Merge pull request #83 from alibaba-damo-academy/dev_lzr

hnluo

2023-02-09 579b998ded8756f1c0213c94fc7adb5ef8bb5647

Merge pull request #83 from alibaba-damo-academy/dev_lzr

remove useless vars and fix bug in predictor tail_process_fn

 funasr/bin/asr_inference.py

@@ -464,16 +464,6 @@
    
    return _forward

def set_parameters(language: str = None,
                   sample_rate: Union[int, Dict[Any, int]] = None):
    if language is not None:
        global global_asr_language
        global_asr_language = language
    if sample_rate is not None:
        global global_sample_rate
        global_sample_rate = sample_rate


def get_parser():
    parser = config_argparse.ArgumentParser(
        description="ASR Decoding",

 funasr/models/predictor/cif.py

@@ -68,7 +68,8 @@
            mask_2 = torch.cat([ones_t, mask], dim=1)

            mask = mask_2 - mask_1

            tail_threshold = mask * tail_threshold

            alphas = torch.cat([alphas, tail_threshold], dim=1)

            alphas = torch.cat([alphas, zeros_t], dim=1)

            alphas = torch.add(alphas, tail_threshold)

        else:

            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

            tail_threshold = torch.reshape(tail_threshold, (1, 1))

@@ -597,7 +598,8 @@
            mask_2 = torch.cat([ones_t, mask], dim=1)

            mask = mask_2 - mask_1

            tail_threshold = mask * tail_threshold

            alphas = torch.cat([alphas, tail_threshold], dim=1)

            alphas = torch.cat([alphas, zeros_t], dim=1)

            alphas = torch.add(alphas, tail_threshold)

        else:

            tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)

            tail_threshold = torch.reshape(tail_threshold, (1, 1))


 funasr/utils/asr_utils.py

@@ -186,22 +186,11 @@

    return wav_list


def set_parameters(language: str = None):
    if language is not None:
        global global_asr_language
        global_asr_language = language


def compute_wer(hyp_list: List[Any],
                ref_list: List[Any],
                lang: str = None) -> Dict[str, Any]:
    assert len(hyp_list) > 0, 'hyp list is empty'
    assert len(ref_list) > 0, 'ref list is empty'

    if lang is not None:
        global global_asr_language
        global_asr_language = lang

    rst = {
        'Wrd': 0,
@@ -216,12 +205,15 @@
        'wrong_sentences': 0
    }

    if lang is None:
        lang = global_asr_language

    for h_item in hyp_list:
        for r_item in ref_list:
            if h_item['key'] == r_item['key']:
                out_item = compute_wer_by_line(h_item['value'],
                                               r_item['value'],
                                               global_asr_language)
                                               lang)
                rst['Wrd'] += out_item['nwords']
                rst['Corr'] += out_item['cor']
                rst['wrong_words'] += out_item['wrong']