python/FunASR-XL.git

			@@ -529,8 +529,9 @@
			nbest=nbest,
			)
			speech2text = Speech2Text(**speech2text_kwargs)

			text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
			text2punc = None
			if punc_model_file is not None:
			text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)

			if output_dir is not None:
			writer = DatadirWriter(output_dir)
			@@ -560,38 +561,28 @@
			allow_variable_data_keys=allow_variable_data_keys,
			inference=True,
			)

			forward_time_total = 0.0
			length_total = 0.0

			finish_count = 0
			file_count = 1
			lfr_factor = 6
			# 7 .Start for-loop
			asr_result_list = []
			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)
			ibest_writer = writer[f"1best_recog"]
			# ibest_writer["punc_dict"][""] = " ".join(punc_infer_config.punc_list)
			# ibest_writer["token_list"][""] = " ".join(asr_train_config.token_list)
			else:
			writer = None


			for keys, batch in loader:
			assert isinstance(batch, dict), type(batch)
			assert all(isinstance(s, str) for s in keys), keys
			_bs = len(next(iter(batch.values())))
			assert len(keys) == _bs, f"{len(keys)} != {_bs}"
			# batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}

			logging.info("decoding, utt_id: {}".format(keys))
			# N-best list of (text, token, token_int, hyp_object)
			time_beg = time.time()

			vad_results = speech2vadsegment(**batch)
			time_end = time.time()
			fbanks, vadsegments = vad_results[0], vad_results[1]
			for i, segments in enumerate(vadsegments):
			result_segments = [["", [], [], ]]
			result_segments = [["", [], [], []]]
			for j, segment_idx in enumerate(segments):
			bed_idx, end_idx = int(segment_idx[0] / 10), int(segment_idx[1] / 10)
			segment = fbanks[:, bed_idx:end_idx, :].to(device)
			@@ -600,76 +591,51 @@
			"end_time": vadsegments[i][j][1]}
			results = speech2text(**batch)
			if len(results) < 1:
			hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
			results = [[" ", ["sil"], [2], 0, 1, 6]] * nbest
			time_end = time.time()
			forward_time = time_end - time_beg
			lfr_factor = results[0][-1]
			length = results[0][-2]
			forward_time_total += forward_time
			length_total += length
			logging.info(
			"decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
			format(length, forward_time, 100 * forward_time / (length * lfr_factor)))
			continue

			result_cur = [results[0][:-2]]
			if j == 0:
			result_segments = result_cur
			else:
			result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]


			key = keys[0]
			result = result_segments[0]
			text, token, token_int = result[0], result[1], result[2]
			time_stamp = None if len(result) < 4 else result[3]

			# Create a directory: outdir/{n}best_recog

			postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
			text_postprocessed = ""
			time_stamp_postprocessed = ""
			text_postprocessed_punc = postprocessed_result
			if len(postprocessed_result) == 3:
			text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
			postprocessed_result[1], \
			postprocessed_result[2]
			text_postprocessed_punc = ""
			if len(word_lists) > 0 and text2punc is not None:
			text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)

			item = {'key': key, 'value': text_postprocessed_punc}
			if text_postprocessed != "":
			item['text_postprocessed'] = text_postprocessed
			if time_stamp_postprocessed != "":
			item['time_stamp'] = time_stamp_postprocessed

			asr_result_list.append(item)
			finish_count += 1
			# asr_utils.print_progress(finish_count / file_count)
			if writer is not None:
			# Write the result to each file
			ibest_writer["token"][key] = " ".join(token)
			ibest_writer["token_int"][key] = " ".join(map(str, token_int))
			ibest_writer["vad"][key] = "{}".format(vadsegments)

			if text is not None:
			postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
			if len(postprocessed_result) == 3:
			text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
			postprocessed_result[1], \
			postprocessed_result[2]
			if len(word_lists) > 0:
			text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
			text_postprocessed_punc_time_stamp = json.dumps({"predictions": text_postprocessed_punc,
			"time_stamp": time_stamp_postprocessed},
			ensure_ascii=False)
			else:
			text_postprocessed_punc = ""
			punc_id_list = []
			text_postprocessed_punc_time_stamp = ""

			else:
			text_postprocessed = ""
			time_stamp_postprocessed = ""
			word_lists = ""
			text_postprocessed_punc_time_stamp = ""
			punc_id_list = ""
			text_postprocessed_punc = ""

			item = {'key': key, 'value': text_postprocessed_punc, 'text_postprocessed': text_postprocessed,
			'time_stamp': time_stamp_postprocessed, 'token': token}
			asr_result_list.append(item)
			finish_count += 1
			# asr_utils.print_progress(finish_count / file_count)
			if writer is not None:
			ibest_writer["text"][key] = text_postprocessed
			ibest_writer["punc_id"][key] = "{}".format(punc_id_list)
			ibest_writer["text_with_punc"][key] = text_postprocessed_punc_time_stamp
			if time_stamp_postprocessed is not None:
			ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)

			logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc,
			time_stamp_postprocessed))

			logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
			format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor+1e-6)))
			ibest_writer["text"][key] = text_postprocessed
			ibest_writer["text_with_punc"][key] = text_postprocessed_punc
			if time_stamp_postprocessed is not None:
			ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)

			logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
			return asr_result_list
			return _forward