Update app.py

app.py

@@ -5,7 +5,6 @@ from transformers import pipeline, VitsModel, VitsTokenizer, SpeechT5ForTextToSp
 import numpy as np
 os.system("pip install git+https://github.com/openai/whisper.git")
 import gradio as gr
-import whisper
 import requests
 
 MODEL = "gpt-3.5-turbo"
@@ -13,7 +12,6 @@ API_URL = os.getenv("API_URL")
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
 NUM_THREADS = 2
 
-model = whisper.load_model("small")
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
 
@@ -32,12 +30,7 @@ def parse_codeblock(text):
     
 
     
-#Load Whisper-small
-pipe = pipeline("automatic-speech-recognition",
-                model="openai/whisper-small",
-                device=device
-)
-#pipe = pipeline(model="Sleepyp00/whisper-small-Swedish") 
+pipe = pipeline(model="Sleepyp00/whisper-small-Swedish") 
 
 model2 = VitsModel.from_pretrained("facebook/mms-tts-eng")
 tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
@@ -174,10 +167,10 @@ description="Use Whisper pretrained model to convert swedish audio to english (t
 supportLangs = ["Swedish", "French (in training)"]
 transTypes = ["Text", "Audio", "GPT answer"]
 
-#examples = [
-#    ["Text", "Swedish", "./ex1.wav", None],
-#    ["Audio", "Swedish", "./ex2.wav", None]
-#]
+examples = [
+    ["Text", "Swedish", "./ex1.wav", None],
+    ["Audio", "Swedish", "./ex2.wav", None]
+]
 
 examples =[]
 demo = gr.Interface(