Update README.rst

small improvements to readability

Author: derNarr

README.rst

@@ -14,50 +14,33 @@ Currently the package supports the following languages:
    - German
    - English
 
-Version 2.0.0 and later
------------------------
-From version 2.0.0 we are relying on the new segment-to-gesture API introduced
-in VTL 2.3 and use the JD3.speaker instead of the JD2.speaker.
-
-Old version 1.1.0
------------------
-The original version of this tool is based on the work and on the Matlab code
-on Yingming Gao. This can be viewed by checking out the tag ``1.1.0``.
 
-The overall logic is in ``create_corpus.py`` which executes the appropriate
-functions from top to bottom. The functions are supplied by the other files.
 
-.. note::
+Minimal Example
+===============
+If you run the following command the package will align the audio files for you, and then create a pandas DataFrame with the synthesized audio and other information useful for the PAULE model,
+but only for the first 100 words that occur 4 times or more. Since you use multiprocessing, no melspectrograms are generated:
 
-   In the since VTL version 2.3 which can be downloaded as free software from
-   https://www.vocaltractlab.de/index.php?page=vocaltractlab-download most of
-   the functionality implemented here is available directly from the VTL api.
-   Please use the VTL api directly.
+.. code:: bash
 
+    python -m create_vtl_corpus.create_corpus --corpus CORPUS --language de --needs_aligner --use_mp --min_word_count 4 --word_amount 100 --save_df_name SAVE_DF_NAME
 
-Minimal Example
-===============
-Given a german Corpus with the following structure which is what the `Mozilla Common Voice project <https://commonvoice.mozilla.org>`__ provides:
+This works, if we have a German corpus in at the path CORPUS with the following structure, which is what the `Mozilla Common Voice project <https://commonvoice.mozilla.org>`__ provides:
 
  .. code:: bash
 
-    corpus/
+    CORPUS/
     ├── validated.tsv         # a file where the transcripts are stored
     ├── clips/
     │   └── *.mp3             # audio files (mp3)
     └── files_not_relevant_to_this_project
 
-If you run the following command the package will align the audio files for you, and then create a pandas DataFrame with the synthesized audio and other information useful for the PAULE model,
-but only for the first 100 words that occur 4 times or more. Since you use multiprocessing, no melspectrograms are generated.:
-.. code:: bash
-
-    python -m create_vtl_corpus.create_corpus --corpus CORPUS --language de --needs_aligner --use_mp --min_word_count 4 --word_amount 100 --save_df_name SAVE_DF_NAME
 
 The end product should look someting like this
 
 .. code:: bash
 
-   corpus/
+   CORPUS/
    ├── validated.tsv          # a file where the transcripts are stored
    ├── clips/
    │   ├── *.mp3              # mp3 files
@@ -72,39 +55,24 @@ The end product should look someting like this
 
 The DataFrame contains the following columns
 
-.. list-table:: Dataframe Labels
-   :header-rows: 1
-
-   * - Column Name
-     - Description
-   * - file_name
-     - Name of the clip
-   * - label
-     - The spoken word as it is in the aligned textgrid
-   * - lexical_word
-     - The word as it is in the dictionary
-   * - word_position
-     - The position of the word in the sentence
-   * - sentence
-     - The sentence the word is part of
-   * - wav_recording
-     - Spliced out audio as mono audio signal
-   * - sr_recording
-     - Sampling rate of the recording
-   * - sr_synthesized
-     - Sampling rates synthesized
-   * - sampa_phones
-     - The SAMPA(like) phonemes of the word
-   * - mfa_phones
-     - The phonemes as outputted by the aligner
-   * - phone_durations_lists
-     - The duration of each phone in the word as list
-   * - cp_norm
-     - Normalized CP-trajectories
-   * - vector
-     - Embedding vector of the word, based on FastText Embeddings
-   * - client_id
-     - ID of the client
+=======================  ===========================================================
+label                    description
+=======================  ===========================================================
+'file_name'              name of the clip
+'label'                  the spoken word as it is in the aligned textgrid
+'lexical_word'           the word as it is in the dictionary
+'word_position'          the position of the word in the sentence
+'sentence'               the sentence the word is part of
+'wav_recording'          spliced out audio as mono audio signal
+'sr_recording'           sampling rate of the recording
+'sr_synthesized'         sampling_rates_sythesized,
+'sampa_phones'           the sampa(like) phonemes of the word
+'mfa_phones'             the phonemes as outputted by the aligner
+'phone_durations_lists'  the duration of each phone in the word as list
+'cp_norm'                normalized cp-trajectories
+'vector'                 embedding vector of the word, based on fastText Embeddings
+'client_id'              id of the client
+=======================  ===========================================================
 
 
 Copyright
@@ -129,7 +97,29 @@ Konstantin Sering. Predictive articulatory speech synthesis utilizing lexical em
          school={Universität Tübingen}
       }
 
-   
+Older Versions
+==============
+
+Version 2.0.0 and later
+-----------------------
+From version 2.0.0 we are relying on the new segment-to-gesture API introduced
+in VTL 2.3 and use the JD3.speaker instead of the JD2.speaker.
+
+Old version 1.1.0
+-----------------
+The original version of this tool is based on the work and on the Matlab code
+on Yingming Gao. This can be viewed by checking out the tag ``1.1.0``.
+
+The overall logic is in ``create_corpus.py`` which executes the appropriate
+functions from top to bottom. The functions are supplied by the other files.
+
+.. note::
+
+   In the since VTL version 2.3 which can be downloaded as free software from
+   https://www.vocaltractlab.de/index.php?page=vocaltractlab-download most of
+   the functionality implemented here is available directly from the VTL api.
+   Please use the VTL api directly.
+ 
 
 Acknowledgments
 ===============