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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
fix bug in asr_inference_paraformer_vad_punc and support without punc model
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lzr265946
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bcf6be4c90
commit
5db5950e07
@ -529,8 +529,9 @@ def inference_modelscope(
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nbest=nbest,
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)
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speech2text = Speech2Text(**speech2text_kwargs)
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text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
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text2punc = None
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if punc_model_file is not None:
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text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
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if output_dir is not None:
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writer = DatadirWriter(output_dir)
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@ -560,38 +561,28 @@ def inference_modelscope(
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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forward_time_total = 0.0
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length_total = 0.0
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finish_count = 0
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file_count = 1
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lfr_factor = 6
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# 7 .Start for-loop
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asr_result_list = []
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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writer = None
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if output_path is not None:
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writer = DatadirWriter(output_path)
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ibest_writer = writer[f"1best_recog"]
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# ibest_writer["punc_dict"][""] = " ".join(punc_infer_config.punc_list)
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# ibest_writer["token_list"][""] = " ".join(asr_train_config.token_list)
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else:
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writer = None
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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assert all(isinstance(s, str) for s in keys), keys
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_bs = len(next(iter(batch.values())))
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assert len(keys) == _bs, f"{len(keys)} != {_bs}"
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# batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")}
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logging.info("decoding, utt_id: {}".format(keys))
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# N-best list of (text, token, token_int, hyp_object)
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time_beg = time.time()
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vad_results = speech2vadsegment(**batch)
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time_end = time.time()
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fbanks, vadsegments = vad_results[0], vad_results[1]
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for i, segments in enumerate(vadsegments):
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result_segments = [["", [], [], ]]
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result_segments = [["", [], [], []]]
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for j, segment_idx in enumerate(segments):
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bed_idx, end_idx = int(segment_idx[0] / 10), int(segment_idx[1] / 10)
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segment = fbanks[:, bed_idx:end_idx, :].to(device)
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@ -600,76 +591,51 @@ def inference_modelscope(
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"end_time": vadsegments[i][j][1]}
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results = speech2text(**batch)
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if len(results) < 1:
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hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
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results = [[" ", ["sil"], [2], 0, 1, 6]] * nbest
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time_end = time.time()
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forward_time = time_end - time_beg
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lfr_factor = results[0][-1]
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length = results[0][-2]
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forward_time_total += forward_time
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length_total += length
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logging.info(
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"decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
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format(length, forward_time, 100 * forward_time / (length * lfr_factor)))
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continue
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result_cur = [results[0][:-2]]
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if j == 0:
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result_segments = result_cur
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else:
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result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
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key = keys[0]
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result = result_segments[0]
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text, token, token_int = result[0], result[1], result[2]
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time_stamp = None if len(result) < 4 else result[3]
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# Create a directory: outdir/{n}best_recog
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postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
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text_postprocessed = ""
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time_stamp_postprocessed = ""
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text_postprocessed_punc = postprocessed_result
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if len(postprocessed_result) == 3:
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text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
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postprocessed_result[1], \
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postprocessed_result[2]
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text_postprocessed_punc = ""
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if len(word_lists) > 0 and text2punc is not None:
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text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
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item = {'key': key, 'value': text_postprocessed_punc}
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if text_postprocessed != "":
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item['text_postprocessed'] = text_postprocessed
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if time_stamp_postprocessed != "":
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item['time_stamp'] = time_stamp_postprocessed
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asr_result_list.append(item)
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finish_count += 1
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# asr_utils.print_progress(finish_count / file_count)
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if writer is not None:
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# Write the result to each file
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ibest_writer["token"][key] = " ".join(token)
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ibest_writer["token_int"][key] = " ".join(map(str, token_int))
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ibest_writer["vad"][key] = "{}".format(vadsegments)
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if text is not None:
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postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
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if len(postprocessed_result) == 3:
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text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
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postprocessed_result[1], \
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postprocessed_result[2]
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if len(word_lists) > 0:
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text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
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text_postprocessed_punc_time_stamp = json.dumps({"predictions": text_postprocessed_punc,
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"time_stamp": time_stamp_postprocessed},
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ensure_ascii=False)
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else:
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text_postprocessed_punc = ""
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punc_id_list = []
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text_postprocessed_punc_time_stamp = ""
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else:
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text_postprocessed = ""
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time_stamp_postprocessed = ""
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word_lists = ""
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text_postprocessed_punc_time_stamp = ""
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punc_id_list = ""
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text_postprocessed_punc = ""
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item = {'key': key, 'value': text_postprocessed_punc, 'text_postprocessed': text_postprocessed,
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'time_stamp': time_stamp_postprocessed, 'token': token}
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asr_result_list.append(item)
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finish_count += 1
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# asr_utils.print_progress(finish_count / file_count)
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if writer is not None:
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ibest_writer["text"][key] = text_postprocessed
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ibest_writer["punc_id"][key] = "{}".format(punc_id_list)
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ibest_writer["text_with_punc"][key] = text_postprocessed_punc_time_stamp
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if time_stamp_postprocessed is not None:
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ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
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logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc,
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time_stamp_postprocessed))
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logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
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format(length_total, forward_time_total, 100 * forward_time_total / (length_total * lfr_factor+1e-6)))
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ibest_writer["text"][key] = text_postprocessed
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ibest_writer["text_with_punc"][key] = text_postprocessed_punc
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if time_stamp_postprocessed is not None:
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ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
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logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
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return asr_result_list
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return _forward
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