fix(frontend): read cmvn statistics without crashing on blank lines (#3760)

Both package cmvn readers index line_item[0] and lines[i + 1] without
checking, so a blank or whitespace-only line anywhere in the file
(trailing newline at EOF, a separator between sections) aborts frontend
construction with IndexError, and a file whose tags the walker does not
recognize returns empty statistics that only fail much later at inference.
Skip blank lines, bound the lookahead, and raise a ValueError naming the
file when neither <AddShift> nor <Rescale> parsed.

funasr/frontends/default.py

@@ -394,23 +394,25 @@ class MultiChannelFrontend(nn.Module):
                 cmvn_file: TODO.
             """
         with open(cmvn_file, "r", encoding="utf-8") as f:
-            lines = f.readlines()
+            lines = [line for line in f.readlines() if line.split()]
         means_list = []
         vars_list = []
         for i in range(len(lines)):
             line_item = lines[i].split()
             if line_item[0] == "<AddShift>":
-                line_item = lines[i + 1].split()
-                if line_item[0] == "<LearnRateCoef>":
+                line_item = lines[i + 1].split() if i + 1 < len(lines) else []
+                if line_item and line_item[0] == "<LearnRateCoef>":
                     add_shift_line = line_item[3 : (len(line_item) - 1)]
                     means_list = list(add_shift_line)
                     continue
             elif line_item[0] == "<Rescale>":
-                line_item = lines[i + 1].split()
-                if line_item[0] == "<LearnRateCoef>":
+                line_item = lines[i + 1].split() if i + 1 < len(lines) else []
+                if line_item and line_item[0] == "<LearnRateCoef>":
                     rescale_line = line_item[3 : (len(line_item) - 1)]
                     vars_list = list(rescale_line)
                     continue
+        if not means_list or not vars_list:
+            raise ValueError(f"No <AddShift>/<Rescale> statistics found in cmvn file: {cmvn_file}")
         means = np.array(means_list).astype(np.float64)
         vars = np.array(vars_list).astype(np.float64)
         return means, vars

funasr/frontends/wav_frontend.py

@@ -19,23 +19,25 @@ def load_cmvn(cmvn_file):
             cmvn_file: TODO.
         """
     with open(cmvn_file, "r", encoding="utf-8") as f:
-        lines = f.readlines()
+        lines = [line for line in f.readlines() if line.split()]
     means_list = []
     vars_list = []
     for i in range(len(lines)):
         line_item = lines[i].split()
         if line_item[0] == "<AddShift>":
-            line_item = lines[i + 1].split()
-            if line_item[0] == "<LearnRateCoef>":
+            line_item = lines[i + 1].split() if i + 1 < len(lines) else []
+            if line_item and line_item[0] == "<LearnRateCoef>":
                 add_shift_line = line_item[3 : (len(line_item) - 1)]
                 means_list = list(add_shift_line)
                 continue
         elif line_item[0] == "<Rescale>":
-            line_item = lines[i + 1].split()
-            if line_item[0] == "<LearnRateCoef>":
+            line_item = lines[i + 1].split() if i + 1 < len(lines) else []
+            if line_item and line_item[0] == "<LearnRateCoef>":
                 rescale_line = line_item[3 : (len(line_item) - 1)]
                 vars_list = list(rescale_line)
                 continue
+    if not means_list or not vars_list:
+        raise ValueError(f"No <AddShift>/<Rescale> statistics found in cmvn file: {cmvn_file}")
     means = np.array(means_list).astype(np.float32)
     vars = np.array(vars_list).astype(np.float32)
     cmvn = np.array([means, vars])

tests/test_numpy_compatibility.py

@@ -37,6 +37,62 @@ def test_cmvn_load_returns_float64_arrays(cmvn_path):
     np.testing.assert_array_equal(scales, np.full(8, 1.25, dtype=np.float64))
 
 
+def test_cmvn_readers_skip_blank_lines(tmp_path):
+    from funasr.frontends.default import MultiChannelFrontend
+    from funasr.frontends.wav_frontend import load_cmvn
+
+    path = tmp_path / "blank_lines.cmvn"
+    path.write_text(
+        "\n<AddShift>\n \t \n<LearnRateCoef> 0 0 "
+        + " ".join(["0.1"] * 8)
+        + " </LearnRateCoef>\n</AddShift>\n\n"
+        + "<Rescale>\n<LearnRateCoef> 0 0 "
+        + " ".join(["1.25"] * 8)
+        + " </LearnRateCoef>\n</Rescale>\n\n",
+        encoding="utf-8",
+    )
+    means, scales = MultiChannelFrontend._load_cmvn(None, path)
+    np.testing.assert_array_equal(means, np.full(8, 0.1, dtype=np.float64))
+    np.testing.assert_array_equal(scales, np.full(8, 1.25, dtype=np.float64))
+    cmvn = load_cmvn(str(path))
+    assert cmvn.shape == (2, 8)
+    torch.testing.assert_close(cmvn[0], torch.full((8,), 0.1))
+    torch.testing.assert_close(cmvn[1], torch.full((8,), 1.25))
+
+
+def test_cmvn_readers_reject_files_without_statistics(tmp_path):
+    from funasr.frontends.default import MultiChannelFrontend
+    from funasr.frontends.wav_frontend import load_cmvn
+
+    # Kaldi also emits the whole nnet on a single line; the readers walk lines,
+    # so nothing matches and empty statistics used to be returned silently.
+    compact = tmp_path / "compact.cmvn"
+    compact.write_text(
+        "<Nnet> <AddShift> <LearnRateCoef> 1 [ -1.0 -2.0 ] </LearnRateCoef> "
+        "<Rescale> <LearnRateCoef> 1 [ 0.5 0.5 ] </Rescale> </Nnet>\n",
+        encoding="utf-8",
+    )
+    truncated = tmp_path / "truncated.cmvn"
+    truncated.write_text("<AddShift> 2 2\n", encoding="utf-8")
+    for path in (compact, truncated):
+        with pytest.raises(ValueError) as excinfo:
+            MultiChannelFrontend._load_cmvn(None, path)
+        assert str(path) in str(excinfo.value)
+        with pytest.raises(ValueError) as excinfo:
+            load_cmvn(str(path))
+        assert str(path) in str(excinfo.value)
+
+
+def test_cmvn_reader_loads_the_am_mvn_shipped_by_the_runtime():
+    from funasr.frontends.wav_frontend import load_cmvn
+
+    path = Path(__file__).resolve().parents[1] / (
+        "runtime/triton_gpu/model_repo_sense_voice_small/feature_extractor/am.mvn"
+    )
+    cmvn = load_cmvn(str(path))
+    assert cmvn.shape == (2, 560) and torch.isfinite(cmvn).all()
+
+
 def test_frontend_applies_cmvn_and_preserves_padding(cmvn_path):
     from funasr.frontends.default import MultiChannelFrontend