Update app.py

app.py

@@ -1,3 +1,4 @@
+import logging
 from importlib.metadata import version
 from timeit import default_timer as timer
 
@@ -6,7 +7,11 @@ import numpy as np
 
 import onnx_asr
 
-print(f"onnx_asr version: {version('onnx_asr')}")
+logging.basicConfig(format="%(asctime)s %(levelname)s %(message)s", level=logging.WARNING)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+logger.info("onnx_asr version: %s", version("onnx_asr"))
+
 models = {
     name: onnx_asr.load_model(name)
     for name in [
@@ -22,14 +27,22 @@ models = {
 
 
 def recognize(audio: tuple[int, np.ndarray]):
+    if audio is None:
+        return None
+
     sample_rate, waveform = audio
+    logger.debug("recognize: sample_rate %s, waveform.shape %s.", sample_rate, waveform.shape)
     try:
         waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1)
+        if waveform.ndim == 2:
+            waveform = waveform.mean(axis=1)
+
         results = []
         for name, model in models.items():
             start = timer()
             result = model.recognize(waveform, sample_rate=sample_rate, language="ru")
             time = timer() - start
+            logger.debug("recognized by %s: result '%s', time %.3f s.", name, result, time)
             results.append([name, result, f"{time:.3f} s."])
     except Exception as e:
         raise gr.Error(f"{e} Audio: sample_rate: {sample_rate}, waveform.shape: {waveform.shape}.") from e