fix(auto): do not divide by zero speech time in AutoModel.inference

funasr/auto/auto_model.py

@@ -983,7 +983,9 @@ def inference(
             speed_stats["extract_feat"] = meta_data.get("extract_feat", 0.0)
             speed_stats["forward"] = f"{time_escape:0.3f}"
             speed_stats["batch_size"] = f"{len(results)}"
-            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
+            # Input shorter than one feature frame reports zero speech time.
+            rtf = time_escape / batch_data_time if batch_data_time else float("nan")
+            speed_stats["rtf"] = f"{rtf:0.3f}"
             description = f"{speed_stats}, "
             if pbar:
                 pbar.update(end_idx - beg_idx)
@@ -998,7 +1000,8 @@ def inference(
 
         if pbar:
             # pbar.update(1)
-            pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
+            rtf_avg = time_escape_total / time_speech_total if time_speech_total else float("nan")
+            pbar.set_description(f"rtf_avg: {rtf_avg:0.3f}")
 
         device = next(model.parameters()).device
         if device.type == "cuda":

tests/test_auto_model_zero_speech_time.py

@@ -0,0 +1,39 @@
+import unittest
+
+import torch
+
+from funasr.auto.auto_model import AutoModel
+
+
+class _ZeroSpeechTimeModel:
+    """Reports no speech time, as models do for input shorter than one feature frame."""
+
+    def __init__(self):
+        self.param = torch.nn.Parameter(torch.zeros(1))
+
+    def parameters(self):
+        return iter([self.param])
+
+    def inference(self, data_in, **kwargs):
+        return [{"key": d, "value": []} for d in data_in], {"batch_data_time": 0.0}
+
+
+def _auto_model(disable_pbar):
+    am = AutoModel.__new__(AutoModel)
+    am.model = _ZeroSpeechTimeModel()
+    am.kwargs = {"batch_size": 1, "disable_pbar": disable_pbar}
+    return am
+
+
+class TestInferenceZeroSpeechTime(unittest.TestCase):
+    def test_zero_speech_time_returns_results_without_progress_bar(self):
+        res = AutoModel.inference(_auto_model(True), ["a", "b"])
+        self.assertEqual(res, [{"key": "a", "value": []}, {"key": "b", "value": []}])
+
+    def test_zero_speech_time_returns_results_with_progress_bar(self):
+        res = AutoModel.inference(_auto_model(False), ["a"])
+        self.assertEqual(res, [{"key": "a", "value": []}])
+
+
+if __name__ == "__main__":
+    unittest.main()