update

shixian.shi

2023-05-04 c91430542e219463b12530145cc338d26ef7c358

update

 egs_modelscope/asr/paraformer/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/finetune.py

New file
@@ -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)

 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 @@
        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(

 funasr/bin/asr_inference_paraformer.py

@@ -41,6 +41,7 @@
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 @@
        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)

 funasr/models/e2e_asr_contextual_paraformer.py

@@ -68,7 +68,7 @@
        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 @@
            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 @@
                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 @@
                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