From 8cd93a1fc77e8a4a119f248ec0eb9018a74f728b Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期二, 21 三月 2023 19:41:40 +0800
Subject: [PATCH] asr_inference pipeline supports combine tp model
---
funasr/bin/asr_inference_paraformer.py | 24 +++++++++++++-----------
funasr/bin/tp_inference.py | 4 ++--
2 files changed, 15 insertions(+), 13 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index 7e159fd..2eeffcd 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -756,16 +756,18 @@
key = keys[batch_id]
for n, result in zip(range(1, nbest + 1), result):
- # import pdb; pdb.set_trace()
text, token, token_int, hyp = result[0], result[1], result[2], result[3]
- time_stamp = None if len(result) < 5 else result[4]
+ timestamp = None if len(result) < 5 else result[4]
# conduct timestamp prediction here
- if time_stamp is None and speechtext2timestamp:
+ # timestamp inference requires token length
+ # thus following inference cannot be conducted in batch
+ if timestamp is None and speechtext2timestamp:
ts_batch = {}
- ts_batch['speech'] = batch['speech'][batch_id].squeeze(0)
+ ts_batch['speech'] = batch['speech'][batch_id].unsqueeze(0)
ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
ts_batch['text_lengths'] = torch.tensor([len(token)])
- import pdb; pdb.set_trace()
+ us_alphas, us_peaks = speechtext2timestamp(**ts_batch)
+ ts_str, timestamp = ts_prediction_lfr6_standard(us_alphas[0], us_peaks[0], token, force_time_shift=-3.0)
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
@@ -777,20 +779,20 @@
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
- if use_timestamp and time_stamp is not None:
- postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
+ if use_timestamp and timestamp is not None:
+ postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
else:
postprocessed_result = postprocess_utils.sentence_postprocess(token)
- time_stamp_postprocessed = ""
+ timestamp_postprocessed = ""
if len(postprocessed_result) == 3:
- text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
+ text_postprocessed, timestamp_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
+ if timestamp_postprocessed != "":
+ item['timestamp'] = timestamp_postprocessed
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)
diff --git a/funasr/bin/tp_inference.py b/funasr/bin/tp_inference.py
index e374a22..6360b17 100644
--- a/funasr/bin/tp_inference.py
+++ b/funasr/bin/tp_inference.py
@@ -116,8 +116,8 @@
enc = enc[0]
# c. Forward Predictor
- _, _, us_alphas, us_cif_peak = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
- return us_alphas, us_cif_peak
+ _, _, us_alphas, us_peaks = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
+ return us_alphas, us_peaks
def inference(
--
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