Allows for 10 instances to be running simultaneously (requires ~11GB of VRAM at most for all to run) assuming filenames are different

app.py

@@ -80,6 +80,8 @@ def Transcribe(file):
         preprocessAudio(file)
         block_size = 30
         batch_size = 1
+        filename = os.path.split(os.path.splitext(file.name)[0])[1]
+        print(filename)
 
         transcripts = []
         speech_segments = []
@@ -97,8 +99,8 @@ def Transcribe(file):
         #Code for timestamping
         encoding_start = 0
         encoding_end = 0
-        sbv_file = open("subtitle.sbv", "w")
-        transcription_file = open("transcription.txt", "w")
+        sbv_file = open(f"{filename}_subtitle.sbv", "w")
+        transcription_file = open(f"{filename}_transcription.txt", "w")
 
         # Create an empty list to hold batches
         batch = []
@@ -183,7 +185,7 @@ def Transcribe(file):
 
         end_time = time.time()
         print(f"The script ran for {end_time - start_time} seconds.")
-        return(["./subtitle.sbv", "./transcription.txt"])
+        return([f"./{filename}_subtitle.sbv", f"./{filename}_transcription.txt"])
     except Exception as e:
         error_log = open("error_log.txt", "w")
         error_log.write(f"Exception occurred: {e}")
@@ -224,4 +226,5 @@ with demo:
 #                     description="This application uses Meta MMS and an Amharic kenLM model to transcribe Amharic Audio files of arbitrary length into .sbv and .txt files. Upload an Amharic audio file and get your transcription! \n(Note: Transcription quality is quite low, you should review and edit transcriptions before making them publicly available)"
 #                     )
 
+demo.queue(concurrency_count=10)
 demo.launch()