diff --git a/.ipynb_checkpoints/ayman_chaouki_speech_commands_mva_2019-checkpoint.ipynb b/.ipynb_checkpoints/ayman_chaouki_speech_commands_mva_2019-checkpoint.ipynb new file mode 100644 index 0000000..fbc14f9 --- /dev/null +++ b/.ipynb_checkpoints/ayman_chaouki_speech_commands_mva_2019-checkpoint.ipynb @@ -0,0 +1,1870 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copie de speech_commands_mva_2019_py3.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "meJObgLjaTcS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Speech Commands\n", + "In this Practical work you will be given voice commands recorded by devices such as Amazon Alexa or Google Echo assistants. The task is to recognize the voice command from the audio signal. The dataset is stored as waveforms, each one being a 1 second file containing one voice command. \n", + "\n", + "## I. Classification of single voice commands\n", + "\n", + "### What you are expected to do in this first part\n", + "The first part of the TP above contains the different blocks that allow you to train a simple speech command recognizer. The results provided with the features and classifiers below are far from optimal.\n", + "\n", + "You are expected to explore various ways to improve the performance. Here are the main things to try:\n", + "* The parameters of the speech features such as the min/max frequency or window size are *poorly chosen*, look inside the resources mentioned in the class to find the best parameters for mel-filterbanks and MFCC.\n", + "* The logistic regression can be regularized in different ways (by controlling the C parameter) and you can try to find the best one.\n", + "* The neural net proposed is a shallow neural net, far from the best you can train. You should try bigger, deeper architectures, different types of regularization, activation functions, learning rate and so on. You can change the** Runtime of your colab instance and use a GPU**. \n", + "* A standard way of improving generalization is to do mean-variance normalization on your data set. This is done by computing the mean and variance of each feature dimension on the entire training set, and then use it to normalize train, valid and test set\n", + "* The dataset provides noises samples, either artificial (pink, white noise) or real (dishes, bike) in the folder _background_noise. You can try augmenting your dataset by adding noise to the waveforms before computing the features\n", + "* The model is only trained on 300 examples per class, if your hardware allows it, try training on more examples\n", + "* Feel free to also experiment with different classifiers\n", + "\n", + "You should find the best model by comparing validation accuracies. After you find your best model, finally test it on the test set and print the result. Comment on the results (best model, best features, classes that are the most difficult to recognize). The **grade will not depend on the performance of the final system**, but on how you **experimented, tried different ideas, and comment** on them.\n", + "\n", + "\n", + "[Documentation for logistic regression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html)\n", + "\n", + "[Documentation for neural network](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)" + ] + }, + { + "metadata": { + "id": "pCfPTAEC291d", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Extraction of speech features\n", + "\n", + "The first step will be to extract speech features, either Melfilterbanks or MFCC. Then you will train different types of classifiers to recognize the spoken voice command from the speech features. These classifiers are a Logistic Regression and a Neural Network.\n", + "\n", + "### The next block downloads the dataset and extracts it to your Colab instance. RUN ONLY ONCE!" + ] + }, + { + "metadata": { + "id": "YsWy2be5aIbZ", + "colab_type": "code", + "outputId": "11fd4f89-dcf4-40ce-80dd-4552ec10774d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + } + }, + "cell_type": "code", + "source": [ + "!wget http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\n", + "!rm -rf speech_commands\n", + "!mkdir speech_commands\n", + "!tar -zxf speech_commands_v0.01.tar.gz -C speech_commands" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2019-02-25 02:47:01-- http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz\n", + "Resolving download.tensorflow.org (download.tensorflow.org)... 74.125.202.128, 2607:f8b0:4001:c06::80\n", + "Connecting to download.tensorflow.org (download.tensorflow.org)|74.125.202.128|: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 133MB/s in 9.2s \n", + "\n", + "2019-02-25 02:47:11 (155 MB/s) - ‘speech_commands_v0.01.tar.gz’ saved [1489096277/1489096277]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "24aNUNpXae8M", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### This block installs and imports the necessary libraries" + ] + }, + { + "metadata": { + "id": "WVv1dCCD3wOl", + "colab_type": "code", + "outputId": "85eb45e5-2dc9-4a0d-8987-6f7c1a9d1c03", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 683 + } + }, + "cell_type": "code", + "source": [ + "!pip install git+https://github.com/bootphon/spectral.git\n", + "!pip install jiwer\n", + "!pip install scikit-learn \n", + "!pip install matplotlib\n", + "!pip install tqdm\n", + "!pip install librosa\n", + "import numpy as np\n", + "import jiwer\n", + "import spectral\n", + "import librosa\n", + "import IPython\n", + "import os, glob\n", + "import ast\n", + "import scipy.io.wavfile as wav\n", + "import sklearn\n", + "from sklearn.neural_network import MLPClassifier\n", + "import time\n", + "from tqdm import tqdm\n", + "import random\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "random.seed(777)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/bootphon/spectral.git\n", + " Cloning https://github.com/bootphon/spectral.git to /tmp/pip-req-build-6fbc7ffu\n", + "Requirement already satisfied: numpy>=1.6.2 in /usr/local/lib/python3.6/dist-packages (from spectral==0.1.7) (1.14.6)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from spectral==0.1.7) (1.1.0)\n", + "Building wheels for collected packages: spectral\n", + " Building wheel for spectral (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Stored in directory: /tmp/pip-ephem-wheel-cache-gc_sz1sc/wheels/73/fd/30/6edc3e8687a96ba559a35ad0de5fa98922066731d716e4c22d\n", + "Successfully built spectral\n", + "Installing collected packages: spectral\n", + "Successfully installed spectral-0.1.7\n", + "Collecting jiwer\n", + " Downloading https://files.pythonhosted.org/packages/0d/fa/87dbadc0f584c49494c72be2d2068de2b42a36f4c93e6aeea6cb1665cadf/jiwer-1.3.2-py3-none-any.whl\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from jiwer) (1.14.6)\n", + "Installing collected packages: jiwer\n", + "Successfully installed jiwer-1.3.2\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (0.20.2)\n", + "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.1.0)\n", + "Requirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scikit-learn) (1.14.6)\n", + "Requirement already satisfied: matplotlib 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already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.1)\n", + "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.40.1)\n", + "Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.27.1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "pJz3CS41arWp", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "These functions are just utilities that will help you for features extraction. Feel free to modify them.\n" + ] + }, + { + "metadata": { + "id": "aGWbInYuauU0", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def txt2list(filename):\n", + " lines_list = []\n", + " with open(filename, 'r') as txt:\n", + " for line in txt:\n", + " lines_list.append(line.rstrip('\\n'))\n", + " return lines_list\n", + "\n", + "def transform_wavs(wavs_list, feature_func):\n", + " features = []\n", + " for wav in wavs_list:\n", + " features.append(feature_func.transform(wav).flatten())\n", + " return features\n", + "\n", + "def pad(features, min_len=1616):\n", + " padded_features =[]\n", + " for feat_file in features:\n", + " min_len = max(min_len, feat_file.shape[0])\n", + " for feat_file in features:\n", + " pad_size = min_len - feat_file.shape[0]\n", + " left_pad = pad_size // 2\n", + " right_pad = pad_size - left_pad\n", + " padded_features.append(np.pad(feat_file, ((left_pad, right_pad),), 'constant', constant_values=(0, 0)))\n", + " return padded_features" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-w_lszq2C8mD", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "### You might change your pad function and the parameter min_len especially as you change your input features to your model" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "bY6nLYKcayG3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Load the data. In this practical work, you are expected to train your models on the training set and evaluate them on the validation set. When you will get your final best model, report the results on the test set." + ] + }, + { + "metadata": { + "id": "xZoT8Xvraynf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "## Loading data\n", + "# label_set = ['no', 'yes', 'up', 'right', 'down', 'left', 'one', 'on', 'off', 'stop', 'go']\n", + "\n", + "path_to_wav = '/content/speech_commands/'\n", + "directories_speech_commands = os.listdir(path_to_wav)\n", + "directories_speech_commands = [x for x in directories_speech_commands if os.path.isdir(os.path.join(path_to_wav,x))]\n", + "directories_speech_commands = [x for x in directories_speech_commands if x != '_background_noise_']\n", + "directories_speech_commands\n", + "label_set = directories_speech_commands\n", + "nb_ex_per_class = 1000 # To have balance classes for the NN/Logistic Regression\n", + "# Valid set\n", + "valid_list = txt2list('/content/speech_commands/validation_list.txt')\n", + "# Test set\n", + "test_list = txt2list('/content/speech_commands/testing_list.txt')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "QIHVFoGxgAp-", + "colab_type": "code", + "outputId": "73fd5ccd-e71b-4bfd-a9d8-5397d7c75bed", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + } + }, + "cell_type": "code", + "source": [ + "print('speech commands ', directories_speech_commands)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "speech commands ['four', 'bed', 'zero', 'go', 'marvin', 'three', 'tree', 'sheila', 'no', 'house', 'left', 'eight', 'seven', 'yes', 'right', 'off', 'stop', 'cat', 'two', 'down', 'dog', 'bird', 'happy', 'one', 'wow', 'on', 'nine', 'up', 'six', 'five']\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "oE8lB8qdayqe", + "colab_type": "code", + "outputId": "d04c2d8b-82d8-4862-deec-82ebd82481fb", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + } + }, + "cell_type": "code", + "source": [ + "# Iterate over files\n", + "train_wavs = []\n", + "train_labels = []\n", + "\n", + "valid_wavs = []\n", + "valid_labels = []\n", + "\n", + "test_wavs = []\n", + "test_labels = []\n", + "\n", + "start = time.time()\n", + "\n", + "for root, dirs, files in os.walk(\"speech_commands\"):\n", + " if \"_background_noise_\" in root:\n", + " continue\n", + " for filename in files:\n", + " if not filename.endswith('.wav'):\n", + " continue\n", + " command = root.split(\"/\")[-1]\n", + " if command not in label_set:\n", + " continue\n", + " else:\n", + " label = label_set.index(command)\n", + " full_name = os.path.join(root, filename)\n", + " partial_path = '/'.join([command, filename])\n", + " if partial_path in valid_list and len(valid_labels) <= len(valid_list): # Take advantage of the whole validation set\n", + " fs, waveform = wav.read(full_name)\n", + " valid_wavs.append(waveform)\n", + " valid_labels.append(label)\n", + " elif partial_path in test_list and len(test_labels) < 5000:\n", + " fs, waveform = wav.read(full_name)\n", + " test_wavs.append(waveform)\n", + " test_labels.append(label)\n", + " elif train_labels.count(label) < nb_ex_per_class:\n", + " fs, waveform = wav.read(full_name)\n", + " train_wavs.append(waveform)\n", + " train_labels.append(label)\n", + "# Print data statistics\n", + "\n", + "print(\"Train files:\" + str(len(train_wavs)))\n", + "print(\"Valid files:\" + str(len(valid_wavs)))\n", + "print(\"Test files:\" + str(len(test_wavs)))\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Time to output features: \" + str(end-start))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Train files:30000\n", + "Valid files:6798\n", + "Test files:5000\n", + "Time to output features: 24.009681463241577\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "mVkig4bUa9tW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The following cells create feature functions that have a .transform method that takes a waveform as input. You should not change the framerate parameter (the dataset is sampled at 16kHz).\n", + "The other parameters are the following:\n", + "* nfilt = number of mel-filters to average spectrograms\n", + "* ncep = number of cepstral coefficients to use for MFCCs\n", + "* do_dct = True to compute MFCC (otherwise mel-filterbanks are the output)\n", + "* lowerf = lowest frequency spanned by the filters (and that will be taken into account by the features)\n", + "* higherf = highest frequency spanned by the filters\n", + "* alpha = parameter use for the pre-emphasis\n", + "* frate = number of frames per second (1/frate is the stride of the windows in seconds)\n", + "* wlen = length of windows in seconds\n", + "* nfft = number of frequency bins used to compute the spectrogram\n", + "* compression = the final compression performed on the mel-filterbanks (before DCT if you put do_dct=True)\n", + "* do_deltas = compute the first derivatives of MFCC\n", + "* do_deltasdeltas = compute the second derivatives of MFCC" + ] + }, + { + "metadata": { + "id": "CTi1kR9la-xo", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "framerate = 16000" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "43v6buW3bGzP", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Mel-filterbanks function" + ] + }, + { + "metadata": { + "id": "-UFQAVg8bF93", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "melfbanks = spectral.Spectral(nfilt=20,\n", + " ncep=0,\n", + " do_dct=False,\n", + " lowerf=133.3333,\n", + " upperf=6855.4976,\n", + " alpha=0.6,\n", + " fs=framerate,\n", + " frate=100,\n", + " wlen=0.025,\n", + " nfft=512,\n", + " compression='log',\n", + " do_deltas=False,\n", + " do_deltasdeltas=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7e-rnjnrbPwO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# MFCC function" + ] + }, + { + "metadata": { + "id": "j1_X2uIqbOdG", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mfcc = spectral.Spectral(nfilt=20,\n", + " ncep=12,\n", + " do_dct=True,\n", + " lowerf=133.3333,\n", + " upperf=6855.4976,\n", + " alpha=0.6,\n", + " fs=framerate,\n", + " frate=100,\n", + " wlen=0.025,\n", + " nfft=512,\n", + " compression='log',\n", + " do_deltas=True,\n", + " do_deltasdeltas=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "HIdrAouObYA8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# The following cell generates the features for train, valid and test from the waveforms" + ] + }, + { + "metadata": { + "id": "I7e5MNzsbVWe", + "colab_type": "code", + "outputId": "eb933902-c8f8-421c-e1b6-1b5bfe0517b8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "# Choose the feature function\n", + "feature_function = mfcc\n", + "\n", + "start = time.time()\n", + "train_feats = np.asarray(pad(transform_wavs(train_wavs, feature_function)))\n", + "valid_feats = np.asarray(pad(transform_wavs(valid_wavs, feature_function), 2424))\n", + "test_feats = np.asarray(pad(transform_wavs(test_wavs, feature_function)))\n", + "end = time.time()\n", + "\n", + "print(\"Time to output features: \" + str(end-start))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Time to output features: 274.4406988620758\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "qEDKUxvg2YMf", + "colab_type": "code", + "outputId": "51da84dc-cb8d-42c1-e4c4-5c8005afb709", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "print(train_feats.shape)\n", + "print(valid_feats.shape)\n", + "print(test_feats.shape)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(30000, 2424)\n", + "(6798, 2424)\n", + "(5000, 2424)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "6otMmc20t6NT", + "colab_type": "code", + "outputId": "1d5acdc0-0828-40b5-aa34-31f6e3a05cce", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + } + }, + "cell_type": "code", + "source": [ + "import seaborn as sns\n", + "\n", + "sns.distplot(valid_labels, kde = False, bins = 30, label = \"valid\")\n", + "sns.distplot(test_labels, kde = False, bins = 30, label = \"test\")\n", + "plt.title(\"Data distribution in the validation and test sets\")\n", + "plt.legend()\n", + "plt.savefig(\"hist_val_test.png\")\n", + "plt.show()\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n", + "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n", + " alternative=\"'density'\", removal=\"3.1\")\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "5TxYZHiy3A9o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Mean-Variance normalization" + ] + }, + { + "metadata": { + "id": "Aw3dNFCF2iZp", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mean, std = train_feats.mean(axis = 0), train_feats.std(axis = 0)\n", + "train_feats, valid_feats, test_feats = (train_feats - mean)/std, (valid_feats - mean)/std, (test_feats - mean)/std" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "m69q9N2abdwg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Next cell trains a logistic regression on the speech features" + ] + }, + { + "metadata": { + "id": "mST1NBWWbeU7", + "colab_type": "code", + "outputId": "5b9cb241-2db9-4cef-b14c-94e3eaf58ca8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88 + } + }, + "cell_type": "code", + "source": [ + "#Logistic regression\n", + "\n", + "# logreg = sklearn.linear_model.LogisticRegression(verbose=1, tol=1e-3, random_state=777) # Does not scale\n", + "logreg = sklearn.linear_model.SGDClassifier(verbose=0, loss='log', n_jobs=-1)\n", + "start = time.time()\n", + "logreg.fit(train_feats, train_labels)\n", + "end = time.time()\n", + "print(\"Training time: \" + str(end-start))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/stochastic_gradient.py:166: FutureWarning: max_iter and tol parameters have been added in SGDClassifier in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n", + " FutureWarning)\n" + ], + "name": "stderr" + }, + { + "output_type": "stream", + "text": [ + "Training time: 12.798158884048462\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "4oJmjRtKdNBL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# You can now evaluate it on the validation set\n" + ] + }, + { + "metadata": { + "id": "dkPvO7DadNcY", + "colab_type": "code", + "outputId": "5fe6b416-f175-47f4-c34e-02a240ba7129", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "print(\"Accuracy on valid set \" + str(100*logreg.score(valid_feats, valid_labels)) + \"%\")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Accuracy on valid set 40.77669902912621%\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "GUl7O7HHdP0z", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Next cell trains a neural network\n", + "\n" + ] + }, + { + "metadata": { + "id": "cRmCWhJbdUsf", + "colab_type": "code", + "outputId": "f3bee4b0-ba7d-4d2a-d713-94c94f628fad", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 428 + } + }, + "cell_type": "code", + "source": [ + "neural_net = MLPClassifier(hidden_layer_sizes=(100,64), validation_fraction = 0.2, early_stopping = False,\n", + " verbose = True, random_state = 777, learning_rate='constant',\n", + " learning_rate_init=0.001, max_iter = 20)\n", + "\n", + "start = time.time()\n", + "neural_net.fit(train_feats, train_labels)\n", + "end = time.time()\n", + "\n", + "print(\"Training time: \" + str(end-start))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Iteration 1, loss = 2.13859670\n", + "Iteration 2, loss = 1.23838844\n", + "Iteration 3, loss = 0.96152939\n", + "Iteration 4, loss = 0.80799602\n", + "Iteration 5, loss = 0.69587146\n", + "Iteration 6, loss = 0.61078948\n", + "Iteration 7, loss = 0.52848997\n", + "Iteration 8, loss = 0.47839709\n", + "Iteration 9, loss = 0.43515761\n", + "Iteration 10, loss = 0.38351946\n", + "Iteration 11, loss = 0.34698915\n", + "Iteration 12, loss = 0.31174632\n", + "Iteration 13, loss = 0.27754228\n", + "Iteration 14, loss = 0.25457787\n", + "Iteration 15, loss = 0.23826945\n", + "Iteration 16, loss = 0.20279296\n", + "Iteration 17, loss = 0.18003176\n", + "Iteration 18, loss = 0.18418484\n", + "Iteration 19, loss = 0.18387219\n", + "Iteration 20, loss = 0.16694140\n", + "Training time: 59.55784821510315\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ], + "name": "stderr" + } + ] + }, + { + "metadata": { + "id": "cRlFfxGZI5nG", + "colab_type": "code", + "outputId": "5f32e78c-f5d7-4aec-f19a-0f87a8ebe22a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "print(\"Accuracy on train set \" + str(100*neural_net.score(train_feats, train_labels)) + \"%\")\n", + "print(\"Accuracy on valid set \" + str(100*neural_net.score(valid_feats, valid_labels)) + \"%\")\n", + "print(\"Accuracy on test set \" + str(100*neural_net.score(test_feats, test_labels)) + \"%\")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Accuracy on train set 95.64%\n", + "Accuracy on valid set 71.27096204766107%\n", + "Accuracy on test set 69.94%\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "KRkZ8_vrE3N8", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from keras.layers import Dense, Conv1D, Flatten, Dropout\n", + "from keras.optimizers import SGD, Adam\n", + "from keras.models import Sequential\n", + "from keras.utils import to_categorical\n", + "from keras.metrics import categorical_accuracy\n", + "from keras import regularizers\n", + "\n", + "train_labels_one_hot = to_categorical(train_labels)\n", + "valid_labels_one_hot = to_categorical(valid_labels, num_classes=30)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "fPK4YWBl0dVD", + "colab_type": "code", + "outputId": "36ae56bb-d72f-4a50-9ed6-fa57d8cad996", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 394 + } + }, + "cell_type": "code", + "source": [ + "model_adam = Sequential()\n", + "\n", + "model_adam.add(Dense(1024, activation = 'relu', kernel_initializer='glorot_uniform'))\n", + "model_adam.add(Dropout(0.25))\n", + "model_adam.add(Dense(512, activation = 'relu', kernel_initializer='glorot_uniform'))\n", + "model_adam.add(Dropout(0.25))\n", + "\n", + "model_adam.add(Dense(30, activation = 'softmax', kernel_initializer='glorot_uniform'))\n", + "\n", + "adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, decay = 1e-8)\n", + "model_adam.compile(adam, loss = 'categorical_crossentropy', metrics = [\"accuracy\"])\n", + "model_adam.fit(train_feats, train_labels_one_hot, batch_size = 64, validation_data = (valid_feats, valid_labels_one_hot), \n", + " epochs = 10, verbose = 1);\n", + "\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Train on 30000 samples, validate on 6798 samples\n", + "Epoch 1/10\n", + "30000/30000 [==============================] - 6s 186us/step - loss: 2.4134 - acc: 0.3269 - val_loss: 1.5823 - val_acc: 0.5559\n", + "Epoch 2/10\n", + "30000/30000 [==============================] - 5s 152us/step - loss: 1.5440 - acc: 0.5495 - val_loss: 1.2200 - val_acc: 0.6559\n", + "Epoch 3/10\n", + "30000/30000 [==============================] - 5s 150us/step - loss: 1.2223 - acc: 0.6430 - val_loss: 1.0626 - val_acc: 0.7005\n", + "Epoch 4/10\n", + "30000/30000 [==============================] - 4s 149us/step - loss: 1.0303 - acc: 0.6961 - val_loss: 0.9695 - val_acc: 0.7261\n", + "Epoch 5/10\n", + "30000/30000 [==============================] - 4s 148us/step - loss: 0.8844 - acc: 0.7375 - val_loss: 0.9098 - val_acc: 0.7396\n", + "Epoch 6/10\n", + "30000/30000 [==============================] - 4s 149us/step - loss: 0.7888 - acc: 0.7627 - val_loss: 0.8793 - val_acc: 0.7510\n", + "Epoch 7/10\n", + "30000/30000 [==============================] - 4s 149us/step - loss: 0.6927 - acc: 0.7878 - val_loss: 0.8146 - val_acc: 0.7658\n", + "Epoch 8/10\n", + "30000/30000 [==============================] - 4s 149us/step - loss: 0.6319 - acc: 0.8078 - val_loss: 0.8132 - val_acc: 0.7643\n", + "Epoch 9/10\n", + "30000/30000 [==============================] - 4s 148us/step - loss: 0.5709 - acc: 0.8255 - val_loss: 0.7939 - val_acc: 0.7746\n", + "Epoch 10/10\n", + "30000/30000 [==============================] - 4s 148us/step - loss: 0.5116 - acc: 0.8429 - val_loss: 0.7746 - val_acc: 0.7821\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ieyvskEddY_b", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Evaluate it on the valid set" + ] + }, + { + "metadata": { + "id": "uo475SIZdZXv", + "colab_type": "code", + "outputId": "5dca39fd-348b-4238-851f-ac481ced10e1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "print(\"Accuracy on train set %.2f\" %(100*accuracy_score(train_labels, model_adam.predict_classes(train_feats))), \"%\")\n", + "print(\"Accuracy on valid set %.2f\" %(100*accuracy_score(valid_labels, model_adam.predict_classes(valid_feats))), \"%\")\n", + "print(\"Accuracy on test set %.2f\" %(100*accuracy_score(test_labels, model_adam.predict_classes(test_feats))), \"%\")" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Accuracy on train set 94.00 %\n", + "Accuracy on valid set 78.21 %\n", + "Accuracy on test set 76.58 %\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "sFIgubgEdbx_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#Listen to a random waveform from the training set\n", + "\n" + ] + }, + { + "metadata": { + "id": "fzCu-4JCdi0E", + "colab_type": "code", + "outputId": "5d86a8dc-3937-4a7c-8187-3e97019ff25a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + } + }, + "cell_type": "code", + "source": [ + "random_idx = random.randint(0, len(valid_wavs))\n", + "random_wav = valid_wavs[random_idx]\n", + "IPython.display.Audio(random_wav, rate=16000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 27 + } + ] + }, + { + "metadata": { + "id": "sCmjGOMkdl4B", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Visualize mel-filterbanks and MFCC" + ] + }, + { + "metadata": { + "id": "xkEgMN89dnz-", + "colab_type": "code", + "outputId": "4db6a770-4704-4747-90fc-eca52ed90be0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 114 + } + }, + "cell_type": "code", + "source": [ + "plt.imshow(melfbanks.transform(random_wav).transpose())\n", + "plt.gca().invert_yaxis()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "RFj9mPmWdqkN", + "colab_type": "code", + "outputId": "dc1f2922-659c-4447-c369-e6491c760867", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127 + } + }, + "cell_type": "code", + "source": [ + "plt.imshow(mfcc.transform(random_wav).transpose())\n", + "plt.gca().invert_yaxis()" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "WN7-jhoXduur", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# See what your classifier has predicted" + ] + }, + { + "metadata": { + "id": "QLV19RDudvI7", + "colab_type": "code", + "outputId": "dcb26d54-1b9d-4159-cdd8-cdfebd1b93bc", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + } + }, + "cell_type": "code", + "source": [ + "print(label_set[model_adam.predict_classes(valid_feats[random_idx][np.newaxis])[0]])" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "no\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "vSIu0e96DZsJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## II. Classification of segmented voice commands\n", + "\n", + "\n", + "\n", + "### What you are expected to do in this second part\n", + "The second part of the TP above contains the different blocks that allow you to decode sequence of speech commands. You are expected to implement the different decoding algorithms and compare them in WER performance, time and memory costs. \n", + " There are several questions in this part, you have to include your answers in the report. The **grade will not depend on the performance of the final system**, but on how you **experimented, tried different ideas, and comment** them.\n", + "\n", + "\n", + "\n", + "### ASR: Prediction of Sequences of words\n", + "In the second part of the practical work, you are now given a new dataset composed of sequences of commands. This dataset is still composed of audio signal, but obtained from concatenation of the different ~1sec speech commands of the Google Dataset. The sequence of commands have not been generated randomly, so you can exploit higher information with language modelling. \n", + "\n", + "We usually denote by $X_{i=1...M}$ the input sequence of speech features, and the goal is to find the most likely sequence of words $W_{i=1...T}$. \n", + "\n", + " $M$ represents the number of obtained features of the signal (ex: MFCCs) for a given command, and $T$ is number of words that was spoken. \n", + "\n", + ">>>> >>>>>$ \\operatorname*{argmax}_W P(W|X) \\approx P(X|W) P(W) $\n", + "\n", + "\n", + "You saw in class that estimate $P(X|W)$ is the acoustic model part and $P(W)$ is the language model part. \n", + "\n", + "Solving this $argmax$ problem is a **search** problem. It can be solved with dynamic programming with the [Viterbi algorithm](https://en.wikipedia.org/wiki/Viterbi_algorithm) or with heuristics such as [beam-search](https://en.wikipedia.org/wiki/Beam_search) techniques. \n", + "\n", + "\n", + "### Evalution with Word Error Rate\n", + "To evaluate the performance of an ASR system, we use the **Word Error Rate**. It uses dynamic programming to align and compare the hypothesis and reference sequences. There are three types of errors under consideration: Insertion, Deletion and Substitution. \n", + "\n", + ">>>> >>>>> $WER = 100.\\frac{S+D+I}{N}\\%$ \n", + "\n", + "- S is the total number of substitutions\n", + "- D is the total number of deletions\n", + "- I is the total number of insertions \n", + "- N is the number of words in the reference sentence \n", + "\n", + "**Question 2.1**: Is it possible that WER<0? and WER>100? \n", + "\n", + "- WER cannot be negative as $S, D, I, N$ are all positive.\n", + "\n", + "- $S$ and $D$ are constrained by $N$, it is not the case for the number of insertions $I$, a high number of insertions can therefore yield $WER>100$, this is in the generale case, however in our case there are no insertions nor deletions as the model predicts each word, therefore there may only be substitutions which makes $WER \\le 100$" + ] + }, + { + "metadata": { + "id": "Y5HC0xAsiZTA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Setup of the practical work\n", + "\n", + "As you can imagine, you will not build an ASR from scracth in this practical work. There are some simplification and we will exploit your work of the previous part.\n", + "\n", + "Usually, we have $M>>T$, however in this case, we simplify this. You have the alignement information and the words are padded to have equal lengths: one word spoken every second. \n", + "\n", + "Therefore , the acoustic model is taken from the discriminator trained in the first part. The discriminator has been trained with balanced dataset $P(W_i) = constant$. $X_i$ now corresponds to the concatenation of the speech features of the word $W_i$. \n", + "\n", + "We can model the acoustic model as such: \n", + "\n", + ">>>> >>>>>$ P(X_i|W_i) \\propto P_{\\text{discriminator single word}}(W_i|X_i) $ \n", + "\n", + "**Question 2.2**: Can you point which line in the code above approximated the prior probability of each word $W_i$ to be equal?\n", + "\n", + "nb_ex_per_class = 1000 , and it was used as a condition when loading the data, this way we have nb_ex_per_class training samples per class, such balanced training dataset yields a uniform prior probability distribution.\n" + ] + }, + { + "metadata": { + "id": "Biks25ikbmrO", + "colab_type": "code", + "outputId": "ab4030ca-2ac0-4653-ff76-850827531183", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 411 + } + }, + "cell_type": "code", + "source": [ + "!wget https://raw.githubusercontent.com/Rachine/Rachine.github.io/master/data/train_sequence_list.txt \n", + "!wget https://raw.githubusercontent.com/Rachine/Rachine.github.io/master/data/test_sequence_list.txt \n", + "\n", + "\n", + "path_to_wavs = '/content/speech_commands/'\n", + "train_sequence_list = [ast.literal_eval(ex) for ex in txt2list('/content/train_sequence_list.txt') ]\n", + "test_sequence_list = [ast.literal_eval(ex) for ex in txt2list('/content/test_sequence_list.txt') ]\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "--2019-02-25 04:17:16-- https://raw.githubusercontent.com/Rachine/Rachine.github.io/master/data/train_sequence_list.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1277360 (1.2M) [text/plain]\n", + "Saving to: ‘train_sequence_list.txt’\n", + "\n", + "\rtrain_sequence_list 0%[ ] 0 --.-KB/s \rtrain_sequence_list 100%[===================>] 1.22M --.-KB/s in 0.06s \n", + "\n", + "2019-02-25 04:17:16 (19.6 MB/s) - ‘train_sequence_list.txt’ saved [1277360/1277360]\n", + "\n", + "--2019-02-25 04:17:17-- https://raw.githubusercontent.com/Rachine/Rachine.github.io/master/data/test_sequence_list.txt\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 76236 (74K) [text/plain]\n", + "Saving to: ‘test_sequence_list.txt’\n", + "\n", + "test_sequence_list. 100%[===================>] 74.45K --.-KB/s in 0.02s \n", + "\n", + "2019-02-25 04:17:17 (3.62 MB/s) - ‘test_sequence_list.txt’ saved [76236/76236]\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "StrCL66TdxUq", + "colab_type": "code", + "outputId": "431a2631-4a8c-451d-90b7-474b40d287ea", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + } + }, + "cell_type": "code", + "source": [ + "def generate_wav_from_list_sequence(generated_list_sequence): \n", + " wavs = np.array([])\n", + " for command in generated_list_sequence[1:]:\n", + " wav_name = os.path.join((path_to_wavs),command[1])\n", + " sample_rate, signal = wav.read(wav_name)\n", + " wavs = np.append(wavs,signal)\n", + " return wavs\n", + "\n", + "\n", + "# Again warning do not forget to change your pad function according to your choice of input features\n", + " \n", + "def generate_posteriors_from_list_sequence(generated_list_sequence, model_predict_proba_function, feature_func): \n", + " posteriors = []\n", + " for command in generated_list_sequence[1:]:\n", + " wav_name = os.path.join((path_to_wavs),command[1])\n", + " sample_rate, signal = wav.read(wav_name)\n", + " features_input = np.asarray(pad(transform_wavs([signal], feature_function), 2424))\n", + " features_input = (features_input - mean)/std\n", + " posterior = model_predict_proba_function(features_input)\n", + " posteriors.append(posterior)\n", + " \n", + " posteriors = np.array(posteriors).reshape(-1,len(label_set))\n", + " return posteriors\n", + "\n", + "example_sequence = train_sequence_list[20]\n", + "sequence_wav = generate_wav_from_list_sequence(example_sequence)\n", + "\n", + "IPython.display.Audio(sequence_wav, rate=16000)\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 61 + } + ] + }, + { + "metadata": { + "id": "4wmosB3rDiqz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "###Independent inputs\n", + "#### Greedy search\n", + "\n", + "You will find below an implementation of a greedy decoder assuming full independence between spoken word commands without the alignement problem (each input is matched with one single output). \n", + "\n", + "We use a simple model from the previous part, **up to you** to use another one to improve the overall performance. \n", + "\n" + ] + }, + { + "metadata": { + "id": "bPA8IM8QDe50", + "colab_type": "code", + "outputId": "cc03ada9-f40e-4e70-a8e8-f0665655022a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331 + } + }, + "cell_type": "code", + "source": [ + "example_sequence = train_sequence_list[10]\n", + "\n", + "posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(example_sequence, neural_net.predict_proba, mfcc))\n", + "\n", + "\n", + "list_plots = plt.plot(['Word_'+str(el) for el in range(posteriors_neural_mfcc.shape[0])],posteriors_neural_mfcc)\n", + "plt.legend(list_plots, label_set, loc=\"upper left\", bbox_to_anchor=(1,1), ncol=6)\n", + "plt.ylabel('Posterior activations')\n", + "plt.title('Neural network activations with bad MFCCs')\n", + "plt.show()\n", + "\n", + "\n", + "def greedy_decoder(data):\n", + " predicted_sequence = [np.argmax(s) for s in data]\n", + " predicted_sequence = [label_set[el] for el in predicted_sequence]\n", + " return predicted_sequence\n", + "\n", + "def sequence_format_to_text(sentence):\n", + " return [el[0] for el in sentence[1:]]\n", + "\n", + "predicted_sequence = greedy_decoder(posteriors_neural_mfcc)\n", + "# predicted_sequence = viterbi_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + "# predicted_sequence = beam_search_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + "print('True sentence: ', ' '.join(sequence_format_to_text(example_sequence)))\n", + "print('Predicted sentence with greedy search: ', ' '.join(predicted_sequence))\n", + "print('WER: ',jiwer.wer(' '.join(sequence_format_to_text(example_sequence)),' '.join(predicted_sequence)))" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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EqY11ltS5xS2PqxlZ3mdZXg7TKkrY2dHN5rbORIhmpDFtDauAfX8eDcMYMa3A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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "True sentence: go marvin one right stop\n", + "Predicted sentence with greedy search: go marvin one right stop\n", + "WER: 0.0\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "KBh-6RW4YRNF", + "colab_type": "code", + "outputId": "418ec670-8f4d-4fae-fa59-9a46deeaa5ee", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + } + }, + "cell_type": "code", + "source": [ + "sequence_wav = generate_wav_from_list_sequence(example_sequence)\n", + "IPython.display.Audio(sequence_wav, rate=16000)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 334 + } + ] + }, + { + "metadata": { + "id": "NCIZqP8xDpJ5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Question 2.3**: Can you detail the computations of the WER for the example above? " + ] + }, + { + "metadata": { + "id": "iHoMkZdFDr4d", + "colab_type": "code", + "outputId": "6221c0bc-90bf-4563-ec43-fa4edd8cf66c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "\n", + "# Evaluation of WER for the Greedy Search decoding\n", + "\n", + "train_hypothesis = []\n", + "train_reference = []\n", + "\n", + "# EVALUATION on the full train set takes too much time, so you should evaluate on a subset\n", + "\n", + "for sentence in np.random.choice(train_sequence_list, 300, replace=False):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, neural_net.predict_proba, mfcc))\n", + " predicted_sequence = greedy_decoder(posteriors_neural_mfcc)\n", + " train_hypothesis.append(' '.join(predicted_sequence))\n", + " train_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + " \n", + " \n", + "test_hypothesis_greedy = []\n", + "test_reference = []\n", + "for idx, sentence in enumerate(test_sequence_list):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, model_adam.predict, mfcc))\n", + " predicted_sequence = greedy_decoder(posteriors_neural_mfcc)\n", + " test_hypothesis_greedy.append(' '.join(predicted_sequence))\n", + " test_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + "\n", + " \n", + "start = time.time()\n", + "print('Subset Train WER: ',jiwer.wer(train_reference, train_hypothesis))\n", + "print('Test WER: ',jiwer.wer(test_reference, test_hypothesis_greedy))\n", + "\n", + "end = time.time()\n", + "\n", + "print(\"Evaluation time: \" + str(end-start))\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Subset Train WER: 0.2740899357601713\n", + "Test WER: 0.29193899782135074\n", + "Evaluation time: 51.06467938423157\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "rAuP3gitD1S_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "You will notice with greedy search and without language model, the train WER and test WER are approximately the same." + ] + }, + { + "metadata": { + "id": "T3DmRnFCD53H", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Inject Language Models in the path scoring\n", + "\n", + "Now, you will incorporate higher information, with the training sequences. You need to model the transition states $i\\rightarrow j$. \n", + "\n", + "#### N-gram model\n", + "You need to estimate and build now this transition matrix. You are only allowed to use the sequences from *train_sequence_list.txt* ti build your language model. \n", + "You can refer to the class to have some ideas, or this part in this paper:\n", + "\n", + "[The Application of Hidden Markov Models in Speech Recognition](https://s3.amazonaws.com/academia.edu.documents/40150101/The_Application_of_Hidden_Markov_Models_20151118-22759-1ab3mz1.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1549387878&Signature=Q%2FQiFLEgWJAvttO1LbP%2Fkb2OGaw%3D&response-content-disposition=inline%3B%20filename%3DThe_Application_of_Hidden_Markov_Models.pdf) part 2.4\n", + "\n", + "**Question 2.4**: Write the Bigram approximation formula of the language model\n", + "\n", + "\\begin{equation}\n", + "p(w_1, \\ldots, w_K) = p(w_1)\\prod_{k=2}^K p(w_k|w_{k-1})\n", + "\\end{equation}\n", + "\n", + "**Question 2.5**: Explain briefly your implementation choices\n", + "\n", + "**Question 2.6**: What are the advantages and drawbacks to increase the N ?\n", + "\n", + "- Increasing $N$ in the $N$-gram model has the advantage of taken a larger past of a word to model its conditional probability.\n", + "- The problem with increasing $N$ is the computational cost because the number of possible conditional combinations for a single word becomes too high, and estimating the transition probabilities becomes very difficult.\n", + "\n" + ] + }, + { + "metadata": { + "id": "4rfFA4qVD3D7", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Build HERE the transition matrix with Uni-Gram and Bi-gram modelling. You can also do an implementation with bigger N.\n", + "command_index = dict((command, label_set.index(command)) for command in label_set) # Dict mapping commands to indices.\n", + "index_command = dict((label_set.index(command), command) for command in label_set) # Dict mapping indices to commands.\n", + "\n", + "def unigram(train_seq_list):\n", + " \n", + " probas = np.ones(len(label_set)) # Initialization to 1 in order to avoid zeros for non existent words in the corpus.\n", + " for sequence in train_seq_list:\n", + " for word in sequence[1:]:\n", + " word = word[0]\n", + " probas[command_index[word]] += 1\n", + " \n", + " return probas/probas.sum()\n", + "\n", + "def bigram(train_seq_list):\n", + " \n", + " probas = np.ones((len(label_set), len(label_set))) # Initialization to 1 in order to avoid zeros for non existent 2-sequences in the corpus.\n", + " for sequence in train_seq_list:\n", + " for i in range(2, len(sequence)):\n", + " index_1 = command_index[sequence[i - 1][0]] # The previous word in the sequene.\n", + " index_2 = command_index[sequence[i][0]] # The current word in the sequene.\n", + " probas[index_1, index_2] += 1\n", + " \n", + " return probas/((probas.sum(axis = 1).reshape(-1, 1)))\n", + "\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "JLU9-xsD5Rzt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "probas_init = unigram(train_sequence_list)\n", + "trans_matrix = bigram(train_sequence_list)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hZC6ymcVD8si", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Beam-Search\n", + "\n", + "Implement the Beam-Search algorithm, and apply it with your transition matrix and your best discriminator.\n", + "\n", + "**Question** 2.7: What is the complexity of the algorithm ?\n" + ] + }, + { + "metadata": { + "id": "a1_c5jxAYuAI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import heapq\n", + "\n", + "def beam_search_decoder(data, transition_matrix, probas, beam_size=5):\n", + " \n", + " h = []\n", + " score = np.inf \n", + " for i in range(len(label_set)):\n", + " heapq.heappush(h, (-np.log(probas[i]) - np.log(posteriors_neural_mfcc[0, i]), [i]))\n", + " \n", + " h = heapq.nsmallest(beam_size, h)\n", + " h_new = []\n", + " while True:\n", + " for node in h:\n", + " log_proba = node[0]\n", + " sequence = node[-1]\n", + " if len(sequence) == data.shape[0]: # Check if we have reached a leaf.\n", + " solution = heapq.heappop(h)[-1]\n", + " return [index_command[i] for i in solution]\n", + "\n", + " i = sequence[-1] # Take the last word of the sequence as we use bigram model.\n", + " for j in range(len(label_set)):\n", + " heapq.heappush(h_new, (log_proba - np.log(transition_matrix[i, j]) - \n", + " np.log(posteriors_neural_mfcc[len(sequence), j]), sequence + [j]))\n", + " \n", + " h = heapq.nsmallest(beam_size, h_new)\n", + " h_new = []\n", + " " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "GQwVlrXXEAqq", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Dynamic progamming with the Viterbi algorithm\n", + "**Question** 2.8: What is the relationship between the probability to be in state $j$ at step $k$, and the probabilities to be in state $j'$ at step $k-1$? What is the final complexity of the Viterbi algorithm? \n", + "\n", + "Implement the Viterbi algorithm, and apply it with your transition matrix and your best discriminator.\n", + "\n" + ] + }, + { + "metadata": { + "id": "fCfMLltIvnYP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Viterbi decoder\n", + "# BELOW IMPLEMENT YOUR viterbi algorithm\n", + "def viterbi_decoder(data, transition_matrix, probas): \n", + " T = len(data)\n", + " K = len(label_set)\n", + " max_messages = np.zeros((T, K))\n", + " argmax = np.zeros((T, K))\n", + " max_messages[0,:] = np.log(probas) + np.log(data[0])\n", + "\n", + " for t in range(1, T):\n", + " max_messages[t, :] = np.max(max_messages[t-1,:].reshape(1,-1) + np.log(transition_matrix.T), axis=1) + np.log(data[t])\n", + " argmax[t, :] = np.argmax(max_messages[t-1,:].reshape(1,-1) + np.log(transition_matrix.T), axis=1)\n", + "\n", + " labels = np.zeros(T, dtype=int)\n", + " labels[-1] = np.argmax(max_messages[-1,:])\n", + " for t in range(T-1,0,-1): \n", + " labels[t-1] = argmax[t, labels[t]]\n", + " \n", + " return [index_command[i] for i in labels]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hWtwpANREFDJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Results for decoding algorithms\n", + "\n", + "In your report for this practical work, you should examine your experiments: the different strategies with the different implementation choices you made and the various parameters. \n", + "\n", + "**Question** 2.9: Can you spot systematic errors due to language model you derived from the training sequences? Provide us some examples of these errors.\n", + "\n", + "**Question** 2.10: Can you implement some backoff strategies to face rare seen words(or sequence of words) and out of vocabulary words? Does it improve your Word Error Rate? \n", + "\n", + "**Question** 2.11: How would you optimize jointly an acoustic model and language model? " + ] + }, + { + "metadata": { + "id": "-5-JOv80OZUu", + "colab_type": "code", + "outputId": "add28ac6-ac92-450c-9346-52424598f6e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "# Evaluation of WER for the Beam search decoding\n", + "\n", + "train_hypothesis = []\n", + "train_reference = []\n", + "\n", + "# EVALUATION on the full train set takes too much time, so you should evaluate on a subset\n", + "\n", + "for sentence in np.random.choice(train_sequence_list, 300, replace=False):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, neural_net.predict_proba, mfcc))\n", + " predicted_sequence = beam_search_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + " train_hypothesis.append(' '.join(predicted_sequence))\n", + " train_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + " \n", + " \n", + "test_hypothesis = []\n", + "test_reference = []\n", + "for idx, sentence in enumerate(test_sequence_list):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, model_adam.predict, mfcc))\n", + " predicted_sequence = beam_search_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + " test_hypothesis.append(' '.join(predicted_sequence))\n", + " test_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + "\n", + "start = time.time()\n", + "print('Subset Train WER for beam search: ',jiwer.wer(train_reference, train_hypothesis))\n", + "print('Test WER for beam search: ',jiwer.wer(test_reference, test_hypothesis))\n", + "end = time.time()\n", + "\n", + "print(\"Evaluation time: \" + str(end-start))\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Subset Train WER: 0.12761506276150628\n", + "Test WER: 0.10185185185185185\n", + "Evaluation time: 51.06467938423157\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "egEBMDyvF0kz", + "colab_type": "code", + "outputId": "f9c6be5a-bedd-4d64-ec0f-9908cc17ae3a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + } + }, + "cell_type": "code", + "source": [ + "# Evaluation of WER for the Viterbi decoding\n", + "\n", + "train_hypothesis = []\n", + "train_reference = []\n", + "\n", + "# EVALUATION on the full train set takes too much time, so you should evaluate on a subset\n", + "\n", + "for sentence in np.random.choice(train_sequence_list, 300, replace=False):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, neural_net.predict_proba, mfcc))\n", + " predicted_sequence = viterbi_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + " train_hypothesis.append(' '.join(predicted_sequence))\n", + " train_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + " \n", + " \n", + "test_hypothesis = []\n", + "test_reference = []\n", + "for idx, sentence in enumerate(test_sequence_list):\n", + " posteriors_neural_mfcc = np.array(generate_posteriors_from_list_sequence(sentence, model_adam.predict, mfcc))\n", + " predicted_sequence = viterbi_decoder(posteriors_neural_mfcc, trans_matrix, probas_init)\n", + " test_hypothesis.append(' '.join(predicted_sequence))\n", + " test_reference.append(' '.join(sequence_format_to_text(sentence)))\n", + "\n", + "start = time.time()\n", + "print('Subset Train WER for viterbi: ',jiwer.wer(train_reference, train_hypothesis))\n", + "print('Test WER for viterbi: ',jiwer.wer(test_reference, test_hypothesis))\n", + "end = time.time()\n", + "\n", + "print(\"Evaluation time: \" + str(end-start))\n" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Subset Train WER: 0.12835349815886377\n", + "Test WER: 0.09368191721132897\n", + "Evaluation time: 51.06467938423157\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "UntHIfiCNwTF", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examples of great errors" + ] + }, + { + "metadata": { + "id": "evqpjOBWLdLx", + "colab_type": "code", + "outputId": "285125f3-f450-4bd8-e0d1-b92fe23b239d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + } + }, + "cell_type": "code", + "source": [ + "# We look for sequences where greedy is better than viterbi in order to see if the language model does not\n", + "# handle well certain sequences.\n", + "for i in range(len(test_reference)) : \n", + " if (jiwer.wer(test_reference[i], test_hypothesis_greedy[i]) < jiwer.wer(test_reference[i], test_hypothesis[i])):\n", + " print('predicted with greedy : ' + test_hypothesis_greedy[i])\n", + " print('predicted with viterbi : '+ test_hypothesis[i])\n", + " print('reference : '+ test_reference[i] +'\\n')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "predicted with greedy : happy house five happy marvin go\n", + "predicted with viterbi : happy house no tree wow go\n", + "reference : happy house off happy bird no\n", + "\n", + "predicted with greedy : wow on wow dog\n", + "predicted with viterbi : wow dog wow dog\n", + "reference : house on house no\n", + "\n", + "predicted with greedy : three off three wow\n", + "predicted with viterbi : three up three down\n", + "reference : tree off tree wow\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "6vlThZlrELCi", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Final Report and Notebook\n", + "Write your report in a external PDF file, and send the **commented clean** ipython notebook along your assignment at mva.speech.language@gmail.com with email object :\n", + "\n", + "[TP1_2019] FirstName_LastName\n", + "\n", + "Due date 25/01/2019\n", + "\n", + "The report will not exceed 4 Pages, in pdf format, will include formula, results graphs, and your critical analysis of the experiments. \n", + "\n", + "Good luck!\n", + "\n", + "\n", + "---\n", + "\n" + ] + } + ] +} \ No newline at end of file diff --git a/.ipynb_checkpoints/parser-checkpoint.ipynb b/.ipynb_checkpoints/parser-checkpoint.ipynb index a9c07cc..3812f4b 100644 --- a/.ipynb_checkpoints/parser-checkpoint.ipynb +++ b/.ipynb_checkpoints/parser-checkpoint.ipynb @@ -2,7 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install PYEVALB" + ] + }, + { + "cell_type": "code", + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +22,10 @@ "import random\n", "import queue\n", "import pickle as pkl\n", - "from scipy.spatial import distance" + "from scipy.spatial import distance\n", + "from PYEVALB import scorer\n", + "from PYEVALB import parser\n", + "from sklearn.metrics import precision_recall_fscore_support" ] }, { @@ -198,7 +210,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PCFG" + "# PCFG" ] }, { @@ -353,7 +365,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CYK algorithm" + "# CYK algorithm" ] }, { @@ -416,11 +428,31 @@ " return scores, back" ] }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7297" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dict_probas['unary']) + len(dict_probas['binary'])" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Backtrack : Build parse tree from CYK" + "# Backtrack : Build parse tree from CYK" ] }, { @@ -490,39 +522,39 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "Tree('', [Tree('SENT', [Tree('AdP', [Tree('PONCT', ['\"']), Tree('AdP|', [Tree('ADV', ['Tout']), Tree('AdP|', [Tree('ADV', ['simplement']), Tree('PONCT', ['\"'])])])]), Tree('SENT|', [Tree('Sint', [Tree('PONCT', [',']), Tree('Sint|', [Tree('VN', [Tree('V', ['a']), Tree('VPP', ['précisé'])]), Tree('NP', [Tree('NPP', ['Roger']), Tree('NPP', ['Thiriot'])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('PONCT', ['\"']), Tree('SENT|', [Tree('Ssub', [Tree('CS', ['parce_que']), Tree('Ssub|', [Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['histoire']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['travail']), Tree('AP', [Tree('ADJ', ['industriel'])])])])])]), Tree('Ssub|', [Tree('VN', [Tree('V', ['est'])]), Tree('Ssub|', [Tree('PONCT', [',']), Tree('Ssub|', [Tree('ADV', ['ici']), Tree('Ssub|', [Tree('PONCT', [',']), Tree('NP', [Tree('DET', ['une']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['longue']), Tree('COORD', [Tree('CC', ['et']), Tree('AP', [Tree('ADJ', ['vieille'])])])]), Tree('NC', ['histoire'])])])])])])])])]), Tree('PONCT', ['.'])])])])])])])" + "Tree('', [Tree('SENT', [Tree('NP', [Tree('DET', ['Une']), Tree('NP|', [Tree('NC', ['heure']), Tree('COORD', [Tree('CC', ['et']), Tree('NP', [Tree('DET', ['vingt']), Tree('NC', ['minutes'])])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('VN', [Tree('CLS', [\"c'\"]), Tree('V', ['est'])]), Tree('SENT|', [Tree('NP', [Tree('DET', ['le']), Tree('NP|', [Tree('NC', ['temps']), Tree('Srel', [Tree('NP', [Tree('PROREL', [\"qu'\"])]), Tree('Srel|', [Tree('VN', [Tree('CLS', ['il']), Tree('VN|', [Tree('V', ['aura']), Tree('VPP', ['fallu'])])]), Tree('Srel|', [Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('NPP', ['Thierry']), Tree('NP|', [Tree('NPP', ['Guerry']), Tree('NP|', [Tree('PONCT', [',']), Tree('NP', [Tree('NC', ['chauffeur-routier']), Tree('PP', [Tree('P', ['chez']), Tree('NP', [Tree('NPP', ['Caillaud']), Tree('NP|', [Tree('PONCT', [',']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['entreprise']), Tree('NP|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['charpente'])])]), Tree('VPpart', [Tree('VPP', ['chargée']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['pose']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['toiture']), Tree('PP', [Tree('P+D', ['des']), Tree('NP', [Tree('NC', ['cours']), Tree('NP|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis'])])]), Tree('AP', [Tree('ADJ', ['couverts'])])])])])])])])])])])])])])])])])])])])])])]), Tree('Srel|', [Tree('PONCT', [',']), Tree('VPinf', [Tree('P', ['pour']), Tree('VPinf|', [Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('VPinf|', [Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['enceinte']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['complexe']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])])])]), Tree('VPinf|', [Tree('PONCT', [',']), Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NP|', [Tree('NC', ['semi-remorque']), Tree('NP|', [Tree('PONCT', [',']), Tree('Srel', [Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])]), Tree('Srel|', [Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('NP', [Tree('DET', ['quatorze']), Tree('NP|', [Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments']), Tree('NP|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois'])])]), Tree('NP|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NP|', [Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('ADJ', ['long'])])])])])]), Tree('NP|', [Tree('PONCT', [',']), Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['couverture']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])]), Tree('PONCT', ['.'])])])])])])" ] }, - "execution_count": 17, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t = Tree.fromstring(ignore_functional_labels(data[15]))\n", + "t = Tree.fromstring(ignore_functional_labels(data[97]))\n", "t.chomsky_normal_form()\n", "t" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "27" + "83" ] }, - "execution_count": 18, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -534,26 +566,37 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2643.805163383484\n" + ] + } + ], "source": [ - "scores, back = cyk(sentence)" + "from time import time\n", + "start_time = time()\n", + "scores, back = cyk(sentence)\n", + "print(time() - start_time)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "Tree('', [Tree('SENT', [Tree('AdP', [Tree('PONCT', ['\"']), Tree('ADV', ['Tout']), Tree('ADV', ['simplement']), Tree('PONCT', ['\"'])]), Tree('Sint', [Tree('PONCT', [',']), Tree('VN', [Tree('V', ['a']), Tree('VPP', ['précisé'])]), Tree('NP', [Tree('NPP', ['Roger']), Tree('NPP', ['Thiriot'])])]), Tree('PONCT', [',']), Tree('PONCT', ['\"']), Tree('Ssub', [Tree('CS', ['parce_que']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NC', ['histoire']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['travail']), Tree('AP', [Tree('ADJ', ['industriel'])])])])]), Tree('VN', [Tree('V', ['est'])]), Tree('PONCT', [',']), Tree('ADV', ['ici']), Tree('PONCT', [',']), Tree('NP', [Tree('DET', ['une']), Tree('AP', [Tree('ADJ', ['longue']), Tree('COORD', [Tree('CC', ['et']), Tree('AP', [Tree('ADJ', ['vieille'])])])]), Tree('NC', ['histoire'])])]), Tree('PONCT', ['.'])])])" + "Tree('', [Tree('SENT', [Tree('NP', [Tree('DET', ['Une']), Tree('NC', ['heure']), Tree('COORD', 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['des'])]), Tree('NP', [Tree('NC', ['cours']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis'])])]), Tree('AP', [Tree('ADJ', ['couverts'])])])])])])])])])])])])])])]), Tree('PONCT', [',']), Tree('VPinf', [Tree('P', ['pour']), Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NC', ['enceinte']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['complexe']), Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])]), Tree('PONCT', [',']), Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NC', ['semi-remorque']), Tree('PONCT', [',']), Tree('Srel', [Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])]), Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('NP', [Tree('DET', ['quatorze']), Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois'])])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('ADJ', ['long'])])])])]), Tree('PONCT', [',']), Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['couverture']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])])])])])])])])])]), Tree('PONCT', ['.'])])])" ] }, - "execution_count": 70, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -565,24 +608,24 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "image/png": 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['chargée']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['pose']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['toiture']), Tree('PP', [Tree('P+D', ['des']), Tree('NP', [Tree('NC', ['cours'])])])])])])])])])])])])])])])])])])])])])])])]), Tree('Sint|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis']), Tree('AP', [Tree('ADJ', ['couverts'])])])]), Tree('PONCT', [','])])])])]), Tree('VPinf', [Tree('P', ['pour']), Tree('VPinf|', [Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('VPinf|', [Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['enceinte']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['complexe']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])])])]), Tree('VPinf|', [Tree('PONCT', [',']), Tree('VPinf|', [Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NC', ['semi-remorque'])])]), Tree('PONCT', [','])])])])])])])])]), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])])])])]), Tree('SENT|', [Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('SENT|', [Tree('NP', [Tree('DET', ['quatorze']), Tree('NP|', [Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments'])])])])]), Tree('SENT|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NP|', [Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('AP', [Tree('ADJ', ['long'])])])])])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['couverture']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])]), Tree('PONCT', ['.'])])])])])])])])" ] }, - "execution_count": 72, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_test = Tree.fromstring(build_parentheses(back))\n", - "t_test.un_chomsky_normal_form()\n", + "# t_test.chomsky_normal_form()\n", "t_test" ] }, @@ -610,12 +653,96 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## OOV module" + "## Evaluation" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "def score(true_parse, proposed_parse):\n", + " \n", + " \"\"\"\n", + " Description\n", + " -----------------\n", + " Evaluate a proposed parse given the true one, this function prints recall and precision of the whole parse and on POS tags only also.\n", + " \n", + " Parameters\n", + " -----------------\n", + " true_parse, proposed_parse : Bracketed strings, the true and proposed parse trees.\n", + " \n", + " Returns\n", + " -----------------\n", + " \"\"\"\n", + " \n", + " true_parse = true_parse[2:-1]\n", + " proposed_parse= proposed_parse[2:-1]\n", + " \n", + " gold_tree = parser.create_from_bracket_string(true_parse)\n", + " test_tree = parser.create_from_bracket_string(proposed_parse)\n", + " \n", + " # Compute recall and precision for POS tags\n", + " y_true = np.array(gold_tree.poss)\n", + " y_pred = np.array(test_tree.poss)\n", + "\n", + " y_pred = (y_true == y_pred).astype(int)\n", + " y_true = np.ones(len(y_true)).astype(int)\n", + "\n", + " (POS_precision, POS_recall, POS_f_score, beta) = precision_recall_fscore_support(y_true,y_pred, labels=[1])\n", + " \n", + " # Compute recall and precision for the whole parse\n", + " thescorer = scorer.Scorer() \n", + " result = thescorer.score_trees(gold_tree, test_tree)\n", + " \n", + " print('Parse recall : {:.2f}%'.format(result.recall*100))\n", + " print('Parse precision : {:.2f}%'.format(result.prec*100), end=\"\\n\\n\")\n", + " \n", + " print('POS recall : {:.2f}%'.format(POS_recall[0]*100))\n", + " print('POS precision : {:.2f}%'.format(POS_precision[0]*100))\n", + "\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse recall : 50.79%\n", + "Parse precision : 50.79%\n", + "\n", + "POS recall : 100.00%\n", + "POS precision : 100.00%\n" + ] + } + ], + "source": [ + "score(\" \".join(str(t).split()), \" \".join(str(t_test).split()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# OOV module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding similarity" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -644,7 +771,7 @@ " (0.31358325481414795, 'prêtre')]" ] }, - "execution_count": 28, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -658,9 +785,16 @@ "sorted(scores)[:10]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formal similarity" + ] + }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -751,32 +885,82 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## Language model" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def unigram(data):\n", + " \n", + " \"\"\"\n", + " Description\n", + " ---------------\n", + " Compute a unigram model from the corpus data.\n", + " \n", + " Parameters\n", + " ---------------\n", + " data : List of bracketed strings\n", + " \n", + " Returns\n", + " ---------------\n", + " np.array of shape (#words_in_data,) containing the probabilities p(word).\n", + " \"\"\"\n", + " \n", + " probas = np.zeros(len(terminals))\n", + " for bracketed in data:\n", + " t = Tree.fromstring(bracketed)\n", + " for word in t.leaves():\n", + " probas[dict_terminals_indices[word]] += 1\n", + "\n", + " return probas/probas.sum()\n", + "\n", + "def bigram(data):\n", + " \n", + " \"\"\"\n", + " Description\n", + " ---------------\n", + " Compute a bigram model from the corpus data.\n", + " \n", + " Parameters\n", + " ---------------\n", + " data : List of bracketed strings\n", + " \n", + " Returns\n", + " ---------------\n", + " np.array of shape (#words_in_data, #words_in_data) containing the probabilities p(word_current|word_previous).\n", + " \"\"\"\n", + " \n", + " probas = np.ones((len(terminals), len(terminals)))\n", + " for bracketed in data:\n", + " t = Tree.fromstring(bracketed)\n", + " sentence = t.leaves()\n", + " if len(sentence) >= 2:\n", + " for i in range(1, len(sentence)):\n", + " index_1 = dict_terminals_indices[sentence[i - 1]] # The previous word in the sequene.\n", + " index_2 = dict_terminals_indices[sentence[i]] # The current word in the sequene.\n", + " probas[index_1, index_2] += 1\n", + "\n", + " else:\n", + " continue\n", + "\n", + " return probas/((probas.sum(axis = 1).reshape(-1, 1)))" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "probas_unigram, probas_bigram = unigram(data), bigram(data)" + ] }, { "cell_type": "code", diff --git a/parser.ipynb b/parser.ipynb index a9c07cc..3812f4b 100644 --- a/parser.ipynb +++ b/parser.ipynb @@ -2,7 +2,16 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 77, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install PYEVALB" + ] + }, + { + "cell_type": "code", + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +22,10 @@ "import random\n", "import queue\n", "import pickle as pkl\n", - "from scipy.spatial import distance" + "from scipy.spatial import distance\n", + "from PYEVALB import scorer\n", + "from PYEVALB import parser\n", + "from sklearn.metrics import precision_recall_fscore_support" ] }, { @@ -198,7 +210,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PCFG" + "# PCFG" ] }, { @@ -353,7 +365,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## CYK algorithm" + "# CYK algorithm" ] }, { @@ -416,11 +428,31 @@ " return scores, back" ] }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7297" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(dict_probas['unary']) + len(dict_probas['binary'])" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Backtrack : Build parse tree from CYK" + "# Backtrack : Build parse tree from CYK" ] }, { @@ -490,39 +522,39 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "Tree('', [Tree('SENT', [Tree('AdP', [Tree('PONCT', ['\"']), Tree('AdP|', [Tree('ADV', ['Tout']), Tree('AdP|', [Tree('ADV', ['simplement']), Tree('PONCT', ['\"'])])])]), Tree('SENT|', [Tree('Sint', [Tree('PONCT', [',']), Tree('Sint|', [Tree('VN', [Tree('V', ['a']), Tree('VPP', ['précisé'])]), Tree('NP', [Tree('NPP', ['Roger']), Tree('NPP', ['Thiriot'])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('PONCT', ['\"']), Tree('SENT|', [Tree('Ssub', [Tree('CS', ['parce_que']), Tree('Ssub|', [Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['histoire']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['travail']), Tree('AP', [Tree('ADJ', ['industriel'])])])])])]), Tree('Ssub|', [Tree('VN', [Tree('V', ['est'])]), Tree('Ssub|', [Tree('PONCT', [',']), Tree('Ssub|', [Tree('ADV', ['ici']), Tree('Ssub|', [Tree('PONCT', [',']), Tree('NP', [Tree('DET', ['une']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['longue']), Tree('COORD', [Tree('CC', ['et']), Tree('AP', [Tree('ADJ', ['vieille'])])])]), Tree('NC', ['histoire'])])])])])])])])]), Tree('PONCT', ['.'])])])])])])])" + "Tree('', [Tree('SENT', [Tree('NP', [Tree('DET', ['Une']), Tree('NP|', [Tree('NC', ['heure']), Tree('COORD', [Tree('CC', ['et']), Tree('NP', [Tree('DET', ['vingt']), Tree('NC', ['minutes'])])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('VN', [Tree('CLS', [\"c'\"]), Tree('V', ['est'])]), Tree('SENT|', [Tree('NP', [Tree('DET', ['le']), Tree('NP|', [Tree('NC', ['temps']), Tree('Srel', [Tree('NP', [Tree('PROREL', [\"qu'\"])]), Tree('Srel|', [Tree('VN', [Tree('CLS', ['il']), Tree('VN|', [Tree('V', ['aura']), Tree('VPP', ['fallu'])])]), Tree('Srel|', [Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('NPP', ['Thierry']), Tree('NP|', [Tree('NPP', ['Guerry']), Tree('NP|', [Tree('PONCT', [',']), Tree('NP', [Tree('NC', ['chauffeur-routier']), Tree('PP', [Tree('P', ['chez']), Tree('NP', [Tree('NPP', ['Caillaud']), Tree('NP|', [Tree('PONCT', [',']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['entreprise']), Tree('NP|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['charpente'])])]), Tree('VPpart', [Tree('VPP', ['chargée']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['pose']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['toiture']), Tree('PP', [Tree('P+D', ['des']), Tree('NP', [Tree('NC', ['cours']), Tree('NP|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis'])])]), Tree('AP', [Tree('ADJ', ['couverts'])])])])])])])])])])])])])])])])])])])])])])]), Tree('Srel|', [Tree('PONCT', [',']), Tree('VPinf', [Tree('P', ['pour']), Tree('VPinf|', [Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('VPinf|', [Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['enceinte']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['complexe']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])])])]), Tree('VPinf|', [Tree('PONCT', [',']), Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NP|', [Tree('NC', ['semi-remorque']), Tree('NP|', [Tree('PONCT', [',']), Tree('Srel', [Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])]), Tree('Srel|', [Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('NP', [Tree('DET', ['quatorze']), Tree('NP|', [Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments']), Tree('NP|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois'])])]), Tree('NP|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NP|', [Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('ADJ', ['long'])])])])])]), Tree('NP|', [Tree('PONCT', [',']), Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['couverture']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])])]), Tree('PONCT', ['.'])])])])])])" ] }, - "execution_count": 17, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t = Tree.fromstring(ignore_functional_labels(data[15]))\n", + "t = Tree.fromstring(ignore_functional_labels(data[97]))\n", "t.chomsky_normal_form()\n", "t" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "27" + "83" ] }, - "execution_count": 18, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -534,26 +566,37 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2643.805163383484\n" + ] + } + ], "source": [ - "scores, back = cyk(sentence)" + "from time import time\n", + "start_time = time()\n", + "scores, back = cyk(sentence)\n", + "print(time() - start_time)" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 90, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "Tree('', [Tree('SENT', [Tree('AdP', [Tree('PONCT', ['\"']), Tree('ADV', ['Tout']), Tree('ADV', ['simplement']), Tree('PONCT', ['\"'])]), Tree('Sint', [Tree('PONCT', [',']), Tree('VN', [Tree('V', ['a']), Tree('VPP', ['précisé'])]), Tree('NP', [Tree('NPP', ['Roger']), Tree('NPP', ['Thiriot'])])]), Tree('PONCT', [',']), Tree('PONCT', ['\"']), Tree('Ssub', [Tree('CS', ['parce_que']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NC', ['histoire']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['travail']), Tree('AP', [Tree('ADJ', ['industriel'])])])])]), Tree('VN', [Tree('V', ['est'])]), Tree('PONCT', [',']), Tree('ADV', ['ici']), Tree('PONCT', [',']), Tree('NP', [Tree('DET', ['une']), Tree('AP', [Tree('ADJ', ['longue']), Tree('COORD', [Tree('CC', ['et']), Tree('AP', [Tree('ADJ', ['vieille'])])])]), Tree('NC', ['histoire'])])]), Tree('PONCT', ['.'])])])" + "Tree('', [Tree('SENT', [Tree('NP', [Tree('DET', ['Une']), Tree('NC', ['heure']), Tree('COORD', 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['des'])]), Tree('NP', [Tree('NC', ['cours']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis'])])]), Tree('AP', [Tree('ADJ', ['couverts'])])])])])])])])])])])])])])]), Tree('PONCT', [',']), Tree('VPinf', [Tree('P', ['pour']), Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NC', ['enceinte']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['complexe']), Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])]), Tree('PONCT', [',']), Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NC', ['semi-remorque']), Tree('PONCT', [',']), Tree('Srel', [Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])]), Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('NP', [Tree('DET', ['quatorze']), Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois'])])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('ADJ', ['long'])])])])]), Tree('PONCT', [',']), Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['couverture']), Tree('PP', [Tree('P', [Tree('D', ['du'])]), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])])])])])])])])])]), Tree('PONCT', ['.'])])])" ] }, - "execution_count": 70, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -565,24 +608,24 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "image/png": 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['chargée']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['pose']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['toiture']), Tree('PP', [Tree('P+D', ['des']), Tree('NP', [Tree('NC', ['cours'])])])])])])])])])])])])])])])])])])])])])])])]), Tree('Sint|', [Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('NC', ['tennis']), Tree('AP', [Tree('ADJ', ['couverts'])])])]), Tree('PONCT', [','])])])])]), Tree('VPinf', [Tree('P', ['pour']), Tree('VPinf|', [Tree('VN', [Tree('VINF', ['pénétrer'])]), Tree('VPinf|', [Tree('PP', [Tree('P', ['dans']), Tree('NP', [Tree('DET', [\"l'\"]), Tree('NP|', [Tree('NC', ['enceinte']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['complexe']), Tree('NP|', [Tree('AP', [Tree('ADJ', ['sportif'])]), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['la']), Tree('NC', ['commune'])])])])])])])])]), Tree('VPinf|', [Tree('PONCT', [',']), Tree('VPinf|', [Tree('PP', [Tree('P', ['avec']), Tree('NP', [Tree('DET', ['son']), Tree('NC', ['semi-remorque'])])]), Tree('PONCT', [','])])])])])])])])]), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('PROREL', ['lequel'])])])])])]), Tree('SENT|', [Tree('VN', [Tree('V', ['étaient']), Tree('VPP', ['chargées'])]), Tree('SENT|', [Tree('NP', [Tree('DET', ['quatorze']), Tree('NP|', [Tree('NC', ['tonnes']), Tree('PP', [Tree('P', [\"d'\"]), Tree('NP', [Tree('NC', ['éléments'])])])])]), Tree('SENT|', [Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['bois']), Tree('PP', [Tree('P', ['de']), Tree('NP', [Tree('DET', ['trente']), Tree('NP|', [Tree('NC', ['mètres']), Tree('PP', [Tree('P', ['de']), Tree('AP', [Tree('ADJ', ['long'])])])])])])])]), Tree('SENT|', [Tree('PONCT', [',']), Tree('SENT|', [Tree('VPpart', [Tree('VPP', ['destinés']), Tree('PP', [Tree('P', ['à']), Tree('NP', [Tree('DET', ['la']), Tree('NP|', [Tree('NC', ['couverture']), Tree('PP', [Tree('P+D', ['du']), Tree('NP', [Tree('NC', ['bâtiment']), Tree('PP', [Tree('P', ['en']), Tree('NP', [Tree('NC', ['construction']), Tree('PP', [Tree('P', ['sur']), Tree('NP', [Tree('DET', ['le']), Tree('NC', ['stade'])])])])])])])])])])]), Tree('PONCT', ['.'])])])])])])])])" ] }, - "execution_count": 72, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t_test = Tree.fromstring(build_parentheses(back))\n", - "t_test.un_chomsky_normal_form()\n", + "# t_test.chomsky_normal_form()\n", "t_test" ] }, @@ -610,12 +653,96 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## OOV module" + "## Evaluation" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 120, + "metadata": {}, + "outputs": [], + "source": [ + "def score(true_parse, proposed_parse):\n", + " \n", + " \"\"\"\n", + " Description\n", + " -----------------\n", + " Evaluate a proposed parse given the true one, this function prints recall and precision of the whole parse and on POS tags only also.\n", + " \n", + " Parameters\n", + " -----------------\n", + " true_parse, proposed_parse : Bracketed strings, the true and proposed parse trees.\n", + " \n", + " Returns\n", + " -----------------\n", + " \"\"\"\n", + " \n", + " true_parse = true_parse[2:-1]\n", + " proposed_parse= proposed_parse[2:-1]\n", + " \n", + " gold_tree = parser.create_from_bracket_string(true_parse)\n", + " test_tree = parser.create_from_bracket_string(proposed_parse)\n", + " \n", + " # Compute recall and precision for POS tags\n", + " y_true = np.array(gold_tree.poss)\n", + " y_pred = np.array(test_tree.poss)\n", + "\n", + " y_pred = (y_true == y_pred).astype(int)\n", + " y_true = np.ones(len(y_true)).astype(int)\n", + "\n", + " (POS_precision, POS_recall, POS_f_score, beta) = precision_recall_fscore_support(y_true,y_pred, labels=[1])\n", + " \n", + " # Compute recall and precision for the whole parse\n", + " thescorer = scorer.Scorer() \n", + " result = thescorer.score_trees(gold_tree, test_tree)\n", + " \n", + " print('Parse recall : {:.2f}%'.format(result.recall*100))\n", + " print('Parse precision : {:.2f}%'.format(result.prec*100), end=\"\\n\\n\")\n", + " \n", + " print('POS recall : {:.2f}%'.format(POS_recall[0]*100))\n", + " print('POS precision : {:.2f}%'.format(POS_precision[0]*100))\n", + "\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parse recall : 50.79%\n", + "Parse precision : 50.79%\n", + "\n", + "POS recall : 100.00%\n", + "POS precision : 100.00%\n" + ] + } + ], + "source": [ + "score(\" \".join(str(t).split()), \" \".join(str(t_test).split()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# OOV module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Embedding similarity" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -626,7 +753,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -644,7 +771,7 @@ " (0.31358325481414795, 'prêtre')]" ] }, - "execution_count": 28, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -658,9 +785,16 @@ "sorted(scores)[:10]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formal similarity" + ] + }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -751,32 +885,82 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "## Language model" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def unigram(data):\n", + " \n", + " \"\"\"\n", + " Description\n", + " ---------------\n", + " Compute a unigram model from the corpus data.\n", + " \n", + " Parameters\n", + " ---------------\n", + " data : List of bracketed strings\n", + " \n", + " Returns\n", + " ---------------\n", + " np.array of shape (#words_in_data,) containing the probabilities p(word).\n", + " \"\"\"\n", + " \n", + " probas = np.zeros(len(terminals))\n", + " for bracketed in data:\n", + " t = Tree.fromstring(bracketed)\n", + " for word in t.leaves():\n", + " probas[dict_terminals_indices[word]] += 1\n", + "\n", + " return probas/probas.sum()\n", + "\n", + "def bigram(data):\n", + " \n", + " \"\"\"\n", + " Description\n", + " ---------------\n", + " Compute a bigram model from the corpus data.\n", + " \n", + " Parameters\n", + " ---------------\n", + " data : List of bracketed strings\n", + " \n", + " Returns\n", + " ---------------\n", + " np.array of shape (#words_in_data, #words_in_data) containing the probabilities p(word_current|word_previous).\n", + " \"\"\"\n", + " \n", + " probas = np.ones((len(terminals), len(terminals)))\n", + " for bracketed in data:\n", + " t = Tree.fromstring(bracketed)\n", + " sentence = t.leaves()\n", + " if len(sentence) >= 2:\n", + " for i in range(1, len(sentence)):\n", + " index_1 = dict_terminals_indices[sentence[i - 1]] # The previous word in the sequene.\n", + " index_2 = dict_terminals_indices[sentence[i]] # The current word in the sequene.\n", + " probas[index_1, index_2] += 1\n", + "\n", + " else:\n", + " continue\n", + "\n", + " return probas/((probas.sum(axis = 1).reshape(-1, 1)))" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "probas_unigram, probas_bigram = unigram(data), bigram(data)" + ] }, { "cell_type": "code",