{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fluF3_oOgkWF" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-08-16T07:47:16.549129Z", "iopub.status.busy": "2024-08-16T07:47:16.548739Z", "iopub.status.idle": "2024-08-16T07:47:16.552401Z", "shell.execute_reply": "2024-08-16T07:47:16.551835Z" }, "id": "AJs7HHFmg1M9" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "jYysdyb-CaWM" }, "source": [ "# Simple audio recognition: Recognizing keywords" ] }, { "cell_type": "markdown", "metadata": { "id": "CNbqmZy0gbyE" }, "source": [ "
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ resizing (Resizing) │ (None, 32, 32, 1) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ normalization (Normalization) │ (None, 32, 32, 1) │ 3 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d (Conv2D) │ (None, 30, 30, 32) │ 320 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_1 (Conv2D) │ (None, 28, 28, 64) │ 18,496 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d (MaxPooling2D) │ (None, 14, 14, 64) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout (Dropout) │ (None, 14, 14, 64) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten (Flatten) │ (None, 12544) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (Dense) │ (None, 128) │ 1,605,760 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_1 (Dropout) │ (None, 128) │ 0 │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_1 (Dense) │ (None, 8) │ 1,032 │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n", "\n" ], "text/plain": [ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", "│ resizing (\u001b[38;5;33mResizing\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ normalization (\u001b[38;5;33mNormalization\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m3\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m18,496\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m1,605,760\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n", "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", "│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m) │ \u001b[38;5;34m1,032\u001b[0m │\n", "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Total params: 1,625,611 (6.20 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,625,611\u001b[0m (6.20 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Trainable params: 1,625,608 (6.20 MB)\n", "\n" ], "text/plain": [ "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,625,608\u001b[0m (6.20 MB)\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Non-trainable params: 3 (16.00 B)\n", "\n" ], "text/plain": [ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m3\u001b[0m (16.00 B)\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "input_shape = example_spectrograms.shape[1:]\n", "print('Input shape:', input_shape)\n", "num_labels = len(label_names)\n", "\n", "# Instantiate the `tf.keras.layers.Normalization` layer.\n", "norm_layer = layers.Normalization()\n", "# Fit the state of the layer to the spectrograms\n", "# with `Normalization.adapt`.\n", "norm_layer.adapt(data=train_spectrogram_ds.map(map_func=lambda spec, label: spec))\n", "\n", "model = models.Sequential([\n", " layers.Input(shape=input_shape),\n", " # Downsample the input.\n", " layers.Resizing(32, 32),\n", " # Normalize.\n", " norm_layer,\n", " layers.Conv2D(32, 3, activation='relu'),\n", " layers.Conv2D(64, 3, activation='relu'),\n", " layers.MaxPooling2D(),\n", " layers.Dropout(0.25),\n", " layers.Flatten(),\n", " layers.Dense(128, activation='relu'),\n", " layers.Dropout(0.5),\n", " layers.Dense(num_labels),\n", "])\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "de52e5afa2f3" }, "source": [ "Configure the Keras model with the Adam optimizer and the cross-entropy loss:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:47:35.221273Z", "iopub.status.busy": "2024-08-16T07:47:35.220621Z", "iopub.status.idle": "2024-08-16T07:47:35.230943Z", "shell.execute_reply": "2024-08-16T07:47:35.230343Z" }, "id": "wFjj7-EmsTD-" }, "outputs": [], "source": [ "model.compile(\n", " optimizer=tf.keras.optimizers.Adam(),\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "f42b9e3a4705" }, "source": [ "Train the model over 10 epochs for demonstration purposes:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:47:35.234446Z", "iopub.status.busy": "2024-08-16T07:47:35.233933Z", "iopub.status.idle": "2024-08-16T07:47:46.273388Z", "shell.execute_reply": "2024-08-16T07:47:46.272725Z" }, "id": "ttioPJVMcGtq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1723794456.367614 244224 service.cc:146] XLA service 0x7f52d4004720 initialized for platform CUDA (this does not guarantee that XLA will be used). 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- accuracy: 0.8685 - loss: 0.3810" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8684 - loss: 0.3811 - val_accuracy: 0.8542 - val_loss: 0.5053\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 10/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[1m 1/100\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.8750 - loss: 0.2674" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 10/100\u001b[0m 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- accuracy: 0.8833 - loss: 0.3342" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 64/100\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8825 - loss: 0.3359" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 73/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8817 - loss: 0.3379" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "\u001b[1m100/100\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.8802 - loss: 0.3423 - val_accuracy: 0.8451 - val_loss: 0.4709\n" ] } ], "source": [ "EPOCHS = 10\n", "history = model.fit(\n", " train_spectrogram_ds,\n", " validation_data=val_spectrogram_ds,\n", " epochs=EPOCHS,\n", " callbacks=tf.keras.callbacks.EarlyStopping(verbose=1, patience=2),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "gjpCDeQ4mUfS" }, "source": [ "Let's plot the training and validation loss curves to check how your model has improved during training:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-08-16T07:47:46.277290Z", "iopub.status.busy": "2024-08-16T07:47:46.276985Z", "iopub.status.idle": "2024-08-16T07:47:46.551824Z", "shell.execute_reply": "2024-08-16T07:47:46.551207Z" }, "id": "nzhipg3Gu2AY" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Accuracy [%]')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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