refactor: switch to argparse, add --stream and --lang flags

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

transcribe.py

@@ -1,51 +1,74 @@
 import sys
+import argparse
 import numpy as np
 import sounddevice as sd
 from transformers import AutoProcessor, CohereAsrForConditionalGeneration
 from transformers.audio_utils import load_audio
 from huggingface_hub import hf_hub_download
 
-# Load model
+MODEL_ID = "CohereLabs/cohere-transcribe-03-2026"
+SAMPLE_RATE = 16000
+
+
+def load_model():
     print("Loading model...")
-processor = AutoProcessor.from_pretrained("CohereLabs/cohere-transcribe-03-2026")
+    processor = AutoProcessor.from_pretrained(MODEL_ID)
     model = CohereAsrForConditionalGeneration.from_pretrained(
-    "CohereLabs/cohere-transcribe-03-2026",
-    device_map="auto"
+        MODEL_ID, device_map="auto"
     )
+    return processor, model
 
-def transcribe_audio(audio, language="en"):
-    inputs = processor(audio, sampling_rate=16000, return_tensors="pt", language=language)
+
+def transcribe_audio(processor, model, audio, language="en"):
+    inputs = processor(audio, sampling_rate=SAMPLE_RATE, return_tensors="pt", language=language)
     inputs.to(model.device, dtype=model.dtype)
-    
     outputs = model.generate(**inputs, max_new_tokens=256)
-    text = processor.decode(outputs, skip_special_tokens=True)
-    return text
+    return processor.decode(outputs, skip_special_tokens=True)
 
-def record_audio(duration, samplerate=16000):
+
+def record_audio(duration):
     print(f"Recording for {duration} seconds...")
-    audio = sd.rec(int(duration * samplerate), samplerate=samplerate, channels=1, dtype='float32')
+    audio = sd.rec(int(duration * SAMPLE_RATE), samplerate=SAMPLE_RATE, channels=1, dtype="float32")
     sd.wait()
     return audio.flatten()
 
-# Parse arguments
-if len(sys.argv) > 1 and sys.argv[1] == "--mic":
-    duration = int(sys.argv[2]) if len(sys.argv) > 2 else 5
+
+def main():
+    parser = argparse.ArgumentParser(description="Cohere ASR Transcription")
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument("--mic", type=int, nargs="?", const=5, metavar="SECONDS",
+                       help="Record from microphone for N seconds (default: 5)")
+    group.add_argument("--stream", action="store_true",
+                       help="Live streaming transcription with VAD")
+    parser.add_argument("--lang", default="en", help="Language code (default: en)")
+    args = parser.parse_args()
+
+    if args.stream:
+        processor, model = load_model()
+        stream_transcribe(processor, model, args.lang)
+    elif args.mic is not None:
+        processor, model = load_model()
         try:
-        mic_audio = record_audio(duration)
+            mic_audio = record_audio(args.mic)
             print("Transcribing...")
-        text = transcribe_audio(mic_audio)
+            text = transcribe_audio(processor, model, mic_audio, args.lang)
             print(f"\nTranscription:\n{text}\n")
         except OSError as e:
             print(f"Microphone error: {e}")
             print("Hint: Run with nix-shell for PortAudio support")
     else:
+        processor, model = load_model()
         print("Loading demo audio...")
-    audio_file = hf_hub_download(
-        repo_id="CohereLabs/cohere-transcribe-03-2026",
-        filename="demo/voxpopuli_test_en_demo.wav",
-    )
-    audio = load_audio(audio_file, sampling_rate=16000)
-
+        audio_file = hf_hub_download(repo_id=MODEL_ID, filename="demo/voxpopuli_test_en_demo.wav")
+        audio = load_audio(audio_file, sampling_rate=SAMPLE_RATE)
         print("Transcribing...")
-    text = transcribe_audio(audio)
+        text = transcribe_audio(processor, model, audio, args.lang)
         print(f"\nTranscription:\n{text}\n")
+
+
+def stream_transcribe(processor, model, language):
+    print("TODO: streaming mode")
+
+
+if __name__ == "__main__":
+    main()