From c91430542e219463b12530145cc338d26ef7c358 Mon Sep 17 00:00:00 2001 From: "shixian.shi" Date: Thu, 4 May 2023 16:15:26 +0800 Subject: [PATCH] update --- .../finetune.py | 36 +++++++++++++++++++ .../infer_aishell1_subtest_demo.py | 5 +++ funasr/bin/asr_inference_paraformer.py | 3 +- .../models/e2e_asr_contextual_paraformer.py | 6 ++-- 4 files changed, 45 insertions(+), 5 deletions(-) create mode 100644 egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/finetune.py diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/finetune.py b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/finetune.py new file mode 100644 index 000000000..796cf3715 --- /dev/null +++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/finetune.py @@ -0,0 +1,36 @@ +import os + +from modelscope.metainfo import Trainers +from modelscope.trainers import build_trainer + +from funasr.datasets.ms_dataset import MsDataset +from funasr.utils.modelscope_param import modelscope_args + + +def modelscope_finetune(params): + if not os.path.exists(params.output_dir): + os.makedirs(params.output_dir, exist_ok=True) + # dataset split ["train", "validation"] + ds_dict = MsDataset.load(params.data_path) + kwargs = dict( + model=params.model, + data_dir=ds_dict, + dataset_type=params.dataset_type, + work_dir=params.output_dir, + batch_bins=params.batch_bins, + max_epoch=params.max_epoch, + lr=params.lr) + trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs) + trainer.train() + + +if __name__ == '__main__': + params = modelscope_args(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", data_path="./data") + 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 = 2000 # 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 = 50 # 最大训练轮数 + params.lr = 0.00005 # 设置学习率 + + modelscope_finetune(params) diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer_aishell1_subtest_demo.py b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer_aishell1_subtest_demo.py index 18897b1a1..c3e18b43e 100644 --- a/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer_aishell1_subtest_demo.py +++ b/egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/infer_aishell1_subtest_demo.py @@ -1,3 +1,4 @@ +from itertools import count import os import tempfile import codecs @@ -19,11 +20,15 @@ if __name__ == '__main__': os.makedirs(work_dir) wav_file_path = os.path.join(work_dir, "wav.scp") + counter = 0 with codecs.open(wav_file_path, 'w') as fin: for line in ds_dict: + counter += 1 wav = line["Audio:FILE"] idx = wav.split("/")[-1].split(".")[0] fin.writelines(idx + " " + wav + "\n") + if counter == 50: + break audio_in = wav_file_path inference_pipeline = pipeline( diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py index 5546c92ae..5335860f4 100644 --- a/funasr/bin/asr_inference_paraformer.py +++ b/funasr/bin/asr_inference_paraformer.py @@ -41,6 +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 +from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard from funasr.bin.tp_inference import SpeechText2Timestamp @@ -236,7 +237,7 @@ class Speech2Text: pre_token_length = pre_token_length.round().long() if torch.max(pre_token_length) < 1: return [] - if not isinstance(self.asr_model, ContextualParaformer): + if not isinstance(self.asr_model, ContextualParaformer) and not isinstance(self.asr_model, NeatContextualParaformer): if self.hotword_list: logging.warning("Hotword is given but asr model is not a ContextualParaformer.") decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length) diff --git a/funasr/models/e2e_asr_contextual_paraformer.py b/funasr/models/e2e_asr_contextual_paraformer.py index e1dfe6cf0..93027ec2a 100644 --- a/funasr/models/e2e_asr_contextual_paraformer.py +++ b/funasr/models/e2e_asr_contextual_paraformer.py @@ -68,7 +68,7 @@ class NeatContextualParaformer(Paraformer): target_buffer_length: int = -1, inner_dim: int = 256, bias_encoder_type: str = 'lstm', - use_decoder_embedding: bool = True, + use_decoder_embedding: bool = False, crit_attn_weight: float = 0.0, crit_attn_smooth: float = 0.0, bias_encoder_dropout_rate: float = 0.0, @@ -340,7 +340,7 @@ class NeatContextualParaformer(Paraformer): input_mask_expand_dim, 0) return sematic_embeds * tgt_mask, decoder_out * tgt_mask - def cal_decoder_with_predictor_with_hwlist_advanced(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None): + def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None): if hw_list is None: hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list hw_list_pad = pad_list(hw_list, 0) @@ -350,7 +350,6 @@ class NeatContextualParaformer(Paraformer): hw_embed = self.bias_embed(hw_list_pad) hw_embed, (h_n, _) = self.bias_encoder(hw_embed) else: - # hw_list = hw_list[1:] + [hw_list[0]] # reorder hw_lengths = [len(i) for i in hw_list] hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device) if self.use_decoder_embedding: @@ -366,7 +365,6 @@ class NeatContextualParaformer(Paraformer): if _h_n is not None: h_n = _h_n hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1) - # import pdb; pdb.set_trace() decoder_outs = self.decoder( encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed