L20.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"L20.ipynb","provenance":[],"collapsed_sections":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"u-O-IaFDLM3u","colab_type":"text"},"source":["In this notebook we will build a speech recognition model.  \n","\n","Below we'll import the libraries we'll be using."]},{"cell_type":"code","metadata":{"id":"wrWNC6CiH7Dd","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1596142272224,"user_tz":240,"elapsed":2727,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}}},"source":["import os\n","import librosa   #for audio processing\n","import IPython.display as ipd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import warnings\n","warnings.filterwarnings(\"ignore\")"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VuKhes1QLe3b","colab_type":"text"},"source":["Next, we'll download the dataset of speech commands from tensorflow."]},{"cell_type":"code","metadata":{"id":"LUrISUQjkscm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1596142287202,"user_tz":240,"elapsed":17694,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"46d974c1-eca8-4457-c8a5-6432e9a69a07"},"source":["!wget http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz"],"execution_count":2,"outputs":[{"output_type":"stream","text":["--2020-07-30 20:51:12--  http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\n","Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.197.128, 2607:f8b0:400e:c03::80\n","Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.197.128|:80... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 1489096277 (1.4G) [application/gzip]\n","Saving to: ‘speech_commands_v0.01.tar.gz’\n","\n","speech_commands_v0. 100%[===================>]   1.39G  88.6MB/s    in 14s     \n","\n","2020-07-30 20:51:26 (101 MB/s) - ‘speech_commands_v0.01.tar.gz’ saved [1489096277/1489096277]\n","\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JZVW2lmaLkHz","colab_type":"text"},"source":["Here, we unzip the file we downloaded from tensorflow."]},{"cell_type":"code","metadata":{"id":"52b5XAo0k1lT","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1596142331149,"user_tz":240,"elapsed":58040,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}}},"source":["!mkdir speech_commands\n","!tar -C ./speech_commands -xf speech_commands_v0.01.tar.gz "],"execution_count":3,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"eI-bgCqOLoEA","colab_type":"text"},"source":["Let's plot the waveform of an example spoken command, `samples`."]},{"cell_type":"code","metadata":{"id":"DVUj3Z5cI4Vm","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":293},"executionInfo":{"status":"ok","timestamp":1596142338747,"user_tz":240,"elapsed":62373,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"b12a98d8-1a32-4101-ab29-6477a2d407c4"},"source":["train_path = 'speech_commands/'\n","filename = train_path+'no/afe0b87d_nohash_0.wav'\n","samples, sample_rate = librosa.load(filename, sr = 16000)\n","fig = plt.figure(figsize=(14, 8))\n","ax1 = fig.add_subplot(211)\n","ax1.set_title('Raw signal of ' + filename)\n","ax1.set_xlabel('time')\n","ax1.set_ylabel('Amplitude')\n","ax1.plot(np.linspace(0, sample_rate/len(samples), sample_rate), samples)"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f68628c5748>]"]},"metadata":{"tags":[]},"execution_count":4},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1008x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"rODDxUb3Lx_O","colab_type":"text"},"source":["Below we will play the the `samples` audio command."]},{"cell_type":"code","metadata":{"id":"RURz8GG2vPHr","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":75},"executionInfo":{"status":"ok","timestamp":1596142338748,"user_tz":240,"elapsed":53453,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"2e66f477-4ddb-43f9-91f3-30fc3578e83f"},"source":["ipd.Audio(samples,rate=sample_rate,autoplay=True)"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/html":["\n","                <audio controls=\"controls\" autoplay=\"autoplay\">\n","                    <source src=\"data:audio/wav;base64,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\" type=\"audio/wav\" />\n","                    Your browser does not support the audio element.\n","                </audio>\n","              "],"text/plain":["<IPython.lib.display.Audio object>"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"markdown","metadata":{"id":"4Ve2s44HL6G5","colab_type":"text"},"source":["Below we load the data into `all_wavs` and their respective labels into `all_labs`.  The labels are either `yes` or `no`.\n","\n","We'll also print the number of examples in `all_wavs`."]},{"cell_type":"code","metadata":{"id":"W--i8r_iwMv-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":68},"executionInfo":{"status":"ok","timestamp":1593903868705,"user_tz":240,"elapsed":440915,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"c86b574f-84d4-431a-ddd5-2b24b46cc0ab"},"source":["import os\n","\n","directory = 'speech_commands/'\n","\n","all_wavs = []\n","all_labs = []\n","for label in ['yes', 'no']:\n","    print(label)\n","    wavs = [f for f in os.listdir(directory + label) if f.endswith('.wav')]\n","    for wav in wavs:\n","        samples, sample_rate = librosa.load(directory + label + '/' + wav, sr = 16000)\n","        if(len(samples)== 16000): \n","            all_wavs.append(samples)\n","            all_labs.append(label)\n","print(len(all_wavs))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["yes\n","no\n","4255\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5OS6-Zk1NwJd","colab_type":"text"},"source":["Below we split our training and test data.  `X_train` is our processed audio files for training and `y_train` are their labels.  `X_test` and `y_test` are our test audio files and their labels, respectively."]},{"cell_type":"code","metadata":{"id":"Mi2-T32hwb6T","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import train_test_split\n"," \n","all_wavs = np.array(all_wavs).reshape(-1,16000,1)\n","all_labs = np.array([lab == 'yes' for lab in all_labs])\n","X_train, X_test, y_train, y_test = train_test_split(all_wavs,all_labs,test_size = 0.2)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qyYmvoBbOcMv","colab_type":"text"},"source":["In the following lines, we will build together the layers of our model for speech recognition."]},{"cell_type":"code","metadata":{"id":"_gLlhz_vy3V6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":170},"executionInfo":{"status":"ok","timestamp":1593904125706,"user_tz":240,"elapsed":15330,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"71a4423e-1104-4193-b324-772dfca87cfe"},"source":["!pip install keras=='2.3.1'\n","from keras.layers import Conv1D, Input, MaxPooling1D, Flatten, Dense\n","from keras.models import Model\n"," \n","inputs = Input(shape=(16000,1))\n"," \n","#First Conv1D layer\n","conv = Conv1D(8,13, padding='valid', activation='relu', strides=1)(inputs)\n","conv = MaxPooling1D(3)(conv)\n"," \n","#Second Conv1D layer\n","conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)\n","conv = MaxPooling1D(3)(conv)\n"," \n","#Third Conv1D layer\n","conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)\n","conv = MaxPooling1D(3)(conv)\n"," \n","#Fourth Conv1D layer\n","conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)\n","conv = MaxPooling1D(3)(conv)\n"," \n","#Flatten layer\n","conv = Flatten()(conv)\n"," \n","#Dense Layer 1\n","conv = Dense(256, activation='relu')(conv)\n"," \n","#Dense Layer 2\n","conv = Dense(128, activation='relu')(conv)\n"," \n","outputs = Dense(1, activation='sigmoid')(conv)\n"," \n","model = Model(inputs, outputs)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already satisfied: keras==2.3.1 in /usr/local/lib/python3.6/dist-packages (2.3.1)\n","Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (2.10.0)\n","Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.12.0)\n","Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.18.5)\n","Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.4.1)\n","Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.0.8)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (3.13)\n","Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras==2.3.1) (1.1.2)\n"],"name":"stdout"},{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"IuVQFr34Oj2y","colab_type":"text"},"source":["We then `fit` the model.  We use a `mean_squared_error` `loss` and optimize the weigths using use `adam` as our `optimizer`. We iterate of the data 15 times.  Each time, or `epoch`, we print out the `accuracy` and `loss` of our model so far."]},{"cell_type":"code","metadata":{"id":"oTCjBNo9y8XT","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":544},"executionInfo":{"status":"ok","timestamp":1593904155610,"user_tz":240,"elapsed":38824,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"5eea60a0-f7a4-46a0-9ebd-046f409f9e4f"},"source":["model.compile(loss='mean_squared_error',optimizer='adam',metrics=['accuracy'])\n"," \n","model.fit(X_train, y_train ,epochs=15, batch_size=32)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch 1/15\n","3404/3404 [==============================] - 9s 3ms/step - loss: 0.1585 - accuracy: 0.7609\n","Epoch 2/15\n","3404/3404 [==============================] - 1s 435us/step - loss: 0.0726 - accuracy: 0.9069\n","Epoch 3/15\n","3404/3404 [==============================] - 2s 447us/step - loss: 0.0389 - accuracy: 0.9509\n","Epoch 4/15\n","3404/3404 [==============================] - 1s 433us/step - loss: 0.0330 - accuracy: 0.9559\n","Epoch 5/15\n","3404/3404 [==============================] - 1s 431us/step - loss: 0.0320 - accuracy: 0.9606\n","Epoch 6/15\n","3404/3404 [==============================] - 1s 432us/step - loss: 0.0348 - accuracy: 0.9530\n","Epoch 7/15\n","3404/3404 [==============================] - 1s 429us/step - loss: 0.0212 - accuracy: 0.9718\n","Epoch 8/15\n","3404/3404 [==============================] - 1s 433us/step - loss: 0.0229 - accuracy: 0.9694\n","Epoch 9/15\n","3404/3404 [==============================] - 1s 431us/step - loss: 0.0222 - accuracy: 0.9712\n","Epoch 10/15\n","3404/3404 [==============================] - 2s 444us/step - loss: 0.0252 - accuracy: 0.9677\n","Epoch 11/15\n","3404/3404 [==============================] - 2s 441us/step - loss: 0.0212 - accuracy: 0.9703\n","Epoch 12/15\n","3404/3404 [==============================] - 2s 441us/step - loss: 0.0185 - accuracy: 0.9768\n","Epoch 13/15\n","3404/3404 [==============================] - 2s 451us/step - loss: 0.0305 - accuracy: 0.9600\n","Epoch 14/15\n","3404/3404 [==============================] - 1s 433us/step - loss: 0.0223 - accuracy: 0.9692\n","Epoch 15/15\n","3404/3404 [==============================] - 1s 431us/step - loss: 0.0176 - accuracy: 0.9765\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.callbacks.History at 0x7fc1758f06a0>"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"l7IGY2JePP5B","colab_type":"text"},"source":["We then report the final `accuracy` and `loss` on the `X_test` and `y_test` data."]},{"cell_type":"code","metadata":{"id":"zbnF7hTXy_zr","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":51},"executionInfo":{"status":"ok","timestamp":1593904700475,"user_tz":240,"elapsed":2084,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"cac1f552-df93-4fcc-e1e8-2fdd98f0c6e1"},"source":["model.evaluate(X_test, y_test)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["851/851 [==============================] - 0s 321us/step\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.039459605919490874, 0.9494712352752686]"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"4TyyipuYIUPP","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593904816895,"user_tz":240,"elapsed":1223,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"35804b5f-5fe3-4c0d-a0c2-ad73f9321655"},"source":["X_train.shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3404, 16000, 1)"]},"metadata":{"tags":[]},"execution_count":11}]},{"cell_type":"code","metadata":{"id":"5haffyVcIVly","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1593904949712,"user_tz":240,"elapsed":1377,"user":{"displayName":"Michael Littman","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gh1OI1zp1NevMSZ87UN47k8bTZ--Ijr4v3ORV33Vg=s64","userId":"06751891446459829367"}},"outputId":"3a80ff6f-e7a6-496b-e72e-06f2a6991663"},"source":["sum(sum(X_train))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([2213.4368], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"NKERzJE8IaRq","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}