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* Fix #2839: initialize punc_res before conditional to prevent UnboundLocalError When punc_model is None or not provided, punc_res was only assigned inside the `if self.punc_model is not None:` block. Downstream code paths (punc_segment speaker diarization and sentence_timestamp) accessed punc_res unconditionally, causing UnboundLocalError. Changes: - Initialize punc_res = None before the conditional block - Add punc_res is None guard in punc_segment path with error log - Add punc_res is None guard in sentence_timestamp path with warning log - Add unit tests covering punc_model=None, empty punc_model, and normal flow Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]> Signed-off-by: Lidang-Jiang <[email protected]> * fix: update docstring to only mention punc_model=None case The tests only cover the None case, not the empty string case. Update the module docstring to match actual test coverage. Signed-off-by: Lidang-Jiang <[email protected]> --------- Signed-off-by: Lidang-Jiang <[email protected]>
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Lidang Jiang
@@ -29,14 +29,18 @@ from funasr.train_utils.set_all_random_seed import set_all_random_seed
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from funasr.train_utils.load_pretrained_model import load_pretrained_model
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from funasr.utils import export_utils
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from funasr.utils import misc
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def is_npu_available():
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"""检查NPU是否可用。"""
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try:
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import torch_npu
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return torch_npu.npu.is_available()
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except ImportError:
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return False
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def _resolve_ncpu(config, fallback=4):
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"""Return a positive integer representing CPU threads from config."""
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value = config.get("ncpu", fallback)
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@@ -46,6 +50,7 @@ def _resolve_ncpu(config, fallback=4):
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value = fallback
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return max(value, 1)
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try:
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from funasr.models.campplus.utils import sv_chunk, postprocess, distribute_spk
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from funasr.models.campplus.cluster_backend import ClusterBackend
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@@ -202,11 +207,13 @@ class AutoModel:
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set_all_random_seed(kwargs.get("seed", 0))
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device = kwargs.get("device", "cuda")
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if ((device =="cuda" and not torch.cuda.is_available())
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if (
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(device == "cuda" and not torch.cuda.is_available())
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or (device == "xpu" and not torch.xpu.is_available())
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or (device == "mps" and not torch.backends.mps.is_available())
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or (device == "npu" and not is_npu_available())
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or kwargs.get("ngpu", 1) == 0):
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or kwargs.get("ngpu", 1) == 0
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):
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device = "cpu"
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kwargs["batch_size"] = 1
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kwargs["device"] = device
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@@ -573,8 +580,12 @@ class AutoModel:
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result[k] = []
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for t in restored_data[j][k]:
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if isinstance(t, dict):
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t["start_time"] = (float(t["start_time"]) * 1000 + int(vadsegments[j][0])) / 1000
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t["end_time"] = (float(t["end_time"]) * 1000 + int(vadsegments[j][0])) / 1000
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t["start_time"] = (
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float(t["start_time"]) * 1000 + int(vadsegments[j][0])
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) / 1000
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t["end_time"] = (
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float(t["end_time"]) * 1000 + int(vadsegments[j][0])
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) / 1000
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else:
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t[0] = int(t[0]) + int(vadsegments[j][0])
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t[1] = int(t[1]) + int(vadsegments[j][0])
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@@ -600,6 +611,7 @@ class AutoModel:
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return_raw_text = kwargs.get("return_raw_text", False)
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# step.3 compute punc model
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raw_text = None
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punc_res = None
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if self.punc_model is not None:
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deep_update(self.punc_kwargs, cfg)
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punc_res = self.inference(
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@@ -645,7 +657,12 @@ class AutoModel:
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and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
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can predict timestamp, and speaker diarization relies on timestamps."
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)
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if kwargs.get("en_post_proc", False):
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if punc_res is None:
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logging.error(
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"Missing punc_model, which is required for punc_segment speaker diarization."
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)
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sentence_list = []
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elif kwargs.get("en_post_proc", False):
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sentence_list = timestamp_sentence_en(
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punc_res[0]["punc_array"],
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result["timestamp"],
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@@ -664,6 +681,11 @@ class AutoModel:
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elif kwargs.get("sentence_timestamp", False):
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if not len(result["text"].strip()):
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sentence_list = []
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elif punc_res is None:
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logging.warning(
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"punc_model is required for sentence_timestamp, skipping sentence segmentation."
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)
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sentence_list = []
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else:
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if kwargs.get("en_post_proc", False):
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sentence_list = timestamp_sentence_en(
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@@ -0,0 +1,119 @@
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"""Tests for issue #2839: punc_model=None should not cause UnboundLocalError."""
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import unittest
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from unittest.mock import MagicMock, patch
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import numpy as np
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class TestPuncModelNone(unittest.TestCase):
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"""Test that inference_with_vad works when punc_model is None."""
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def _make_auto_model(self, punc_model=None, spk_model=None, spk_mode=None):
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"""Create a minimal AutoModel instance with mocked dependencies."""
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from funasr.auto.auto_model import AutoModel
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am = AutoModel.__new__(AutoModel)
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am.model = MagicMock()
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am.vad_model = MagicMock()
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am.punc_model = punc_model
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am.punc_kwargs = {}
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am.spk_model = spk_model
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am.cb_model = None
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am.spk_mode = spk_mode
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am.vad_kwargs = {}
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am.kwargs = {
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"batch_size_s": 300,
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"batch_size_threshold_s": 60,
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"device": "cpu",
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"disable_pbar": True,
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"frontend": MagicMock(fs=16000),
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"fs": 16000,
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}
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am._reset_runtime_configs = MagicMock()
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return am
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def _setup_mocks(self, am, mock_slice, mock_load, mock_prep):
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"""Configure standard mocks for a single-segment VAD + ASR flow."""
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# VAD returns one segment [0, 16000ms]
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vad_result = [{"key": "test_utt", "value": [[0, 16000]]}]
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# ASR returns text with timestamps
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asr_result = [{"text": "hello world", "timestamp": [[0, 500], [500, 1000]]}]
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call_count = [0]
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results_seq = [vad_result, asr_result]
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def mock_inference(data, input_len=None, model=None, kwargs=None, **cfg):
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idx = call_count[0]
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call_count[0] += 1
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if idx < len(results_seq):
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return results_seq[idx]
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return [{"text": ""}]
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am.inference = MagicMock(side_effect=mock_inference)
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mock_prep.return_value = (["test_utt"], [np.zeros(16000, dtype=np.float32)])
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mock_load.return_value = np.zeros(16000, dtype=np.float32)
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mock_slice.return_value = ([np.zeros(16000, dtype=np.float32)], [16000])
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@patch("funasr.auto.auto_model.slice_padding_audio_samples")
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@patch("funasr.auto.auto_model.load_audio_text_image_video")
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@patch("funasr.auto.auto_model.prepare_data_iterator")
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def test_punc_model_none_basic(self, mock_prep, mock_load, mock_slice):
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"""Basic inference with punc_model=None should not raise UnboundLocalError."""
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am = self._make_auto_model(punc_model=None)
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self._setup_mocks(am, mock_slice, mock_load, mock_prep)
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results = am.inference_with_vad("dummy_input")
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self.assertEqual(len(results), 1)
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self.assertEqual(results[0]["text"], "hello world")
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self.assertEqual(results[0]["key"], "test_utt")
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@patch("funasr.auto.auto_model.slice_padding_audio_samples")
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@patch("funasr.auto.auto_model.load_audio_text_image_video")
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@patch("funasr.auto.auto_model.prepare_data_iterator")
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def test_sentence_timestamp_with_punc_model_none(self, mock_prep, mock_load, mock_slice):
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"""sentence_timestamp=True with punc_model=None should not crash."""
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am = self._make_auto_model(punc_model=None)
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self._setup_mocks(am, mock_slice, mock_load, mock_prep)
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# This path previously caused UnboundLocalError on punc_res
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results = am.inference_with_vad("dummy_input", sentence_timestamp=True)
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self.assertEqual(len(results), 1)
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# sentence_info should be empty list since punc_res is unavailable
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self.assertEqual(results[0].get("sentence_info"), [])
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@patch("funasr.auto.auto_model.slice_padding_audio_samples")
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@patch("funasr.auto.auto_model.load_audio_text_image_video")
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@patch("funasr.auto.auto_model.prepare_data_iterator")
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def test_punc_model_with_value_still_works(self, mock_prep, mock_load, mock_slice):
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"""When punc_model is provided, punc_res should still be used normally."""
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punc_mock = MagicMock()
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am = self._make_auto_model(punc_model=punc_mock)
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vad_result = [{"key": "test_utt", "value": [[0, 16000]]}]
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asr_result = [{"text": "hello world", "timestamp": [[0, 500], [500, 1000]]}]
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punc_result = [{"text": "Hello, world.", "punc_array": [1, 2]}]
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call_count = [0]
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results_seq = [vad_result, asr_result, punc_result]
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def mock_inference(data, input_len=None, model=None, kwargs=None, **cfg):
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idx = call_count[0]
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call_count[0] += 1
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return results_seq[idx]
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am.inference = MagicMock(side_effect=mock_inference)
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mock_prep.return_value = (["test_utt"], [np.zeros(16000, dtype=np.float32)])
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mock_load.return_value = np.zeros(16000, dtype=np.float32)
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mock_slice.return_value = ([np.zeros(16000, dtype=np.float32)], [16000])
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results = am.inference_with_vad("dummy_input")
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self.assertEqual(len(results), 1)
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# Text should be updated with punctuated version
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self.assertEqual(results[0]["text"], "Hello, world.")
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if __name__ == "__main__":
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unittest.main()
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