3. Wav2Vec_Finetuning.ipynb

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      "source": [
        "# Fine-tuning Wav2Vec for Audio Classification\n",
        "Wav2Vec is a powerful pre-trained model designed for self-supervised learning of audio representations. We can fine-tune it for our audio classification task, this time working with *huggin face* transformers.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "arXjQA0AWqjx"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datasets import load_dataset, Audio\n",
        "from transformers import TrainingArguments\n",
        "from transformers import AutoFeatureExtractor\n",
        "from transformers import AutoModelForAudioClassification\n",
        "from transformers import Trainer\n",
        "import evaluate\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Dataset loading"
      ]
    },
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        {
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              "model_id": "21fc9a5a50ca430e9d842f7f85afb6ce",
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              "Resolving data files:   0%|          | 0/3918 [00:00<?, ?it/s]"
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              "Resolving data files:   0%|          | 0/1179 [00:00<?, ?it/s]"
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              "Downloading data:   0%|          | 0/3918 [00:00<?, ?files/s]"
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        {
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              "model_id": "b135ba1f5c1142b09fc3263ea4b75915",
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            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "dataset = load_dataset(\"audiofolder\", data_dir=\"./split_songs_v2/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghymGYowWqjy",
        "outputId": "1382bb72-94d8-4986-ff50-99772b814f35"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['audio', 'label'],\n",
              "        num_rows: 3918\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['audio', 'label'],\n",
              "        num_rows: 1179\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's have a close look into one element."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzMmKarUWqjz",
        "outputId": "63d31fd1-aa3a-47a5-befb-c091d857355c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'audio': {'path': '/content/drive/.shortcut-targets-by-id/1RsUKtn8GSoGO50v9MBuv2WF86mc7JWQR/split_songs_v2/train/drumbass/preview_10_part1.mp3',\n",
              "  'array': array([-6.84566709e-04, -7.71552703e-04,  2.18750007e-04, ...,\n",
              "          4.01656196e-01,  4.46715504e-01,  3.16405728e-01]),\n",
              "  'sampling_rate': 44100},\n",
              " 'label': 0}"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset[\"train\"][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's print out the labels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L0DR9yi3Wqjz",
        "outputId": "c36dfcd2-2096-4e67-c62a-53062a90c6b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "ClassLabel(names=['drumbass', 'dubtechno', 'dupstep', 'hardcore_breaks', 'house', 'psytrance', 'techno', 'ukgarage'], id=None)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset[\"train\"].features[\"label\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To extract audio features effectively and efficiently, we can use the power of transformer models. Transformers have demonstrated exceptional performance in various natural language processing tasks and can be adapted for audio processing.\n",
        "\n",
        "We select our wav2vec base model and we can rely on the library to extract the necessary audio features automatically, saving us from manual transformations.\n"
      ]
    },
    {
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      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b12075ac39c543f09fab524706ade1b2",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/159 [00:00<?, ?B/s]"
            ]
          },
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        {
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            "text/plain": [
              "config.json:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py:364: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "16000"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model_id = \"facebook/wav2vec2-base\"\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\n",
        "    model_id, do_normalize=True, return_attention_mask=True\n",
        ")\n",
        "sampling_rate = feature_extractor.sampling_rate\n",
        "sampling_rate\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We create the two datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "elRtpULpWqj0"
      },
      "outputs": [],
      "source": [
        "dataset[\"train\"] = dataset[\"train\"].cast_column(\"audio\", Audio(sampling_rate=sampling_rate))\n",
        "dataset[\"test\"] = dataset[\"test\"].cast_column(\"audio\", Audio(sampling_rate=sampling_rate))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's print out 5 examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZjB_3xxWqj0",
        "outputId": "df6894d2-7411-410a-da44-4cfd61d9c2fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Label: ['house']\n",
            "Shape: (48000,), sampling rate: 16000\n",
            "\n",
            "Label: ['drumbass']\n",
            "Shape: (48000,), sampling rate: 16000\n",
            "\n",
            "Label: ['psytrance']\n",
            "Shape: (48000,), sampling rate: 16000\n",
            "\n",
            "Label: ['dubtechno']\n",
            "Shape: (48000,), sampling rate: 16000\n",
            "\n",
            "Label: ['house']\n",
            "Shape: (48000,), sampling rate: 16000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import random\n",
        "for _ in range(5):\n",
        "    rand_idx = random.randint(0, len(dataset[\"train\"])-1)\n",
        "    example = dataset[\"train\"][rand_idx]\n",
        "    audio = example[\"audio\"]\n",
        "\n",
        "    print(f'Label: {dataset[\"train\"].features[\"label\"].int2str([example[\"label\"]])}')\n",
        "    print(f'Shape: {audio[\"array\"].shape}, sampling rate: {audio[\"sampling_rate\"]}')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's preprocess the clips."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "F2MO-ukvWqj0"
      },
      "outputs": [],
      "source": [
        "max_duration = 3.0\n",
        "def preprocess_function(examples):\n",
        "    audio_arrays = [x[\"array\"] for x in examples[\"audio\"]]\n",
        "    inputs = feature_extractor(\n",
        "        audio_arrays,\n",
        "        sampling_rate=feature_extractor.sampling_rate,\n",
        "        max_length=int(feature_extractor.sampling_rate * max_duration),\n",
        "        truncation=True,\n",
        "        return_attention_mask=True,\n",
        "    )\n",
        "    return inputs\n"
      ]
    },
    {
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      },
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        {
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            "text/plain": [
              "Map:   0%|          | 0/3918 [00:00<?, ? examples/s]"
            ]
          },
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        },
        {
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              "Map:   0%|          | 0/1179 [00:00<?, ? examples/s]"
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          "metadata": {},
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      ],
      "source": [
        "encoded_audio = dataset.map(preprocess_function, remove_columns=\"audio\", batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGezDOOIWqj2",
        "outputId": "78fa4c33-2fbc-404a-f8d4-08084dd1b457"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_labels = dataset[\"train\"].features[\"label\"].num_classes\n",
        "num_labels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can login interactivily to upload the model to the hub."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
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            "2bcd5d55245c40f685d8891f5725137a",
            "ae1e3fc8998a4bb2a5801d20a63cceec",
            "ec6710c5e8bd4ae3a940dca94986092e",
            "f3d234459a5d4e60971f5e96d57c3cd6",
            "5e96f68ed6ae4b0d87918dcee16c1ab8",
            "ffe7fca260be42adafcbe720b1ee7bd9",
            "3809d0577e104c4da9c52bcb8d595ff3",
            "d420a1c40ef34e2ba370ce42beafb045",
            "73f77215a6df43e682d426c4d209424e",
            "bf519bc33a524b7081ad9362c52f663d",
            "2a59b15a924b4da4aaa656ab93aa4c04",
            "5ae0cc746ddf4d14bbe08414a5376b46",
            "53d6e4c55baa440caacc5c8e61c7af7c",
            "e9cfb5b5416548a3bf9ff48cd9b71829",
            "54ffee87cd96424fa611de3461013de0",
            "6a0d46b2f1bb46c186b9bcd04af861d0",
            "f8e080421cae441898e13f89047aff84",
            "fcd6a173723f4897935a5d7700ab5891",
            "a315ba0dacbc458d881c64e4116ec17e",
            "13b50a09360f455596c4f3ca7f27f4d2",
            "c8bd0efba8da4fe697250ae3923c16ff",
            "c8216abca8074e06802e71b5857d9c7b",
            "ed1be9deba2f4d8c913fe3ab580c15ff",
            "9ab762843c4f429f8d3d0fcaa54b44d9",
            "cf6ce181fa3b44338f98d8cee393a77d",
            "78a15cffd7fd413eb6b740537eb4ca2f",
            "d5899a6dde4a400dbf2341d730510e7f",
            "ac0e8b691a9a48c9ac591d411d8c0910",
            "506a9a8d7d8b44e390f11865a8b2e45b",
            "047a7cef2a804c02951d169312af6808"
          ]
        },
        "id": "Q8QrSsNdWqj2",
        "outputId": "8066be13-574b-4781-aa9b-f07063b6a85c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d7a13a49544341cb91bf76659e99c642",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142,
          "referenced_widgets": [
            "a49921e9cb9b4dbf9463d1ed7c652df3",
            "6e4d2cac251e4f24b964e4cc6393cfb4",
            "ee09bc88222f44079e1bee43cec51a10",
            "1eae0bf3f5294419a42858e297a9a744",
            "3f57bcc7f3634f0590b227f1ff5b3704",
            "c7e72c95b7e64d6897c2d03fed0cd113",
            "3f2cc2e16d6644f98c7d10b9ee7f1d4c",
            "6f8ede607c974b13814b1ac6fc8778b9",
            "b7734b44ad07438d832ffdac36d91678",
            "f898fd795fa54de39a549c07b58af3f9",
            "a9f53919bc8a49e694deb1fd560d8989"
          ]
        },
        "id": "Jd4MYd7aWqj3",
        "outputId": "8a71a973-ae3d-4893-d818-7781c25cf53f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py:364: UserWarning: Passing `gradient_checkpointing` to a config initialization is deprecated and will be removed in v5 Transformers. Using `model.gradient_checkpointing_enable()` instead, or if you are using the `Trainer` API, pass `gradient_checkpointing=True` in your `TrainingArguments`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a49921e9cb9b4dbf9463d1ed7c652df3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/380M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of Wav2Vec2ForSequenceClassification were not initialized from the model checkpoint at facebook/wav2vec2-base and are newly initialized: ['classifier.bias', 'classifier.weight', 'projector.bias', 'projector.weight', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original0', 'wav2vec2.encoder.pos_conv_embed.conv.parametrizations.weight.original1']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    model_id,\n",
        "    num_labels=num_labels\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Training Arguments Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's define all the training arguments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2oeVnfCWqj3",
        "outputId": "ec20fd10-a771-44ab-ae22-f739217e3fd4"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "model_name = model_id.split(\"/\")[-1]+ \"-music_genre_classifier\"\n",
        "batch_size = 12\n",
        "gradient_accumulation_steps = 1\n",
        "num_train_epochs = 10\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    model_name,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=3e-5,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    report_to='tensorboard',\n",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=5,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    push_to_hub=True,\n",
        "    fp16=True\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluation Function Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's define the metrics to evaluate our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "fiFjvmgpWqj3"
      },
      "outputs": [],
      "source": [
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "\n",
        "def compute_metrics(p):\n",
        "\n",
        "\n",
        "\n",
        "    accuracy_metric = evaluate.load(\"accuracy\")\n",
        "    accuracy = accuracy_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids)['accuracy']\n",
        "\n",
        "    ### ------------------- F1 scores -------------------\n",
        "\n",
        "    f1_score_metric = evaluate.load(\"f1\")\n",
        "    f1_score = f1_score_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average = \"macro\")[\"f1\"]\n",
        "\n",
        "    ### ------------------- recall -------------------\n",
        "\n",
        "    recall_metric = evaluate.load(\"recall\")\n",
        "    recall = recall_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average = \"macro\")[\"recall\"]\n",
        "\n",
        "    ### ------------------- precision -------------------\n",
        "\n",
        "    precision_metric = evaluate.load(\"precision\")\n",
        "    precision = precision_metric.compute(predictions=np.argmax(p.predictions, axis=1), references=p.label_ids, average = \"macro\")[\"precision\"]\n",
        "\n",
        "    return {\"accuracy\" : accuracy,\n",
        "            \"F1\" : f1_score,\n",
        "            \"Recall\" : recall,\n",
        "            \"Precision\" : precision,\n",
        "            }\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model Training\n",
        "Now we can train our model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        },
        "id": "Uq-gXMZKWqj3",
        "outputId": "9d70b9f7-b524-4f29-f762-b97ca53c0846"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:488: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n",
            "  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2290' max='3270' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2290/3270 41:08 < 17:37, 0.93 it/s, Epoch 7/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.170600</td>\n",
              "      <td>2.080968</td>\n",
              "      <td>0.726039</td>\n",
              "      <td>0.719655</td>\n",
              "      <td>0.719744</td>\n",
              "      <td>0.745987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.092300</td>\n",
              "      <td>1.848292</td>\n",
              "      <td>0.731128</td>\n",
              "      <td>0.732166</td>\n",
              "      <td>0.737397</td>\n",
              "      <td>0.762104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.138800</td>\n",
              "      <td>1.740142</td>\n",
              "      <td>0.749788</td>\n",
              "      <td>0.749553</td>\n",
              "      <td>0.750061</td>\n",
              "      <td>0.767742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>1.592242</td>\n",
              "      <td>0.793893</td>\n",
              "      <td>0.792314</td>\n",
              "      <td>0.792737</td>\n",
              "      <td>0.802891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.000900</td>\n",
              "      <td>1.654830</td>\n",
              "      <td>0.788804</td>\n",
              "      <td>0.788668</td>\n",
              "      <td>0.791123</td>\n",
              "      <td>0.795365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>1.702750</td>\n",
              "      <td>0.783715</td>\n",
              "      <td>0.781873</td>\n",
              "      <td>0.787052</td>\n",
              "      <td>0.791813</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='99' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/99 00:36 < 00:23, 1.63 it/s]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2848' max='3270' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2848/3270 52:05 < 07:43, 0.91 it/s, Epoch 8.71/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.170600</td>\n",
              "      <td>2.080968</td>\n",
              "      <td>0.726039</td>\n",
              "      <td>0.719655</td>\n",
              "      <td>0.719744</td>\n",
              "      <td>0.745987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.092300</td>\n",
              "      <td>1.848292</td>\n",
              "      <td>0.731128</td>\n",
              "      <td>0.732166</td>\n",
              "      <td>0.737397</td>\n",
              "      <td>0.762104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.138800</td>\n",
              "      <td>1.740142</td>\n",
              "      <td>0.749788</td>\n",
              "      <td>0.749553</td>\n",
              "      <td>0.750061</td>\n",
              "      <td>0.767742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.057300</td>\n",
              "      <td>1.592242</td>\n",
              "      <td>0.793893</td>\n",
              "      <td>0.792314</td>\n",
              "      <td>0.792737</td>\n",
              "      <td>0.802891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.000900</td>\n",
              "      <td>1.654830</td>\n",
              "      <td>0.788804</td>\n",
              "      <td>0.788668</td>\n",
              "      <td>0.791123</td>\n",
              "      <td>0.795365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.000700</td>\n",
              "      <td>1.702750</td>\n",
              "      <td>0.783715</td>\n",
              "      <td>0.781873</td>\n",
              "      <td>0.787052</td>\n",
              "      <td>0.791813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.000600</td>\n",
              "      <td>1.529715</td>\n",
              "      <td>0.807464</td>\n",
              "      <td>0.806755</td>\n",
              "      <td>0.808768</td>\n",
              "      <td>0.811130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.000400</td>\n",
              "      <td>1.535364</td>\n",
              "      <td>0.810857</td>\n",
              "      <td>0.808830</td>\n",
              "      <td>0.807088</td>\n",
              "      <td>0.820173</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
            "  with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs):  # type: ignore[attr-defined]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "trainer = Trainer(\n",
        "    model = model,\n",
        "    args = training_args,\n",
        "    train_dataset = encoded_audio[\"train\"],\n",
        "    eval_dataset = encoded_audio[\"test\"],\n",
        "    tokenizer = feature_extractor,\n",
        "    compute_metrics = compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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