diff --git a/README.md b/README.md index 6d44e6d82..eaeb9ba4a 100644 --- a/README.md +++ b/README.md @@ -17,21 +17,23 @@ ## What's new: -### 2023.2.16, funasr-0.2.0 -- We support a new feature, export paraformer models into [onnx and torchscripts](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export) from modelscopes. The local finetuned models are also supported. -- We support a new feature, [onnxruntime](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer), you could deploy the runtime without modelscope or funasr, for the [paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model, the rtf of onnxruntime is 3x speedup(0.110->0.038) on cpu. -- We support e new feature, [grpc](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/grpc), you could build the ASR service with grpc, by deploying the modelscope pipeline or onnxruntime. +### 2023.2.17, funasr-0.2.0, modelscope-1.3.0 +- We support a new feature, export paraformer models into [onnx and torchscripts](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export) from modelscope. The local finetuned models are also supported. +- We support a new feature, [onnxruntime](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer), you could deploy the runtime without modelscope or funasr, for the [paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) model, the rtf of onnxruntime is 3x speedup(0.110->0.038) on cpu, [details](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer#speed). +- We support a new feature, [grpc](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/python/grpc), you could build the ASR service with grpc, by deploying the modelscope pipeline or onnxruntime. - We release a new model [paraformer-large-contextual](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary), which supports the hotword customization based on the incentive enhancement, and improves the recall and precision of hotwords. +- We optimize the timestamp alignment of [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), the prediction accuracy of timestamp is much improved, and achieving accumulated average shift (aas) of 74.7ms, [details](https://arxiv.org/abs/2301.12343). - We release a new model, [8k VAD model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary), which could predict the duration of none-silence speech. It could be freely integrated with any ASR models in [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary). -- We release a new model, [MFCCA](https://www.modelscope.cn/models/yufan6/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary), a multi-channel multi-speaker model which is independent of the number and geometry of microphones and supports Mandarin meeting transcription. +- We release a new model, [MFCCA](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary), a multi-channel multi-speaker model which is independent of the number and geometry of microphones and supports Mandarin meeting transcription. - We release several new UniASR model: -[Southern Fujian Dialect model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary), +[Southern Fujian Dialect model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/summary), [French model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/summary), [German model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/summary), [Vietnamese model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/summary), [Persian model](https://modelscope.cn/models/damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/summary). - We release a new model, [paraformer-data2vec model](https://www.modelscope.cn/models/damo/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/summary), an unsupervised pretraining model on AISHELL-2, which is inited for paraformer model and then finetune on AISHEL-1. -### 2023.1.16, funasr-0.1.6 +- Various new types of audio input types are now supported by modelscope inference pipeline, including: mp3、flac、ogg、opus... +### 2023.1.16, funasr-0.1.6， modelscope-1.2.0 - We release a new version model [Paraformer-large-long](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), which integrate the [VAD](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary) model, [ASR](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary), [Punctuation](https://www.modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/summary) model and timestamp together. The model could take in several hours long inputs. - We release a new model, [16k VAD model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/summary), which could predict the duration of none-silence speech. It could be freely integrated with any ASR models in [modelscope](https://www.modelscope.cn/models/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary). @@ -101,4 +103,10 @@ This project is licensed under the [The MIT License](https://opensource.org/lice booktitle={INTERSPEECH}, year={2022} } +@inproceedings{Shi2023AchievingTP, + title={Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model}, + author={Xian Shi and Yanni Chen and Shiliang Zhang and Zhijie Yan}, + booktitle={arXiv preprint arXiv:2301.12343} + year={2023} +} ``` \ No newline at end of file diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/RESULTS.md b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/RESULTS.md index 8f5825966..716d44eeb 100644 --- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/RESULTS.md +++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/RESULTS.md @@ -1,5 +1,5 @@ # Paraformer-Large -- Model link: +- Model link: - Model size: 45M # Environments diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/finetune.py b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/finetune.py index 281292ffa..bf8176ed1 100755 --- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/finetune.py +++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/finetune.py @@ -24,12 +24,12 @@ def modelscope_finetune(params): if __name__ == '__main__': - params = modelscope_args(model="yufan6/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950") + params = modelscope_args(model="NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950") params.output_dir = "./checkpoint" # m模型保存路径 params.data_path = "./example_data/" # 数据路径 params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large params.batch_bins = 1000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒， params.max_epoch = 10 # 最大训练轮数 params.lr = 0.0001 # 设置学习率 - params.model_revision = 'v2.0.0' + params.model_revision = 'v1.0.0' modelscope_finetune(params) diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py index 3054394d3..fa22aad05 100755 --- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py +++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer.py @@ -18,8 +18,8 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx): os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id) inference_pipline = pipeline( task=Tasks.auto_speech_recognition, - model='yufan6/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950', - model_revision='v2.0.0', + model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950', + model_revision='v1.0.0', output_dir=output_dir_job, batch_size=1, ) diff --git a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py index 00faad0c2..e714a3d03 100755 --- a/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py +++ b/egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py @@ -59,7 +59,7 @@ def modelscope_infer_after_finetune(params): if __name__ == '__main__': params = {} - params["modelscope_model_name"] = "yufan6/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950" + params["modelscope_model_name"] = "NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950" params["required_files"] = ["feats_stats.npz", "decoding.yaml", "configuration.json"] params["output_dir"] = "./checkpoint" params["data_dir"] = "./example_data/validation" diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py index be35e78bf..18d788e9f 100644 --- a/funasr/bin/asr_inference_paraformer.py +++ b/funasr/bin/asr_inference_paraformer.py @@ -41,16 +41,7 @@ from funasr.utils.types import str_or_none from funasr.utils import asr_utils, wav_utils, postprocess_utils from funasr.models.frontend.wav_frontend import WavFrontend from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer - - -header_colors = '\033[95m' -end_colors = '\033[0m' - -global_asr_language: str = 'zh-cn' -global_sample_rate: Union[int, Dict[Any, int]] = { - 'audio_fs': 16000, - 'model_fs': 16000 -} +from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export class Speech2Text: @@ -346,6 +337,160 @@ class Speech2Text: # assert check_return_type(results) return results +class Speech2TextExport: + """Speech2TextExport class + + """ + + def __init__( + self, + asr_train_config: Union[Path, str] = None, + asr_model_file: Union[Path, str] = None, + cmvn_file: Union[Path, str] = None, + lm_train_config: Union[Path, str] = None, + lm_file: Union[Path, str] = None, + token_type: str = None, + bpemodel: str = None, + device: str = "cpu", + maxlenratio: float = 0.0, + minlenratio: float = 0.0, + dtype: str = "float32", + beam_size: int = 20, + ctc_weight: float = 0.5, + lm_weight: float = 1.0, + ngram_weight: float = 0.9, + penalty: float = 0.0, + nbest: int = 1, + frontend_conf: dict = None, + hotword_list_or_file: str = None, + **kwargs, + ): + + # 1. Build ASR model + asr_model, asr_train_args = ASRTask.build_model_from_file( + asr_train_config, asr_model_file, cmvn_file, device + ) + frontend = None + if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None: + frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf) + + logging.info("asr_model: {}".format(asr_model)) + logging.info("asr_train_args: {}".format(asr_train_args)) + asr_model.to(dtype=getattr(torch, dtype)).eval() + + token_list = asr_model.token_list + + + + logging.info(f"Decoding device={device}, dtype={dtype}") + + # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text + if token_type is None: + token_type = asr_train_args.token_type + if bpemodel is None: + bpemodel = asr_train_args.bpemodel + + if token_type is None: + tokenizer = None + elif token_type == "bpe": + if bpemodel is not None: + tokenizer = build_tokenizer(token_type=token_type, bpemodel=bpemodel) + else: + tokenizer = None + else: + tokenizer = build_tokenizer(token_type=token_type) + converter = TokenIDConverter(token_list=token_list) + logging.info(f"Text tokenizer: {tokenizer}") + + # self.asr_model = asr_model + self.asr_train_args = asr_train_args + self.converter = converter + self.tokenizer = tokenizer + + self.device = device + self.dtype = dtype + self.nbest = nbest + self.frontend = frontend + + model = Paraformer_export(asr_model, onnx=False) + self.asr_model = model + + @torch.no_grad() + def __call__( + self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None + ): + """Inference + + Args: + speech: Input speech data + Returns: + text, token, token_int, hyp + + """ + assert check_argument_types() + + # Input as audio signal + if isinstance(speech, np.ndarray): + speech = torch.tensor(speech) + + if self.frontend is not None: + feats, feats_len = self.frontend.forward(speech, speech_lengths) + feats = to_device(feats, device=self.device) + feats_len = feats_len.int() + self.asr_model.frontend = None + else: + feats = speech + feats_len = speech_lengths + + enc_len_batch_total = feats_len.sum() + lfr_factor = max(1, (feats.size()[-1] // 80) - 1) + batch = {"speech": feats, "speech_lengths": feats_len} + + # a. To device + batch = to_device(batch, device=self.device) + + decoder_outs = self.asr_model(**batch) + decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1] + + results = [] + b, n, d = decoder_out.size() + for i in range(b): + am_scores = decoder_out[i, :ys_pad_lens[i], :] + + yseq = am_scores.argmax(dim=-1) + score = am_scores.max(dim=-1)[0] + score = torch.sum(score, dim=-1) + # pad with mask tokens to ensure compatibility with sos/eos tokens + yseq = torch.tensor( + yseq.tolist(), device=yseq.device + ) + nbest_hyps = [Hypothesis(yseq=yseq, score=score)] + + for hyp in nbest_hyps: + assert isinstance(hyp, (Hypothesis)), type(hyp) + + # remove sos/eos and get results + last_pos = -1 + if isinstance(hyp.yseq, list): + token_int = hyp.yseq[1:last_pos] + else: + token_int = hyp.yseq[1:last_pos].tolist() + + # remove blank symbol id, which is assumed to be 0 + token_int = list(filter(lambda x: x != 0 and x != 2, token_int)) + + # Change integer-ids to tokens + token = self.converter.ids2tokens(token_int) + + if self.tokenizer is not None: + text = self.tokenizer.tokens2text(token) + else: + text = None + + results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor)) + + return results + def inference( maxlenratio: float, @@ -454,9 +599,11 @@ def inference_modelscope( level=log_level, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) - + + export_mode = False if param_dict is not None: hotword_list_or_file = param_dict.get('hotword') + export_mode = param_dict.get("export_mode", False) else: hotword_list_or_file = None @@ -490,7 +637,10 @@ def inference_modelscope( nbest=nbest, hotword_list_or_file=hotword_list_or_file, ) - speech2text = Speech2Text(**speech2text_kwargs) + if export_mode: + speech2text = Speech2TextExport(**speech2text_kwargs) + else: + speech2text = Speech2Text(**speech2text_kwargs) def _forward( data_path_and_name_and_type, diff --git a/funasr/bin/asr_inference_paraformer_vad.py b/funasr/bin/asr_inference_paraformer_vad.py index dbb271986..c01c6ba5e 100644 --- a/funasr/bin/asr_inference_paraformer_vad.py +++ b/funasr/bin/asr_inference_paraformer_vad.py @@ -38,7 +38,6 @@ from funasr.utils.types import str_or_none from funasr.utils import asr_utils, wav_utils, postprocess_utils from funasr.models.frontend.wav_frontend import WavFrontend from funasr.tasks.vad import VADTask -from funasr.utils.timestamp_tools import time_stamp_lfr6 from funasr.bin.punctuation_infer import Text2Punc from funasr.bin.asr_inference_paraformer_vad_punc import Speech2Text from funasr.bin.asr_inference_paraformer_vad_punc import Speech2VadSegment diff --git a/funasr/bin/asr_inference_paraformer_vad_punc.py b/funasr/bin/asr_inference_paraformer_vad_punc.py index ee361351a..f194830b2 100644 --- a/funasr/bin/asr_inference_paraformer_vad_punc.py +++ b/funasr/bin/asr_inference_paraformer_vad_punc.py @@ -39,7 +39,7 @@ from funasr.utils.types import str_or_none from funasr.utils import asr_utils, wav_utils, postprocess_utils from funasr.models.frontend.wav_frontend import WavFrontend from funasr.tasks.vad import VADTask -from funasr.utils.timestamp_tools import time_stamp_lfr6, time_stamp_lfr6_pl +from funasr.utils.timestamp_tools import time_stamp_lfr6_pl from funasr.bin.punctuation_infer import Text2Punc from funasr.models.e2e_asr_paraformer import BiCifParaformer @@ -282,13 +282,10 @@ class Speech2Text: else: text = None - if isinstance(self.asr_model, BiCifParaformer): - timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time) - results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor)) - else: - time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, - end_time) - results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor)) + + timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time) + results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor)) + # assert check_return_type(results) return results @@ -636,7 +633,8 @@ def inference_modelscope( text, token, token_int = result[0], result[1], result[2] time_stamp = None if len(result) < 4 else result[3] - if use_timestamp and time_stamp is not None: + + if use_timestamp and time_stamp is not None: postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp) else: postprocessed_result = postprocess_utils.sentence_postprocess(token) diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py index b5c6fa8f9..972f92f24 100644 --- a/funasr/export/export_model.py +++ b/funasr/export/export_model.py @@ -44,6 +44,7 @@ class ASRModelExportParaformer: model, self.export_config, ) + model.eval() # self._export_onnx(model, verbose, export_dir) if self.onnx: self._export_onnx(model, verbose, export_dir) diff --git a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py index d51c6bf1b..64dbaf8ee 100644 --- a/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py +++ b/funasr/runtime/python/onnxruntime/paraformer/rapid_paraformer/paraformer_onnx.py @@ -41,8 +41,8 @@ class Paraformer(): self.ort_infer = OrtInferSession(model_file, device_id) self.batch_size = batch_size - def __call__(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List: - waveform_list = self.load_data(wav_content, fs) + def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List: + waveform_list = self.load_data(wav_content, self.frontend.opts.samp_freq) waveform_nums = len(waveform_list) asr_res = [] diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py index 33d1255cc..f966aeee9 100644 --- a/funasr/utils/timestamp_tools.py +++ b/funasr/utils/timestamp_tools.py @@ -4,88 +4,6 @@ import logging import numpy as np from typing import Any, List, Tuple, Union -def cut_interval(alphas: torch.Tensor, start: int, end: int, tail: bool): - if not tail: - if end == start + 1: - cut = (end + start) / 2.0 - else: - alpha = alphas[start+1: end].tolist() - reverse_steps = 1 - for reverse_alpha in alpha[::-1]: - if reverse_alpha > 0.35: - reverse_steps += 1 - else: - break - cut = end - reverse_steps - else: - if end != len(alphas) - 1: - cut = end + 1 - else: - cut = start + 1 - return float(cut) - -def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text: List[str], begin: int = 0, end: int = None): - time_stamp_list = [] - alphas = alphas[0] - text = copy.deepcopy(raw_text) - if end is None: - time = speech_lengths * 60 / 1000 - sacle_rate = (time / speech_lengths[0]).tolist() - else: - time = (end - begin) / 1000 - sacle_rate = (time / speech_lengths[0]).tolist() - - predictor = (alphas > 0.5).int() - fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist() - - cuts = [] - npeak = int(predictor.sum()) - nchar = len(raw_text) - if npeak - 1 == nchar: - fire_places = torch.where((alphas > 0.5) == 1)[0].tolist() - for i in range(len(fire_places)): - if fire_places[i] < len(alphas) - 1: - if 0.05 < alphas[fire_places[i]+1] < 0.5: - fire_places[i] += 1 - elif npeak < nchar: - lost_num = nchar - npeak - lost_fire = speech_lengths[0].tolist() - fire_places[-1] - interval_distance = lost_fire // (lost_num + 1) - for i in range(1, lost_num + 1): - fire_places.append(fire_places[-1] + interval_distance) - elif npeak - 1 > nchar: - redundance_num = npeak - 1 - nchar - for i in range(redundance_num): - fire_places.pop() - - cuts.append(0) - start_sil = True - if start_sil: - text.insert(0, '') - - for i in range(len(fire_places)-1): - cuts.append(cut_interval(alphas, fire_places[i], fire_places[i+1], tail=(i==len(fire_places)-2))) - - for i in range(2, len(fire_places)-2): - if fire_places[i-2] == fire_places[i-1] - 1 and fire_places[i-1] != fire_places[i] - 1: - cuts[i-1] += 1 - - if cuts[-1] != len(alphas) - 1: - text.append('') - cuts.append(speech_lengths[0].tolist()) - cuts.insert(-1, (cuts[-1] + cuts[-2]) * 0.5) - sec_fire_places = np.array(cuts) * sacle_rate - for i in range(1, len(sec_fire_places) - 1): - start, end = sec_fire_places[i], sec_fire_places[i+1] - if i == len(sec_fire_places) - 2: - end = time - time_stamp_list.append([int(round(start, 2) * 1000) + begin, int(round(end, 2) * 1000) + begin]) - text = text[1:] - if npeak - 1 == nchar or npeak > nchar: - return time_stamp_list[:-1] - else: - return time_stamp_list - def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None): START_END_THRESHOLD = 5 TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled