fixed pipeline

app.py

@@ -14,8 +14,7 @@ api_token = os.getenv("API_TOKEN")
 asr_processor = AutoProcessor.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew")
 asr_model = AutoModelForCTC.from_pretrained("imvladikon/wav2vec2-xls-r-300m-hebrew")
 
-en_he_translator = pipeline("translation_en_to_he")
-
+he_en_translator = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-he-en")
 
 def process_audio_file(file):
     data, sr = librosa.load(file)
@@ -59,7 +58,7 @@ def parse_transcription(wav_file):
     logits = trans_model(input_values).logits
     predicted_ids = torch.argmax(logits, dim=-1)
     transcription = trans_processor.decode(predicted_ids[0], skip_special_tokens=True)
-    translated = en_he_translator(trasncription)
+    translated = he_en_translator(trasncription)
     return transcription
 
 output = gr.outputs.Textbox(label="TEXT")