From 9fcb3cc06b4e324f0913d2f61b89becc2baeef1b Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期一, 11 九月 2023 17:40:03 +0800
Subject: [PATCH] Merge pull request #932 from alibaba-damo-academy/dev_lhn
---
funasr/bin/asr_infer.py | 6 ++++--
funasr/bin/asr_inference_launch.py | 12 ++++++++++++
2 files changed, 16 insertions(+), 2 deletions(-)
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 2e002b7..7746821 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -399,7 +399,7 @@
@torch.no_grad()
def __call__(
self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
- begin_time: int = 0, end_time: int = None,
+ decoding_ind: int = None, begin_time: int = 0, end_time: int = None,
):
"""Inference
@@ -429,7 +429,9 @@
batch = to_device(batch, device=self.device)
# b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
+ if decoding_ind is None:
+ decoding_ind = self.decoding_ind
+ enc, enc_len = self.asr_model.encode(**batch, ind=decoding_ind)
if isinstance(enc, tuple):
enc = enc[0]
# assert len(enc) == 1, len(enc)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index bc62b51..6fa57a7 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -236,6 +236,7 @@
timestamp_infer_config: Union[Path, str] = None,
timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
+ decoding_ind: int = 0,
**kwargs,
):
ncpu = kwargs.get("ncpu", 1)
@@ -290,6 +291,7 @@
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
clas_scale=clas_scale,
+ decoding_ind=decoding_ind,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -312,6 +314,7 @@
**kwargs,
):
+ decoding_ind = None
hotword_list_or_file = None
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
@@ -319,6 +322,8 @@
hotword_list_or_file = kwargs['hotword']
if hotword_list_or_file is not None or 'hotword' in kwargs:
speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
+ if param_dict is not None and "decoding_ind" in param_dict:
+ decoding_ind = param_dict["decoding_ind"]
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:
@@ -365,6 +370,7 @@
# N-best list of (text, token, token_int, hyp_object)
time_beg = time.time()
+ batch["decoding_ind"] = decoding_ind
results = speech2text(**batch)
if len(results) < 1:
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
@@ -1786,6 +1792,12 @@
default=1,
help="The batch size for inference",
)
+ group.add_argument(
+ "--decoding_ind",
+ type=int,
+ default=0,
+ help="chunk select for chunk encoder",
+ )
group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses")
group.add_argument("--beam_size", type=int, default=20, help="Beam size")
group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
--
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