Update README.md

README.md formatting suggestions

CITATION.cff

@@ -5,7 +5,7 @@ authors:
   given-names: "Kristofer Rolf"
   orcid: "https://orcid.org/0000-0002-5322-3350"
 title: "transcribe"
-version: 1.0
+version: 1.1.1
 doi: 10.5281/zenodo.7760511
 date-released: 2023-03-22
 url: "https://github.com/soderstromkr/transcribe"

GUI.py

@@ -14,11 +14,12 @@ class App:
         self.master = master
         master.title("Local Transcribe")
 
-        #style = ttk.Style()
-        #style.configure('TLabel', font=('Arial', 10), padding=10)
-        #style.configure('TEntry', font=('Arial', 10), padding=10)
-        #style.configure('TButton', font=('Arial', 10), padding=10)
-        #style.configure('TCheckbutton', font=('Arial', 10), padding=10)
+        #style options
+        style = ttk.Style()
+        style.configure('TLabel', font=('Arial', 10), padding=10)
+        style.configure('TEntry', font=('Arial', 10), padding=10)
+        style.configure('TButton', font=('Arial', 10), padding=10)
+        style.configure('TCheckbutton', font=('Arial', 10), padding=10)
 
         # Folder Path
         path_frame = ttk.Frame(master, padding=10)
@@ -95,6 +96,5 @@ class App:
 if __name__ == "__main__":
 #    root = tk.Tk()
     root = ThemedTk(theme="clearlooks")
-    root.geometry("300x200")
     app = App(root) 
     root.mainloop()

Mac_instructions.txt

@@ -0,0 +1,5 @@
+### How to run on Mac
+Unfortunately, I have not found a permament solution for this, not being a Mac user has limited the ways I can test this. For now, these are the recommended steps for a beginner user:
+1. Open a terminal and navigate to the root folder (transcribe-main if you downloaded the folder). You can also right-click (or equivalent) on the root folder to open a Terminal within the folder.
+2. Run the following command: 
+python GUI.py

README.md

@@ -1,30 +1,71 @@
-## transcribe
-Simple script that uses OpenAI's Whisper to transcribe audio files from your local folders. 
-## Note
-This implementation and guide is mostly made for researchers not familiar with programming that want a way to transcribe their files locally, without internet connection, usually required within ethical data practices and frameworks. Two examples are shown, a normal workflow with interent connection. And one in which the model is loaded first, via openai-whisper, and then the transcription can be done without being connected to the internet. 
+## Local Transcribe
+
+Local Transcribe uses OpenAI's Whisper to transcribe audio files from your local folders, creating text files on disk. 
+
+## Note
+
+This implementation and guide is mostly made for researchers not familiar with programming that want a way to transcribe their files locally, without internet connection, usually required within ethical data practices and frameworks. Two examples are shown, a normal workflow with internet connection. And one in which the model is loaded first, via openai-whisper, and then the transcription can be done without being connected to the internet. There is now also a GUI implementation, read below for more information.  
+
+### Instructions
 
-### Instructions 
 #### Requirements
-1. This script was made and tested in an Anaconda environment with python 3.10. I recommend this method if you're not familiar with python.
+
+1. This script was made and tested in an Anaconda environment with Python 3.10. I recommend this method if you're not familiar with Python.
 See [here](https://docs.anaconda.com/anaconda/install/index.html) for instructions. You might need administrator rights. 
+
 2. Whisper requires some additional libraries. The [setup](https://github.com/openai/whisper#setup) page states: "The codebase also depends on a few Python packages, most notably HuggingFace Transformers for their fast tokenizer implementation and ffmpeg-python for reading audio files."
-Users might not need to specifically install Transfomers. However, a conda installation might be needed for ffmepg[^1], which takes care of setting up PATH variables. From the anaconda prompt, type or copy the following:
+Users might not need to specifically install Transfomers. However, a conda installation might be needed for ffmpeg[^1], which takes care of setting up PATH variables. From the anaconda prompt, type or copy the following:
+
 ```
- conda install -c conda-forge ffmpeg-python
- ```
+conda install -c conda-forge ffmpeg-python
+```
+
 3. The main functionality comes from openai-whisper. See their [page](https://github.com/openai/whisper) for details. As of 2023-03-22 you can install via:
+
 ```
 pip install -U openai-whisper
 ```
+
+4. There is an option to run a batch file, which launches a GUI built on TKinter and TTKthemes. If using these options, make sure they are installed in your Python build. You can install them via pip.
+
+```
+pip install tk
+```
+
+and
+
+```
+pip install ttkthemes 
+```
+
 #### Using the script
-This is a simple script with no installation. You can either clone the repository with
+
+This is a simple script with no installation. You can download the zip folder and extract it to your preferred working folder.
+
+![](Picture1.png)  
+
+Or by cloning the repository with:
+
 ```
 git clone https://github.com/soderstromkr/transcribe.git
 ```
-and use the example.ipynb template to use the script **OR (for beginners)** download the ```transcribe.py``` file into your work folder. Then you can either import it to another script or notebook for use. I recommend jupyter notebook for new users, see the example below. (Remember to have transcribe.py and example.ipynb in the same working folder).
 
-### Example
-See [example](example.ipynb) for an implementation on jupyter notebook, also added an example for a simple [workaround](example_no_internet.ipynb) to transcribe while offline. 
+
+#### Example with Jupyter Notebook
+
+See [example](example.ipynb) for an implementation on Jupyter Notebook, also added an example for a simple [workaround](example_no_internet.ipynb) to transcribe while offline.
+
+#### Using the GUI
+
+You can also run the GUI version from your terminal running ```python GUI.py``` or with the batch file called run_Windows.bat (for Windows users), just make sure to add your conda path to it. If you want to download a model first, and then go offline for transcription, I recommend running the model with the default sample folder, which will download the model locally. 
+
+The GUI should look like this:
+
+![python GUI.py](gui_jpeg.jpg?raw=true)
+
+or this, on a Mac, by running `python GUI.py` or `python3 GUI.py`:
+
+![python GUI Mac.py](gui-mac.png)
 
 [^1]: Advanced users can use ```pip install ffmpeg-python``` but be ready to deal with some [PATH issues](https://stackoverflow.com/questions/65836756/python-ffmpeg-wont-accept-path-why), which I encountered in Windows 11.
 

run_Windows.bat

@@ -0,0 +1,5 @@
+@echo off
+echo Starting...
+call conda activate base
+REM OPTION 2 : (KEEP TEXT WITHIN QUOTES AND CHANGE USERNAME) "C:/Users/user/Anaconda3/condabin/activate.bat"
+call python GUI.py

run_gui.bat

@@ -1,5 +0,0 @@
-@echo off
-call 'PATH_TO_CONDA' 
-call 'ACTIVATE_NEEDED_ENVS'
-call python GUI.py
-PAUSE

sample_audio/transcriptions/Armstrong_Small_Step.txt

@@ -1,3 +1,5 @@
 Armstrong_Small_Step
 In seconds:
-[0.00 --> 24.00]: That's one small step for man, one giant leap for mankind.
+[0.00 --> 7.00]: I'm going to step off the limb now.
+[7.00 --> 18.00]: That's one small step for man.
+[18.00 --> 24.00]: One giant leap for mankind.

sample_audio/transcriptions/Axel_Pettersson_röstinspelning.txt

@@ -1,3 +1,4 @@
 Axel_Pettersson_röstinspelning
 In seconds:
-[0.00 --> 16.00]: Hej, jag heter Axel Pettersson, jag föddes i Örebro 1976. Jag har varit Wikipedia sen 2008 och jag har översatt röstintroduktionsprojektet till svenska.
+[0.00 --> 6.14]: Hej, jag heter Axel Pettersson. Jag följer bror 1976.
+[6.40 --> 15.10]: Jag har varit vikerpedjan sen 2008 och jag har översatt röstintroduktionsprojektet till svenska.

transcribe.py

@@ -1,7 +1,9 @@
 import whisper 
 import glob, os
+#import torch #uncomment if using torch with cuda, below too
+import datetime
 
-def transcribe(path, file_type, model=None, language=None, verbose=True):
+def transcribe(path, file_type, model=None, language=None, verbose=False):
     '''Implementation of OpenAI's whisper model. Downloads model, transcribes audio files in a folder and returns the text files with transcriptions'''
 
     try:
@@ -10,11 +12,16 @@ def transcribe(path, file_type, model=None, language=None, verbose=True):
         pass
     
     glob_file = glob.glob(path+'/*{}'.format(file_type))
+
+    #if torch.cuda.is_available():
+    #    generator = torch.Generator('cuda').manual_seed(42)
+    #else:
+    #    generator = torch.Generator().manual_seed(42)
         
-    print('Using {} model, you can change this by specifying model="medium" for example'.format(model))
-    print('Only looking for file type {}, you can change this by specifying file_type="mp3"'.format(file_type))    
-    print('Expecting {} language, you can change this by specifying language="English". None will try to auto-detect'.format(language))
-    print('Verbosity is {}. If TRUE it will print out the text as it is transcribed, you can turn this off by setting verbose=False'.format(verbose))
+    print('Using {} model'.format(model))
+    print('File type is {}'.format(file_type))    
+    print('Language is being detected automatically for each file')
+    print('Verbosity is set to {}'.format(verbose))
     print('\nThere are {} {} files in path: {}\n\n'.format(len(glob_file), file_type, path))
     
     print('Loading model...')
@@ -28,21 +35,21 @@ def transcribe(path, file_type, model=None, language=None, verbose=True):
         result = model.transcribe(
             file, 
             language=language, 
-            verbose=True
+            verbose=verbose
         )
         start=[]
         end=[]
         text=[]
         for i in range(len(result['segments'])):
-            start.append(result['segments'][i]['start'])
-            end.append(result['segments'][i]['end'])
+            start.append(str(datetime.timedelta(seconds=(result['segments'][i]['start']))))
+            end.append(str(datetime.timedelta(seconds=(result['segments'][i]['end']))))
             text.append(result['segments'][i]['text'])
         
         with open("{}/transcriptions/{}.txt".format(path,title), 'w', encoding='utf-8') as file:
             file.write(title)
             file.write('\nIn seconds:')
             for i in range(len(result['segments'])):
-                file.writelines('\n[{:.2f} --> {:.2f}]:{}'.format(start[i], end[i], text[i]))
+                file.writelines('\n[{} --> {}]:{}'.format(start[i], end[i], text[i]))
                 
         print('\nFinished file number {}.\n\n\n'.format(idx+1))