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Merge pull request #181 from alibaba-damo-academy/dev_lzr

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support BiCifParaformer timestamp output without vad
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zhifu gao 2023-03-03 11:01:54 +08:00 committed by GitHub
commit 026ad27d9d
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1 changed files with 33 additions and 4 deletions
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37
funasr/bin/asr_inference_paraformer.py
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@ -42,6 +42,7 @@ 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.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import time_stamp_lfr6_pl, time_stamp_sentence
class Speech2Text:
@ -190,7 +191,8 @@ class Speech2Text:
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
begin_time: int = 0, end_time: int = None,
):
"""Inference
@ -242,6 +244,10 @@ class Speech2Text:
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]
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):
@ -284,7 +290,11 @@ class Speech2Text:
else:
text = None
results.append((text, token, token_int, hyp, 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, hyp, timestamp, enc_len_batch_total, lfr_factor))
else:
results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
@ -683,6 +693,11 @@ def inference_modelscope(
inference=True,
)
if param_dict is not None:
use_timestamp = param_dict.get('use_timestamp', True)
else:
use_timestamp = True
forward_time_total = 0.0
length_total = 0.0
finish_count = 0
@ -724,7 +739,9 @@ def inference_modelscope(
result = [results[batch_id][:-2]]
key = keys[batch_id]
for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
for n, result in zip(range(1, nbest + 1), result):
text, token, token_int, hyp = result[0], result[1], result[2], result[3]
time_stamp = None if len(result) < 5 else result[4]
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
@ -736,8 +753,20 @@ def inference_modelscope(
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
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)
time_stamp_postprocessed = ""
if len(postprocessed_result) == 3:
text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
postprocessed_result[1], \
postprocessed_result[2]
else:
text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
item = {'key': key, 'value': text_postprocessed}
if time_stamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)