From 8e45daec878f667a88dd905ab79e602981eff685 Mon Sep 17 00:00:00 2001
From: Adolfo Reyna <aeroreyna@gmail.com>
Date: Thu, 26 Feb 2026 21:13:03 -0500
Subject: [PATCH] Switch to dedicated MarianMT for cleaner, non-LLM translation

---
 transcribe.py | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/transcribe.py b/transcribe.py
index 596a8c9..5fee545 100644
--- a/transcribe.py
+++ b/transcribe.py
@@ -5,12 +5,12 @@ import queue
 import sys
 import torch
 from silero_vad import load_silero_vad, get_speech_timestamps
-from mlx_lm import load, generate
+from transformers import MarianMTModel, MarianTokenizer
 
 # Parameters
 WHISPER_MODEL = "mlx-community/whisper-small.en-mlx"
-LLM_MODEL = "mlx-community/SmolLM2-135M-Instruct" # Verified public model
-TARGET_LANG = "Spanish"
+# Dedicated EN-ES translation model (very fast and accurate)
+TRANSLATE_MODEL = "Helsinki-NLP/opus-mt-en-es"
 CHANNELS = 1
 SAMPLERATE = 16000
 BLOCK_SIZE = 512
@@ -25,26 +25,23 @@ def callback(indata, frames, time, status):
         print(status, file=sys.stderr)
     audio_queue.put(indata.copy())
 
-def translate_text(model, tokenizer, text, target_lang):
-    prompt = f"Translate the following English text to {target_lang}. Only provide the translation, no extra text.\n\nEnglish: {text}\n\n{target_lang}:"
-    messages = [{"role": "user", "content": prompt}]
-    prompt_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-    
-    response = generate(model, tokenizer, prompt=prompt_text, max_tokens=100, verbose=False)
-    return response.strip()
-
 def main():
+    # Set device for translation model
+    device = "mps" if torch.backends.mps.is_available() else "cpu"
+    print(f"Using device for translation: {device}")
+
     print(f"Loading Whisper model '{WHISPER_MODEL}'...")
     
-    print(f"Loading LLM for translation '{LLM_MODEL}'...")
-    llm_model, llm_tokenizer = load(LLM_MODEL)
+    print(f"Loading dedicated translation model '{TRANSLATE_MODEL}'...")
+    tokenizer = MarianTokenizer.from_pretrained(TRANSLATE_MODEL)
+    model = MarianMTModel.from_pretrained(TRANSLATE_MODEL).to(device)
 
     print("Loading Silero VAD model...")
     vad_model = load_silero_vad()
     
     print("Models loaded.")
 
-    print(f"\nStarting live transcription & translation to {TARGET_LANG}... (Press Ctrl+C to stop)")
+    print(f"\nStarting live transcription & translation... (Press Ctrl+C to stop)")
     
     audio_buffer = []
     speech_started = False
@@ -80,11 +77,14 @@ def main():
                             original_text = result['text'].strip()
                             
                             if original_text:
-                                # 2. Translate using LLM
-                                translated_text = translate_text(llm_model, llm_tokenizer, original_text, TARGET_LANG)
+                                # 2. Translate using dedicated MarianMT
+                                inputs = tokenizer(original_text, return_tensors="pt", padding=True).to(device)
+                                with torch.no_grad():
+                                    translated_tokens = model.generate(**inputs)
+                                translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
                                 
                                 print(f"\nEN: {original_text}")
-                                print(f"{TARGET_LANG[:2].upper()}: {translated_text}")
+                                print(f"ES: {translated_text}")
                             
                             audio_buffer = []
                             speech_started = False