1.attn.value.weight 2 (384, 384)\n",
            "model.encoder.layers.1.self_attn.v_proj.bias  ->  encoder.blocks.1.attn.value.bias\n",
            "encoder.blocks.1.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.self_attn.q_proj.weight  ->  encoder.blocks.1.attn.query.weight\n",
            "encoder.blocks.1.attn.query.weight 2 (384, 384)\n",
            "model.encoder.layers.1.self_attn.q_proj.bias  ->  encoder.blocks.1.attn.query.bias\n",
            "encoder.blocks.1.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.self_attn.out_proj.weight  ->  encoder.blocks.1.attn.out.weight\n",
            "encoder.blocks.1.attn.out.weight 2 (384, 384)\n",
            "model.encoder.layers.1.self_attn.out_proj.bias  ->  encoder.blocks.1.attn.out.bias\n",
            "encoder.blocks.1.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.self_attn_layer_norm.weight  ->  encoder.blocks.1.attn_ln.weight\n",
            "encoder.blocks.1.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.self_attn_layer_norm.bias  ->  encoder.blocks.1.attn_ln.bias\n",
            "encoder.blocks.1.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.fc1.weight  ->  encoder.blocks.1.mlp.0.weight\n",
            "encoder.blocks.1.mlp.0.weight 2 (1536, 384)\n",
            "model.encoder.layers.1.fc1.bias  ->  encoder.blocks.1.mlp.0.bias\n",
            "encoder.blocks.1.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.fc2.weight  ->  encoder.blocks.1.mlp.2.weight\n",
            "encoder.blocks.1.mlp.2.weight 2 (384, 1536)\n",
            "model.encoder.layers.1.fc2.bias  ->  encoder.blocks.1.mlp.2.bias\n",
            "encoder.blocks.1.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.final_layer_norm.weight  ->  encoder.blocks.1.mlp_ln.weight\n",
            "encoder.blocks.1.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.1.final_layer_norm.bias  ->  encoder.blocks.1.mlp_ln.bias\n",
            "encoder.blocks.1.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.self_attn.k_proj.weight  ->  encoder.blocks.2.attn.key.weight\n",
            "encoder.blocks.2.attn.key.weight 2 (384, 384)\n",
            "model.encoder.layers.2.self_attn.v_proj.weight  ->  encoder.blocks.2.attn.value.weight\n",
            "encoder.blocks.2.attn.value.weight 2 (384, 384)\n",
            "model.encoder.layers.2.self_attn.v_proj.bias  ->  encoder.blocks.2.attn.value.bias\n",
            "encoder.blocks.2.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.self_attn.q_proj.weight  ->  encoder.blocks.2.attn.query.weight\n",
            "encoder.blocks.2.attn.query.weight 2 (384, 384)\n",
            "model.encoder.layers.2.self_attn.q_proj.bias  ->  encoder.blocks.2.attn.query.bias\n",
            "encoder.blocks.2.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.self_attn.out_proj.weight  ->  encoder.blocks.2.attn.out.weight\n",
            "encoder.blocks.2.attn.out.weight 2 (384, 384)\n",
            "model.encoder.layers.2.self_attn.out_proj.bias  ->  encoder.blocks.2.attn.out.bias\n",
            "encoder.blocks.2.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.self_attn_layer_norm.weight  ->  encoder.blocks.2.attn_ln.weight\n",
            "encoder.blocks.2.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.self_attn_layer_norm.bias  ->  encoder.blocks.2.attn_ln.bias\n",
            "encoder.blocks.2.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.fc1.weight  ->  encoder.blocks.2.mlp.0.weight\n",
            "encoder.blocks.2.mlp.0.weight 2 (1536, 384)\n",
            "model.encoder.layers.2.fc1.bias  ->  encoder.blocks.2.mlp.0.bias\n",
            "encoder.blocks.2.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.fc2.weight  ->  encoder.blocks.2.mlp.2.weight\n",
            "encoder.blocks.2.mlp.2.weight 2 (384, 1536)\n",
            "model.encoder.layers.2.fc2.bias  ->  encoder.blocks.2.mlp.2.bias\n",
            "encoder.blocks.2.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.final_layer_norm.weight  ->  encoder.blocks.2.mlp_ln.weight\n",
            "encoder.blocks.2.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.2.final_layer_norm.bias  ->  encoder.blocks.2.mlp_ln.bias\n",
            "encoder.blocks.2.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.self_attn.k_proj.weight  ->  encoder.blocks.3.attn.key.weight\n",
            "encoder.blocks.3.attn.key.weight 2 (384, 384)\n",
            "model.encoder.layers.3.self_attn.v_proj.weight  ->  encoder.blocks.3.attn.value.weight\n",
            "encoder.blocks.3.attn.value.weight 2 (384, 384)\n",
            "model.encoder.layers.3.self_attn.v_proj.bias  ->  encoder.blocks.3.attn.value.bias\n",
            "encoder.blocks.3.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.self_attn.q_proj.weight  ->  encoder.blocks.3.attn.query.weight\n",
            "encoder.blocks.3.attn.query.weight 2 (384, 384)\n",
            "model.encoder.layers.3.self_attn.q_proj.bias  ->  encoder.blocks.3.attn.query.bias\n",
            "encoder.blocks.3.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.self_attn.out_proj.weight  ->  encoder.blocks.3.attn.out.weight\n",
            "encoder.blocks.3.attn.out.weight 2 (384, 384)\n",
            "model.encoder.layers.3.self_attn.out_proj.bias  ->  encoder.blocks.3.attn.out.bias\n",
            "encoder.blocks.3.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.self_attn_layer_norm.weight  ->  encoder.blocks.3.attn_ln.weight\n",
            "encoder.blocks.3.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.self_attn_layer_norm.bias  ->  encoder.blocks.3.attn_ln.bias\n",
            "encoder.blocks.3.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.fc1.weight  ->  encoder.blocks.3.mlp.0.weight\n",
            "encoder.blocks.3.mlp.0.weight 2 (1536, 384)\n",
            "model.encoder.layers.3.fc1.bias  ->  encoder.blocks.3.mlp.0.bias\n",
            "encoder.blocks.3.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.fc2.weight  ->  encoder.blocks.3.mlp.2.weight\n",
            "encoder.blocks.3.mlp.2.weight 2 (384, 1536)\n",
            "model.encoder.layers.3.fc2.bias  ->  encoder.blocks.3.mlp.2.bias\n",
            "encoder.blocks.3.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.final_layer_norm.weight  ->  encoder.blocks.3.mlp_ln.weight\n",
            "encoder.blocks.3.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layers.3.final_layer_norm.bias  ->  encoder.blocks.3.mlp_ln.bias\n",
            "encoder.blocks.3.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layer_norm.weight  ->  encoder.ln_post.weight\n",
            "encoder.ln_post.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.encoder.layer_norm.bias  ->  encoder.ln_post.bias\n",
            "encoder.ln_post.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.embed_tokens.weight  ->  decoder.token_embedding.weight\n",
            "decoder.token_embedding.weight 2 (51865, 384)\n",
            "model.decoder.embed_positions.weight  ->  decoder.positional_embedding\n",
            "decoder.positional_embedding 2 (448, 384)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.self_attn.k_proj.weight  ->  decoder.blocks.0.attn.key.weight\n",
            "decoder.blocks.0.attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.0.self_attn.v_proj.weight  ->  decoder.blocks.0.attn.value.weight\n",
            "decoder.blocks.0.attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.0.self_attn.v_proj.bias  ->  decoder.blocks.0.attn.value.bias\n",
            "decoder.blocks.0.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.self_attn.q_proj.weight  ->  decoder.blocks.0.attn.query.weight\n",
            "decoder.blocks.0.attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.0.self_attn.q_proj.bias  ->  decoder.blocks.0.attn.query.bias\n",
            "decoder.blocks.0.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.self_attn.out_proj.weight  ->  decoder.blocks.0.attn.out.weight\n",
            "decoder.blocks.0.attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.0.self_attn.out_proj.bias  ->  decoder.blocks.0.attn.out.bias\n",
            "decoder.blocks.0.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.self_attn_layer_norm.weight  ->  decoder.blocks.0.attn_ln.weight\n",
            "decoder.blocks.0.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.self_attn_layer_norm.bias  ->  decoder.blocks.0.attn_ln.bias\n",
            "decoder.blocks.0.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.encoder_attn.k_proj.weight  ->  decoder.blocks.0.cross_attn.key.weight\n",
            "decoder.blocks.0.cross_attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.0.encoder_attn.v_proj.weight  ->  decoder.blocks.0.cross_attn.value.weight\n",
            "decoder.blocks.0.cross_attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.0.encoder_attn.v_proj.bias  ->  decoder.blocks.0.cross_attn.value.bias\n",
            "decoder.blocks.0.cross_attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.encoder_attn.q_proj.weight  ->  decoder.blocks.0.cross_attn.query.weight\n",
            "decoder.blocks.0.cross_attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.0.encoder_attn.q_proj.bias  ->  decoder.blocks.0.cross_attn.query.bias\n",
            "decoder.blocks.0.cross_attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.encoder_attn.out_proj.weight  ->  decoder.blocks.0.cross_attn.out.weight\n",
            "decoder.blocks.0.cross_attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.0.encoder_attn.out_proj.bias  ->  decoder.blocks.0.cross_attn.out.bias\n",
            "decoder.blocks.0.cross_attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.encoder_attn_layer_norm.weight  ->  decoder.blocks.0.cross_attn_ln.weight\n",
            "decoder.blocks.0.cross_attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.encoder_attn_layer_norm.bias  ->  decoder.blocks.0.cross_attn_ln.bias\n",
            "decoder.blocks.0.cross_attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.fc1.weight  ->  decoder.blocks.0.mlp.0.weight\n",
            "decoder.blocks.0.mlp.0.weight 2 (1536, 384)\n",
            "model.decoder.layers.0.fc1.bias  ->  decoder.blocks.0.mlp.0.bias\n",
            "decoder.blocks.0.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.fc2.weight  ->  decoder.blocks.0.mlp.2.weight\n",
            "decoder.blocks.0.mlp.2.weight 2 (384, 1536)\n",
            "model.decoder.layers.0.fc2.bias  ->  decoder.blocks.0.mlp.2.bias\n",
            "decoder.blocks.0.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.final_layer_norm.weight  ->  decoder.blocks.0.mlp_ln.weight\n",
            "decoder.blocks.0.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.0.final_layer_norm.bias  ->  decoder.blocks.0.mlp_ln.bias\n",
            "decoder.blocks.0.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.self_attn.k_proj.weight  ->  decoder.blocks.1.attn.key.weight\n",
            "decoder.blocks.1.attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.1.self_attn.v_proj.weight  ->  decoder.blocks.1.attn.value.weight\n",
            "decoder.blocks.1.attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.1.self_attn.v_proj.bias  ->  decoder.blocks.1.attn.value.bias\n",
            "decoder.blocks.1.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.self_attn.q_proj.weight  ->  decoder.blocks.1.attn.query.weight\n",
            "decoder.blocks.1.attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.1.self_attn.q_proj.bias  ->  decoder.blocks.1.attn.query.bias\n",
            "decoder.blocks.1.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.self_attn.out_proj.weight  ->  decoder.blocks.1.attn.out.weight\n",
            "decoder.blocks.1.attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.1.self_attn.out_proj.bias  ->  decoder.blocks.1.attn.out.bias\n",
            "decoder.blocks.1.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.self_attn_layer_norm.weight  ->  decoder.blocks.1.attn_ln.weight\n",
            "decoder.blocks.1.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.self_attn_layer_norm.bias  ->  decoder.blocks.1.attn_ln.bias\n",
            "decoder.blocks.1.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.encoder_attn.k_proj.weight  ->  decoder.blocks.1.cross_attn.key.weight\n",
            "decoder.blocks.1.cross_attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.1.encoder_attn.v_proj.weight  ->  decoder.blocks.1.cross_attn.value.weight\n",
            "decoder.blocks.1.cross_attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.1.encoder_attn.v_proj.bias  ->  decoder.blocks.1.cross_attn.value.bias\n",
            "decoder.blocks.1.cross_attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.encoder_attn.q_proj.weight  ->  decoder.blocks.1.cross_attn.query.weight\n",
            "decoder.blocks.1.cross_attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.1.encoder_attn.q_proj.bias  ->  decoder.blocks.1.cross_attn.query.bias\n",
            "decoder.blocks.1.cross_attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.encoder_attn.out_proj.weight  ->  decoder.blocks.1.cross_attn.out.weight\n",
            "decoder.blocks.1.cross_attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.1.encoder_attn.out_proj.bias  ->  decoder.blocks.1.cross_attn.out.bias\n",
            "decoder.blocks.1.cross_attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.encoder_attn_layer_norm.weight  ->  decoder.blocks.1.cross_attn_ln.weight\n",
            "decoder.blocks.1.cross_attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.encoder_attn_layer_norm.bias  ->  decoder.blocks.1.cross_attn_ln.bias\n",
            "decoder.blocks.1.cross_attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.fc1.weight  ->  decoder.blocks.1.mlp.0.weight\n",
            "decoder.blocks.1.mlp.0.weight 2 (1536, 384)\n",
            "model.decoder.layers.1.fc1.bias  ->  decoder.blocks.1.mlp.0.bias\n",
            "decoder.blocks.1.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.fc2.weight  ->  decoder.blocks.1.mlp.2.weight\n",
            "decoder.blocks.1.mlp.2.weight 2 (384, 1536)\n",
            "model.decoder.layers.1.fc2.bias  ->  decoder.blocks.1.mlp.2.bias\n",
            "decoder.blocks.1.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.final_layer_norm.weight  ->  decoder.blocks.1.mlp_ln.weight\n",
            "decoder.blocks.1.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.1.final_layer_norm.bias  ->  decoder.blocks.1.mlp_ln.bias\n",
            "decoder.blocks.1.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.self_attn.k_proj.weight  ->  decoder.blocks.2.attn.key.weight\n",
            "decoder.blocks.2.attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.2.self_attn.v_proj.weight  ->  decoder.blocks.2.attn.value.weight\n",
            "decoder.blocks.2.attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.2.self_attn.v_proj.bias  ->  decoder.blocks.2.attn.value.bias\n",
            "decoder.blocks.2.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.self_attn.q_proj.weight  ->  decoder.blocks.2.attn.query.weight\n",
            "decoder.blocks.2.attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.2.self_attn.q_proj.bias  ->  decoder.blocks.2.attn.query.bias\n",
            "decoder.blocks.2.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.self_attn.out_proj.weight  ->  decoder.blocks.2.attn.out.weight\n",
            "decoder.blocks.2.attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.2.self_attn.out_proj.bias  ->  decoder.blocks.2.attn.out.bias\n",
            "decoder.blocks.2.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.self_attn_layer_norm.weight  ->  decoder.blocks.2.attn_ln.weight\n",
            "decoder.blocks.2.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.self_attn_layer_norm.bias  ->  decoder.blocks.2.attn_ln.bias\n",
            "decoder.blocks.2.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.encoder_attn.k_proj.weight  ->  decoder.blocks.2.cross_attn.key.weight\n",
            "decoder.blocks.2.cross_attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.2.encoder_attn.v_proj.weight  ->  decoder.blocks.2.cross_attn.value.weight\n",
            "decoder.blocks.2.cross_attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.2.encoder_attn.v_proj.bias  ->  decoder.blocks.2.cross_attn.value.bias\n",
            "decoder.blocks.2.cross_attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.encoder_attn.q_proj.weight  ->  decoder.blocks.2.cross_attn.query.weight\n",
            "decoder.blocks.2.cross_attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.2.encoder_attn.q_proj.bias  ->  decoder.blocks.2.cross_attn.query.bias\n",
            "decoder.blocks.2.cross_attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.encoder_attn.out_proj.weight  ->  decoder.blocks.2.cross_attn.out.weight\n",
            "decoder.blocks.2.cross_attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.2.encoder_attn.out_proj.bias  ->  decoder.blocks.2.cross_attn.out.bias\n",
            "decoder.blocks.2.cross_attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.encoder_attn_layer_norm.weight  ->  decoder.blocks.2.cross_attn_ln.weight\n",
            "decoder.blocks.2.cross_attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.encoder_attn_layer_norm.bias  ->  decoder.blocks.2.cross_attn_ln.bias\n",
            "decoder.blocks.2.cross_attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.fc1.weight  ->  decoder.blocks.2.mlp.0.weight\n",
            "decoder.blocks.2.mlp.0.weight 2 (1536, 384)\n",
            "model.decoder.layers.2.fc1.bias  ->  decoder.blocks.2.mlp.0.bias\n",
            "decoder.blocks.2.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.fc2.weight  ->  decoder.blocks.2.mlp.2.weight\n",
            "decoder.blocks.2.mlp.2.weight 2 (384, 1536)\n",
            "model.decoder.layers.2.fc2.bias  ->  decoder.blocks.2.mlp.2.bias\n",
            "decoder.blocks.2.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.final_layer_norm.weight  ->  decoder.blocks.2.mlp_ln.weight\n",
            "decoder.blocks.2.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.2.final_layer_norm.bias  ->  decoder.blocks.2.mlp_ln.bias\n",
            "decoder.blocks.2.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.self_attn.k_proj.weight  ->  decoder.blocks.3.attn.key.weight\n",
            "decoder.blocks.3.attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.3.self_attn.v_proj.weight  ->  decoder.blocks.3.attn.value.weight\n",
            "decoder.blocks.3.attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.3.self_attn.v_proj.bias  ->  decoder.blocks.3.attn.value.bias\n",
            "decoder.blocks.3.attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.self_attn.q_proj.weight  ->  decoder.blocks.3.attn.query.weight\n",
            "decoder.blocks.3.attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.3.self_attn.q_proj.bias  ->  decoder.blocks.3.attn.query.bias\n",
            "decoder.blocks.3.attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.self_attn.out_proj.weight  ->  decoder.blocks.3.attn.out.weight\n",
            "decoder.blocks.3.attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.3.self_attn.out_proj.bias  ->  decoder.blocks.3.attn.out.bias\n",
            "decoder.blocks.3.attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.self_attn_layer_norm.weight  ->  decoder.blocks.3.attn_ln.weight\n",
            "decoder.blocks.3.attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.self_attn_layer_norm.bias  ->  decoder.blocks.3.attn_ln.bias\n",
            "decoder.blocks.3.attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.encoder_attn.k_proj.weight  ->  decoder.blocks.3.cross_attn.key.weight\n",
            "decoder.blocks.3.cross_attn.key.weight 2 (384, 384)\n",
            "model.decoder.layers.3.encoder_attn.v_proj.weight  ->  decoder.blocks.3.cross_attn.value.weight\n",
            "decoder.blocks.3.cross_attn.value.weight 2 (384, 384)\n",
            "model.decoder.layers.3.encoder_attn.v_proj.bias  ->  decoder.blocks.3.cross_attn.value.bias\n",
            "decoder.blocks.3.cross_attn.value.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.encoder_attn.q_proj.weight  ->  decoder.blocks.3.cross_attn.query.weight\n",
            "decoder.blocks.3.cross_attn.query.weight 2 (384, 384)\n",
            "model.decoder.layers.3.encoder_attn.q_proj.bias  ->  decoder.blocks.3.cross_attn.query.bias\n",
            "decoder.blocks.3.cross_attn.query.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.encoder_attn.out_proj.weight  ->  decoder.blocks.3.cross_attn.out.weight\n",
            "decoder.blocks.3.cross_attn.out.weight 2 (384, 384)\n",
            "model.decoder.layers.3.encoder_attn.out_proj.bias  ->  decoder.blocks.3.cross_attn.out.bias\n",
            "decoder.blocks.3.cross_attn.out.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.encoder_attn_layer_norm.weight  ->  decoder.blocks.3.cross_attn_ln.weight\n",
            "decoder.blocks.3.cross_attn_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.encoder_attn_layer_norm.bias  ->  decoder.blocks.3.cross_attn_ln.bias\n",
            "decoder.blocks.3.cross_attn_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.fc1.weight  ->  decoder.blocks.3.mlp.0.weight\n",
            "decoder.blocks.3.mlp.0.weight 2 (1536, 384)\n",
            "model.decoder.layers.3.fc1.bias  ->  decoder.blocks.3.mlp.0.bias\n",
            "decoder.blocks.3.mlp.0.bias 1 (1536,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.fc2.weight  ->  decoder.blocks.3.mlp.2.weight\n",
            "decoder.blocks.3.mlp.2.weight 2 (384, 1536)\n",
            "model.decoder.layers.3.fc2.bias  ->  decoder.blocks.3.mlp.2.bias\n",
            "decoder.blocks.3.mlp.2.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.final_layer_norm.weight  ->  decoder.blocks.3.mlp_ln.weight\n",
            "decoder.blocks.3.mlp_ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layers.3.final_layer_norm.bias  ->  decoder.blocks.3.mlp_ln.bias\n",
            "decoder.blocks.3.mlp_ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layer_norm.weight  ->  decoder.ln.weight\n",
            "decoder.ln.weight 1 (384,)\n",
            "  Converting to float32\n",
            "model.decoder.layer_norm.bias  ->  decoder.ln.bias\n",
            "decoder.ln.bias 1 (384,)\n",
            "  Converting to float32\n",
            "Skipping proj_out.weight\n",
            "Done. Output file:  ggml-updated/ggml-model.bin\n",
            "\n",
            "whisper_model_quantize: loading model from './ggml-updated/ggml-model.bin'\n",
            "whisper_model_quantize: n_vocab       = 51865\n",
            "whisper_model_quantize: n_audio_ctx   = 1500\n",
            "whisper_model_quantize: n_audio_state = 384\n",
            "whisper_model_quantize: n_audio_head  = 6\n",
            "whisper_model_quantize: n_audio_layer = 4\n",
            "whisper_model_quantize: n_text_ctx    = 448\n",
            "whisper_model_quantize: n_text_state  = 384\n",
            "whisper_model_quantize: n_text_head   = 6\n",
            "whisper_model_quantize: n_text_layer  = 4\n",
            "whisper_model_quantize: n_mels        = 80\n",
            "whisper_model_quantize: ftype (src)   = 1\n",
            "whisper_model_quantize: qntvr (src)   = 0\n",
            "whisper_model_quantize: ftype (dst)   = 2007\n",
            "whisper_model_quantize: qntvr (dst)   = 2\n",
            "                                            encoder.conv1.weight - [    3,    80,   384], type =    f16 size =    0.176 MB\n",
            "                                              encoder.conv1.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.conv2.weight - [    3,   384,   384], type =    f16 size =    0.844 MB\n",
            "                                              encoder.conv2.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.positional_embedding - [  384,  1500,     1], type =    f32 size =    2.197 MB\n",
            "                                encoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              encoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  encoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              encoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  encoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              encoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  encoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              encoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                encoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  encoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     encoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                          encoder.ln_post.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.ln_post.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.token_embedding.weight - [  384, 51865,     1], type =    f16 size =    75.97 MB ->    20.18 MB\n",
            "                                    decoder.positional_embedding - [  384,   448,     1], type =    f32 size =    0.656 MB\n",
            "                                decoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              decoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  decoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                        decoder.blocks.0.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.0.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.0.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.0.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                            decoder.blocks.0.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.0.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.0.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              decoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  decoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                        decoder.blocks.1.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.1.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.1.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.1.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                            decoder.blocks.1.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.1.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.1.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              decoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  decoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                        decoder.blocks.2.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.2.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.2.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.2.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                            decoder.blocks.2.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.2.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.2.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                              decoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                decoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                                  decoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                        decoder.blocks.3.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.3.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.3.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                          decoder.blocks.3.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.15 MB\n",
            "                            decoder.blocks.3.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.3.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.3.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.60 MB\n",
            "                                     decoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                               decoder.ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                                 decoder.ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "ggml_common_quantize_0: model size  =   144.05 MB\n",
            "ggml_common_quantize_0: quant size  =    40.97 MB | ftype = 7 (q8_0)\n",
            "\n",
            "main: quantize time =   630.45 ms\n",
            "main:    total time =   630.45 ms\n",
            "whisper_model_quantize: loading model from './ggml-updated/ggml-model.bin'\n",
            "whisper_model_quantize: n_vocab       = 51865\n",
            "whisper_model_quantize: n_audio_ctx   = 1500\n",
            "whisper_model_quantize: n_audio_state = 384\n",
            "whisper_model_quantize: n_audio_head  = 6\n",
            "whisper_model_quantize: n_audio_layer = 4\n",
            "whisper_model_quantize: n_text_ctx    = 448\n",
            "whisper_model_quantize: n_text_state  = 384\n",
            "whisper_model_quantize: n_text_head   = 6\n",
            "whisper_model_quantize: n_text_layer  = 4\n",
            "whisper_model_quantize: n_mels        = 80\n",
            "whisper_model_quantize: ftype (src)   = 1\n",
            "whisper_model_quantize: qntvr (src)   = 0\n",
            "whisper_model_quantize: ftype (dst)   = 2008\n",
            "whisper_model_quantize: qntvr (dst)   = 2\n",
            "                                            encoder.conv1.weight - [    3,    80,   384], type =    f16 size =    0.176 MB\n",
            "                                              encoder.conv1.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.conv2.weight - [    3,   384,   384], type =    f16 size =    0.844 MB\n",
            "                                              encoder.conv2.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.positional_embedding - [  384,  1500,     1], type =    f32 size =    2.197 MB\n",
            "                                encoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              encoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  encoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              encoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  encoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              encoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  encoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              encoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                encoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  encoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     encoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                          encoder.ln_post.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.ln_post.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.token_embedding.weight - [  384, 51865,     1], type =    f16 size =    75.97 MB ->    13.06 MB\n",
            "                                    decoder.positional_embedding - [  384,   448,     1], type =    f32 size =    0.656 MB\n",
            "                                decoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              decoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  decoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                        decoder.blocks.0.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.0.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.0.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.0.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                            decoder.blocks.0.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.0.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.0.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              decoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  decoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                        decoder.blocks.1.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.1.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.1.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.1.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                            decoder.blocks.1.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.1.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.1.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              decoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  decoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                        decoder.blocks.2.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.2.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.2.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.2.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                            decoder.blocks.2.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.2.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.2.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                              decoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                decoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                                  decoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                        decoder.blocks.3.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.3.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.3.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                          decoder.blocks.3.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.10 MB\n",
            "                            decoder.blocks.3.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.3.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.3.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.39 MB\n",
            "                                     decoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                               decoder.ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                                 decoder.ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "ggml_common_quantize_0: model size  =   144.05 MB\n",
            "ggml_common_quantize_0: quant size  =    27.94 MB | ftype = 8 (q5_0)\n",
            "\n",
            "main: quantize time =   502.26 ms\n",
            "main:    total time =   502.26 ms\n",
            "whisper_model_quantize: loading model from './ggml-updated/ggml-model.bin'\n",
            "whisper_model_quantize: n_vocab       = 51865\n",
            "whisper_model_quantize: n_audio_ctx   = 1500\n",
            "whisper_model_quantize: n_audio_state = 384\n",
            "whisper_model_quantize: n_audio_head  = 6\n",
            "whisper_model_quantize: n_audio_layer = 4\n",
            "whisper_model_quantize: n_text_ctx    = 448\n",
            "whisper_model_quantize: n_text_state  = 384\n",
            "whisper_model_quantize: n_text_head   = 6\n",
            "whisper_model_quantize: n_text_layer  = 4\n",
            "whisper_model_quantize: n_mels        = 80\n",
            "whisper_model_quantize: ftype (src)   = 1\n",
            "whisper_model_quantize: qntvr (src)   = 0\n",
            "whisper_model_quantize: ftype (dst)   = 2002\n",
            "whisper_model_quantize: qntvr (dst)   = 2\n",
            "                                            encoder.conv1.weight - [    3,    80,   384], type =    f16 size =    0.176 MB\n",
            "                                              encoder.conv1.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.conv2.weight - [    3,   384,   384], type =    f16 size =    0.844 MB\n",
            "                                              encoder.conv2.bias - [    1,   384,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.positional_embedding - [  384,  1500,     1], type =    f32 size =    2.197 MB\n",
            "                                encoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              encoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  encoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              encoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  encoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              encoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  encoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              encoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              encoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                encoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                encoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  encoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 encoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   encoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   encoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     encoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  encoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    encoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                          encoder.ln_post.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                            encoder.ln_post.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.token_embedding.weight - [  384, 51865,     1], type =    f16 size =    75.97 MB ->    10.68 MB\n",
            "                                    decoder.positional_embedding - [  384,   448,     1], type =    f32 size =    0.656 MB\n",
            "                                decoder.blocks.0.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              decoder.blocks.0.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.0.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.0.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.0.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.0.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  decoder.blocks.0.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.0.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                        decoder.blocks.0.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.0.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.0.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.0.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.0.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                            decoder.blocks.0.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.0.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.0.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.0.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.0.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.0.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.0.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.0.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.0.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              decoder.blocks.1.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.1.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.1.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.1.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.1.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  decoder.blocks.1.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.1.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                        decoder.blocks.1.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.1.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.1.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.1.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.1.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                            decoder.blocks.1.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.1.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.1.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.1.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.1.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.1.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.1.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.1.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.1.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              decoder.blocks.2.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.2.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.2.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.2.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.2.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  decoder.blocks.2.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.2.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                        decoder.blocks.2.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.2.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.2.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.2.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.2.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                            decoder.blocks.2.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.2.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.2.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.2.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.2.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.2.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.2.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.2.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.2.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                              decoder.blocks.3.attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.3.attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                              decoder.blocks.3.attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                decoder.blocks.3.attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                decoder.blocks.3.attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                                  decoder.blocks.3.attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                 decoder.blocks.3.attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.key.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                        decoder.blocks.3.cross_attn.value.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.3.cross_attn.value.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                        decoder.blocks.3.cross_attn.query.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                          decoder.blocks.3.cross_attn.query.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                          decoder.blocks.3.cross_attn.out.weight - [  384,   384,     1], type =    f16 size =     0.56 MB ->     0.08 MB\n",
            "                            decoder.blocks.3.cross_attn.out.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                           decoder.blocks.3.cross_attn_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                             decoder.blocks.3.cross_attn_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                   decoder.blocks.3.mlp.0.weight - [  384,  1536,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.3.mlp.0.bias - [ 1536,     1,     1], type =    f32 size =    0.006 MB\n",
            "                                   decoder.blocks.3.mlp.2.weight - [ 1536,   384,     1], type =    f16 size =     2.25 MB ->     0.32 MB\n",
            "                                     decoder.blocks.3.mlp.2.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                  decoder.blocks.3.mlp_ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                    decoder.blocks.3.mlp_ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                               decoder.ln.weight - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "                                                 decoder.ln.bias - [  384,     1,     1], type =    f32 size =    0.001 MB\n",
            "ggml_common_quantize_0: model size  =   144.05 MB\n",
            "ggml_common_quantize_0: quant size  =    23.59 MB | ftype = 2 (q4_0)\n",
            "\n",
            "main: quantize time =   420.69 ms\n",
            "main:    total time =   420.69 ms\n"
          ]
        }
      ],
      "source": [
        "!mkdir ./ggml-updated\n",
        "!python whisper.cpp/models/convert-h5-to-ggml.py ./final-model ./whisper-github ./ggml-updated\n",
        "\n",
        "# Quantize. See https://github.com/ggerganov/whisper.cpp/discussions/838\n",
        "!./whisper.cpp/build/bin/quantize ./ggml-updated/ggml-model.bin ./ggml-updated/ggml-model-q8_0.bin q8_0\n",
        "!./whisper.cpp/build/bin/quantize ./ggml-updated/ggml-model.bin ./ggml-updated/ggml-model-q5_0.bin q5_0\n",
        "!./whisper.cpp/build/bin/quantize ./ggml-updated/ggml-model.bin ./ggml-updated/ggml-model-q4_0.bin q4_0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNH_WV-lMW9n"
      },
      "source": [
        "Now, let's make sure that the `.ggml` model works. Start by downloading some test audio:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFcwkwhXMYKt",
        "outputId": "368cb689-f76d-448c-ef26-eb397e7cc6df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2025-03-25 22:29:59--  https://www.archive.org/download/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3\n",
            "Resolving www.archive.org (www.archive.org)... 207.241.224.2\n",
            "Connecting to www.archive.org (www.archive.org)|207.241.224.2|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://archive.org/download/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3 [following]\n",
            "--2025-03-25 22:29:59--  https://archive.org/download/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3\n",
            "Resolving archive.org (archive.org)... 207.241.224.2\n",
            "Connecting to archive.org (archive.org)|207.241.224.2|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ia803201.us.archive.org/25/items/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3 [following]\n",
            "--2025-03-25 22:30:00--  https://ia803201.us.archive.org/25/items/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3\n",
            "Resolving ia803201.us.archive.org (ia803201.us.archive.org)... 207.241.234.121\n",
            "Connecting to ia803201.us.archive.org (ia803201.us.archive.org)|207.241.234.121|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20279887 (19M) [audio/mpeg]\n",
            "Saving to: ‘./test-audio/aliceaupays_04_carroll_128kb.mp3’\n",
            "\n",
            "aliceaupays_04_carr 100%[===================>]  19.34M  75.2MB/s    in 0.3s    \n",
            "\n",
            "2025-03-25 22:30:00 (75.2 MB/s) - ‘./test-audio/aliceaupays_04_carroll_128kb.mp3’ saved [20279887/20279887]\n",
            "\n",
            "ffmpeg version 4.4.2-0ubuntu0.22.04.1 Copyright (c) 2000-2021 the FFmpeg developers\n",
            "  built with gcc 11 (Ubuntu 11.2.0-19ubuntu1)\n",
            "  configuration: --prefix=/usr --extra-version=0ubuntu0.22.04.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libdav1d --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librabbitmq --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzimg --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-pocketsphinx --enable-librsvg --enable-libmfx --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\n",
            "  libavutil      56. 70.100 / 56. 70.100\n",
            "  libavcodec     58.134.100 / 58.134.100\n",
            "  libavformat    58. 76.100 / 58. 76.100\n",
            "  libavdevice    58. 13.100 / 58. 13.100\n",
            "  libavfilter     7.110.100 /  7.110.100\n",
            "  libswscale      5.  9.100 /  5.  9.100\n",
            "  libswresample   3.  9.100 /  3.  9.100\n",
            "  libpostproc    55.  9.100 / 55.  9.100\n",
            "Input #0, mp3, from './test-audio/aliceaupays_04_carroll_128kb.mp3':\n",
            "  Metadata:\n",
            "    title           : 04 - L'Habitation du Lapin Blanc\n",
            "    artist          : Lewis Carroll\n",
            "    album           : Aventures d'Alice au Pays des Merveilles\n",
            "    genre           : speech\n",
            "    track           : 5\n",
            "  Duration: 00:21:07.20, start: 0.025057, bitrate: 128 kb/s\n",
            "  Stream #0:0: Audio: mp3, 44100 Hz, mono, fltp, 128 kb/s\n",
            "    Metadata:\n",
            "      encoder         : LAME3.99r\n",
            "Stream mapping:\n",
            "  Stream #0:0 -> #0:0 (mp3 (mp3float) -> pcm_s16le (native))\n",
            "Press [q] to stop, [?] for help\n",
            "Output #0, wav, to './test-audio/recording-fr-4.wav':\n",
            "  Metadata:\n",
            "    INAM            : 04 - L'Habitation du Lapin Blanc\n",
            "    IART            : Lewis Carroll\n",
            "    IPRD            : Aventures d'Alice au Pays des Merveilles\n",
            "    IGNR            : speech\n",
            "    IPRT            : 5\n",
            "    ISFT            : Lavf58.76.100\n",
            "  Stream #0:0: Audio: pcm_s16le ([1][0][0][0] / 0x0001), 16000 Hz, mono, s16, 256 kb/s\n",
            "    Metadata:\n",
            "      encoder         : Lavc58.134.100 pcm_s16le\n",
            "size=     313kB time=00:00:09.99 bitrate= 256.2kbits/s speed= 380x    \n",
            "video:0kB audio:312kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.068125%\n"
          ]
        }
      ],
      "source": [
        "!mkdir ./test-audio\n",
        "# Download the first chapter of Alice in Wonderland (in French)\n",
        "!wget -P ./test-audio/ https://www.archive.org/download/alice_au_pays_des_merveilles_1811_librivox/aliceaupays_04_carroll_128kb.mp3\n",
        "# Convert it to a format that's understandable by whisper.cpp:\n",
        "# -t 30                 Take the first 30s\n",
        "# -i ...                Input path\n",
        "# -ar 16000             Sample rate of 16000 HZ\n",
        "# -ac 1                 1 audio channel\n",
        "# -codec:a pcm_s16le    Audio codec\n",
        "!ffmpeg -t 10 -i ./test-audio/aliceaupays_04_carroll_128kb.mp3 -ar 16000 -ac 1 -codec:a pcm_s16le ./test-audio/recording-fr-4.wav"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kg5hkQpyq1io"
      },
      "source": [
        "Next, use the `whisper-cli` command to transcribe the audio using our GGML model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ur0wEF6SULr4",
        "outputId": "85816704-9225-4a5a-f1dd-086598e35215"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            " Chapez trop quatre de aventures talices au pays d'Emerveilleux par Louis Carroll. C'est un registrement libre-voix fait partie.\n"
          ]
        }
      ],
      "source": [
        "# Test converting the WAV file to text using the GGML file that we built.\n",
        "# The \"-np\" argument causes only the recognised text to be printed:\n",
        "!./whisper.cpp/build/bin/whisper-cli --language fr -np --no-timestamps -m ./ggml-updated/ggml-model.bin ./test-audio/recording-fr-4.wav"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNKGqVn0zRg6"
      },
      "source": [
        "## Building the Joplin-compatible .zip file\n",
        "\n",
        "Next, we need to convert the model to a format compatible with Joplin. A `.zip` file is created with the following structure:\n",
        "```\n",
        "model_name.zip/\n",
        "| model_name/\n",
        "| | README.md\n",
        "| | model.bin\n",
        "| | config.json\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "ucsOUrTqzRSz"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import shutil, json, zipfile\n",
        "\n",
        "def package_output(source_model: Path, output_dir: Path, output_filename: str):\n",
        "    if not output_dir.exists():\n",
        "        output_dir.mkdir()\n",
        "    unzipped_dir = output_dir / output_filename\n",
        "    if unzipped_dir.exists():\n",
        "        shutil.rmtree(unzipped_dir)\n",
        "    unzipped_dir.mkdir()\n",
        "\n",
        "    shutil.copyfile(source_model, unzipped_dir / 'model.bin')\n",
        "    # config.json\n",
        "    config_filepath = unzipped_dir / 'config.json'\n",
        "    config_filepath.write_text(json.dumps({\n",
        "        # 'languages': [ 'fr' ], # For French-only language models\n",
        "        'prompts': {\n",
        "            # Custom prompts can improve accuracy.\n",
        "            'en': 'Joplin is a note-taking application. This is a Joplin note.'\n",
        "        },\n",
        "        # Performance: Informs Joplin that the model supports a shortened audio context\n",
        "        'shortAudioContext': True,\n",
        "        'output': {\n",
        "            '//': 'Each of the replacements is in the form [ original, replaceWith ]. For example, [\"test\", \"\"] replaces all instances of \"test\" with the empty string.',\n",
        "            'stringReplacements': [\n",
        "                [ '[BLANK_AUDIO]', '' ],\n",
        "            ],\n",
        "            'regexReplacements': [\n",
        "                [ r'^\\([^(),.?]+\\)$', ''],\n",
        "                [ r'^\\[[^(),.?]+\\]$', ''],\n",
        "                [ r'^[.,?!]$', '' ],\n",
        "                [ r'\\[swearB1\\]', 'BS' ],\n",
        "                [ r'\\[swear[A-Z][0-9]+\\]', '****' ],\n",
        "            ],\n",
        "        }\n",
        "    }, indent='\\t'))\n",
        "    # README.md\n",
        "    readme_filepath = unzipped_dir / 'README.md'\n",
        "    readme_filepath.write_text('\\n'.join([\n",
        "        '# {}'.format(output_filename),\n",
        "        '',\n",
        "        'This model is a version of `whisper-' + whisper_mode + '` with an [adjusted vocab.json](https://github.com/personalizedrefrigerator/joplin-voice-typing-test/blob/main/whisper_vocab_cleanup.ipynb) to reduce the probability of profanity when given noisy non-speech input.',\n",
        "        '',\n",
        "        'This model has also been [fine-tuned](https://github.com/joplin/voice-typing-models/blob/240c4de34b76aa516482f6e3155c19e14a414e37/whisper_more_efficient_encoding.ipynb) to improve efficiency.',\n",
        "        '',\n",
        "        '## License',\n",
        "        '',\n",
        "        'The Whisper model from which this is modified has the following license:',\n",
        "        '''\n",
        "        MIT License\n",
        "\n",
        "        Copyright (c) 2022 OpenAI\n",
        "\n",
        "        Permission is hereby granted, free of charge, to any person obtaining a copy\n",
        "        of this software and associated documentation files (the \"Software\"), to deal\n",
        "        in the Software without restriction, including without limitation the rights\n",
        "        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
        "        copies of the Software, and to permit persons to whom the Software is\n",
        "        furnished to do so, subject to the following conditions:\n",
        "\n",
        "        The above copyright notice and this permission notice shall be included in all\n",
        "        copies or substantial portions of the Software.\n",
        "\n",
        "        THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
        "        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
        "        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
        "        SOFTWARE.\n",
        "        ''',\n",
        "        '',\n",
        "        'The fine-tuning code that helped generate this model has the following license:',\n",
        "        '''\n",
        "        MIT License\n",
        "\n",
        "        Copyright (c) 2024 FUTO Organization\n",
        "\n",
        "        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
        "\n",
        "        The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
        "\n",
        "        THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
        "\n",
        "        '''\n",
        "    ]))\n",
        "\n",
        "    # Make the .zip file\n",
        "    # See https://docs.python.org/3/library/shutil.html\n",
        "    shutil.make_archive(\n",
        "        output_dir / output_filename,\n",
        "        'zip',\n",
        "        root_dir=output_dir,\n",
        "        base_dir=output_filename,\n",
        "    )\n",
        "\n",
        "package_output(\n",
        "    Path('./ggml-updated/ggml-model.bin'),\n",
        "    Path('./joplin-model'),\n",
        "    'whisper-{}'.format(whisper_mode)\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCEyuCYi3Phx"
      },
      "source": [
        "The models are now built! They're stored in the `./joplin-model` and `./joplin-model-q8_0` directories.\n",
        "\n",
        "Optionally also create packages for the more quantized versions of the models:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "knBgdcxbpdbw"
      },
      "outputs": [],
      "source": [
        "for quantization in ['q8_0', 'q5_0', 'q4_0']:\n",
        "    package_output(\n",
        "        Path(f'./ggml-updated/ggml-model-{quantization}.bin'),\n",
        "        Path(f'./joplin-model-{quantization}'),\n",
        "        f'whisper-{whisper_mode}-{quantization}'\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "razNbAnik9Um"
      },
      "source": [
        "## Google-colab-specific\n",
        "\n",
        "If running on Google Colab, download the final packaged models:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "O94RKzrZk8ud",
        "outputId": "4e339ae8-2a96-4005-843d-2b47d5d9c66c"
      },
      "outputs": [
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          "data": {
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          "data": {
            "application/javascript": "download(\"download_ab82f2b0-752e-41d8-a8af-af2ae76c1cae\", \"whisper-tiny-q4_0.zip\", 21489751)",
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          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
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      ],
      "source": [
        "# Google colab only: Save the packaged files to the local machine\n",
        "from google.colab import files\n",
        "import glob\n",
        "\n",
        "for model_path in glob.glob('./joplin-model*/whisper-*.zip'):\n",
        "    files.download(model_path)\n"
      ]
    },
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      "cell_type": "code",
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