From 35caed5dbc9eb83efab3051ed6b7504d42ae652b Mon Sep 17 00:00:00 2001
From: Lizerui9926 <110582652+Lizerui9926@users.noreply.github.com>
Date: 星期二, 10 十月 2023 16:00:50 +0800
Subject: [PATCH] Merge pull request #996 from alibaba-damo-academy/dev_lzr_en
---
funasr/bin/asr_inference_launch.py | 17 ++++++++++-------
1 files changed, 10 insertions(+), 7 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 15dbdd4..1288777 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -498,6 +498,7 @@
):
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
+ language = kwargs.get("model_lang", None)
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
@@ -704,10 +705,13 @@
text, token, token_int = result[0], result[1], result[2]
time_stamp = result[4] if len(result[4]) > 0 else None
- if use_timestamp and time_stamp is not None and len(time_stamp):
- postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
+ if language == "en-bpe":
+ postprocessed_result = postprocess_utils.sentence_postprocess_sentencepiece(token)
else:
- postprocessed_result = postprocess_utils.sentence_postprocess(token)
+ if use_timestamp and time_stamp is not None and len(time_stamp):
+ 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
@@ -787,7 +791,7 @@
time_stamp_writer: bool = True,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
- sv_model_file: Optional[str] = None,
+ sv_model_file: Optional[str] = "~/.cache/modelscope/hub/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/campplus_cn_common.bin",
streaming: bool = False,
embedding_node: str = "resnet1_dense",
sv_threshold: float = 0.9465,
@@ -933,7 +937,7 @@
##### speaker_verification #####
##################################
# load sv model
- sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
+ sv_model_dict = torch.load(sv_model_file.replace("~", os.environ['HOME']), map_location=torch.device('cpu'))
sv_model = CAMPPlus()
sv_model.load_state_dict(sv_model_dict)
sv_model.eval()
@@ -1084,7 +1088,6 @@
logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
torch.cuda.empty_cache()
distribute_spk(asr_result_list[0]['sentences'], sv_output)
- import pdb; pdb.set_trace()
return asr_result_list
return _forward
@@ -2030,7 +2033,7 @@
return inference_paraformer(**kwargs)
elif mode == "paraformer_streaming":
return inference_paraformer_online(**kwargs)
- elif mode == "paraformer_vad_speaker":
+ elif mode.startswith("paraformer_vad_speaker"):
return inference_paraformer_vad_speaker(**kwargs)
elif mode.startswith("paraformer_vad"):
return inference_paraformer_vad_punc(**kwargs)
--
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