{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ni4QFjWo8cp",
        "outputId": "072d5f25-c78d-4d43-9c6d-9e718b3953ef"
      },
      "outputs": [],
      "source": [
        "# !pip install gradio evaluate datasets\n",
        "# !pip install accelerate -U\n",
        "# !pip install git+https://github.com/huggingface/transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310,
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            "e0400f0834174e4fa947ef02373929b0",
            "c47f3d7685e6476aa6021647bae767b7",
            "c6c6c32e5b1e46c3bf74c0a100620a15",
            "ce305f78898a4f1682ccc24c9768d9bc",
            "63e5ba242ec8464c8b23c73679acc0e6",
            "5f908f6a0a9047118cc8459016bef43b",
            "c82118635df9483fb6d7e8a96d720c71",
            "85b25b0aebb945c9b4275c43b5ff25b8"
          ]
        },
        "id": "04RTpQVvnRrQ",
        "outputId": "a329e439-771b-4a82-ee29-6ad302194a01"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "gtzan = load_dataset(\"marsyas/gtzan\", \"all\")\n",
        "gtzan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uFZjfW0n6cE",
        "outputId": "111b8376-927c-4d4f-b516-8941b0331453"
      },
      "outputs": [],
      "source": [
        "gtzan = gtzan[\"train\"].train_test_split(seed=42, shuffle=True, test_size=0.1)\n",
        "gtzan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBYE2mttoR7c",
        "outputId": "f193c9de-8f0e-4dd0-d6ae-47540c76a2ca"
      },
      "outputs": [],
      "source": [
        "gtzan[\"train\"][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "UDyiAFEwoeH9",
        "outputId": "a17d65c1-0c88-4204-eb88-c22c03002a93"
      },
      "outputs": [],
      "source": [
        "id2label_fn = gtzan[\"train\"].features[\"genre\"].int2str\n",
        "id2label_fn(gtzan[\"train\"][0][\"genre\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ydaWOEJ2ohgR"
      },
      "outputs": [],
      "source": [
        "#from IPython.display import Audio as displayAudio\n",
        "\n",
        "#Audio(gtzan[\"train\"][0][\"audio\"][\"array\"], rate=gtzan[\"train\"][0][\"audio\"][\"sampling_rate\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q-GC0FqGozFR"
      },
      "outputs": [],
      "source": [
        "# import gradio as gr\n",
        "# def generate_audio():\n",
        "#     example = gtzan[\"train\"].shuffle()[0]\n",
        "#     audio = example[\"audio\"]\n",
        "#     return (\n",
        "#         audio[\"sampling_rate\"],\n",
        "#         audio[\"array\"],\n",
        "#     ), id2label_fn(example[\"genre\"])\n",
        "\n",
        "\n",
        "# with gr.Blocks() as demo:\n",
        "#     with gr.Column():\n",
        "#         for _ in range(4):\n",
        "#             audio, label = generate_audio()\n",
        "#             output = gr.Audio(audio, label=label)\n",
        "\n",
        "# demo.launch(debug=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "6705d6a87ece43f38cb8b97aa7879a60",
            "eeacae19074941dd86420224a9bc2a5a",
            "16053dc329874fc69ca7e0341df95b5c",
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            "9f3f4a59602348d5a1857747e7aeb856"
          ]
        },
        "id": "otPBHNafpXbP",
        "outputId": "9cb47552-0f63-49a9-c961-54b337bf5313"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoFeatureExtractor\n",
        "from transformers import AutoConfig\n",
        "\n",
        "model_id = \"ntu-spml/distilhubert\" #\"NicolasDenier/distilhubert-finetuned-gtzan\"\n",
        "\n",
        "configuration = AutoConfig.from_pretrained(model_id)\n",
        "configuration.hidden_dropout = 0.5\n",
        "configuration.attention_dropout = 0.1\n",
        "\n",
        "feature_extractor = AutoFeatureExtractor.from_pretrained(\n",
        "    model_id, do_normalize=True, return_attention_mask=True, config=configuration\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdN3TpY6qiZQ",
        "outputId": "414653f2-5a1b-4417-d5c9-763869fa3c7f"
      },
      "outputs": [],
      "source": [
        "sampling_rate = feature_extractor.sampling_rate\n",
        "sampling_rate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvX22bjJql93",
        "outputId": "d421b5b7-e4fc-446a-8c9c-f01ded406c97"
      },
      "outputs": [],
      "source": [
        "from datasets import Audio\n",
        "\n",
        "gtzan = gtzan.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))\n",
        "gtzan[\"train\"][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sm4p3J38JzgE"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import librosa\n",
        "import random\n",
        "\n",
        "test = gtzan[\"train\"][4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B8MuaIxmJ6L2"
      },
      "outputs": [],
      "source": [
        "def change_pitch(data, sampling_rate, pitch_factor):\n",
        "    data =  np.asarray(data, dtype=np.float64)\n",
        "    augmented_data = librosa.effects.pitch_shift(data, sr=sampling_rate, n_steps=pitch_factor)\n",
        "    return augmented_data\n",
        "\n",
        "def add_noise(data, noise_factor):\n",
        "    data =  np.asarray(data, dtype=np.float64)\n",
        "    noise = np.random.randn(len(data))\n",
        "    augmented_data = data + noise_factor * noise\n",
        "    # Cast back to same data type\n",
        "    augmented_data = augmented_data.astype(type(data[0]))\n",
        "    return augmented_data\n",
        "\n",
        "def transform_data(data, sr=16000):\n",
        "    # 1/2 chance of getting transformed\n",
        "    if random.random()>0.6:\n",
        "        data = np.asarray(data, dtype=np.float64)\n",
        "        # random pitch in [-4,-2]U[2,4]\n",
        "        pitch_factor = random.randint(2,4) *(1 if random.random()>0.5 else -1)\n",
        "        data_augmented = change_pitch(data, sr, pitch_factor)\n",
        "        # random noise in [0.05,0.2]\n",
        "        noise_factor = random.random()*0.15+0.05\n",
        "        data_augmented = add_noise(data_augmented, noise_factor)\n",
        "        return data_augmented\n",
        "    else:\n",
        "        return data\n",
        "\n",
        "transform_data_vect = np.vectorize(transform_data , signature='(n)->(n)')\n",
        "\n",
        "test_pitch = change_pitch(test[\"audio\"][\"array\"], sampling_rate, 8) # -4\n",
        "test_noise = add_noise(test[\"audio\"][\"array\"], 0.2)\n",
        "test_pitch_noise = transform_data(test[\"audio\"][\"array\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "M_f0MPCln2Et",
        "outputId": "054a5bce-4be1-4b93-ca80-5745daff9769"
      },
      "outputs": [],
      "source": [
        "#displayAudio(test_pitch_noise, rate=sampling_rate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PxNhv2VwwUmU",
        "outputId": "6c697a16-ac63-4c76-e651-13839588be04"
      },
      "outputs": [],
      "source": [
        "# sample = gtzan[\"train\"][0][\"audio\"]\n",
        "\n",
        "# print(f\"Mean: {np.mean(sample['array']):.3}, Variance: {np.var(sample['array']):.3}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8elhfk0wjHd",
        "outputId": "1512fee3-88e6-4bd5-90d4-fc2986f86cd6"
      },
      "outputs": [],
      "source": [
        "# inputs = feature_extractor(sample[\"array\"], sampling_rate=sample[\"sampling_rate\"])\n",
        "\n",
        "# print(f\"inputs keys: {list(inputs.keys())}\")\n",
        "\n",
        "# print(\n",
        "#     f\"Mean: {np.mean(inputs['input_values']):.3}, Variance: {np.var(inputs['input_values']):.3}\"\n",
        "# )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6TMGN7QZ7-ib"
      },
      "outputs": [],
      "source": [
        "max_duration = 30.0\n",
        "\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",
        "      padding=True,\n",
        "      return_attention_mask=True,\n",
        "  )\n",
        "  return inputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198,
          "referenced_widgets": [
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            "2b90419190dc42d58d6352b1677a61c8",
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            "bed4cc07399f4796b61414a23fc06d4f",
            "8d586a0b5e044b3f998e68daf00d0829"
          ]
        },
        "id": "XDL7-anz8Gg2",
        "outputId": "84758812-3e60-447f-d073-b299f2ce671a"
      },
      "outputs": [],
      "source": [
        "gtzan_encoded = gtzan.map(\n",
        "    preprocess_function, remove_columns=[\"audio\", \"file\"], batched=True, num_proc=1, batch_size=100\n",
        ")\n",
        "gtzan_encoded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FEzdivosy-c8"
      },
      "outputs": [],
      "source": [
        "gtzan_encoded = gtzan_encoded.rename_column(\"genre\", \"label\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import copy\n",
        "# copy the test dataset to not apply tranformations on it\n",
        "gtzan_encoded_test = copy.deepcopy(gtzan_encoded['test'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def transform_batch(batch):\n",
        "  augmented_data = transform_data_vect(batch[\"input_values\"])\n",
        "  augmented_batch = {'input_values': augmented_data}\n",
        "  return augmented_batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# define a function that will be called when the data is accessed\n",
        "gtzan_encoded.set_transform(transform_batch, columns=\"input_values\", output_all_columns=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Zmo_ia4LzOvw",
        "outputId": "f91b3462-d2bd-48ba-9841-2a56071b45d9"
      },
      "outputs": [],
      "source": [
        "id2label = {\n",
        "    str(i): id2label_fn(i)\n",
        "    for i in range(len(gtzan_encoded[\"train\"].features[\"label\"].names))\n",
        "}\n",
        "label2id = {v: k for k, v in id2label.items()}\n",
        "\n",
        "id2label[\"7\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
            "a3cbacbc3b1b40b3bd145df72a07a778",
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            "bc59e3149c384a1a862c2a2dbcdc5cb6",
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            "150b7dc6d536416885a0feac36590d60",
            "c3efdfb00ae4409c88af13eccc9feafc",
            "4c30a61ec8ec49398fce28254d525c9e",
            "f497c9b05ba7450699bb5249efdcdd78",
            "d22bc41a3cc24a3b8af5a0b757a50772",
            "8d2aaedb349740be9631a8d06dadaa0d",
            "75aab13f7e3545bd8bfcaa93588be53d",
            "38f925efe22c4e07ad4e26b43c77efbf",
            "2965fd0946a240ccb2a107733892e2e3"
          ]
        },
        "id": "jFjF5q2E0qyK",
        "outputId": "a3fb48ec-2487-4fb9-d93c-dc993020a52d"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForAudioClassification\n",
        "\n",
        "num_labels = len(id2label)\n",
        "\n",
        "model = AutoModelForAudioClassification.from_pretrained(\n",
        "    model_id,\n",
        "    num_labels=num_labels,\n",
        "    label2id=label2id,\n",
        "    id2label=id2label,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "7ca139ff1841490484b7e668effd52c8"
          ]
        },
        "id": "R2dV29rf1Dvj",
        "outputId": "d22284bc-b1e1-4f5f-81c3-1108d2548108"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xxv5arIP1cyI"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = \"distilhubert\"\n",
        "batch_size = 8\n",
        "gradient_accumulation_steps = 1\n",
        "num_train_epochs = 12\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    f\"{model_name}-finetuned-gtzan\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=5e-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",
        "    warmup_ratio=0.1,\n",
        "    logging_steps=5,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    fp16=True,\n",
        "    push_to_hub=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
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        "outputId": "ca2ec7c8-80b0-4eea-98df-5ef644d818ae"
      },
      "outputs": [],
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    \"\"\"Computes accuracy on a batch of predictions\"\"\"\n",
        "    predictions = np.argmax(eval_pred.predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=eval_pred.label_ids)"
      ]
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        },
        "id": "7OwwlTZe17kG",
        "outputId": "b6050257-ae63-42d7-a87e-14c5d631fd57"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model,\n",
        "    training_args,\n",
        "    train_dataset=gtzan_encoded[\"train\"],\n",
        "    eval_dataset=gtzan_encoded_test,\n",
        "    tokenizer=feature_extractor,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZYmItvzH3JUp"
      },
      "outputs": [],
      "source": [
        "kwargs = {\n",
        "    \"dataset_tags\": \"marsyas/gtzan\",\n",
        "    \"dataset\": \"GTZAN\",\n",
        "    \"model_name\": f\"{model_name}-finetuned-gtzan\",\n",
        "    \"finetuned_from\": model_id,\n",
        "    \"tasks\": \"audio-classification\",\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BDzHhpmB3NW7"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub(**kwargs)"
      ]
    }
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