Allow one to set a custom progress callback (#2609)
* Allow one to set a custom progress callback
so that they can show it own progrss bar
* Uncomment an existing test
* restore indentation
---------
Co-authored-by: Tony Mak <tony@Tonys-MacBook-Air-1802.local>
| | |
| | | res = self.model(*args, kwargs) |
| | | return res |
| | | |
| | | def generate(self, input, input_len=None, **cfg): |
| | | def generate(self, input, input_len=None, progress_callback=None, **cfg): |
| | | if self.vad_model is None: |
| | | return self.inference(input, input_len=input_len, **cfg) |
| | | return self.inference( |
| | | input, input_len=input_len, progress_callback=progress_callback, **cfg |
| | | ) |
| | | |
| | | else: |
| | | return self.inference_with_vad(input, input_len=input_len, **cfg) |
| | | return self.inference_with_vad( |
| | | input, input_len=input_len, progress_callback=progress_callback, **cfg |
| | | ) |
| | | |
| | | def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg): |
| | | def inference( |
| | | self, |
| | | input, |
| | | input_len=None, |
| | | model=None, |
| | | kwargs=None, |
| | | key=None, |
| | | progress_callback=None, |
| | | **cfg, |
| | | ): |
| | | kwargs = self.kwargs if kwargs is None else kwargs |
| | | if "cache" in kwargs: |
| | | kwargs.pop("cache") |
| | |
| | | if pbar: |
| | | pbar.update(end_idx - beg_idx) |
| | | pbar.set_description(description) |
| | | if progress_callback: |
| | | try: |
| | | progress_callback(end_idx, num_samples) |
| | | except Exception as e: |
| | | logging.error(f"progress_callback error: {e}") |
| | | time_speech_total += batch_data_time |
| | | time_escape_total += time_escape |
| | | |
| | |
| | | self.assertEqual(model.cb_model.model_config['merge_thr'], merge_thr) |
| | | # res = model.generate(input="/test.wav", |
| | | # batch_size_s=300) |
| | | |
| | | def test_progress_callback_called(self): |
| | | class DummyModel: |
| | | def __init__(self): |
| | | self.param = torch.nn.Parameter(torch.zeros(1)) |
| | | |
| | | def parameters(self): |
| | | return iter([self.param]) |
| | | |
| | | def eval(self): |
| | | pass |
| | | |
| | | def inference(self, data_in=None, **kwargs): |
| | | results = [{"text": str(d)} for d in data_in] |
| | | return results, {"batch_data_time": 1} |
| | | |
| | | am = AutoModel.__new__(AutoModel) |
| | | am.model = DummyModel() |
| | | am.kwargs = {"batch_size": 2, "disable_pbar": True} |
| | | |
| | | progress = [] |
| | | |
| | | res = AutoModel.inference( |
| | | am, |
| | | ["a", "b", "c"], |
| | | progress_callback=lambda idx, total: progress.append((idx, total)), |
| | | ) |
| | | |
| | | self.assertEqual(len(progress), 2) |
| | | self.assertEqual(progress, [(2, 3), (3, 3)]) |
| | | |
| | | |
| | | if __name__ == '__main__': |
| | | unittest.main() |