fix vad results bug

凌匀

2023-02-16 91027ddab49e5791fc42569b4db9dafca55735e6

 funasr/bin/asr_inference_paraformer.py

@@ -3,6 +3,11 @@
import logging
import sys
import time
import copy
import os
import codecs
import tempfile
import requests
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -35,6 +40,8 @@
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'
@@ -78,6 +85,7 @@
            penalty: float = 0.0,
            nbest: int = 1,
            frontend_conf: dict = None,
            hotword_list_or_file: str = None,
            **kwargs,
    ):
        assert check_argument_types()
@@ -168,6 +176,59 @@
        self.asr_train_args = asr_train_args
        self.converter = converter
        self.tokenizer = tokenizer

        # 6. [Optional] Build hotword list from str, local file or url
        # for None 
        if hotword_list_or_file is None:
            self.hotword_list = None
        # for text str input
        elif not os.path.exists(hotword_list_or_file) and not hotword_list_or_file.startswith('http'):
            logging.info("Attempting to parse hotwords as str...")
            self.hotword_list = []
            hotword_str_list = []
            for hw in hotword_list_or_file.strip().split():
                hotword_str_list.append(hw)
                self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
            self.hotword_list.append([self.asr_model.sos])
            hotword_str_list.append('<s>')
            logging.info("Hotword list: {}.".format(hotword_str_list))
        # for local txt inputs
        elif os.path.exists(hotword_list_or_file):
            logging.info("Attempting to parse hotwords from local txt...")
            self.hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))
        # for url, download and generate txt
        else:
            logging.info("Attempting to parse hotwords from url...")
            work_dir = tempfile.TemporaryDirectory().name
            if not os.path.exists(work_dir):
                os.makedirs(work_dir)
            text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
            local_file = requests.get(hotword_list_or_file)
            open(text_file_path, "wb").write(local_file.content)
            hotword_list_or_file = text_file_path
            self.hotword_list = []
            hotword_str_list = []
            with codecs.open(hotword_list_or_file, 'r') as fin:
                for line in fin.readlines():
                    hw = line.strip()
                    hotword_str_list.append(hw)
                    self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
                self.hotword_list.append([self.asr_model.sos])
                hotword_str_list.append('<s>')
            logging.info("Initialized hotword list from file: {}, hotword list: {}."
                .format(hotword_list_or_file, hotword_str_list))


        is_use_lm = lm_weight != 0.0 and lm_file is not None
        if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
            beam_search = None
@@ -229,8 +290,14 @@
        pre_token_length = pre_token_length.round().long()
        if torch.max(pre_token_length) < 1:
            return []
        decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        if not isinstance(self.asr_model, ContextualParaformer):
            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)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
        else:
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        results = []
        b, n, d = decoder_out.size()
@@ -388,6 +455,11 @@
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
    else:
        hotword_list_or_file = None

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
@@ -416,6 +488,7 @@
        ngram_weight=ngram_weight,
        penalty=penalty,
        nbest=nbest,
        hotword_list_or_file=hotword_list_or_file,
    )
    speech2text = Speech2Text(**speech2text_kwargs)

@@ -497,7 +570,7 @@
                        ibest_writer["rtf"][key] = rtf_cur

                    if text is not None:
                        text_postprocessed = postprocess_utils.sentence_postprocess(token)
                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                        item = {'key': key, 'value': text_postprocessed}
                        asr_result_list.append(item)
                        finish_count += 1
@@ -551,7 +624,12 @@
        default=1,
        help="The number of workers used for DataLoader",
    )

    parser.add_argument(
        "--hotword",
        type=str_or_none,
        default=None,
        help="hotword file path or hotwords seperated by space"
    )
    group = parser.add_argument_group("Input data related")
    group.add_argument(
        "--data_path_and_name_and_type",
@@ -679,8 +757,10 @@
    print(get_commandline_args(), file=sys.stderr)
    parser = get_parser()
    args = parser.parse_args(cmd)
    param_dict = {'hotword': args.hotword}
    kwargs = vars(args)
    kwargs.pop("config", None)
    kwargs['param_dict'] = param_dict
    inference(**kwargs)