Complete rework for GUI, experimental EXE file and other minor changes, see readme for more info

src/_LocalTranscribe.py

@@ -0,0 +1,90 @@
+import os
+import datetime
+from glob import glob
+import whisper
+from torch import cuda, Generator
+import colorama
+from colorama import Back,Fore
+colorama.init(autoreset=True)
+
+
+# Get the path
+def get_path(path):
+    glob_file = glob(path + '/*')
+    return glob_file
+
+# Main function
+def transcribe(path, glob_file, model=None, language=None, verbose=False):
+    """
+    Transcribes audio files in a specified folder using OpenAI's Whisper model.
+
+    Args:
+        path (str): Path to the folder containing the audio files.
+        glob_file (list): List of audio file paths to transcribe.
+        model (str, optional): Name of the Whisper model to use for transcription.
+            Defaults to None, which uses the default model.
+        language (str, optional): Language code for transcription. Defaults to None,
+            which enables automatic language detection.
+        verbose (bool, optional): If True, enables verbose mode with detailed information
+            during the transcription process. Defaults to False.
+
+    Returns:
+        str: A message indicating the result of the transcription process.
+
+    Raises:
+        RuntimeError: If an invalid file is encountered, it will be skipped.
+
+    Notes:
+        - The function downloads the specified model if not available locally.
+        - The transcribed text files will be saved in a "transcriptions" folder
+          within the specified path.
+
+    """    
+    # Check for GPU acceleration
+    if cuda.is_available():
+        Generator('cuda').manual_seed(42)
+    else:
+        Generator().manual_seed(42)
+    # Load model
+    model = whisper.load_model(model)
+    # Start main loop
+    files_transcripted=[]   
+    for file in glob_file:
+        title = os.path.basename(file).split('.')[0]
+        print(Back.CYAN + '\nTrying to transcribe file named: {}\U0001f550'.format(title))
+        try:
+            result = model.transcribe(
+                file, 
+                language=language, 
+                verbose=verbose
+                )
+            files_transcripted.append(result)
+            # Make folder if missing 
+            try:
+                os.makedirs('{}/transcriptions'.format(path), exist_ok=True)
+            except FileExistsError:
+                pass
+            # Create segments for text files
+            start = []
+            end = []
+            text = []
+            for segment in result['segments']:
+                start.append(str(datetime.timedelta(seconds=segment['start'])))
+                end.append(str(datetime.timedelta(seconds=segment['end'])))
+                text.append(segment['text'])
+            # Save files to transcriptions folder
+            with open("{}/transcriptions/{}.txt".format(path, title), 'w', encoding='utf-8') as file:
+                file.write(title)
+                for i in range(len(result['segments'])):
+                    file.write('\n[{} --> {}]:{}'.format(start[i], end[i], text[i]))
+        # Skip invalid files
+        except RuntimeError:
+            print(Fore.RED + 'Not a valid file, skipping.')
+            pass
+     # Check if any files were processed.
+    if len(files_transcripted) > 0:
+        output_text = 'Finished transcription, {} files can be found in {}/transcriptions'.format(len(files_transcripted), path)
+    else:
+        output_text = 'No files elligible for transcription, try adding audio or video files to this folder or choose another folder!'
+    # Return output text
+    return output_text