Update app.py

app.py

@@ -10,6 +10,9 @@ from flores200_codes import flores_codes
 import tempfile
 import os
 
+global model_dict
+model_dict = load_models()
+
 MODEL_NAME = "openai/whisper-large-v2"
 BATCH_SIZE = 8
 FILE_LIMIT_MB = 1000
@@ -49,7 +52,7 @@ def transcribe(inputs, task):
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
 
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
-    translated_text = translate_text(text, source_language, target_language)
+    translated_text = translation(source_lang, target_lang, text)["result"]
     return  text, translated_text
 
 
@@ -106,8 +109,9 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
     inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
 
     text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    translated_text = translation(source_lang, target_lang, text)["result"]
 
-    return html_embed_str, text
+    return html_embed_str, text, translated_text
 
 
 lang_codes = list(flores_codes.keys())
@@ -163,7 +167,7 @@ yt_transcribe = gr.Interface(
         gr.inputs.Dropdown(lang_codes, default='English', label='Source Language'),
         gr.inputs.Dropdown(lang_codes, default='French', label='Target Language'),
     ],
-    outputs=["html", "text"],
+    outputs=["html", "text", "translated_text"],
     layout="horizontal",
     theme="huggingface",
     title="Whisper Large V2: Transcribe YouTube",