Update app.py

app.py

@@ -11,9 +11,6 @@ import random
 from openai import OpenAI
 import subprocess
 from starlette.requests import ClientDisconnect
-import hashlib
-from functools import lru_cache
-
 
 LLAMA_3B_API_ENDPOINT = os.environ.get("LLAMA_3B_API_ENDPOINT")
 LLAMA_3B_API_KEY = os.environ.get("LLAMA_3B_API_KEY")
@@ -123,20 +120,16 @@ def models(text, model="Llama 3 8B Service", seed=42):
 
         return output
 
-translation_cache = {}
+def translate_speech(audio_file, target_language):
+    if audio_file is None:
+        return None
 
-@lru_cache(maxsize=100)
-def cached_translate_speech(audio_file_hash, target_language):
-    # This function will be called if the result is not in the cache
     language_code = LANGUAGE_CODES[target_language]
-    output_file = f"translated_audio_{audio_file_hash[:10]}.wav"
-
-    if os.path.exists(output_file):
-        return output_file
+    output_file = "translated_audio.wav"
 
     command = [
         "expressivity_predict",
-        audio_file_hash,  # We're using the hash as a unique identifier
+        audio_file,
         "--tgt_lang", language_code,
         "--model_name", "seamless_expressivity",
         "--vocoder_name", "vocoder_pretssel",
@@ -144,39 +137,15 @@ def cached_translate_speech(audio_file_hash, target_language):
         "--output_path", output_file
     ]
 
-    try:
-        subprocess.run(command, check=True, capture_output=True, text=True)
-        if os.path.exists(output_file):
-            print(f"File created successfully: {output_file}")
-            return output_file
-        else:
-            print(f"File not found: {output_file}")
-            return None
-    except subprocess.CalledProcessError as e:
-        print(f"Translation process failed: {e.stderr}")
-        return None
+    subprocess.run(command, check=True)
 
-def translate_speech(audio_file, target_language):
-    if audio_file is None:
+    if os.path.exists(output_file):
+        print(f"File created successfully: {output_file}")
+        return output_file
+    else:
+        print(f"File not found: {output_file}")
         return None
 
-    # Generate a hash of the audio file content
-    with open(audio_file, "rb") as f:
-        file_hash = hashlib.md5(f.read()).hexdigest()
-
-    # Check if we have a cached result
-    cache_key = (file_hash, target_language)
-    if cache_key in translation_cache:
-        return translation_cache[cache_key]
-
-    # If not in cache, call the cached function
-    result = cached_translate_speech(file_hash, target_language)
-    
-    # Store the result in our custom cache
-    translation_cache[cache_key] = result
-
-    return result
-
 async def respond(audio, model, seed, target_language):
     try:
         if audio is None:
@@ -212,7 +181,7 @@ def clear_history():
 
 with gr.Blocks(css="style.css") as demo:
     gr.Markdown("# <center><b>Optimus Prime: Your Personal AI Voice Assistant with Speech Translation</b></center>")
-    gr.Markdown("<center><b>For speech translation, start with the phrase 'Please translate' followed by the speech you want to translate</b></center>")
+    gr.Markdown("## <center><b>For speech translation, start with the phrase 'Please translate' followed by the speech you want to translate</b></center>")
 
     with gr.Row():
         with gr.Column(scale=1):