update com define

雾聪

2024-03-14 3549c0106e5a35ef2ddffdfd7381e613ed5310bd

 runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py

@@ -5,9 +5,9 @@
import os.path
from pathlib import Path
from typing import List, Union, Tuple
import json

import copy
import torch
import librosa
import numpy as np

@@ -18,7 +18,7 @@
                                      sentence_postprocess_sentencepiece)
from .utils.frontend import WavFrontend
from .utils.timestamp_utils import time_stamp_lfr6_onnx
from .utils.utils import pad_list, make_pad_mask
from .utils.utils import pad_list

logging = get_logger()

@@ -56,25 +56,24 @@
        if not os.path.exists(model_file):
            print(".onnx is not exist, begin to export onnx")
            try:
                from funasr.export.export_model import ModelExport
                from funasr import AutoModel
            except:
                raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
                      "\npip3 install -U funasr\n" \
                      "For the users in China, you could install with the command:\n" \
                      "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
            export_model = ModelExport(
                cache_dir=cache_dir,
                onnx=True,
                device="cpu",
                quant=quantize,
            )
            export_model.export(model_dir)

            model = AutoModel(model=model_dir)
            model_dir = model.export(type="onnx", quantize=quantize)
            
        config_file = os.path.join(model_dir, 'config.yaml')
        cmvn_file = os.path.join(model_dir, 'am.mvn')
        config = read_yaml(config_file)
        token_list = os.path.join(model_dir, 'tokens.json')
        with open(token_list, 'r', encoding='utf-8') as f:
            token_list = json.load(f)

        self.converter = TokenIDConverter(config['token_list'])
        self.converter = TokenIDConverter(token_list)
        self.tokenizer = CharTokenizer()
        self.frontend = WavFrontend(
            cmvn_file=cmvn_file,
@@ -309,7 +308,7 @@
        # index from bias_embed
        bias_embed = bias_embed.transpose(1, 0, 2)
        _ind = np.arange(0, len(hotwords)).tolist()
        bias_embed = bias_embed[_ind, hotwords_length.cpu().numpy().tolist()]
        bias_embed = bias_embed[_ind, hotwords_length.tolist()]
        waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
        waveform_nums = len(waveform_list)
        asr_res = []
@@ -336,7 +335,7 @@
        hotwords = hotwords.split(" ")
        hotwords_length = [len(i) - 1 for i in hotwords]
        hotwords_length.append(0)
        hotwords_length = torch.Tensor(hotwords_length).to(torch.int32)
        hotwords_length = np.array(hotwords_length)
        # hotwords.append('<s>')
        def word_map(word):
            hotwords = []
@@ -346,11 +345,12 @@
                    logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word))
                else:
                    hotwords.append(self.vocab[c])
            return torch.tensor(hotwords)
            return np.array(hotwords)
        hotword_int = [word_map(i) for i in hotwords]
        # import pdb; pdb.set_trace()
        hotword_int.append(torch.tensor([1]))
        hotword_int.append(np.array([1]))
        hotwords = pad_list(hotword_int, pad_value=0, max_len=10)
        # import pdb; pdb.set_trace()
        return hotwords, hotwords_length

    def bb_infer(self, feats: np.ndarray,
@@ -359,7 +359,7 @@
        return outputs

    def eb_infer(self, hotwords, hotwords_length):
        outputs = self.ort_infer_eb([hotwords.to(torch.int32).numpy(), hotwords_length.to(torch.int32).numpy()])
        outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)])
        return outputs

    def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]: