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"_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "Clasificación de audio utilizando embeddings de Wav2vec\n", "=======================================================\n", "\n", "Wav2Vec2 fue propuesto en el paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) por Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, y Michael Auli." ], "metadata": { "id": "zgNwBDVUx3wT" } }, { "cell_type": "markdown", "source": [ "## Preparacion del ambiente\n", "\n", "Instalamos las librerias necesarias: `librosa`, `transformers`, `datasets`, `evaluate`." ], "metadata": { "id": "i3nEi92zIvW_" } }, { "cell_type": "code", "source": [ "%pip install transformers[torch] datasets accelerate evaluate --quiet" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rbwRPnQl0DkD", "outputId": "27f8ca40-15a0-45df-9615-f7b8afd15ec7" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m57.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m34.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m 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entrenamiento para clasificar sonidos de diferentes animales.\n", "\n", "> Nota: La siguiente celda realiza varias manipulaciones de directorios para que el nombre del directorio coincida con el nombre para mostrar de la clase." ], "metadata": { "id": "qbUuah52I8dN" } }, { "cell_type": "code", "source": [ "!git clone https://github.com/YashNita/Animal-Sound-Dataset\n", "!mv Animal-Sound-Dataset/Aslan Animal-Sound-Dataset/Leon\n", "!mv Animal-Sound-Dataset/Esek Animal-Sound-Dataset/Burro\n", "!mv Animal-Sound-Dataset/Inek Animal-Sound-Dataset/Vaca\n", "!mv Animal-Sound-Dataset/Kedi-Part1 Animal-Sound-Dataset/Gato\n", "!mv Animal-Sound-Dataset/Kedi-Part2/* Animal-Sound-Dataset/Gato\n", "!rm -d Animal-Sound-Dataset/Kedi-Part2\n", "!mv Animal-Sound-Dataset/Kopek-Part1 Animal-Sound-Dataset/Perro\n", "!mv Animal-Sound-Dataset/Kopek-Part2/* Animal-Sound-Dataset/Perro\n", "!rm -d Animal-Sound-Dataset/Kopek-Part2\n", "!mv Animal-Sound-Dataset/Koyun Animal-Sound-Dataset/Oveja\n", "!mv Animal-Sound-Dataset/Kus-Part1 Animal-Sound-Dataset/Pajaro\n", "!mv Animal-Sound-Dataset/Kus-Part2/* Animal-Sound-Dataset/Pajaro\n", "!rm -d Animal-Sound-Dataset/Kus-Part2\n", "!mv Animal-Sound-Dataset/Maymun Animal-Sound-Dataset/Mono\n", "!mv Animal-Sound-Dataset/Tavuk Animal-Sound-Dataset/Gallina\n", "!mv Animal-Sound-Dataset/Kurbaga Animal-Sound-Dataset/Rana" ], "metadata": { "id": "NEn9_5J1HzbC", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "58890e03-79ac-47a7-eafb-dbdacf640f65" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Cloning into 'Animal-Sound-Dataset'...\n", "remote: Enumerating objects: 887, done.\u001b[K\n", "remote: Total 887 (delta 0), reused 0 (delta 0), pack-reused 887\u001b[K\n", "Receiving objects: 100% (887/887), 100.68 MiB | 16.56 MiB/s, done.\n", "Resolving deltas: 100% (69/69), done.\n", "Updating files: 100% (876/876), done.\n" ] } ] }, { "cell_type": "markdown", "source": [ "Este conjunto de datos dispone de sonidos de diferentes animales, donde el nombre del directorio corresponde al animal. Este tipo de conjuntos de datos se pueden cargar facilmente utilizando la libraría `datasets`:" ], "metadata": { "id": "GTKTBFDtLbSz" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset, Audio\n", "\n", "dataset = load_dataset(\"audiofolder\", data_dir=\"/content/Animal-Sound-Dataset\", split='train')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 201, "referenced_widgets": [ "f18a96d0d2034e4688bffe2be7d47218", "c588b48f7dc747888fbb2dbc199b25ad", "f75691bd314d4ca5844dba093d89ca6c", "4fdedd66b41f4c71964f554aff01cfff", "b8b367ddd04f4b4798ad72f5fbdfd8aa", "bb5e62d09fd44cb7be301ea2fe248ca8", "06656befe4ab4f1ab10684964d53cfb3", "7998c389739348c7bf386edf26b94f65", "6340d2ceb13c42339e6713ab646d6d54", "e7f12c56b4264942815b2625deff5386", "022a02411941426190845651661d1af5", "950fca438ba740a89b6128c9ab1aa795", "1f1c83c63f7f40409ce373cf71cc4a0b", "6826a29b7f994cbd936650bf18b89a39", "e8ea3cce812246499b1944868b6d8037", "a23fad315ff2420a8d3f5f4b787116b0", "f61c19ee69374d5697a1fb6a503ee509", "d9fc169a34cb4bf1b7c42df9bbc86cd7", "15df7013247a4bee9f9e80fc97e94e22", "816af6705ae144738c42d4cfbdcc9d16", "2bbb892f18724162a5a3baf04dc9379c", "bc37d7e958624655a6b866206ef0e2cc", "0b0db8c0ec1d45688c4c408f561529a8", "0b25f3847b29482a96ddd11f6710d0f0", "2b2c0fea5c09423dadef3f18d8578bbb", "f323711db396455c92489db012fef5b3", "1f85afb615964379b09755bf1d07fbc1", "eb00629720584f36a46bd18cf544909e", "6c8b743ae0fe46feb9643141812dadfd", "928e3bb363e642a4b4c1dfb83ae27ad2", "5b89ef13f8bc46268d18ece3841edb5b", "1a01f4b88aa446de88f3029e0bdad175", "e5af2dcb10cc4536908c8bf645201235", "1adf7264688a47e4b8d884ed12dcb027", "01c592b4e69247bebe826e40fda787e8", "7a2f0615a2d64cca8353f2bd52a17768", "ae9adb6013c3490ab9d6d092a3e34282", "1b1dc0cd9b0046748ae7f1a0d4b146e1", "abd98ec8ca324786a667ef325f7500ce", "3fd5e3118f674ccf82c67e0ec225c9a3", "901b6a9b46ec4c5cb3852cfea1e10de3", "6ff903ef9e5b4f5db6d8de72019c821b", "7f1ec7fea8be481088e8fc95a74ed273", "ca769cd9a05c4a259c7b0958d2e1cbe2", "5386016b97e247e38b679290e2408ba3", "0003443d859445d9b85d0633416a8d65", "1f203afdc0514754b43ce32e48b3cd50", "c639257792d24e319ae8b9f6f47892c0", "bbcaa6e6f7ac46548bfcf5a175bed2fe", "95b0fd882e404e1686aa82b0ca2eefa8", "2e7b471162344957ad723bfcacdba436", "51d3b5427a944d1d8cffd37314d10790", "60d43ea3e0a74f9eabea0cc1935efb4f", "495cdcfcdf284c73b4f23bcf2f06e9f8", "2fa85f80df3d4b079c25b4ce35b47307" ] }, "id": "GI1ZeDER0Oqs", "outputId": "ee278d03-44f6-4446-af76-b0f6541e114f" }, "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Resolving data files: 0%| | 0/875 [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [50/50 04:59, Epoch 9/10]\n", "
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EpochTraining LossValidation LossAccuracy
0No log2.2053600.217143
2No log1.9699200.502857
2No log1.8266150.560000
4No log1.6846810.582857
4No log1.5847150.634286
6No log1.5123870.657143
6No log1.4713380.668571
8No log1.4014760.708571
8No log1.3751050.714286
9No log1.3751850.714286

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=50, training_loss=1.7717454528808594, metrics={'train_runtime': 309.5449, 'train_samples_per_second': 22.614, 'train_steps_per_second': 0.162, 'total_flos': 5.7777674221824e+16, 'train_loss': 1.7717454528808594, 'epoch': 9.09})" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "markdown", "source": [ "### Verificando el modelo\n", "\n", "Podemos probar el modelo con un ejemplo. Tomemos un archivo aleatorio del directorio de datos:" ], "metadata": { "id": "G5-2mYIaSwB6" } }, { "cell_type": "code", "source": [ "wav_file_name = '/content/Animal-Sound-Dataset/Pajaro/Kus_161.wav'" ], "metadata": { "id": "gjzCN2VVCg4g" }, "execution_count": 30, "outputs": [] }, { "cell_type": "markdown", "source": [ "Podemos reproducir este archivo de audio para familiarizarnos con él:" ], "metadata": { "id": "bCCivo2mS3I7" } }, { "cell_type": "code", "source": [ "import librosa\n", "\n", "sample_rate: float = 16000.0\n", "wave = librosa.load(wav_file_name, sr=sample_rate)" ], "metadata": { "id": "Cqgle_oTH-K7" }, "execution_count": 31, "outputs": [] }, { "cell_type": "code", "source": [ "from IPython.display import Audio\n", "\n", "Audio(wave[0], rate=sample_rate)" ], "metadata": { "id": "uBaU_CX_C3Rz", "outputId": "d7a3bf4d-2f87-4086-a16b-9c28a36a1c40", "colab": { "base_uri": "https://localhost:8080/", "height": 75 } }, "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "Creamos un pipeline con el modelo que entrenamos anteriormente:" ], "metadata": { "id": "-wE4eaQlS7Mx" } }, { "cell_type": "code", "source": [ "from transformers import pipeline\n", "\n", "classifier = pipeline(\"audio-classification\", feature_extractor=feature_extractor, model=trainer.model, device=trainer.model.device)" ], "metadata": { "id": "_k0ZtQ_EDLRK" }, "execution_count": 33, "outputs": [] }, { "cell_type": "code", "source": [ "classifier([wav_file_name], top_k=1)" ], "metadata": { "id": "_EY7-LowEXy8", "outputId": "2459cbcf-a057-4a9f-d328-36dfd2342a7f", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 34, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[[{'score': 0.35037851333618164, 'label': 'Pajaro'}]]" ] }, "metadata": {}, "execution_count": 34 } ] } ] }