Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

游雁

2023-02-10 59a791121fccd3c9ca177c4f6d33105a82d23ef3

 funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -14,6 +14,7 @@
from typing import Any
from typing import List
import math
import copy
import numpy as np
import torch
from typeguard import check_argument_types
@@ -38,8 +39,9 @@
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_lfr6
from funasr.utils.timestamp_tools import time_stamp_lfr6, time_stamp_lfr6_pl
from funasr.bin.punctuation_infer import Text2Punc
from funasr.models.e2e_asr_paraformer import BiCifParaformer

header_colors = '\033[95m'
end_colors = '\033[0m'
@@ -234,6 +236,10 @@
        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 isinstance(self.asr_model, BiCifParaformer):
            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                   pre_token_length)  # test no bias cif2

        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
@@ -276,9 +282,12 @@
                else:
                    text = None

                time_stamp = time_stamp_lfr6(alphas[i:i+1,], enc_len[i:i+1,], token, begin_time, end_time)
    
                results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
                if isinstance(self.asr_model, BiCifParaformer):
                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
                    results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
                    results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))

        # assert check_return_type(results)
        return results
@@ -561,6 +570,11 @@
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )

        if param_dict is not None:
            use_timestamp = param_dict.get('use_timestamp', True)
        else:
            use_timestamp = True
    
        finish_count = 0
        file_count = 1
@@ -603,8 +617,11 @@
                result = result_segments[0]
                text, token, token_int = result[0], result[1], result[2]
                time_stamp = None if len(result) < 4 else result[3]
    
                postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
   
                if use_timestamp and time_stamp is not None: 
                    postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
                else:
                    postprocessed_result = postprocess_utils.sentence_postprocess(token)
                text_postprocessed = ""
                time_stamp_postprocessed = ""
                text_postprocessed_punc = postprocessed_result
@@ -612,9 +629,12 @@
                    text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
                                                                               postprocessed_result[1], \
                                                                               postprocessed_result[2]
                    text_postprocessed_punc = text_postprocessed
                    if len(word_lists) > 0 and text2punc is not None:
                        text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
                else:
                    text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]

                text_postprocessed_punc = text_postprocessed
                if len(word_lists) > 0 and text2punc is not None:
                    text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
    
                item = {'key': key, 'value': text_postprocessed_punc}
                if text_postprocessed != "":