Update README.md

README.md formatting suggestions

Mac_2_instructions.txt

@@ -1,6 +0,0 @@
-### Steps to make command file executable 
-To make a file executable on a Mac, you need to open a terminal window in the directory where the file is located. Then run the following command: 
-
-chmod +x run_MAC_2.command
-
-After running this command, the file should be marked as executable and you should be able to run it by double-clicking on it.

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

@@ -1,42 +1,71 @@
-## transcribe
-Simple script that uses OpenAI's Whisper to transcribe audio files from your local folders. 
+## 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.
+
+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** 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 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_gui.bat, 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:  
+
+
+#### 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_Mac_1.sh

@@ -1,4 +0,0 @@
-#!/bin/bash
-echo Starting...
-conda activate venv
-python -u GUI.py

run_Mac_2.command

@@ -1,3 +0,0 @@
-#!/bin/bash
-echo Running Script
-python -u GUI.py

run_Windows.bat

@@ -1,5 +1,5 @@
 @echo off
 echo Starting...
-call conda activate venv
+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
+call python GUI.py

transcribe.py

@@ -1,5 +1,7 @@
 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=False):
     '''Implementation of OpenAI's whisper model. Downloads model, transcribes audio files in a folder and returns the text files with transcriptions'''
@@ -10,6 +12,11 @@ def transcribe(path, file_type, model=None, language=None, verbose=False):
         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'.format(model))
     print('File type is {}'.format(file_type))    
@@ -34,15 +41,15 @@ def transcribe(path, file_type, model=None, language=None, verbose=False):
         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))