python/FunASR-XL.git

			@@ -54,7 +54,7 @@
			assert check_argument_types()
			# 1. Build ASR model
			tp_model, tp_train_args = ASRTask.build_model_from_file(
			timestamp_infer_config, timestamp_model_file, device
			timestamp_infer_config, timestamp_model_file, device=device
			)
			if 'cuda' in device:
			tp_model = tp_model.cuda() # force model to cuda
			@@ -222,6 +222,13 @@
			split_with_space=split_with_space,
			seg_dict_file=seg_dict_file,
			)

			if output_dir is not None:
			writer = DatadirWriter(output_dir)
			tp_writer = writer[f"timestamp_prediction"]
			# ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
			else:
			tp_writer = None

			def _forward(
			data_path_and_name_and_type,
			@@ -230,7 +237,14 @@
			fs: dict = None,
			param_dict: dict = None,
			**kwargs
			):
			):
			output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
			writer = None
			if output_path is not None:
			writer = DatadirWriter(output_path)
			tp_writer = writer[f"timestamp_prediction"]
			else:
			tp_writer = None
			# 3. Build data-iterator
			if data_path_and_name_and_type is None and raw_inputs is not None:
			if isinstance(raw_inputs, torch.Tensor):
			@@ -268,6 +282,9 @@
			ts_str, ts_list = ts_prediction_lfr6_standard(us_alphas[batch_id], us_cif_peak[batch_id], token, force_time_shift=-3.0)
			logging.warning(ts_str)
			item = {'key': key, 'value': ts_str, 'timestamp':ts_list}
			if tp_writer is not None:
			tp_writer["tp_sync"][key+'#'] = ts_str
			tp_writer["tp_time"][key+'#'] = str(ts_list)
			tp_result_list.append(item)
			return tp_result_list