Upload app.py

app.py

@@ -28,8 +28,6 @@ for required_variable in required_variables:
 
 # Create the transcription pipeline.
 model_name = os.environ["MODEL_NAME"]
-model_name = "openai/whisper-tiny" # TODO: Remove this.
-logger.warning("Using hardcoded model name 'openai/whisper-tiny'.")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 logger.info(f"Loading model {model_name} with device {device}...")
 transcriber = pipeline(
@@ -145,7 +143,7 @@ def transcribe_audio(audio: Tuple[int, np.ndarray], password: str = None) -> str
     # Calculate elapsed time
     elapsed_time = time.time() - start_time
     audio_time = len(y) / sr
-    status_string = f"Transcription took {elapsed_time:.2f}s for {audio_time:.2f}s of audio"
+    status_string = f"Transcription took {elapsed_time:.2f}s for {audio_time:.2f}s of audio with model {model_name}."
     return result["text"], status_string