initial commit

.gitignore

+*.wav
+.ipynb_checkpoints
+__pycache__
+creapy.egg-info
+!audio/example.wav
+.vscode
+.python-version
+.DS_Store

README.md

+# creapy − a Python-based tool for the automatic detection of creak in conversational speech
+creapy [ˈkriːpaɪ] is a tool to detect creak in speech. 
+
+## Prerequisites
+### git
+The most convenient way to work with `creapy` right now is to `clone` this repository using `git`. Find a handy git tutorial [here](https://rogerdudler.github.io/git-guide/).
+
+### python
+
+`creapy` is written in the programming language `Python 3`. You can download the most recent version (as of 02/10/2023) for your operating system using the following links
+- [Windows Installer (64-bit)](https://www.python.org/ftp/python/3.10.10/python-3.10.10-amd64.exe)
+- [macOS 64-bit universal2 installer](https://www.python.org/ftp/python/3.10.10/python-3.10.10-macos11.pkg)
+
+If you are a user of Windows make sure to check the mark **Add Python 3.10 to PATH** to add python to the PATH so that you can find the `python`-keyword that links to the `python`-executable globally in your terminal.
+![](https://i.imgur.com/qfydlwD.png)
+
+To check if you successfully installed Python open up a new Terminal. Press the Windows key, type `cmd` and hit enter. 
+
+A new terminal window opens where you can type `python --version` which should give you the latest version of python 3.10 installed on your system.
+![](https://i.imgur.com/A3fHfcS.png)
+
+macOS-users can do the same thing in their terminal by typing `python3 --version` instead.
+
+### jupyter lab
+We recommend using jupyter lab which provides an interactive python environment and is also needed to open the demo-notebook at `examples/creapy_demo.ipynb`.
+To install, type
+```bash
+pip install jupyterlab
+```
+in your terminal or
+```bash
+pip3 install jupyterlab
+```
+if you are using macOS. You can open jupyter lab in your terminal by typing
+```bash
+jupyter lab
+```
+## Installation
+
+`creapy` will be available on PyPI once published.
+<!-- 
+```
+pip install creapy
+```
+-->
+Until then, you may clone the repository from git. 
+<!-- For now as creapy is not yet published the git repository can be cloned from Git. -->
+
+Before cloning you should navigate to your desired directory in the terminal like `C:/Users/myusername/<path_where_repo_will_be_cloned_into>`. With the following command you can clone the repository either with ssh
+```bash
+git clone git@git.spsc.tugraz.at:troeck/ti-project-creak.git
+```
+or html 
+```bash
+git clone https://git.spsc.tugraz.at/troeck/ti-project-creak.git
+```
+
+After cloning, a new folder should be present: `C:/Users/myusername/<path_to_creapy_repository>`.
+
+To finally install creapy you need to navigate into the new folder using your terminal (The command `cd <folder>` **c**hanges the **d**irectory to a folder given with `<folder>`). To install `creapy` execute
+```bash
+pip install -e .
+```
+
+<!-- To check if the installation was succesfull you can try to run a python script or a jupyter notebook with:
+```python
+import creapy
+``` -->
+## Disclaimer
+
+Please note that `creapy` will modify your hand-labelled `TextGrid` files in an automated fashion. While the tool should generally only add additional tiers to your `TextGrid`, these new tiers might overpopulate quite soon, especially after messing with the tool a bit and processing whole folders. Make sure to **copy** your files beforhand to assure a backup and the originality of your precious files.
+
+## Basic Usage
+### Classifying an audio file
+
+After you imported `creapy` with
+```python
+import creapy
+```
+you can classify creak on your own audio file calling the function `process_file` with the path to the audio- and respective TextGrid-file
+```python!
+X_test, y_pred, sr = creapy.process_file(
+    audio_path='<path_to_your_audio_file>', 
+    textgrid_path='<path_to_your_textgrid_file>')
+```
+`creapy` will add a new tier to the file at `textgrid_path` containing the detected creak intervals. **Note**: We recommend to work on a copy of your original TextGrid file.
+
+### Choosing different models
+Depending on the speaker you may choose another pre-trained model given by the paramter `gender_model`. Per default, `creapy` uses the model trained on the genders male and female (=`all`). This is an example of the classification using a model trained on female speakers only
+```python!
+X_test, y_pred, sr = creapy.process_file(
+    audio_path='<path_to_your_audio_file>', 
+    textgrid_path='<path_to_your_textgrid_file>',
+    gender_model='female')
+```
+`process_file` returns a `tuple` where the first element (`X_test`) are the calculated feature values for each block, the second element is the calculated creak probability (`y_pred`) and the third element (`sr`) is the samplingrate in `Samples/s`. 
+
+<!-- The calculated creak probability is shown in the following plot:
+
+|![](examples/creapy_creak_probability_example.png 'creak probability')|
+| - |
+|*Creak probability over time (blue) and hand labelled intervals (red)*| -->
+
+<!-- The function `get_time_vector` returns an array containing the timesteps for each block in seconds. -->
+
+The `TextGrid` File written at `textgrid_path` could look like this in Praat:
+
+|![](examples/creapy_creak_example_praat.PNG 'creak probability')|
+| :-: |
+|*Modified TextGrid in Praat with new creak tier*|
+
+
+### Classifying a folder containing multiple audio files
+You can perform the classification on a whole folder using the function `process_folder`.
+```python!
+creapy.process_folder(
+    audio_directory='<path_to_your_audio_directory>',
+    textgrid_directory='<path_to_your_textgrid_directory>')
+```
+
+Here the folder structure is expected to be as follows:
+```
+audio_directory
+├── speaker_1.wav
+├── speaker_2.wav
+├── ...
+└── speaker_n.wav
+
+textgrid_directory
+├── speaker_1.TextGrid
+├── speaker_2.TextGrid
+├── ...
+└── speaker_n.TextGrid
+```
+
+i.e. the maximum folder level is 1 and the filenames for the respective `TextGrid`-files are expected to be the same as for the audio-files. **Note**: The audio directory and textgrid directory can be the same, but it is really important that the names and suffixes are correct.
+
+## Set your configuration/Change parameters
+All of the functions above use the default configuration. If you want to change various parameters you can do so in the configuration file. This file will be saved to `C:/Users/myusername/.creapy/config.yaml` after you **called the function** `set_config` **once**.
+
+```python!
+creapy.set_config()
+```
+A detailed description on the changeable parameters is given in the following table:
+
+| Parameter | Explanation| default value |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --- |
+| `audio_directory`| Path to the directory containing audio files like `C:/Users/myusername/Documents/audio`.| `null`|
+| `textgrid_directory` | Path to the directory containing the respective `TextGrid` files `C:/Users/myusername/Documents/textgrid`. This is optional. It will also work without texgrid files. |`null`|
+| `csv_directory`| Path to a folder where the csv-files containing the classification results should be stored `C:/Users/myusername/Documents/results`.| `null`|
+| `audio_start`| Starttime in seconds where the audiofile should be analysed. |$0$|
+| `audio_end`| endtime in seconds until the audiofile should be analysed (if -1 the file gets processed until the end).|$-1$|
+|`audio_suffix`| suffix of the audio-file(s) (compressed audio formats like `.mp3` are not supported). | `.wav`|
+|`textgrid_suffix`|suffix of the textgrid-file(s).|.TextGrid|
+|`gender_model`|The gender model chosen for creak-classification. Can be `all`, `male` or `female`. We recommend using the `all` model for male speakers and the `female` model for female speakers. |`all`|
+|`tier_name`|The tiername of the new tier with creapy's annotations in the textgrid file.| `creapy`|
+|`block_size`| Classification blocksize in seconds. Smaller blocks are computationally more expensive but achieve a better time resolution. |$0.04$|
+|`hop_size`|Classification hopsize in seconds. Should be in the range of $\frac{1}{4}\texttt{block\_size}\leq\texttt{hop\_size}\leq\frac{1}{2}\texttt{block\_size}$.|$0.01$|
+|`creak_threshold`|Probability-threshold of the classifier where creak is classified. Can be a decimal value between 0 and 1. If you get too many false positives try increasing this value. If a lot of creaky voice gets missed try decreasing it.|$0.75$|
+|`zcr_threshold`|Threshold for the zero-crossing-rate pre-elimination feature. Blocks with a $\text{zcr}\geq\text{zcr\_threshold}$ get eliminated. For female speakers we achieved better results with a higher value ranging around 0.08-0.12. For male speakers a value <0.08 will yield good results. **Note:** This is highly speaker dependent. |$0.08$|
+|`ste_threshold`|Threshold for the short-term-energy pre-elimination feature. This value does not normally need to be changed. It mostly eliminiates blocks of silence or noise. |$1\cdot10^{-5}$|
+
+You can change these parameters in the `config.yaml` file itself or by using the function `set_config`, e.g.
+```python!
+creapy.set_config(block_size=0.05, creak_threshold=0.7)
+```
+If you want to reset the configuration file, you can do so, using the function `reset_config`, e.g.
+```python!
+creapy.reset_config()
+```
+This will set all parameters to their default values as seen in the table [above](#set-your-configurationchange-parameters).
+
+Especially the `zcr_threshold`, `ste_threshold` and `creak_threshold` are parameters which can change the result the most. 
+
+<!-- With the `plot` function those parameters get visualized. 
+```python!
+creapy.plot(X_test, y_pred, sr)
+``` -->
+
+
+### Plotting
+While not a dependency for this tool, `plotly` is needed in order to evoke the plotting function provided by `creapy`:
+```bash
+pip install plotly
+```
+The function call
+```python
+creapy.plot(X_test, y_pred, sr)
+```
+on the example results in this plot
+![](https://i.imgur.com/1nZUR8L.png)
+
+The plot is interactive: one can check specific timeranges and select the features to display. In this way, a good estimate of the values for the parameters can be obtained.
+
+<!-- ## Useful links (TEMP)
+
+If you have a problem with setting up a SSH connection to GitHub the following tutorial should help: 
+
+https://docs.github.com/en/authentication/connecting-to-github-with-ssh
+
+We recommend working in a Jupyter enviroment. If you do not have one yet following link might help you:
+
+https://jupyter.org/install
+
+Python (brauch ma des?)
+
+https://wiki.python.org/moin/BeginnersGuide/Download -->
+
+
+<!-- On Windows, replace the Backslashes `\` or `\\` in your path with single slashes `/` so that you get something like `C:/Users/myusername/Documents/creak` -->
+
+<!-- ### Classify audio file
+You can use the pretrained models to detect creak in your speech signals. For more information on the training process, see [Pretrained Model](#pretrained-model).
+1. change paths as described [above](#set-your-configuration).
+2. run script `xyz.py` and wait; note: takes approx. 3 times as long as the audio file on a computer with processor xyz, ...; so for longer sound files, you need to be patient -->
+
+

creapy/__init__.py

+from .feature_extraction import *
+from .utils import *
+from .model import *
+
+__version__ = '0.0.2'

creapy/config.yaml

+# USER:
+#   audio_directory: null
+#   textgrid_directory: null
+#   csv_directory: null
+#   audio_start: 0
+#   audio_end: -1
+#   audio_suffix: .wav
+#   textgrid_suffix: .TextGrid
+#   gender_model: all
+#   tier_name: creapy
+#   block_size: 0.040 # seconds
+#   hop_size: 0.010 # in seconds
+#   creak_threshold: 0.75
+#   zcr_threshold: 0.08
+#   ste_threshold: 0.00001
+
+DEFAULT: 
+  sample_dir: audio/samples
+  audio_suffix: .wav # file name extension of audio file(s)
+  audio_re: (?P<speakers>[a-zA-Z\d]+)_(?P<single_speaker>\d{3}[a-zA-Z])_.+\.wav # [optional] define regex to match your files
+  re_speaker_group: single_speaker # ??
+
+FEATURE_EXTRACTION: # choose which features should be extracted
+  cpp: false
+  hnr: true
+  jitter: true
+  h1h2: true
+  shimmer: true
+  f0mean: true
+  zcr: true
+  ste: true
+  VALUES: # set parameters/thresholds for the features
+    HNR: 1
+    JITTER: 1
+    H1H2: 1
+    SHIMMER: 1
+    F0MEAN: # fundamental frequency
+      fmin: 60 # pitch range
+      fmax: 500 # pitch range
+    ZCR: 
+      window: hamming
+    STE:
+      window: hamming
+
+CLASSIFICATION: # set parameters for the (random forest) classification
+  impute_strategy: median # mean, most_frequent, null
+  random_state: 42 # int, null
+  test_size: 0.33 # float
+
+MODEL:
+  PREPROCESSING:
+    impute_strategy: median # mean, most_frequent, null
+    impute_at_fit: true
+    test_size: 0.33 # int, Pfloat
+    block_size: 0.040 # in seconds
+    hop_size: 0.010 # in seconds
+    random_state: 42
+    UNVOICED_EXCLUSION:
+      zcr: true
+      ste: true
+      VALUES:
+        ZCR:
+          threshold: 0.08
+          replace_value: 0.0
+          operator: ">="
+          normalize: true
+        STE:
+          threshold: 0.00001
+          replace_value: 0.0
+          operator: "<="
+          normalize: false
+
+  POSTPROCESSING:
+    MAVG:
+      mavg: false
+      VALUES:
+        length: 3
+        mode: same
+    INTERVALS:
+      creak_threshold: 0.75
+      min_creak_length: 0.03
+      max_gap: 0.015
+
+  CLASSIFIER:
+    clf: rfc # rfc, mlp
+    VALUES:
+      RFC:
+        kwargs: 
+          n_estimators: 99
+          random_state: 42 # int, null
+      MLP:
+        kwargs:
+          solver: lbfgs
+          hidden_layer_sizes: [5, 5, 5, 2]
+          random_state: 42
+          max_iter: 500
+    predict_proba: true # true, false
+    target_name: "c"
+  FEATURES:
+    for_classification: ['hnr', 'jitter', 'h1h2', 'shimmer', 'f0mean']
+  model_location: model/training_models/model
+  save_pickle: false
+  target_label: class
+
+
+PRAAT:
+  creak_tier_name: creapy
+  interval_text: c
+  creak_re: (?P<speaker>[a-zA-Z\d]+)-creak
+  creak_fstr: '{}-creak'

creapy/feature_extraction/__init__.py

+from .feature_extraction import calculate_features, calculate_features_for_folder, get_feature_list, WINDOW_MAPPING, blockwise_feature_calculation

creapy/feature_extraction/feature_extraction.py

+from __future__ import annotations
+
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+# import opensmile
+import pandas as pd
+import parselmouth as pm
+import soundfile as sf
+from scipy import interpolate
+from scipy.signal.windows import hamming, hann, kaiser
+from rich.progress import track
+from ..utils import get_config
+
+
+def _cpp(data: np.ndarray, sound: pm.Sound, sr: int,
+         config: Optional[dict] = None) -> float:
+    """Calculates the cepstral peak prominence using praat
+
+    Args:
+        data (np.ndarray): The sound data
+        sr (int): The sampling rate
+        config (dict, optional): The default configuration file. Defaults to None.
+
+    Returns:
+        float: The cepstral peak prominence
+    """
+    if config is None:
+        config = get_config()
+
+    spectrum = sound.to_spectrum()
+    power_cepstrum = pm.praat.call(spectrum, 'To PowerCepstrum')
+
+    *args, = map(config["FEATURE_EXTRACTION"]["VALUES"]["CPP"].get, [
+        "fmin", "fmax", "interpolation", "qmin", "qmax", "trend_type", "fit_method"
+    ])
+
+    cpp = pm.praat.call(power_cepstrum, "Get peak prominence...", *args)
+    return cpp
+
+
+def _h1_h2(data: np.ndarray, sound: pm.Sound, sr: int,
+           config: Optional[dict] = None) -> float:
+    # if config is None:
+    #     config = get_config()
+
+    try:
+        pitch = sound.to_pitch(sound.duration)
+        spectrum = sound.to_spectrum()
+        h1 = pitch.selected_array[0][0]
+        h2 = h1 * 2
+
+        x = np.arange(0, spectrum.nf) * spectrum.df
+        y = np.sqrt(spectrum.values[0]**2 + spectrum.values[1]**2)
+        f = interpolate.interp1d(x, y, 'quadratic')
+        h1_amp = f(h1)
+        h2_amp = f(h2)
+    except:
+        [h1_amp, h2_amp] = [np.nan, np.nan]
+
+    return h1_amp - h2_amp
+
+
+def _hnr(data: np.ndarray, sound: pm.Sound, sr: int,
+         config: Optional[dict] = None) -> float:
+    try:
+        harmonicity = sound.to_harmonicity()
+    except pm.PraatError:
+        hnr = np.nan
+    else:
+        # taken from
+        # https://parselmouth.readthedocs.io/en/stable/examples/batch_processing.html?highlight=harmonicity#Batch-processing-of-files
+        hnr = harmonicity.values[harmonicity.values != -200].mean()
+    return hnr
+
+
+def _jitter(data: np.ndarray, sound: pm.Sound, sr: int,
+            config: Optional[dict] = None) -> float:
+    try:
+        pointProcess = pm.praat.call(
+            sound, "To PointProcess (periodic, cc)", 75, 500)
+        local_jitter = pm.praat.call(
+            pointProcess, "Get jitter (local)", 0, 0, 0.0001, 0.02, 1.3)
+    except:
+        local_jitter = np.nan
+
+    return local_jitter
+
+
+def _shimmer(data: np.ndarray, sound: pm.Sound, sr: int,
+             config: Optional[dict] = None) -> float:
+    try:
+        pointProcess = pm.praat.call(
+            sound, "To PointProcess (periodic, cc)", 75, 500)
+        local_shimmer = pm.praat.call(
+            [sound, pointProcess], "Get shimmer (local)", 0, 0, 0.0001, 0.02, 1.3, 1.6)
+    except:
+        local_shimmer = np.nan
+
+    return local_shimmer
+
+
+def _f0mean(data: np.ndarray, sound: pm.Sound, sr: int,
+            config: Optional[dict] = None) -> float:
+    try:
+        pitch = sound.to_pitch(sound.duration).selected_array[0][0]
+    except:
+        pitch = np.nan
+
+    return pitch
+
+
+def _zcr(data: np.ndarray, sound: pm.Sound, sr: int,
+         config: Optional[dict] = None):
+    """
+    calculates the Zero-Crossing-Rate (ZCR)
+    @param data: buffer signal data with blocklength N in form of an array of shape N data num_blocks
+    @param w: Window with length N
+    @return Zero-Crossing-rate 
+
+    """
+    # if config is None:
+    #     config = get_config()
+    # w = WINDOW_MAPPING[config["FEATURE_EXTRACTION"]
+    #                    ["VALUES"]["ZCR"]["window"]](data.shape[0])
+
+    # sgn = lambda data: 1 if data >= 0 else -1
+    sign_arr = np.sign(data)
+    sign_arr[sign_arr == 0] = 1
+    N = data.shape[0]
+    return 0.5 * np.sum(np.abs(np.diff(sign_arr))) / N
+
+
+def _ste(data: np.ndarray, sound: pm.Sound, sr: int,
+         config: Optional[dict] = None):
+    """
+    calculates the short-term-energy (STE)
+    @param x: buffer signal x with blocklength N in form of an array of shape N x num_blocks
+    @param w: Window with length N, shape (N,)
+    @return short-term-energy
+    """
+    # assert len(w) == x.shape[0], "Dimension Mismatch: Windowlength != blocklength"
+    # assert len(x.shape) == 2, "Signal must be already buffered (2D)"
+    # assert len(w.shape) == 1, "Window must be 1D"
+
+    # if config is None:
+    #     config = get_config()
+    # if w is None:
+    #     w = WINDOW_MAPPING[config["FEATURE_EXTRACTION"]
+    #                        ["VALUES"]["STE"]["window"]](data.shape[0])
+    N = data.shape[0]
+    return np.sum(data ** 2) / N
+    # return np.sum(np.abs(data)) / N
+
+
+def get_feature_list(config: Optional[dict] = None) -> list[str]:
+    if config is None:
+        config = get_config()
+    return [key for key, value in config["FEATURE_EXTRACTION"].items() if value is True]
+
+
+def calculate_features(data: np.ndarray, sr: int,
+                       return_header: bool = False,
+                       config: Optional[dict] = None,
+                       features: Optional[list[str]] = None) -> tuple[np.ndarray, list] | np.ndarray:
+    if config is None:
+        config = get_config()
+    if features is None:
+        features = get_feature_list(config)
+    sound = pm.Sound(values=data, sampling_frequency=sr)
+    result = np.array([FEATURE_MAPPING[feature](data, sound, sr, config=config)
+                      for feature in features])
+
+    return (result, features) if return_header is True else result
+
+
+def blockwise_feature_calculation(data: np.ndarray, sr,
+                                  feature, config: Optional[dict] = None):
+    if config is None:
+        config = get_config()
+
+    sounds = [pm.Sound(values=block, sampling_frequency=sr) for block in data]
+    function = FEATURE_MAPPING[feature]
+    res = []
+    # for block, sound in zip(track(data, description=f"Calculating {feature}"), sounds):
+    #     res.append(function(block, sound, sr))
+    res = [function(block, sound, sr) for block, sound in zip(data, sounds)]
+    return np.array(res)
+
+
+def calculate_features_for_folder(path: str,
+                   file_suffix: str = ".wav",
+                   features=None) -> tuple[pd.DataFrame, pd.Series, list[str]]:
+    files = list(Path(path).glob(f"**/*{file_suffix}"))
+    config = get_config()
+    if features is None:
+        features = get_feature_list(config)
+    feature_matrix = np.array(
+        [calculate_features(*sf.read(file_), config=config,
+                            features=features) for file_ in files]
+    )
+    # TODO put regex in config file
+    target_vector = np.array(
+        [str(x).split('.')[0].split('_')[-1] for x in files])
+    return pd.DataFrame(feature_matrix, columns=features), pd.Series(target_vector)
+
+
+FEATURE_MAPPING = {
+    "cpp": _cpp,
+    "hnr": _hnr,
+    "h1h2": _h1_h2,
+    "jitter": _jitter,
+    "shimmer": _shimmer,
+    "f0mean": _f0mean,
+    "zcr": _zcr,
+    "ste": _ste,
+}
+WINDOW_MAPPING = {
+    "hann": hann,
+    "kaiser": kaiser,
+    "hamming": hamming,
+    "rect": lambda N: np.ones((N)) / N
+}

creapy/model/__init__.py

+from re import S
+from .model import Model, load_model
+from .preprocessing import impute, split_data, buffer
+from .postprocessing import moving_average
+from .classify import process_file, process_folder

creapy/model/classify.py

+from __future__ import annotations
+
+import time
+import warnings
+from functools import partial
+from pathlib import Path
+# from threading import Thread
+from typing import Optional
+import os
+import numpy as np
+import pandas as pd
+from rich.progress import track
+from scipy.signal.windows import hann
+
+from ..feature_extraction import WINDOW_MAPPING
+from ..feature_extraction.feature_extraction import (
+    blockwise_feature_calculation, calculate_features, get_feature_list)
+from ..utils.config import get_config
+from ..utils.helpers import (ThreadWithReturnValue, get_creak_intervals,
+                             get_root, get_time_vector, intervals_to_textgrid,
+                             intervals_to_csv)
+from ..utils.read_wav import read_wav
+from .model import load_model
+from .postprocessing import thresholding
+from .preprocessing import buffer
+
+
+def process_file(audio_path,
+                 textgrid_path: Optional[str] = None,
+                 csv_folder_path: Optional[str] = None,
+                 gender_model: Optional[str] = None):
+    _config = get_config()
+    verbosity = _config['USER']['verbose']
+    start, end = _config['USER']['audio_start'], _config['USER']['audio_end']
+    data, sr = read_wav(audio_path, start=start, end=end)
+
+    w = hann(int(_config['USER']["block_size"] * sr))
+    creak_data_buff = buffer(data, sr, window=w)
+    PREPROCESSING_FEATURES = [key for key, val in _config['MODEL']
+                              ['PREPROCESSING']['UNVOICED_EXCLUSION'].items() if val is True]
+    t = time.time()
+    threads = [ThreadWithReturnValue(target=blockwise_feature_calculation, args=[
+                                     creak_data_buff.T.copy(), sr, feature, _config]) for feature in PREPROCESSING_FEATURES]
+    for thread in threads:
+        thread.start()
+    elimination_chunks = np.array([thread.join() for thread in threads]).T
+
+    preprocessing_values = _config["MODEL"]['PREPROCESSING']['UNVOICED_EXCLUSION']["VALUES"]
+    preprocessing_values['ZCR']['threshold'] = _config['USER']['zcr_threshold']
+    preprocessing_values['STE']['threshold'] = _config['USER']['ste_threshold']
+    
+    thresholds = np.array(
+        [thresholding(series=chunk, **preprocessing_values
+                      [feature.upper()]) for chunk, feature in zip(elimination_chunks.T, PREPROCESSING_FEATURES)]
+    )
+
+    included_indices = thresholds.sum(axis=0) == 0
+
+    features_for_classification = _config["MODEL"]["FEATURES"]["for_classification"]
+
+    t = time.time()
+    threads = [ThreadWithReturnValue(target=blockwise_feature_calculation, args=[
+                                     creak_data_buff.T[included_indices].copy(), sr, feature, _config.copy()]) for feature in features_for_classification]
+    for thread in threads:
+        thread.start()
+    _X_test = np.array([thread.join() for thread in threads]).T
+    # print("time ellapsed:", time.time() - t)
+
+    _X_test = pd.DataFrame(_X_test, columns=features_for_classification,
+                           index=np.argwhere(included_indices).ravel())
+
+    X_test = pd.concat((pd.DataFrame(elimination_chunks,
+                       columns=PREPROCESSING_FEATURES), _X_test), axis=1)
+    y_pred = np.zeros((creak_data_buff.shape[1]))
+    if not any(included_indices):
+        warnings.warn("Did not make classification. Consider setting new values "
+                      "for Zero-Crossing-Rate (zcr) or Short-Term-Energy (ste).")
+        return X_test, y_pred
+
+    # Load model
+    if gender_model is not None:
+        gender_model = gender_model.lower().strip()
+        if gender_model not in ("male", "female", "all"):
+            raise ValueError(
+                f'Gender must be \"male\", \"female\", \"all\" or None is {gender_model}')
+
+    else:
+        gender_model = _config['USER']['gender_model']
+    model_path = get_root() / _config["MODEL"]["model_location"]
+    model_path = (model_path.parent /
+                  (f"{model_path.stem}_{gender_model.upper()}")).with_suffix(".csv")
+    model = load_model(model_path)
+
+    y_pred[included_indices] = model.predict(_X_test)
+
+    if textgrid_path is not None:
+        tier_name = _config['USER']['tier_name']
+        filename_extension = _config['USER']['filename_extension']
+        
+        if filename_extension:
+            _textgrid_path = Path(textgrid_path)
+            new_filename = _textgrid_path.stem + filename_extension + _textgrid_path.suffix
+            result_path = str(_textgrid_path.parent / new_filename)
+        else:
+            result_path = textgrid_path
+
+        intervals = get_creak_intervals(
+            y_pred, get_time_vector(y_pred, sr, start), tgt_intervals=True)
+        intervals_to_textgrid(
+            intervals=intervals,
+            textgrid_path=textgrid_path,
+            # result_path=textgrid_path if textgrid_dst is None else textgrid_dst,
+            result_path=result_path,
+            tier_name=tier_name,
+            verbose=verbosity
+        )
+
+    if csv_folder_path is not None:
+        intervals = get_creak_intervals(
+            y_pred, get_time_vector(y_pred, sr, start), tgt_intervals=True)
+        intervals_to_csv(
+            intervals=intervals,
+            csv_dst=csv_folder_path
+        )
+        pass
+    return X_test, y_pred, sr
+
+
+def process_folder(audio_directory: Optional[str] = None,
+                   textgrid_directory: Optional[str] = None,
+                   csv_directory: Optional[str] = None):
+    _config = get_config()
+
+    if audio_directory is None:
+        audio_directory = _config['USER']['audio_directory']
+    if not os.path.isdir(audio_directory):
+        raise ValueError(
+            f"Invalid path, given audio-directory \"{audio_directory}\" is not a directory.")
+    if textgrid_directory is None:
+        textgrid_directory = _config['USER']['textgrid_directory']
+    if textgrid_directory:
+        if not os.path.isdir(textgrid_directory):
+            raise ValueError(
+                f"Invalid path, given textgrid-directory \"{textgrid_directory}\" is not a directory.")
+
+    if csv_directory is None:
+        csv_directory = _config['USER']['csv_directory']
+    if csv_directory:
+        if not os.path.isdir(csv_directory):
+            raise ValueError(
+                f"Invalid path, given csv_directory \"{csv_directory}\" is not a directory.")
+
+    audio_suffix = _config['USER']['audio_suffix']
+    wav_files = list(Path(audio_directory).glob(f'**/*{audio_suffix}'))
+
+    if textgrid_directory:
+        wav_tg_map = dict()
+        textgrid_path = Path(textgrid_directory)
+        textgrid_suffix = _config['USER']['textgrid_suffix']
+        for wav_file in wav_files:
+            wav_tg_map[wav_file] = (
+                textgrid_path / wav_file.stem).with_suffix(textgrid_suffix)
+
+    # for wav_file in track(wav_files, "Processing folder..."):
+    for wav_file in wav_files:
+        process_file(
+            wav_file,
+            textgrid_path=wav_tg_map[wav_file] if textgrid_directory else None,
+            csv_folder_path=csv_directory if csv_directory else None
+        )

creapy/model/model.py

+from __future__ import annotations
+
+import os
+import pickle
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn.impute import SimpleImputer
+
+from ..utils import get_config, get_root
+from .postprocessing import moving_average
+from .preprocessing import impute
+
+
+class NotFittedError(Exception):
+    pass
+
+class Model:
+    """The Model for creaky voice classification.
+    """
+    def __init__(self):
+        self._config = get_config()["MODEL"]
+        self._X_train: pd.DataFrame
+        self._y_train: pd.Series
+        self._imputer: SimpleImputer
+        self._features = self._config["FEATURES"]["for_classification"]
+        self._fitted = False
+        _clf = self._config["CLASSIFIER"]["clf"]
+        self._clf = clfs[_clf](
+            **self._config["CLASSIFIER"]["VALUES"][_clf.upper()]["kwargs"])
+
+    def fit(self, X_train: pd.DataFrame, y_train: pd.DataFrame):
+        """Function to fit the model with training data.
+
+        Args:
+            X_train (pd.DataFrame): Features of training data.
+            y_train (pd.Dataframe): Targets of training data (creak, no-creak).
+        """
+        if isinstance(y_train, pd.DataFrame):
+            y_train = y_train.to_numpy()
+        if self._config["PREPROCESSING"]["impute_at_fit"] is True:
+            self._X_train, self._imputer = impute(
+                X_train=X_train.loc[:, self._features], return_imputer=True)
+        else:
+            self._X_train = X_train
+        self._y_train = pd.Series(y_train, name=self._config["target_label"])
+        self._clf.fit(
+            self._X_train.loc[:, self._features], self._y_train.ravel())
+        self._fitted = True
+
+    def predict(self, X_test: pd.DataFrame, predict_proba: bool=None) -> np.ndarray:
+        """Predicts the given features. 
+
+        Args:
+            X_test (pd.DataFrame): Features to be predicted.
+            predict_proba (bool, optional): If `True` the likelihood to be creak will be returned, else the predicted target. 
+            Defaults to None.
+
+        Returns:
+            np.ndarray: Predicted targets, or probability of creak.
+        """
+        self._config = get_config()["MODEL"]
+        if predict_proba is not None:
+            assert isinstance(predict_proba, bool)
+        else:
+            predict_proba = self._config["CLASSIFIER"]["predict_proba"]
+        if hasattr(self, "_imputer"):
+            X_test = pd.DataFrame(self._imputer.transform(
+                X_test.loc[:, self._features]), columns=self._X_train.columns, index=X_test.index)
+        if predict_proba is True:
+            _target_index = np.argwhere(
+                self._clf.classes_ == self._config["CLASSIFIER"]["target_name"]).item()
+            y_pred = self._clf.predict_proba(X_test[self._features])[
+                :, _target_index].flatten()
+            if self._config["POSTPROCESSING"]["MAVG"]["mavg"] is True:
+                length, mode = map(
+                    self._config["POSTPROCESSING"]["MAVG"]["VALUES"].get, ("length", "mode"))
+                y_pred = moving_average(y_pred, length, mode)
+        else:
+            y_pred = self._clf.predict(X_test[self._features])
+
+        return y_pred
+
+    def save(self, filepath: str = None):
+        """Saves a fitted model to the given location as csv file
+
+        Args:
+            filepath (str, optional): Destination path of saved model. Defaults to None.
+
+        Raises:
+            NotFittedError: If the model is not yet fitted it can not be saved.
+        """
+        if self._fitted is False:
+            raise NotFittedError(
+                "Can't save model because it is not fitted yet")
+        _config = get_config()
+        if filepath is None:
+            # filepath without suffix!
+            filepath = get_root() / _config["MODEL"]["model_location"]
+        else:
+            filepath = Path(filepath)
+        # merge X_train and y_train
+        _X_combined = pd.concat((self._X_train, self._y_train), axis=1)
+        _X_combined.to_csv((filepath.parent /
+                           (filepath.name)).with_suffix(".csv"), index=False)
+        if _config["MODEL"]["save_pickle"] is True:
+            with open(filepath.parent / (filepath.name + '.pickle'), "wb") as f:
+                pickle.dump(self, f)
+
+
+def load_model(filepath: str = None) -> Model:
+    """Loads a already fitted model from a csv file.    
+
+    Args:
+        filepath (str, optional): Location of the model csv file. Defaults to None.
+
+    Returns:
+        Model: Fitted Model for creak classification.
+    """
+    if filepath is None:
+        filepath = get_root() / get_config()["MODEL"]["model_location"]
+        filepath = (filepath.parent / (filepath.name)).with_suffix(".csv")
+    else:
+        filepath = Path(filepath)
+
+    if filepath.suffix == ".csv":
+        _config = get_config()
+        _X_combined = pd.read_csv(filepath)
+        model = Model()
+        _target_column = _config["MODEL"]["target_label"]
+        _feature_columns = _config["MODEL"]["FEATURES"]["for_classification"]
+        _X_train, _y_train = _X_combined[_feature_columns], _X_combined[_target_column]
+        model.fit(_X_train, _y_train)
+        return model
+    if filepath.suffix == ".pickle":
+        with open(filepath, "rb") as f:
+            return pickle.load(f)
+
+
+clfs = {
+    "rfc": RandomForestClassifier,
+    "mlp": MLPClassifier
+}

creapy/model/postprocessing.py

+from __future__ import annotations
+
+import numpy as np
+import operator as operator_
+
+def thresholding(
+                 series: np.ndarray, 
+                 threshold: float,
+                 y: np.ndarray = None, 
+                 replace_value: float = 0.0,
+                 operator: str = ">=",
+                 normalize: bool = False):
+    
+    _operators = {
+        ">": operator_.gt,
+        ">=": operator_.ge,
+        "<": operator_.lt,
+        "<=": operator_.le
+    }
+    
+    # assert y.shape == series.shape
+    if normalize is True: series /= max(series)
+    if y is not None:
+        y[_operators[operator](series, threshold)] = replace_value
+        return y
+    else:
+        return _operators[operator](series, threshold)
+    
+
+def moving_average(series: np.ndarray, N: int = 10, mode: str = "same"):
+    if len(series) < N: 
+        return series
+    return np.convolve(series, np.ones((N)) / N, mode=mode)
+
+    
\ No newline at end of file

creapy/model/preprocessing.py

+from __future__ import annotations
+
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from sklearn.impute import SimpleImputer
+from sklearn.model_selection import train_test_split
+
+from ..utils import get_config
+
+
+def impute(X_train: pd.DataFrame,
+           X_test: Optional[pd.DataFrame] = None,
+           return_imputer: bool = False):
+
+    config_ = get_config()["MODEL"]["PREPROCESSING"]
+    impute_strategy = config_["impute_strategy"]
+    _imputer = SimpleImputer(strategy=impute_strategy)
+    if X_test is None:
+        res = pd.DataFrame(_imputer.fit_transform(
+            X_train), columns=X_train.columns)
+    else:
+        X_train = pd.DataFrame(_imputer.fit_transform(
+            X_train), columns=X_train.columns)
+        X_test = pd.DataFrame(_imputer.transform(X_test),
+                              columns=X_train.columns)
+        res = X_train, X_test
+    return (res, _imputer) if return_imputer is True else res
+
+
+def split_data(X: pd.DataFrame, y: pd.Series):
+    config_ = get_config()["MODEL"]["PREPROCESSING"]
+    impute_strategy = config_["impute_strategy"]
+    test_size = config_["test_size"]
+    if impute_strategy is not None:
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=test_size, random_state=config_["random_state"])
+        X_train, X_test = impute(X_train, X_test)
+    else:
+        _tmp = X.copy()
+        _tmp["label"] = y
+        _tmp.dropna(inplace=True)
+        _X_drop = _tmp.drop(["label"], axis=1)
+        _y_drop = _tmp["label"]
+        X_train, X_test, y_train, y_test = train_test_split(
+            _X_drop, _y_drop, test_size=test_size, random_state=config_["random_state"])
+        del _tmp, _X_drop, _y_drop
+
+    return X_train, X_test, y_train, y_test
+
+
+def buffer(x, sr, opt: str = "nodelay", window=None):
+    """
+    Buffer signal vector into matrix of data frames
+
+    x: Signal
+    N: block size
+    OL: overlap
+    window: predefined window
+
+    * works only for 1D Signals!
+    """
+
+    config_ = get_config()["USER"]
+    N = int(config_["block_size"] * sr)
+    R = int(config_["hop_size"] * sr)
+
+    if window is None:
+        window = np.ones(N)
+
+    assert len(window) == N, "windowlength does not match blocksize N"
+    n = len(x)
+    OL = int(N - R)
+    if opt == 'nodelay':
+        assert n >= OL, "in 'nodelay' mode, len(x) must be OL or longer"
+        n_seg = int(np.ceil((n - N) / R + 1))
+        # print('num_seg:', n_seg)
+    else:
+        n_seg = int(np.ceil(n / R))
+        x = np.concatenate([np.zeros(OL), x.squeeze()])
+
+    res = np.zeros((N, n_seg))
+    for i in range(n_seg - 1):
+        res[:, i] = x[i * R: i * R + N] * window
+
+    i = n_seg - 1
+    # last block
+    nLast = x[i * R:].size
+    res[range(nLast), n_seg - 1] = x[i * R:]
+    return res

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+7.76682956050803,0.038069882417918546,-0.00011166242848222247,0.07646869226454797,75.69584360930206,c
+6.995947058788982,0.060005037181075846,-1.1367361371856936e-05,0.18665722342437543,599.5601816326141,c
+23.843196396346862,0.006379161600897725,-0.000378839519680307,0.061546210377920414,159.45198245927656,no-c
+-4.3237621106906055,0.01905211944152302,0.0,0.07646869226454797,0.0,c?
+0.22686116090162445,0.01905211944152302,0.0,0.07646869226454797,0.0,c

creapy/model/update_model.py

+from __future__ import annotations
+
+import creapy
+
+if __name__ == "__main__":
+    print("Extracting features..")
+    _config = creapy.get_config()
+    X, y = creapy.calculate_features_for_folder("/home/creaker/tip/audio/samples_new",
+                                 features=_config["MODEL"]["FEATURES"]["for_classification"])
+    
+    print("Fitting model..")
+    model = creapy.Model()
+    model.fit(X, y)
+    model.save()

creapy/user_config.yaml

+audio_directory: null
+textgrid_directory: null
+csv_directory: null
+audio_start: 0
+audio_end: -1
+audio_suffix: .wav
+filename_extension: 
+textgrid_suffix: .TextGrid
+gender_model: all
+tier_name: creapy
+verbose: true
+block_size: 0.040 # seconds
+hop_size: 0.010 # in seconds
+creak_threshold: 0.75
+zcr_threshold: 0.08
+ste_threshold: 0.00001

creapy/utils/__init__.py

+from .text_grid_to_intervals import read_textgrid, generate_sample_wavs
+from .config import get_config, get_user_config, set_config, reset_config, CONFIG_DIR
+from .read_wav import read_wav
+from .helpers import *
+from .evaluation import evaluation_metrics, evaluate
+from .plot import plot

creapy/utils/config.py

+from __future__ import annotations
+
+import yaml
+import ruamel.yaml
+from os.path import isfile
+from pathlib import Path
+import sys
+_RELATIVE_PATH_TO_CONFIG = "../config.yaml"
+_RELATIVE_PATH_TO_USER_CONFIG = "../user_config.yaml"
+_CONFIG_DIR = Path(__file__).parent / _RELATIVE_PATH_TO_CONFIG
+_USER_CONFIG_DIR = Path(__file__).parent / _RELATIVE_PATH_TO_USER_CONFIG
+
+USER_CONFIG_DIR = Path("~/.creapy/config.yaml").expanduser()
+
+CONFIG_DIR = str(USER_CONFIG_DIR)
+
+yaml_str = """\
+audio_directory: null # Path to the directory containing audio files
+textgrid_directory: null # Path to the directory containing the respective textgrid files
+csv_directory: null # Path to a folder where the csv-files containing the classification results should be stored
+audio_start: 0 # starttime in seconds where the audiofile should be analysed
+audio_end: -1 # endtime in seconds until the audiofile should be analysed
+audio_suffix: .wav # suffix of the audio-file(s)
+filename_extension: null # string to append to the original textgrid filename. Creates a new file with the corresponding name.
+textgrid_suffix: .TextGrid # suffix of the textgrid-file(s)
+gender_model: all # The gender model chosen for creak-classification. Can be all, male or female
+tier_name: creapy # tiername chosen creapy's annotations in praat
+verbose: true # Verbosity of the tool
+block_size: 0.04 # classification blocksize in seconds
+hop_size: 0.01 # classification hopsize in seconds
+creak_threshold: 0.75 # probability-threshold of the classifies where creak is classified. Can be a decimal value between 0 and 1
+zcr_threshold: 0.08 # Threshold for the zero-crossing-rate pre-elimination feature
+ste_threshold: 1.0e-05 # Threshold for the short-term-energy crossing rate pre-elimination feature
+"""
+
+
+def get_config() -> dict:
+    """
+    returns the configuration file as a dictionary
+
+    Returns:
+        dict: the configuration
+    """
+
+    with open(_CONFIG_DIR) as config_file:
+        config: dict = ruamel.yaml.safe_load(config_file.read())
+
+    with open(_USER_CONFIG_DIR) as user_config_file:
+        _user_config: dict = ruamel.yaml.safe_load(user_config_file.read())
+        
+    config['USER'] = _user_config
+    
+    if not isfile(USER_CONFIG_DIR):
+        return config
+
+    with open(USER_CONFIG_DIR) as user_config_file:
+        user_config: dict = ruamel.yaml.safe_load(user_config_file.read())
+
+    for key in user_config.keys():
+        if key not in config['USER']:
+            raise ValueError(f'Invalid key found in user config: {key}')
+
+    config['USER'].update(user_config)
+    return config
+
+
+def get_user_config() -> dict:
+    return get_config()['USER']
+
+
+def set_config(**kwargs) -> None:
+    _default_config: dict = get_config()['USER']
+    for key in kwargs.keys():
+        if key not in _default_config:
+            raise ValueError(
+                f"key \"{key}\" can't be set in config, possible keys: {list(_default_config.keys())}")
+
+    _default_config.update(kwargs)
+    ruamel_yaml = ruamel.yaml.YAML()
+    code = ruamel_yaml.load(yaml_str)
+    code.update(_default_config)
+    USER_CONFIG_DIR.parent.mkdir(parents=True, exist_ok=True)
+    with open(USER_CONFIG_DIR, "w") as user_config_file:
+        ruamel_yaml.dump(code, user_config_file)
+
+
+def reset_config() -> None:
+    with open(_USER_CONFIG_DIR) as cfg_file:
+        _default_config: dict = ruamel.yaml.safe_load(cfg_file.read())
+    set_config(**_default_config)

creapy/utils/evaluation.py

+from __future__ import annotations
+
+import numpy as np
+from pandas import Series
+import tgt
+from datetime import datetime
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
+
+if __name__ == "__main__":  # Todo
+    from creapy import get_config
+else:
+    try:
+        from ..utils import get_config
+    except ImportError as ie:
+        print(ie)
+
+
+# %%
+
+def evaluation_metrics(y_true, y_pred):
+    assert all((isinstance(y_pred, (np.ndarray, Series)),
+               isinstance(y_true, (np.ndarray, Series))))
+    assert y_pred.shape == y_true.shape
+    _accuracy = accuracy_score(y_true, y_pred)
+    recall = recall_score(y_true, y_pred, average=None)
+    f1 = f1_score(y_true, y_pred, average=None)
+    precision = precision_score(y_true, y_pred, average=None)
+    print(
+        f"""accuracy: {_accuracy}
+        recall: {recall}
+        f1: {f1},
+        precision: {precision}""")
+    return recall, f1, precision, confusion_matrix(y_true, y_pred)
+
+
+class CreakInterval(tgt.core.Interval):
+
+    def __init__(self, start_time, end_time, delta_start, delta_end, text=''):
+        super().__init__(start_time, end_time, text)
+        self.delta_start = delta_start
+        self.delta_end = delta_end
+
+    def __repr__(self):
+        return u'Interval({0}, {1}, "{2}")'.format(self.start_time, self.end_time, self.text)
+
+
+def evaluate(textgrid, tier_name_true, tier_name_creapy, boundary_tier_name=None,
+             tier_name_evaluation=None):
+    """Only possible as a function inside creapy module!"""
+
+    TEXTGRID_PATH = textgrid
+    OWN_TIER_NAME = tier_name_true
+    OUTFILE = None
+    CREAPY_TIER_NAME = tier_name_creapy
+    # INTERVAL_LABEL = "c"
+    EVALUATION_TIER_NAME = tier_name_evaluation if tier_name_evaluation is not None else "creapy-evaluation"
+    for encoding in ("utf-8", "utf-16"):
+        try:
+            tg = tgt.io.read_textgrid(TEXTGRID_PATH, encoding=encoding)
+        except UnicodeDecodeError as e:
+            print(f"Error occured reading textfile:\n\n{e}")
+        else:
+            break
+
+    own_tier: tgt.core.Tier = tg.get_tier_by_name(OWN_TIER_NAME)
+    creapy_tier: tgt.core.Tier = tg.get_tier_by_name(CREAPY_TIER_NAME)
+    # Grausliche loesung im moment
+    t_min = max(own_tier.intervals[0].start_time,
+                creapy_tier.intervals[0].start_time)
+    t_max = min(own_tier.intervals[-1].end_time,
+                creapy_tier.intervals[-1].end_time)
+
+    own_tier = tgt.core.IntervalTier(objects=own_tier.get_annotations_between_timepoints(
+        start=t_min, end=t_max, left_overlap=True, right_overlap=True))
+    creapy_tier = tgt.core.IntervalTier(objects=creapy_tier.get_annotations_between_timepoints(
+        start=t_min, end=t_max, left_overlap=True, right_overlap=True))
+    if boundary_tier_name is not None:
+        boundary_tier: tgt.core.IntervalTier = tg.get_tier_by_name(
+            boundary_tier_name)
+        # creapy_tier = tgt.core.IntervalTier(objects=[
+        #     interval for interval in creapy_tier.intervals if not any(map(lambda x: x.text == "###" or x.text == "", boundary_tier.get_annotations_between_timepoints(
+        #         start=interval.start_time, end=interval.end_time, left_overlap=True, right_overlap=True
+        #     )))
+        # ])
+
+        in_boundary = []
+        for interval in creapy_tier.intervals:
+            ols = boundary_tier.get_annotations_between_timepoints(
+                start=interval.start_time, end=interval.end_time, left_overlap=True,
+                right_overlap=True
+            )
+            if (not all(map(lambda x: x.text in ("###", "SIL"), ols))) and bool(ols):
+                in_boundary.append(interval)
+        creapy_tier = tgt.core.IntervalTier(objects=in_boundary)
+        in_boundary = []
+        for interval in own_tier.intervals:
+            ols = boundary_tier.get_annotations_between_timepoints(
+                start=interval.start_time, end=interval.end_time, left_overlap=True,
+                right_overlap=True
+            )
+            if (not all(map(lambda x: x.text in ("###", "SIL"), ols))) and bool(ols):
+                in_boundary.append(interval)
+        own_tier = tgt.core.IntervalTier(objects=in_boundary)
+
+        # print([interval for interval in creapy_tier.intervals if not any(map(lambda x: x.text == "###" or x.text == "", boundary_tier.get_annotations_between_timepoints(
+        #         start=interval.start_time, end=interval.end_time, left_overlap=True, right_overlap=True])
+
+        # return np.nan, np.nan, np.nan
+    overlaps = tgt.util.get_overlapping_intervals(
+        own_tier,
+        creapy_tier,
+    )
+    own_tier_copy = own_tier.get_copy_with_same_intervals_merged()
+    creapy_tier_copy = creapy_tier.get_copy_with_same_intervals_merged()
+    # %%
+    tps = [ol for ol in overlaps if ol.text in ("c+c")]
+    # %%
+    # print([ol.text for ol in overlaps if ol.text != "c+c"])
+    out_intervals = []
+    tp_intervals = []
+    fp_intervals = []
+    TRUE_POSITIVE_LABELS = ("c+c", "c?+c")
+    FALSE_POSITIVE_LABELS = ("no-c+c")
+    # %%
+    for ol in overlaps:
+        if ol.text in TRUE_POSITIVE_LABELS:
+            annot_creapy = creapy_tier.get_annotations_between_timepoints(
+                ol.start_time, ol.end_time, left_overlap=True, right_overlap=True)[0]
+            annot_own = own_tier.get_annotations_between_timepoints(
+                ol.start_time, ol.end_time, left_overlap=True, right_overlap=True)[0]
+            delta_start = annot_creapy.start_time - annot_own.start_time
+            delta_end = annot_creapy.end_time - annot_own.end_time
+            delta_length = delta_start - delta_end
+            tp_intervals.append(CreakInterval(
+                ol.start_time, ol.end_time, delta_start, delta_end, text="TP"))
+
+        elif ol.text in FALSE_POSITIVE_LABELS:
+            fp_intervals.append(ol)
+
+    # %%
+    # Delete overlaps
+    for ol_interval in overlaps:
+        own_tier_copy.delete_annotations_between_timepoints(
+            ol_interval.start_time, ol_interval.end_time, left_overlap=True, right_overlap=True
+        )
+        creapy_tier_copy.delete_annotations_between_timepoints(
+            ol_interval.start_time, ol_interval.end_time, left_overlap=True, right_overlap=True
+        )
+    # %%
+    own_tier_creak_only = tgt.core.Tier(
+        objects=own_tier_copy.get_annotations_with_text(pattern="c"))
+    creapy_tier_creak_only = tgt.core.Tier(
+        objects=creapy_tier_copy.get_annotations_with_text(pattern="c"))
+
+    # %%
+    eval_tier = tgt.core.IntervalTier(name=EVALUATION_TIER_NAME)
+    for tp in tp_intervals:
+        t0_start = tp.start_time - abs(tp.delta_start)
+        t0_end = tp.end_time + abs(tp.delta_end)
+        try:
+            eval_tier.add_annotation(tp)
+        except ValueError as e:
+            eval_tier.delete_annotations_between_timepoints(
+                tp.start_time, tp.end_time, left_overlap=True, right_overlap=True)
+            try:
+                eval_tier.add_annotation(tp)
+            except ValueError as e:
+                # print(e)
+                pass
+        try:
+            eval_tier.add_annotation(tgt.core.Interval(
+                start_time=t0_start, end_time=tp.start_time, text="+" if tp.delta_start > 0 else "-"))
+
+        except ValueError as e:
+            # print(e)
+            pass
+
+        try:
+            eval_tier.add_annotation(tgt.core.Interval(
+                start_time=tp.end_time, end_time=t0_end, text="+" if tp.delta_end < 0 else "-"))
+        except ValueError as e:
+            # print(e)
+            pass
+    # %%
+    for fns in own_tier_creak_only.annotations:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fns.start_time, fns.end_time, text="FN"))
+        except ValueError as e:
+            print(e)
+
+    for fps in creapy_tier_creak_only.annotations:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fps.start_time, fps.end_time, text="FP"))
+        except ValueError as e:
+            print(e)
+
+    for fps in fp_intervals:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fps.start_time, fps.end_time, text="FP"))
+        except ValueError as e:
+            eval_tier.delete_annotations_between_timepoints(
+                fps.start_time, fps.end_time, left_overlap=True, right_overlap=True)
+            try:
+                eval_tier.add_interval(tgt.core.Interval(
+                    fps.start_time, fps.end_time, text="FP"))
+            except ValueError as e:
+                print(e)
+
+    # %%
+
+    TP = len(tp_intervals)
+    FP = len(fp_intervals) + len(creapy_tier_creak_only)
+    FN = len(own_tier_creak_only)
+
+    F1 = TP / (TP + (FP + FN) / 2) if (TP + (FP + FN) / 2) != 0 else np.nan
+    Precision = TP / (TP + FP) if (TP + FP) != 0 else np.nan
+    Recall = TP / (TP + FN) if (TP + FP) != 0 else np.nan
+
+    tg.add_tier(eval_tier)
+    if tier_name_evaluation is not None:
+        tgt.io.write_to_file(tg, TEXTGRID_PATH, encoding="utf-16")
+
+    return F1, Precision, Recall, TP, FP, FN
+
+
+def main():
+    TEXTGRID_PATH = "/home/creaker/tip/stateofgrass/GRASS/004M024F/004M024F_HM2_HM1_CS_001_creak.TextGrid"
+    OWN_TIER_NAME = "024F-creak"
+    OUTFILE = "/home/creaker/tip/results/evaluation.txt"
+    CREAPY_TIER_NAME = "024F-creapy-F"
+    # INTERVAL_LABEL = "c"
+    EVALUATION_TIER_NAME = "creapy_evaluation"
+
+    # %%
+    for encoding in ("utf-8", "utf-16"):
+        try:
+            tg = tgt.io.read_textgrid(TEXTGRID_PATH, encoding=encoding)
+        except UnicodeDecodeError as e:
+            print(f"Error occured reading textfile:\n\n{e}")
+        else:
+            break
+
+    own_tier: tgt.core.Tier = tg.get_tier_by_name(OWN_TIER_NAME)
+    creapy_tier: tgt.core.Tier = tg.get_tier_by_name(CREAPY_TIER_NAME)
+    # Grausliche loesung im moment
+    t_min = max(own_tier.intervals[0].start_time,
+                creapy_tier.intervals[0].start_time)
+    t_max = min(own_tier.intervals[-1].end_time,
+                creapy_tier.intervals[-1].end_time)
+
+    own_tier = tgt.core.IntervalTier(objects=own_tier.get_annotations_between_timepoints(
+        start=t_min, end=t_max, left_overlap=True, right_overlap=True))
+    creapy_tier = tgt.core.IntervalTier(objects=creapy_tier.get_annotations_between_timepoints(
+        start=t_min, end=t_max, left_overlap=True, right_overlap=True))
+    overlaps = tgt.util.get_overlapping_intervals(
+        own_tier,
+        creapy_tier,
+    )
+    eval_tier = tgt.core.IntervalTier(name="creapy-evaluation")
+    own_tier_copy = own_tier.get_copy_with_same_intervals_merged()
+    creapy_tier_copy = creapy_tier.get_copy_with_same_intervals_merged()
+    # %%
+    tps = [ol for ol in overlaps if ol.text in ("c+c")]
+    # %%
+    print([ol.text for ol in overlaps if ol.text != "c+c"])
+    out_intervals = []
+    tp_intervals = []
+    fp_intervals = []
+    TRUE_POSITIVE_LABELS = ("c+c", "c?+c")
+    FALSE_POSITIVE_LABELS = ("no-c+c")
+    # %%
+    for ol in overlaps:
+        if ol.text in TRUE_POSITIVE_LABELS:
+            annot_creapy = creapy_tier.get_annotations_between_timepoints(
+                ol.start_time, ol.end_time, left_overlap=True, right_overlap=True)[0]
+            annot_own = own_tier.get_annotations_between_timepoints(
+                ol.start_time, ol.end_time, left_overlap=True, right_overlap=True)[0]
+            delta_start = annot_creapy.start_time - annot_own.start_time
+            delta_end = annot_creapy.end_time - annot_own.end_time
+            delta_length = delta_start - delta_end
+            tp_intervals.append(CreakInterval(
+                ol.start_time, ol.end_time, delta_start, delta_end, text="TP"))
+
+        elif ol.text in FALSE_POSITIVE_LABELS:
+            fp_intervals.append(ol)
+
+    # %%
+    # Delete overlaps
+    for ol_interval in overlaps:
+        own_tier_copy.delete_annotations_between_timepoints(
+            ol_interval.start_time, ol_interval.end_time, left_overlap=True, right_overlap=True
+        )
+        creapy_tier_copy.delete_annotations_between_timepoints(
+            ol_interval.start_time, ol_interval.end_time, left_overlap=True, right_overlap=True
+        )
+    # %%
+    own_tier_creak_only = tgt.core.Tier(
+        objects=own_tier_copy.get_annotations_with_text(pattern="c"))
+    creapy_tier_creak_only = tgt.core.Tier(
+        objects=creapy_tier_copy.get_annotations_with_text(pattern="c"))
+
+    # %%
+    eval_tier = tgt.core.IntervalTier(name="creapy-evaluation")
+    for tp in tp_intervals:
+        t0_start = tp.start_time - abs(tp.delta_start)
+        t0_end = tp.end_time + abs(tp.delta_end)
+        try:
+            eval_tier.add_annotation(tp)
+        except ValueError as e:
+            eval_tier.delete_annotations_between_timepoints(
+                tp.start_time, tp.end_time, left_overlap=True, right_overlap=True)
+            try:
+                eval_tier.add_annotation(tp)
+            except ValueError as e:
+                print(e)
+        try:
+            eval_tier.add_annotation(tgt.core.Interval(
+                start_time=t0_start, end_time=tp.start_time, text="+" if tp.delta_start > 0 else "-"))
+
+        except ValueError as e:
+            print(e)
+            pass
+
+        try:
+            eval_tier.add_annotation(tgt.core.Interval(
+                start_time=tp.end_time, end_time=t0_end, text="+" if tp.delta_end < 0 else "-"))
+        except ValueError as e:
+            print(e)
+            pass
+    # %%
+    for fns in own_tier_creak_only.annotations:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fns.start_time, fns.end_time, text="FN"))
+        except ValueError as e:
+            print(e)
+
+    for fps in creapy_tier_creak_only.annotations:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fps.start_time, fps.end_time, text="FP"))
+        except ValueError as e:
+            print(e)
+
+    for fps in fp_intervals:
+        try:
+            eval_tier.add_interval(tgt.core.Interval(
+                fps.start_time, fps.end_time, text="FP"))
+        except ValueError as e:
+            eval_tier.delete_annotations_between_timepoints(
+                fps.start_time, fps.end_time, left_overlap=True, right_overlap=True)
+            try:
+                eval_tier.add_interval(tgt.core.Interval(
+                    fps.start_time, fps.end_time, text="FP"))
+            except ValueError as e:
+                print(e)
+
+    # %%
+
+    TP = len(tp_intervals)
+    FP = len(fp_intervals) + len(creapy_tier_creak_only)
+    FN = len(own_tier_creak_only)
+    F1 = TP / (TP + (FP + FN) / 2)
+    Precision = TP / (TP + FP)
+    Recall = TP / (TP + FN)
+    # %%
+    tg.add_tier(eval_tier)
+    # tgt.io.write_to_file(tg, TEXTGRID_PATH, encoding="utf-16")
+    # %%
+    own_tier_creak_only
+    # %%
+    print(
+        f"""True Positives (TP):\t{TP}
+    False Positives (FP):\t{FP}
+    False Negatives (FN):\t{FN}
+    F1-Score: {F1:.3f}
+    Precision: {Precision: .3f}
+    Recall: {Recall: .3f}
+    """
+    )
+
+    # %%
+    config = get_config()
+    _ste = config["MODEL"]["POSTPROCESSING"]["NON_MODAL_EXCLUSION"]["VALUES"]["STE"]["threshold"]
+    _zcr = config["MODEL"]["POSTPROCESSING"]["NON_MODAL_EXCLUSION"]["VALUES"]["ZCR"]["threshold"]
+    _block_size = config["MODEL"]["PREPROCESSING"]["block_size"]
+    _hop_size = config["MODEL"]["PREPROCESSING"]["hop_size"]
+    _intervals = config["MODEL"]["POSTPROCESSING"]["INTERVALS"]
+
+    # %%
+
+    content = f"""{datetime.now().strftime(" [%d/%m/%Y, %H:%M:%S] ").center(120, "-")}
+
+    PRAAT STUFF:
+        Textgrid File: {TEXTGRID_PATH}
+        Own Tier: {OWN_TIER_NAME}
+        Creapy Tier: {CREAPY_TIER_NAME}
+
+    THRESHOLDS:
+        STE: {_ste}
+        ZCR: {_zcr}
+        creak_threshold: {_intervals["creak_threshold"]}
+
+    GAPS:
+        min_creak_length: {_intervals["min_creak_length"]}
+        max_gap: {_intervals["max_gap"]}
+
+    EVALUATION METRICS:
+        F1: {F1:.3f}
+        Precision: {Precision: .3f}
+        Recall: {Recall: .3f}
+        True Positives: {TP}
+        False Positives: {FP}
+        False Negatives: {FN}
+
+    """
+    with open(OUTFILE, "a") as f:
+        f.write(content)
+        f.write("-" * 120 + '\n')
+
+
+if __name__ == "__main__":
+    main()