{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "0o5aVfRuH2sg" }, "source": [ "# Clasificación de audio utilizando embeddings de YAMNet" ] }, { "cell_type": "markdown", "metadata": { "id": "xUSWaH9YH2sh" }, "source": [ "En este notebook revisaremos una de las tantas técnicas para el procesamiento de audio: embeddings" ] }, { "cell_type": "markdown", "metadata": { "id": "aef7p0jPH2sh" }, "source": [ "## Preparacion del ambiente" ] }, { "cell_type": "markdown", "metadata": { "id": "KEa4gobKH2si" }, "source": [ "Instalamos las librerias necesarias: `librosa`, `moviepy` y `soundfile`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C_hgb76iH2sj" }, "outputs": [], "source": [ "!wget https://raw.githubusercontent.com/santiagxf/M72109/master/docs/audio/neural/yamnet_class.txt \\\n", " --quiet --no-clobber\n", "!pip install -r yamnet_class.txt --quiet" ] }, { "cell_type": "markdown", "metadata": { "id": "7pqn26SfH2st" }, "source": [ "Descargamos algunas utilidades para graficar audio y el mapa de etiquetas de las predicciones del modelo:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "awPCjMzUH2su" }, "outputs": [], "source": [ "!wget https://raw.githubusercontent.com/santiagxf/M72109/master/Audio/Models/yamnet/yamnet_class_map.csv \\\n", " --directory-prefix ./Models/yamnet/ --quiet --no-clobber\n", "!wget https://raw.githubusercontent.com/santiagxf/M72109/master/m72109/audio/plotting.py \\\n", " --directory-prefix ./m72109/audio/ --quiet --no-clobber" ] }, { "cell_type": "markdown", "metadata": { "id": "gd9eiXZtH2s3" }, "source": [ "Descargamos algunos archivos de audio de ejemplo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ZIuHBdwWH2s3" }, "outputs": [], "source": [ "!wget -N https://raw.githubusercontent.com/santiagxf/M72109/master/Audio/Samples/127_hours_285_295_2.wav \\\n", " --directory-prefix ./Samples/ --quiet\n", "!wget -N https://raw.githubusercontent.com/santiagxf/M72109/master/Audio/Samples/Back_To_The_Future_3648_3658_6.wav \\\n", " --directory-prefix ./Samples/ --quiet" ] }, { "cell_type": "markdown", "metadata": { "id": "D_EtSYSxH2s6" }, "source": [ "## Classificación de audio utilizando CNNs" ] }, { "cell_type": "markdown", "metadata": { "id": "eyfspLVDH2s7" }, "source": [ "La performance de los modelos de clasificación de imágenes ha mejorado enormemente con la utilización de grandes conjuntos de datos como ImageNet, lo que permite utilizar grandes arquitecturas de redes neuronales basadas en convoluciones (CNN) como AlexNet, VGG, Inception y ResNet. Justamente de este último tipo hemos visto un ejemplo de como utilizar un modelo preentrenado en ImageNet para transferir los conceptos aprendidos a otro dominio. ¿Sería posible entonces pensar que la misma ténica podría funcionar para la clasificación de audio?" ] }, { "cell_type": "markdown", "metadata": { "id": "tCrsOYK4H2s8" }, "source": [ "### AudioSet-YouTube corpus" ] }, { "cell_type": "markdown", "metadata": { "id": "kQm67kA0H2s8" }, "source": [ "Equivalente a ImageNet, existe un dataset de un gran tamaño sobre el cual entrenar modelos especificamente tratandose de audio. Este conjunto de datos, AudioSet-YouTube corpus consta de una colección de 2,084,320 clips de sonido de 10 segundos con etiquetas (labels) extraídas de videos de YouTube. Estas anotaciones corresponden a 632 diferentes clases que se especifican como un gráfico jerárquico de categorías de eventos, que cubre una amplia gama de sonidos incluyendo anumales, personas, instrumentos musicales y géneros, y sonidos ambientales de la vida cotidiana.\n", "\n", "Para más información sobre este set de datos pueden visitar: https://research.google.com/audioset/" ] }, { "cell_type": "markdown", "metadata": { "id": "ZUksPIjrH2s9" }, "source": [ "### YAMNet" ] }, { "cell_type": "markdown", "metadata": { "id": "hls15GcHH2s9" }, "source": [ "YAMNet es un modelo que explota las técnicas de Computer Vision al construir imagenes a partir de audio (llamados parches o patchs). Cada uno de estos patchs es el resultado de calcular espectrogramas log-mel, creando asi parches de imágenes 2D para utilizar en nuestro modelo.\n", "\n", "\n", "\n", "Estas representaciones 2D tienen dimensiones de datos en el tiempo y la frecuencia del sonido, con lo cual, el significado es bien distinto al de una imágen tradicional. En este contexto, ¿funcionarán las técnicas de CNN que utilizamos en imagenes convencionales?. Sin embargo, YAMNet logra performance muy interesante que logra predecir 521 clases de eventos de audio empleando la arquitectura de convolución de Mobilenet. Esto tiene sentido ya que las 2 supociones básicas que realiza una CNN se cumplen en estos espectrogramas: **locality and translation invariance**.\n", "\n", "El repositorio puede encontrase en: https://github.com/tensorflow/models/tree/master/research/audioset/yamnet\n", "\n", "Para más información sobre este modelo, puede ver el paper asociado: [CNN Architectures for Large-Scale Audio Classification | arXiv:1609.09430 cs.SD](https://arxiv.org/abs/1609.09430v2)" ] }, { "cell_type": "markdown", "metadata": { "id": "3VMK1bmZH2s-" }, "source": [ "## Explorando los embeddings generados por YAMNet" ] }, { "cell_type": "markdown", "metadata": { "id": "sRq5ae3PH2s-" }, "source": [ "### Instanciamos el modelo" ] }, { "cell_type": "markdown", "metadata": { "id": "9P955rs2H2s_" }, "source": [ "Al igual que hicimos con varios de nuestros modelos, el primer paso para trabajar con este modelo es descargar los pesos (weights) de la red neuronal. Entrenar este modelo desde zero es una tarea muy demandante de hardware teniendo en cuenta el dataset con el que estamos trabajando, por lo cual utilizaremos un modelo ya entrenado para esta tarea. Al igual que hicimos con modelos de imágenes, utilizaremos un modelo pre-entrenado y disponible en TensorFlow Hub:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wqA0nwzXiVrz" }, "outputs": [], "source": [ "import tensorflow_hub as hub\n", "\n", "model = hub.load('https://tfhub.dev/google/yamnet/1')" ] }, { "cell_type": "markdown", "metadata": { "id": "JXGzR-lSH2tF" }, "source": [ "### Representación de un archivo de audio" ] }, { "cell_type": "markdown", "metadata": { "id": "7CT1_ToGH2tF" }, "source": [ "Como estamos acostumbrados, los tipos de datos no estructurados ofrecen desafios para procesarlos. El audio no es una exceptción ya que en general suele estar comprimido. Codecs como MP3, que ademas de ser propietario, son utilizados para comprimir los datos y asi reducir el espacio que ocupa. Nuestros modelos no podrán operar sobre datos comprimidos y por ende deben ser convertidos a un formato como wav. En este formato, podemos cargarlos utiliando la libreria librosa" ] }, { "cell_type": "markdown", "metadata": { "id": "tG_qY7IMH2tG" }, "source": [ "Leemos un archivo de audio para correr el modelo" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Fz6m3GU3H2tK" }, "outputs": [], "source": [ "import numpy as np\n", "import librosa\n", "\n", "sample_rate: float = 16000.0\n", "wav_file_name = 'Samples/127_hours_285_295_2.wav'\n", "wave = librosa.load(wav_file_name, sr=sample_rate)" ] }, { "cell_type": "markdown", "metadata": { "id": "FV9_AozbpKTV" }, "source": [ "> Note que estamos leyendo el archivo de audio en un Sample Rate (`sr`) especifico 16KHz (16000). Esto es un parametro con el cual es modelo ha sido entrenado y por lo tanto, durante inferencia, debemos proporcionar la información utilizando la misma tasa de muestreo. La librería `libraosa` tiene una funcionalidad para ajustar automaticamente el muestreo a un valor objetivo." ] }, { "cell_type": "markdown", "metadata": { "id": "MptiLR5jlwDg" }, "source": [ "El audio está en sterio (2 canales). En este caso nos quedaremos solo con 1 canal suponiendo que ambos tienen la misma informacion:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UVYwMVjHlp9C" }, "outputs": [], "source": [ "wave = wave[0].astype(np.float32)" ] }, { "cell_type": "markdown", "metadata": { "id": "OG45nYJCrq8s" }, "source": [ "Escuchemos el audio:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 76 }, "id": "E5yu9Ou4rebC", "outputId": "d984d167-5fcc-45d4-e2ec-63dc26fceb93" }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Audio\n", "\n", "Audio(wave, rate=sample_rate)" ] }, { "cell_type": "markdown", "metadata": { "id": "YheatMGJH2tN" }, "source": [ "¿Que dimensiones tiene wave?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KVxtQ2bwH2tN", "outputId": "fca43f70-cba0-44ae-80aa-d809e70085c4" }, "outputs": [ { "data": { "text/plain": [ "(160480,)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wave.shape" ] }, { "cell_type": "markdown", "metadata": { "id": "FVDh6cwHuA1W" }, "source": [ "Notar que `wave.shape[0] / sample_rate` nos da la longitud del audio en segundos:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ALSiUj-Btycp", "outputId": "aba60a73-3813-418d-9427-93989a73d4da" }, "outputs": [ { "data": { "text/plain": [ "10.03" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wave.shape[0] / sample_rate\n" ] }, { "cell_type": "markdown", "metadata": { "id": "B_VuCrVXH2tP" }, "source": [ "### Ejecución de nuestro modelo" ] }, { "cell_type": "markdown", "metadata": { "id": "YKAsWJtmH2tQ" }, "source": [ "Ejecutaremos nuestro modelo sobre el audio que acabamos de especificar. Para hacerlo, simplemente llamamos al modelo instanciado anteriormente. Este metodo devuelve 3 valores:\n", " - **scores:** Son las probabilidades de que el audio indicado pertenezca a cada una de las 521 clases que el modelo es capaz de predecir. Noten que este modelo resuelve un problema de clasificación múltiple\n", " - **embeddings:** Son los embeddings (ante-ultima capa de la red) utilizados para predecir cada una de las clases. Estos embeddings son un vector de dimensión 1024 en YAMNet que se representan a lo largo de la duración del audio. Las representaciones finales entonces terminan teniendo dimensiones 20x1024\n", " - **spectrogram:** Es el espectrograma resultante del audio indicado como entrada y que se utilizo como input para la red. Este espectrograma sería el equivalente a una imágen 2D" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "swH1I51_H2tQ" }, "outputs": [], "source": [ "scores, embeddings, spectrogram = model(wave)" ] }, { "cell_type": "markdown", "metadata": { "id": "HyIk9gfYrI5f" }, "source": [ "Convertimos nuestros tensores a Numpy para que sea mas sencillo trabajarlos:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PzDOSoW_rLzA" }, "outputs": [], "source": [ "scores_np = scores.numpy()\n", "embeddings_np = embeddings.numpy()\n", "spectrogram_np = spectrogram.numpy()" ] }, { "cell_type": "markdown", "metadata": { "id": "202yfYbNH2tT" }, "source": [ "¿Que dimensiones tiene scores, embeddings y spectrogram?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aVMBSwDSH2tT", "outputId": "eed98180-7c8a-4fa5-c0eb-f0ddaaebd7c8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(20, 521)\n", "(20, 1024)\n" ] } ], "source": [ "print(scores_np.shape)\n", "print(embeddings_np.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "LqNCAGZRp3Xl" }, "source": [ "> Tip: Estos embeddings pueden ser utilizados como transferencia de aprendizaje en una red neuronal." ] }, { "cell_type": "markdown", "metadata": { "id": "Eoqa2WLlqC5W" }, "source": [ "### Verificando las predicciones" ] }, { "cell_type": "markdown", "metadata": { "id": "2UvOBqvlqGpt" }, "source": [ "El modelo YAMNet está preentrenado para clasificar audios en 521 categorias distintas. Sin embargo, este solo predecirá el número de la clase. Para saber cual es la etiqueta asociada con la misma utilizaremos el siguiente mapa:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rlXf86mQnUsN", "outputId": "8080b9b0-55d2-429a-ebb3-593d2b1d8569" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Por ejemplo, la clase 45 corresponde a Sniff\n" ] } ], "source": [ "import pandas as pd\n", "\n", "class_map_path = '/content/Models/yamnet/yamnet_class_map.csv'\n", "class_names = pd.read_csv(class_map_path)['display_name'].values\n", "\n", "print(f'Por ejemplo, la clase 45 corresponde a {class_names[45]}')" ] }, { "cell_type": "markdown", "metadata": { "id": "97-QXoYfIZzD" }, "source": [ "> El mapa de clases fué descargado en el inicio de este notebook. El modelo de YAMNet también lo hace disponible. Puede obtenerlo usando `class_map_path = bytes.decode(model.class_map_path().numpy())` y luego leyendo el archivo CSV usando la libraría Pandas." ] }, { "cell_type": "markdown", "metadata": { "id": "IT6gkNCeqxnH" }, "source": [ "Verifiquemos cual es la clase mas probable:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0ls_p9hpnOaR", "outputId": "8d443b8c-5072-4287-828c-7aa11d9a3a95" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "La clase principal de es: Music\n" ] } ], "source": [ "infered_class = class_names[scores_np.mean(axis=0).argmax()]\n", "print(f'La clase principal de es: {infered_class}')" ] }, { "cell_type": "markdown", "metadata": { "id": "k3l7LkynsBpA" }, "source": [ "__Importante__: ¿por qué realizamos la operación `mean`? La red YAMNet primero divide el audio en \"parches\" de una determina duración, sobre los cuales luego aplica la red neuronal. El tamaño de este parche (medido en segundos) es un hiperparámetro del modelo. En el caso de YAMNet, el tamaño del parche es de 0.48 segundos. Este es el motivo por el cual el modelo genera 20 predicciones en un archivo de 10 segundos de duración (duración del audio / tamaño del parche). En este ejemplo estamos queriendo ver cúal es la clase que mayormente aparece en la totalidad del audio. Por lo tanto, tomamos el promedio de la probabilidad para cada una de las diferentes clases que predice el modelo. Finalmente, con el método `argmax` obtenemos el número de la clase más probable." ] }, { "cell_type": "markdown", "metadata": { "id": "mDsDZG4fH2tV" }, "source": [ "Podemos revisar los resultados gráficando las predicciones, pero en este caso, verificaremos la predicción en cada parche:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 483 }, "id": "Xwbm0TTDH2ta", "outputId": "fb4285b0-560d-4eb4-a588-6cd1d7617839" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from m72109.audio.plotting import plot_audio_embeddings\n", "import soundfile as sf\n", "\n", "wav_data, sr = sf.read(wav_file_name, dtype=np.int16)\n", "waveform = wav_data / sr\n", "\n", "# Los siguientes valores son hiperparámetros del modelo, que puede verificarlos en la documentación del mismo\n", "patch_window_seconds: float = 0.96\n", "patch_hop_seconds: float = 0.48\n", "\n", "plot_audio_embeddings(scores, embeddings, spectrogram, waveform, class_names, patch_window_seconds, patch_hop_seconds)" ] }, { "cell_type": "markdown", "metadata": { "id": "8xB-ftVSBDxh" }, "source": [ "## Utilizando YAMNet con transferencia de aprendizaje\n", "\n", "
PRECAUCIÓN 😱: El tema presentado en esta sección está clasificado como avanzado. El entendimiento de este contenido es totalmente opcional.
\n", "\n", "En el caso anterior, utilizamos el modelo completo de YAMNet para resolver el problema. Sin embargo, podemos aplicar transferencia de aprendizaje para utilizar este modelo en el contexto de otro problema. Por ejemplo, el siguiente código utiliza los embeddings generados por YAMNet para resolver un problema de clasificación binaria." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WHdyOVqwBHP4" }, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.layers.experimental import preprocessing\n", "\n", "transfer_model = tf.keras.models.Sequential([\n", " hub.KerasLayer('https://tfhub.dev/google/yamnet/1', trainable=False, output_key='Identity_1:0'),\n", " tf.keras.layers.Dense(128, activation='relu'),\n", " tf.keras.layers.Dense(1, activation='sigmoid')\n", "])\n", "\n", "\n", "\n", "transfer_model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "z6LaI8UFFGYU" }, "source": [ "> __¿Que significa `output_key='Identity_1:0'`__? El modelo YAMNet retorna 3 diferentes salidas: las predicciones segun el problema de clasificación de AudioSet, los embeddings y el espectograma generado. Sin embargo, nosotros solo necesitamos la salida #2. Si visualizamos las diferentes salidas de este modelo, esta corresponde a la salida que tiene como nombre `Identity_1:0`. El parametro `output_key` nos permite seleccionar una salida en particular de cualquier modelo. Para ver las salidas que tiene disponibles cualquier modelo en TensorFlow hub utilize el método `outputs`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T2O2oI4eDLdJ", "outputId": "f80c67f3-5297-49b8-f326-a76157367b56" }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.signatures['serving_default'].outputs" ] } ], "metadata": { "colab": { "name": "Clasificacion de audio.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "TensorFlow 2.1 (tf21-py37)", "language": "python", "name": "tf21-py37" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }