From 62a2c299e6f5118798730619cd2033995c22fcd4 Mon Sep 17 00:00:00 2001 From: Akhmed Sakip Date: Thu, 9 Mar 2023 18:22:59 +0400 Subject: [PATCH] First commit --- .gitignore | 1 + README.md | 87 +++ audio_paths.csv | 1441 ++++++++++++++++++++++++++++++++++++ experiments_betti.py | 82 ++ experiments_giottobetti.py | 82 ++ experiments_landscapes.py | 82 ++ experiments_pairwise.py | 82 ++ features_giottobetti.csv | 1441 ++++++++++++++++++++++++++++++++++++ features_gudhi.csv | 1441 ++++++++++++++++++++++++++++++++++++ features_landscapes.csv | 1441 ++++++++++++++++++++++++++++++++++++ features_pairwise.csv | 1441 ++++++++++++++++++++++++++++++++++++ generate_betticurves.py | 74 ++ generate_giotto.py | 120 +++ generate_pairwise.py | 67 ++ generate_persims.py | 47 ++ labels_ts2pi.csv | 1441 ++++++++++++++++++++++++++++++++++++ load_dataset.py | 40 + vgg16_ts2pi.py | 78 ++ 18 files changed, 9488 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 audio_paths.csv create mode 100644 experiments_betti.py create mode 100644 experiments_giottobetti.py create mode 100644 experiments_landscapes.py create mode 100644 experiments_pairwise.py create mode 100644 features_giottobetti.csv create mode 100644 features_gudhi.csv create mode 100644 features_landscapes.csv create mode 100644 features_pairwise.csv create mode 100644 generate_betticurves.py create mode 100644 generate_giotto.py create mode 100644 generate_pairwise.py create mode 100644 generate_persims.py create mode 100644 labels_ts2pi.csv create mode 100644 load_dataset.py create mode 100644 vgg16_ts2pi.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..62c8935 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.idea/ \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..c764152 --- /dev/null +++ b/README.md @@ -0,0 +1,87 @@ +# Topological Data Analysis for Speech Emotion Recognition +## Course Project +## By Akhmed Sakip, Noor Hussein, Nurdaulet Mukhituly +Reach out at akhmedsakip@gmail.com + +### Downloading & extracting the dataset +The project uses the RAVDESS dataset. To download it, head [here](https://www.kaggle.com/datasets/uwrfkaggler/ravdess-emotional-speech-audio) and download the dataset. Create a folder named 'RAVDESS' and extract the archive's contents there. +
The resulting data paths should look like 'RAVDESS / Actor_** / *.wav'. + +### Downloading additional data before running the project +To avoid generating persistence images from scratch in *generate_persims.py*, download the contents (<2 GB) of [this folder](https://mbzuaiac-my.sharepoint.com/:f:/g/personal/akhmed_sakip_mbzuai_ac_ae/EkeSvj0lkdlBtMMjDfXpTMYBbzayUmbmsvegHDSmc1vuLQ?e=bdgJhJ) and place the 2 files (*features_ts2pi.txt*, *labels_ts2pi.csv*) directly to the project's folder (alongside with other project files). + +### Packages to install +The project was tested on **Python 3.10.4**, so to be on the safe side, it is preferable to use one of the releases of **Python 3.10**. +
Once you have created an environment running Python 3.10, make sure to install the following packages using *pip*: +
+
**NumPy**: +
    `pip install numpy` +
+
**pandas**: +
    `pip install pandas` +
+
**scikit-learn**: +
    `pip install scikit-learn` +
+
**librosa**: +
    `pip install librosa` +
+
**gudhi**: +
    `pip install gudhi` +
+
**giotto-tda**: +
    `pip install giotto-tda` +
+
**tqdm**: +
    `pip install tqdm` +
+
**tensorflow**: +
    `pip install tensorflow` +
+
*Note*: if you need GPU acceleration, follow the instructions [here](https://www.tensorflow.org/install/pip). Otherwise, you had better run the TensorFlow code (*vgg16_ts2pi.py*) on Kaggle, since setting up GPU acceleration on the university's lab computers is complicated and problematic. +
+ +*Warning*: **ts2pi** cannot be installed alongside the newest versions of the above packages and is thus incompatible with them. If you wish to regenerate persistence images without downloading them from the link provided above, create a new environment and install ONLY the package below, so that it will pull the specific versions by itself. +
**ts2pi**: +
    `pip install ts2pi` +
+ +### Repository contents + +The project directory consists of the following Python files: +
+
  **load_dataset.py** - conveniently formats the audio paths and sorts them according to emotions, saving the results to *audio_paths.csv*. The code for processing the dataset folder is taken from [here](https://www.kaggle.com/code/shivamburnwal/speech-emotion-recognition). +
+
  **experiments_betti.py** - runs classification experiments on Betti curves generated using the *gudhi* package. +
+
  **experiments_giottobetti.py** - runs classification experiments on Betti curves generated using manual preprocessing and the *giotto-tda* package. +
+
  **experiments_landscapes.py** - runs classification experiments on persistence landscapes generated using manual preprocessing and the *giotto-tda* package. +
+
  **experiments_pairwise.py** - runs classification experiments on pairwise distances between persistence diagrams generated using manual preprocessing and the *giotto-tda* package. +
+
  **vgg16_ts2pi.py** - runs classification experiments by training the VGG16 CNN with persistence images generated using the *ts2pi* package. +
+
  **generate_betticurves.py** - generates Betti curves for the audio files using the *gudhi* package by following the approach outlined [here](https://ieeexplore.ieee.org/document/9356319). The results are stored in *features_gudhi.csv*. +
+
  **generate_giotto.py** - generates Betti curves and persistence landscapes for the audio files using manual preprocessing and the *giotto-tda* package. The results are stored in *features_giottobetti.csv* and *features_landscapes.csv*. +
+
  **generate_pairwise.py** - calculates pairwise distances for the persistence diagrams of the audio files using manual preprocessing and the *giotto-tda* package. The results are stored in *features_pairwise.csv*. +
+
  **generate_persims.py** - generates persistence images for the audio files using the *ts2pi* package. The results are stored in *features_ts2pi.txt* and *labels_ts2pi.csv*. To download them, refer above. + +### Replication of experiments +To replicate the classification experiments, simply run the corresponding Python files named *experiments_\*.py* and *vgg16_ts2pi.py* inside an environment with installed packages. All the sample preprocessed data is already provided in this repository (except for VGG16, which should be downloaded referring to the above instructions), so you do not have to rerun the *generate_\*.py* files. +

+**Warning**: the Python files are set to run experiments for 100 train-test splits, which could take considerable time. To reduce the number of splits, simply change the *NUM_SPLITS* in the files' beginnings to a smaller number. +

+In case you wish to run the preprocessing files, first run *load_dataset.py* and proceed to running the *generate_\*.py* files. + +### References +[1] M. Tlachac, A. Sargent, E. Toto, R. Paffenroth and E. Rundensteiner, "Topological Data Analysis to Engineer Features from Audio Signals for Depression Detection," _2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)_, 2020, pp. 302-307, doi: 10.1109/ICMLA51294.2020.00056. +
+[2] The GUDHI Project, GUDHI User and Reference Manual. GUDHI Editorial Board, 2015. [Online]. Available: http://gudhi.gforge.inria.fr/doc/latest/ +
+[3] G. Tauzin et al., giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration. 2020. [Online]. Available: https://giotto-ai.github.io/gtda-docs/0.5.1/library.html +
+[4] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv, 2014. doi: 10.48550/ARXIV.1409.1556. diff --git a/audio_paths.csv b/audio_paths.csv new file mode 100644 index 0000000..8d08636 --- /dev/null +++ b/audio_paths.csv @@ -0,0 +1,1441 @@ +Emotions,Path +angry,RAVDESS/Actor_05/03-01-05-02-01-01-05.wav +happy,RAVDESS/Actor_05/03-01-03-01-01-02-05.wav +happy,RAVDESS/Actor_05/03-01-03-02-02-02-05.wav +angry,RAVDESS/Actor_05/03-01-05-01-02-01-05.wav +happy,RAVDESS/Actor_05/03-01-03-02-01-02-05.wav +angry,RAVDESS/Actor_05/03-01-05-01-01-01-05.wav +angry,RAVDESS/Actor_05/03-01-05-02-02-01-05.wav +happy,RAVDESS/Actor_05/03-01-03-01-02-02-05.wav +sad,RAVDESS/Actor_05/03-01-04-01-02-01-05.wav +calm,RAVDESS/Actor_05/03-01-02-02-02-02-05.wav +calm,RAVDESS/Actor_05/03-01-02-01-01-02-05.wav +sad,RAVDESS/Actor_05/03-01-04-02-01-01-05.wav +calm,RAVDESS/Actor_05/03-01-02-01-02-02-05.wav +sad,RAVDESS/Actor_05/03-01-04-02-02-01-05.wav 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pd +from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder +from sklearn.metrics import confusion_matrix, classification_report, f1_score +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.neural_network import MLPClassifier +from sklearn.decomposition import PCA + +FEATURES_PATH = 'features_gudhi.csv' +NUM_SPLITS = 100 + +# Load the features/labels from a .csv file with the path specified in the function's argument +# The last column stands for the emotion label, so features are all the columns before that +def load_features(path): + df = pd.read_csv(path) + X = df.iloc[:, :-1].values + y = df['label'].values + return X, y + +# Scale the features according to the passed argument specifying the scaling type (standard/minmax scaling, no scaling) +def scale_features(X, type): + if type == 'standard': + scaler = StandardScaler() + elif type == 'minmax': + scaler = MinMaxScaler() + elif type == 'none': + return X + else: + raise Exception("No valid scaler type passed.") + return scaler.fit_transform(X) + +# Perform dimensionality reduction pf the features via PCA based on the argument specifying n_components argument +# of scikit-learn's PCA +def reduce_features(X, n_components): + if n_components == 'none': + return X + reducer = PCA(n_components=n_components) + return reducer.fit_transform(X) + +# Perform label encoding +def encode_labels(y): + encoder = LabelEncoder() + return encoder.fit_transform(y) + +X_loaded, y_loaded = load_features(FEATURES_PATH) + +approaches = {} + +# Looping through all the approaches +for scaler in ['none', 'standard', 'minmax']: + for reducer in ['none', 10, 0.80]: + for classifier in ['rfc', 'knn', 'svc', 'mlp']: + scores = [] + # Looping through all the train-test splits of data + for i in range(NUM_SPLITS): + X, y = X_loaded.copy(), y_loaded.copy() + X = scale_features(X, type=scaler) + X = reduce_features(X, n_components=reducer) + y = encode_labels(y) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + # Running the respective algorithms of scikit-learn + if classifier == 'rfc': + clf = RandomForestClassifier(n_estimators=100) + elif classifier == 'knn': + clf = KNeighborsClassifier(n_neighbors=5) + elif classifier == 'svc': + clf = SVC(kernel='rbf') + elif classifier == 'mlp': + clf = MLPClassifier() + clf.fit(X_train, y_train) + score = f1_score(y_test, clf.predict(X_test), average='micro') + # Appending the f1-scores to the list of the splits' scores + scores.append(score) + + scores = np.array(scores) + # Filling the dictionary with the mean and stdev of f1-scores for each split + approaches[(scaler, reducer, classifier)] = (np.mean(scores), np.std(scores)) + +print(approaches) \ No newline at end of file diff --git a/experiments_giottobetti.py b/experiments_giottobetti.py new file mode 100644 index 0000000..f18b0f8 --- /dev/null +++ b/experiments_giottobetti.py @@ -0,0 +1,82 @@ +import numpy as np +import pandas as pd +from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder +from sklearn.metrics import confusion_matrix, classification_report, f1_score +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.neural_network import MLPClassifier +from sklearn.decomposition import PCA + +FEATURES_PATH = 'features_giottobetti.csv' +NUM_SPLITS = 100 + +# Load the features/labels from a .csv file with the path specified in the function's argument +# The last column stands for the emotion label, so features are all the columns before that +def load_features(path): + df = pd.read_csv(path) + X = df.iloc[:, :-1].values + y = df['label'].values + return X, y + +# Scale the features according to the passed argument specifying the scaling type (standard/minmax scaling, no scaling) +def scale_features(X, type): + if type == 'standard': + scaler = StandardScaler() + elif type == 'minmax': + scaler = MinMaxScaler() + elif type == 'none': + return X + else: + raise Exception("No valid scaler type passed.") + return scaler.fit_transform(X) + +# Perform dimensionality reduction pf the features via PCA based on the argument specifying n_components argument +# of scikit-learn's PCA +def reduce_features(X, n_components): + if n_components == 'none': + return X + reducer = PCA(n_components=n_components) + return reducer.fit_transform(X) + +# Perform label encoding +def encode_labels(y): + encoder = LabelEncoder() + return encoder.fit_transform(y) + +X_loaded, y_loaded = load_features(FEATURES_PATH) + +approaches = {} + +# Looping through all the approaches +for scaler in ['none', 'standard', 'minmax']: + for reducer in ['none', 10, 0.80]: + for classifier in ['rfc', 'knn', 'svc', 'mlp']: + scores = [] + # Looping through all the train-test splits of data + for i in range(NUM_SPLITS): + X, y = X_loaded.copy(), y_loaded.copy() + X = scale_features(X, type=scaler) + X = reduce_features(X, n_components=reducer) + y = encode_labels(y) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + # Running the respective algorithms of scikit-learn + if classifier == 'rfc': + clf = RandomForestClassifier(n_estimators=100) + elif classifier == 'knn': + clf = KNeighborsClassifier(n_neighbors=5) + elif classifier == 'svc': + clf = SVC(kernel='rbf') + elif classifier == 'mlp': + clf = MLPClassifier() + clf.fit(X_train, y_train) + score = f1_score(y_test, clf.predict(X_test), average='micro') + # Appending the f1-scores to the list of the splits' scores + scores.append(score) + + scores = np.array(scores) + # Filling the dictionary with the mean and stdev of f1-scores for each split + approaches[(scaler, reducer, classifier)] = (np.mean(scores), np.std(scores)) + +print(approaches) \ No newline at end of file diff --git a/experiments_landscapes.py b/experiments_landscapes.py new file mode 100644 index 0000000..74fb4c1 --- /dev/null +++ b/experiments_landscapes.py @@ -0,0 +1,82 @@ +import numpy as np +import pandas as pd +from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder +from sklearn.metrics import confusion_matrix, classification_report, f1_score +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.neural_network import MLPClassifier +from sklearn.decomposition import PCA + +FEATURES_PATH = 'features_landscapes.csv' +NUM_SPLITS = 100 + +# Load the features/labels from a .csv file with the path specified in the function's argument +# The last column stands for the emotion label, so features are all the columns before that +def load_features(path): + df = pd.read_csv(path) + X = df.iloc[:, :-1].values + y = df['label'].values + return X, y + +# Scale the features according to the passed argument specifying the scaling type (standard/minmax scaling, no scaling) +def scale_features(X, type): + if type == 'standard': + scaler = StandardScaler() + elif type == 'minmax': + scaler = MinMaxScaler() + elif type == 'none': + return X + else: + raise Exception("No valid scaler type passed.") + return scaler.fit_transform(X) + +# Perform dimensionality reduction pf the features via PCA based on the argument specifying n_components argument +# of scikit-learn's PCA +def reduce_features(X, n_components): + if n_components == 'none': + return X + reducer = PCA(n_components=n_components) + return reducer.fit_transform(X) + +# Perform label encoding +def encode_labels(y): + encoder = LabelEncoder() + return encoder.fit_transform(y) + +X_loaded, y_loaded = load_features(FEATURES_PATH) + +approaches = {} + +# Looping through all the approaches +for scaler in ['none', 'standard', 'minmax']: + for reducer in ['none', 10, 0.80]: + for classifier in ['rfc', 'knn', 'svc', 'mlp']: + scores = [] + # Looping through all the train-test splits of data + for i in range(NUM_SPLITS): + X, y = X_loaded.copy(), y_loaded.copy() + X = scale_features(X, type=scaler) + X = reduce_features(X, n_components=reducer) + y = encode_labels(y) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + # Running the respective algorithms of scikit-learn + if classifier == 'rfc': + clf = RandomForestClassifier(n_estimators=100) + elif classifier == 'knn': + clf = KNeighborsClassifier(n_neighbors=5) + elif classifier == 'svc': + clf = SVC(kernel='rbf') + elif classifier == 'mlp': + clf = MLPClassifier() + clf.fit(X_train, y_train) + score = f1_score(y_test, clf.predict(X_test), average='micro') + # Appending the f1-scores to the list of the splits' scores + scores.append(score) + + scores = np.array(scores) + # Filling the dictionary with the mean and stdev of f1-scores for each split + approaches[(scaler, reducer, classifier)] = (np.mean(scores), np.std(scores)) + +print(approaches) \ No newline at end of file diff --git a/experiments_pairwise.py b/experiments_pairwise.py new file mode 100644 index 0000000..ecde8f5 --- /dev/null +++ b/experiments_pairwise.py @@ -0,0 +1,82 @@ +import numpy as np +import pandas as pd +from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder +from sklearn.metrics import confusion_matrix, classification_report, f1_score +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.neighbors import KNeighborsClassifier +from sklearn.svm import SVC +from sklearn.neural_network import MLPClassifier +from sklearn.decomposition import PCA + +FEATURES_PATH = 'features_pairwise.csv' +NUM_SPLITS = 100 + +# Load the features/labels from a .csv file with the path specified in the function's argument +# The last column stands for the emotion label, so features are all the columns before that +def load_features(path): + df = pd.read_csv(path) + X = df.iloc[:, :-1].values + y = df['label'].values + return X, y + +# Scale the features according to the passed argument specifying the scaling type (standard/minmax scaling, no scaling) +def scale_features(X, type): + if type == 'standard': + scaler = StandardScaler() + elif type == 'minmax': + scaler = MinMaxScaler() + elif type == 'none': + return X + else: + raise Exception("No valid scaler type passed.") + return scaler.fit_transform(X) + +# Perform dimensionality reduction pf the features via PCA based on the argument specifying n_components argument +# of scikit-learn's PCA +def reduce_features(X, n_components): + if n_components == 'none': + return X + reducer = PCA(n_components=n_components) + return reducer.fit_transform(X) + +# Perform label encoding +def encode_labels(y): + encoder = LabelEncoder() + return encoder.fit_transform(y) + +X_loaded, y_loaded = load_features(FEATURES_PATH) + +approaches = {} + +# Looping through all the approaches +for scaler in ['none', 'standard', 'minmax']: + for reducer in ['none', 10, 0.80]: + for classifier in ['rfc', 'knn', 'svc', 'mlp']: + scores = [] + # Looping through all the train-test splits of data + for i in range(NUM_SPLITS): + X, y = X_loaded.copy(), y_loaded.copy() + X = scale_features(X, type=scaler) + X = reduce_features(X, n_components=reducer) + y = encode_labels(y) + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + # Running the respective algorithms of scikit-learn + if classifier == 'rfc': + clf = RandomForestClassifier(n_estimators=100) + elif classifier == 'knn': + clf = KNeighborsClassifier(n_neighbors=5) + elif classifier == 'svc': + clf = SVC(kernel='rbf') + elif classifier == 'mlp': + clf = MLPClassifier() + clf.fit(X_train, y_train) + score = f1_score(y_test, clf.predict(X_test), average='micro') + # Appending the f1-scores to the list of the splits' scores + scores.append(score) + + scores = np.array(scores) + # Filling the dictionary with the mean and stdev of f1-scores for each split + approaches[(scaler, reducer, classifier)] = (np.mean(scores), np.std(scores)) + +print(approaches) \ No newline at end of file diff --git a/features_giottobetti.csv b/features_giottobetti.csv new file mode 100644 index 0000000..f3ab0e5 --- /dev/null +++ b/features_giottobetti.csv @@ -0,0 +1,1441 @@ 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diff --git a/generate_betticurves.py b/generate_betticurves.py new file mode 100644 index 0000000..0495cdd --- /dev/null +++ b/generate_betticurves.py @@ -0,0 +1,74 @@ +import numpy as np +import pandas as pd +import librosa as lr +import gudhi +from multiprocessing import Pool + +AUDIO_PATHS = 'audio_paths.csv' +BETTI_COMPONENTS = 100 +# Modify the below variable depending on the number of CPU threads you are ready to allocate +N_THREADS = 16 + +# Extracting persistence barcode from an audio wave extracted using librosa +# The code is used from https://github.com/mltlachac/TDA with no significant changes +def get_persistence_from_audio(audio_wave, sample=22050, length=5): + simplex_up = gudhi.SimplexTree() + simplex_dw = gudhi.SimplexTree() + for i in np.arange(len(audio_wave)): + simplex_up.insert([i, i + 1], filtration=audio_wave[i]) + simplex_dw.insert([i, i + 1], filtration=-audio_wave[i]) + for i in np.arange(len(audio_wave) - 1): + simplex_up.insert([i, i + 1], filtration=audio_wave[i]) + simplex_dw.insert([i, i + 1], filtration=-audio_wave[i]) + dig_up = simplex_up.persistence() + dig_dw = simplex_dw.persistence() + return dig_up, dig_dw + +# A helper function +# The code is used from https://github.com/mltlachac/TDA with no significant changes +def functionize(val, descriptor): + def dirichlet(x): + return 1 if (x > descriptor[0]) and (x < descriptor[1]) else 0 + + return np.vectorize(dirichlet)(val) + +# Extracting Betti curves explained by 100 points given a persistence barocde of an audio file +# The code is used from https://github.com/mltlachac/TDA with no significant changes +def get_betti_curve_from_persistence(dig, num_points=100): + dig = np.asarray([[ele[1][0], ele[1][1]] for ele in dig if ele[1][1] < np.inf]) + v = np.zeros(num_points) + try: + mn, mx = np.min(dig), np.max(dig) + val_up = np.linspace(mn, mx, num=num_points) + for ele in dig: + v += functionize(val_up, ele) + return v + except ValueError: + print("Silent, returning 0") + return v + +# Extracting the resulting features (Betti curves) using the above functions +def extract_features(path): + wave, sample_rate = lr.core.load(path) + + dig_up, dig_dw = get_persistence_from_audio(wave, sample=sample_rate) + betti = get_betti_curve_from_persistence(dig_dw, num_points=BETTI_COMPONENTS) + + return betti + +# Parallelizing the feature extraction function to speed up the process +def preprocess_dataset(df): + with Pool(N_THREADS) as p: + X = p.map(extract_features, df['Path']) + + y = df['Emotions'] + + return X, y + +audio_paths = pd.read_csv(AUDIO_PATHS) +X, y = preprocess_dataset(audio_paths) + +# Saving the preprocessed features (Betti curves) for future use +preprocessed = pd.DataFrame(X) +preprocessed['label'] = y +preprocessed.to_csv('features_gudhi.csv', index=False) \ No newline at end of file diff --git a/generate_giotto.py b/generate_giotto.py new file mode 100644 index 0000000..62be4e5 --- /dev/null +++ b/generate_giotto.py @@ -0,0 +1,120 @@ +import numpy as np +import pandas as pd +import librosa as lr +import gudhi +from multiprocessing import Pool +import math +from gtda.diagrams import BettiCurve, PersistenceLandscape + +AUDIO_PATHS = 'audio_paths.csv' +# Modify the below variable depending on the number of CPU threads you are ready to allocate +N_THREADS = 16 + + +# Extracting persistence barcode given the path of an audio file +# This code is significantly changed based on the original code from https://github.com/mltlachac/TDA +def extract_persistence_barcode(path): + data, sr = lr.load(path, sr=16000, duration=2.5, offset=0.6) + simplex = gudhi.SimplexTree() + for i in np.arange(len(data)): + simplex.insert([i, i+1], filtration=data[i]) + barcode = simplex.persistence() + return barcode + + +# Function for postprocessing the diagrams +# from the source code of giotto-tda at https://github.com/giotto-ai/giotto-tda/blob/master/gtda/homology/_utils.py +def _postprocess_diagrams( + Xt, format, homology_dimensions, infinity_values, reduced + ): + + def replace_infinity_values(subdiagram): + np.nan_to_num(subdiagram, posinf=infinity_values, copy=False) + return subdiagram[subdiagram[:, 0] < subdiagram[:, 1]] + + + if format in ["ripser", "flagser"]: + + slices = {dim: slice(None) if (dim or not reduced) else slice(None, -1) + for dim in homology_dimensions} + Xt = [{dim: replace_infinity_values(diagram[dim][slices[dim]]) + for dim in homology_dimensions} for diagram in Xt] + elif format == "gudhi": + + slices = {dim: slice(None) if (dim or not reduced) else slice(1, None) + for dim in homology_dimensions} + Xt = [{dim: replace_infinity_values( + np.array([pers_info[1] for pers_info in diagram + if pers_info[0] == dim]).reshape(-1, 2)[slices[dim]] + ) + for dim in homology_dimensions} for diagram in Xt] + else: + raise ValueError( + f"Unknown input format {format} for collection of diagrams." + ) + + + start_idx_per_dim = np.cumsum( + [0] + [np.max([len(diagram[dim]) for diagram in Xt] + [1]) + for dim in homology_dimensions] + ) + + # Changed min() to np.min() and .size to len() + min_values = [np.min([np.min(diagram[dim][:, 0]) if len(diagram[dim]) + else np.inf for diagram in Xt]) + for dim in homology_dimensions] + min_values = [min_value if min_value != np.inf else 0 + for min_value in min_values] + n_features = start_idx_per_dim[-1] + Xt_padded = np.empty((len(Xt), n_features, 3), dtype=float) + + for i, dim in enumerate(homology_dimensions): + start_idx, end_idx = start_idx_per_dim[i:i + 2] + padding_value = min_values[i] + + Xt_padded[:, start_idx:end_idx, 2] = dim + for j, diagram in enumerate(Xt): + subdiagram = diagram[dim] + end_idx_nontrivial = start_idx + len(subdiagram) + + Xt_padded[j, start_idx:end_idx_nontrivial, :2] = subdiagram + + Xt_padded[j, end_idx_nontrivial:end_idx, :2] = [padding_value] * 2 + + return Xt_padded + +# Parallelizing the feature extraction function to speed up the process +def preprocess_dataset(df): + with Pool(N_THREADS) as p: + X = p.map(extract_persistence_barcode, df['Path']) + + y = df['Emotions'] + + X = _postprocess_diagrams( + X, "gudhi", sorted([0, 1]), np.inf, True) + + return X, y + + +audio_paths = pd.read_csv(AUDIO_PATHS) +X, y = preprocess_dataset(audio_paths) + +# Extracting Betti curves using giotto-tda +bc = BettiCurve() +X_bc = bc.fit_transform(X) +X_bc = X_bc.reshape(X_bc.shape[0], X_bc.shape[1] * X_bc.shape[2]) + +# Saving the preprocessed features (Betti curves) for future use +preprocessed = pd.DataFrame(X_bc) +preprocessed['label'] = y +preprocessed.to_csv('features_giottobetti.csv', index=False) + +# Extracting persistence landscapes using giotto-tda +pc = PersistenceLandscape() +X_pc = pc.fit_transform(X) +X_pc = X_pc.reshape(X_pc.shape[0], X_pc.shape[1] * X_pc.shape[2]) + +# Saving the preprocessed features (persistence landscapes) for future use +preprocessed = pd.DataFrame(X_pc) +preprocessed['label'] = y +preprocessed.to_csv('features_landscapes.csv', index=False) \ No newline at end of file diff --git a/generate_pairwise.py b/generate_pairwise.py new file mode 100644 index 0000000..7efbc14 --- /dev/null +++ b/generate_pairwise.py @@ -0,0 +1,67 @@ +import numpy as np +import pandas as pd +import librosa as lr +import gudhi +from multiprocessing import Pool +import math +import tqdm + +AUDIO_PATHS = 'audio_paths.csv' +# Modify the below variable depending on the number of CPU threads you are ready to allocate +N_THREADS = 16 + + +# Extracting persistence barcode given the path of an audio file +# This code is significantly changed based on the original code from https://github.com/mltlachac/TDA +def extract_persistence_barcode(path): + data, sr = lr.load(path, sr=16000, duration=2.5, offset=0.6) + simplex = gudhi.SimplexTree() + for i in np.arange(len(data)): + simplex.insert([i, i+1], filtration = data[i]) + barcode = simplex.persistence() + return barcode + +# Takes a persistence barcode as input, and iterates over each of its inner lists. If a value in an inner list +# is equal to positive infinity, the function replaces it with a different value and appends the resulting inner list +# to another list. +def format_pd(persistence_barcode): + res_array = [] + for row in persistence_barcode: + push_val_0 = row[1][0] + push_val_1 = row[1][1] + if math.isinf(row[1][0]): + push_val_0 = -0.2 + if math.isinf(row[1][1]): + push_val_1 = 0.2 + res_array.append([push_val_0, push_val_1]) + return res_array + +# Parallelizing the feature extraction function to speed up the process +def preprocess_dataset(df): + with Pool(N_THREADS) as p: + X = p.map(extract_persistence_barcode, df['Path']) + + with Pool(N_THREADS) as p: + X_numpy = p.map(format_pd, X) + + # Calculating bottleneck distances for every pair of persistence diagrams using gudhi + # WARNING: This functions runs for 2+ hours! + total_distances = [] + for pd1 in tqdm.tqdm(X_numpy): + diagram_distances = [] + for pd2 in X_numpy: + diagram_distances.append(gudhi.bottleneck_distance(pd1, pd2, 0.1)) + total_distances.append(diagram_distances) + + y = df['Emotions'] + + return total_distances, y + + +audio_paths = pd.read_csv(AUDIO_PATHS) +X, y = preprocess_dataset(audio_paths) + +# Saving the preprocessed features (pairwise distances) for future use +preprocessed = pd.DataFrame(X) +preprocessed['label'] = y +preprocessed.to_csv('features_pairwise.csv', index=False) \ No newline at end of file diff --git a/generate_persims.py b/generate_persims.py new file mode 100644 index 0000000..0b7c8bf --- /dev/null +++ b/generate_persims.py @@ -0,0 +1,47 @@ +import numpy as np +import pandas as pd +import librosa as lr +from ts2pi import persistence_image +from multiprocessing import Pool + +AUDIO_PATHS = 'audio_paths.csv' +# Modify the below variable depending on the number of CPU threads you are ready to allocate +N_THREADS = 16 +IMAGE_RESOLUTION = [224, 224] + +# Extracting 224x224 persistence images for the provided path of an audio file +def extract_features(path): + wave, sample_rate = lr.core.load(path) + + image = persistence_image(wave, resolution=IMAGE_RESOLUTION, t=3, bandwidth=0.1) + + return image + +# Iterating through the whole dataframe and extracting persistence images for each audio +def preprocess_dataset(df): + X = [] + i = 0 + for path in df['Path']: + feature = extract_features(path) + X.append(feature) + if i % 10 == 0: + print("Extracted", i, "-th feature") + i += 1 + + y = df['Emotions'] + + return X, y + +audio_paths = pd.read_csv(AUDIO_PATHS) +X, y = preprocess_dataset(audio_paths) + +X_np = np.array(X) +# Reshaping X so that it can be saved in a text file (since it is a 3D np.array) +X_reshaped = X_np.reshape(X_np.shape[0], -1) +np.savetxt("features_ts2pi.txt", X_reshaped) +# loading this file +# loaded = np.loadtxt("features_ts2pi.txt") +# X = loaded.reshape(loaded.shape[0], loaded.shape[1] // 224, 224) + +# Separately storing the corresponding emotion labels +y.to_csv("labels_ts2pi.csv", index=False) \ No newline at end of file diff --git a/labels_ts2pi.csv b/labels_ts2pi.csv new file mode 100644 index 0000000..48be5c9 --- /dev/null +++ b/labels_ts2pi.csv @@ -0,0 +1,1441 @@ +Emotions +angry +happy +happy +angry +happy +angry +angry +happy +sad +calm +calm +sad +calm +sad +sad +calm +disgust +neutral +disgust +disgust +disgust +neutral +fear +surprise +surprise +fear +surprise +fear +fear +surprise +surprise +fear +fear +surprise +fear +surprise +surprise +fear +neutral +disgust +disgust +disgust +neutral +disgust +calm +sad +sad +calm +sad +calm +calm +sad +happy +angry +angry +happy +angry +happy +happy +angry +calm +sad +sad +calm +sad +calm +calm +sad +happy +angry +angry +happy +angry +happy +happy +angry +surprise +fear +fear +surprise +fear +surprise +surprise +fear +neutral +disgust +disgust +disgust +neutral +disgust +disgust +neutral +disgust +disgust +disgust +neutral +fear +surprise +surprise +fear +surprise +fear +fear 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+surprise +fear +neutral +disgust +disgust +disgust +neutral +disgust +disgust +neutral +disgust +disgust +disgust +neutral +fear +surprise +surprise +fear +surprise +fear +fear +surprise +angry +happy +happy +angry +happy +angry +angry +happy +sad +calm +calm +sad +calm +sad +sad +calm +neutral +disgust +disgust +disgust +neutral +disgust +surprise +fear +fear +surprise +fear +surprise +surprise +fear +happy +angry +angry +happy +angry +happy +happy +angry +calm +sad +sad +calm +sad +calm +calm +sad +sad +calm +calm +sad +calm +sad +sad +calm +angry +happy +happy +angry +happy +angry +angry +happy +fear +surprise +surprise +fear +surprise +fear +fear +surprise +disgust +neutral +disgust +disgust +disgust +neutral diff --git a/load_dataset.py b/load_dataset.py new file mode 100644 index 0000000..66f9e96 --- /dev/null +++ b/load_dataset.py @@ -0,0 +1,40 @@ +import os +import pandas as pd + +RAVDESS_PATH = 'RAVDESS/' + +# The function below conveniently sorts the datasets' files according the the audio files' emotions. +# The code is taken from: https://www.kaggle.com/code/shivamburnwal/speech-emotion-recognition +# No significant changes to the original code +def load_ravdess(path): + ravdess_directory_list = os.listdir(path) + + file_emotion = [] + file_path = [] + for dir in ravdess_directory_list: + + actor = os.listdir(path + dir) + + for file in actor: + part = file.split('.')[0] + part = part.split('-') + + file_emotion.append(int(part[2])) + file_path.append(path + dir + '/' + file) + + + emotion_df = pd.DataFrame(file_emotion, columns=['Emotions']) + + + path_df = pd.DataFrame(file_path, columns=['Path']) + Ravdess_df = pd.concat([emotion_df, path_df], axis=1) + + + Ravdess_df.Emotions.replace({1:'neutral', 2:'calm', 3:'happy', 4:'sad', 5:'angry', 6:'fear', 7:'disgust', 8:'surprise'}, inplace=True) + + return Ravdess_df + + +audio_paths = load_ravdess(RAVDESS_PATH) +# Exporting the audio path-emotion pairs to a .csv +audio_paths.to_csv('audio_paths.csv', index=False) \ No newline at end of file diff --git a/vgg16_ts2pi.py b/vgg16_ts2pi.py new file mode 100644 index 0000000..5a2afaa --- /dev/null +++ b/vgg16_ts2pi.py @@ -0,0 +1,78 @@ +import numpy as np +import pandas as pd +import librosa as lr +import tensorflow as tf +# Uncomment the line below to disable GPU training if needed +# tf.config.set_visible_devices([], 'GPU') +from tensorflow import keras +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder, StandardScaler +from sklearn.metrics import accuracy_score +from tqdm import tqdm +import warnings +warnings.filterwarnings('ignore', category=UserWarning) +from keras.applications.vgg16 import VGG16 +from keras.layers import Input, Dense, Flatten +from keras.models import Model + +FEATURES_PATH = 'features_ts2pi.txt' +LABELS_PATH = 'labels_ts2pi.csv' + + +loaded = np.loadtxt(FEATURES_PATH) +# Restoring the 3D shape of saved data +X = loaded.reshape(loaded.shape[0], loaded.shape[1] // 224, 224) + +scaler = StandardScaler() +# Performing standard scaling on the 3D np.array +X = scaler.fit_transform(X.reshape(-1, X.shape[-1])).reshape(X.shape) + +y = pd.read_csv(LABELS_PATH)['Emotions'].to_numpy() +features = X +labels = y +# Changing the shape of the features to account for the single color channel -> (1440, 224, 224, 1) +features = features.reshape((features.shape[0], features.shape[1], features.shape[2], 1)) +features = np.array([np.repeat(img, 3, axis=2) for img in features]) + + +# Create the input layer with the desired input shape +inputs = Input(shape=(224, 224, 3)) + +# Use a VGG16 model as the base for the new model (not pretrained) +base_model = VGG16(input_tensor=inputs, include_top=False) + +# Add a new fully-connected layer with 8 output units +x = base_model.output + +# Add a Flatten layer to convert the 7x7x8 tensor output from the VGG16 model to a 7*7*8=392-dimensional vector +x = Flatten()(x) + +# To account for 8 emotion classes +x = Dense(8, activation='softmax')(x) + +model = Model(inputs=inputs, outputs=x) + +model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.categorical_crossentropy, metrics=['accuracy']) + +# Performing label encoding +labels_old = labels.copy() +le = LabelEncoder() +labels = le.fit_transform(labels_old) + +# Basic training code from https://www.kaggle.com/code/nilshmeier/melspectrogram-based-cnn-classification + +TEST_SIZE = 0.3 +BATCH_SIZE = 64 +EPOCHS = 50 + +encoded_labels = tf.one_hot(indices=labels, depth=8) + +X_train, X_test, y_train, y_test = train_test_split(features, encoded_labels.numpy(), test_size=TEST_SIZE) + +early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) +history = model.fit(x=X_train, y=y_train, validation_split=TEST_SIZE, epochs=EPOCHS, batch_size=BATCH_SIZE, callbacks=[early_stopping]) + +y_predicted = np.argmax(model.predict(x=X_test), axis=1) +y_true = np.argmax(y_test, axis=1) + +accuracy_score(y_true, y_predicted) \ No newline at end of file