From d1fa1ffb15366d4d994eb27a14af113694de223c Mon Sep 17 00:00:00 2001
From: Adolfo Reyna <aeroreyna@gmail.com>
Date: Thu, 26 Feb 2026 20:57:31 -0500
Subject: [PATCH] Optimize for Apple Silicon using MLX

---
 transcribe.py | 40 +++++++++++-----------------------------
 1 file changed, 11 insertions(+), 29 deletions(-)

diff --git a/transcribe.py b/transcribe.py
index e872ef5..b29581b 100644
--- a/transcribe.py
+++ b/transcribe.py
@@ -1,4 +1,4 @@
-import whisper
+import mlx_whisper
 import numpy as np
 import sounddevice as sd
 import queue
@@ -7,13 +7,13 @@ import torch
 from silero_vad import load_silero_vad, get_speech_timestamps
 
 # Parameters
-MODEL_TYPE = "tiny.en"
+MODEL_PATH = "mlx-community/whisper-tiny.en-mlx" # MLX optimized model
 CHANNELS = 1
 SAMPLERATE = 16000
-BLOCK_SIZE = 512 # Silero VAD prefers specific block sizes (512, 1024, 1536)
-VAD_THRESHOLD = 0.5 # Confidence threshold for speech
-BUFFER_LIMIT = SAMPLERATE * 30 # Max 30 seconds of audio buffer
-MIN_SILENCE_DURATION_MS = 500 # Silence duration to trigger transcription
+BLOCK_SIZE = 512 # Silero VAD prefers 512, 1024, or 1536
+VAD_THRESHOLD = 0.5 
+BUFFER_LIMIT = SAMPLERATE * 30 
+MIN_SILENCE_DURATION_MS = 500 
 
 audio_queue = queue.Queue()
 
@@ -23,22 +23,15 @@ def callback(indata, frames, time, status):
     audio_queue.put(indata.copy())
 
 def main():
-    print(f"Loading Whisper model '{MODEL_TYPE}'...")
-    whisper_model = whisper.load_model(MODEL_TYPE)
+    print(f"Loading MLX-optimized Whisper model '{MODEL_PATH}'...")
+    # mlx-whisper uses the same model names or Hugging Face paths
     
     print("Loading Silero VAD model...")
     vad_model = load_silero_vad()
     
     print("Models loaded.")
 
-    print("\nAvailable Audio Devices:")
-    devices = sd.query_devices()
-    print(devices)
-    
-    default_device = sd.default.device[0]
-    print(f"\nUsing default input device index: {default_device}")
-
-    print("\nStarting live transcription with VAD... (Press Ctrl+C to stop)")
+    print("\nStarting live transcription (MLX + VAD)... (Press Ctrl+C to stop)")
     
     audio_buffer = []
     speech_started = False
@@ -51,13 +44,9 @@ def main():
                     audio_buffer.append(data.flatten())
 
                 if len(audio_buffer) > 0:
-                    # Concatenate buffer to check for speech
                     current_audio = np.concatenate(audio_buffer)
-                    
-                    # Convert to torch tensor for Silero
                     audio_tensor = torch.from_numpy(current_audio)
                     
-                    # Get speech timestamps
                     speech_timestamps = get_speech_timestamps(
                         audio_tensor, 
                         vad_model, 
@@ -66,31 +55,24 @@ def main():
                         min_silence_duration_ms=MIN_SILENCE_DURATION_MS
                     )
 
-                    # If we have speech and then silence, or buffer is getting too long
                     if len(speech_timestamps) > 0:
                         speech_started = True
-                        
-                        # Check if the last speech segment has "ended" (i.e., we have enough silence after it)
-                        # or if we've reached a significant buffer size
                         last_end = speech_timestamps[-1]['end']
                         buffer_len_samples = len(current_audio)
                         
-                        # If the speech ended more than MIN_SILENCE_DURATION_MS ago
                         if (buffer_len_samples - last_end) > (SAMPLERATE * MIN_SILENCE_DURATION_MS / 1000) or buffer_len_samples > BUFFER_LIMIT:
                             
-                            # Transcribe the valid speech segment
-                            result = whisper_model.transcribe(current_audio, fp16=False, language="en")
+                            # Transcribe with MLX
+                            result = mlx_whisper.transcribe(current_audio, path_or_hf_repo=MODEL_PATH)
                             text = result['text'].strip()
                             
                             if text:
                                 print(f"Transcription: {text}")
                             
-                            # Reset buffer
                             audio_buffer = []
                             speech_started = False
                     
                     elif not speech_started and len(current_audio) > SAMPLERATE * 2:
-                        # Clear buffer if it's just silence for more than 2 seconds
                         audio_buffer = []
 
     except KeyboardInterrupt: