From 4e2fe544ae37174a3e09dfcdbbdae5abfe711e53 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 05 七月 2023 16:57:21 +0800
Subject: [PATCH] funasr sdk
---
funasr/bin/asr_inference_launch.py | 37 ++++++++++++++++++++++---------------
1 files changed, 22 insertions(+), 15 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 026874e..c32357e 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -21,7 +21,6 @@
import torchaudio
import soundfile
import yaml
-from typeguard import check_argument_types
from funasr.bin.asr_infer import Speech2Text
from funasr.bin.asr_infer import Speech2TextMFCCA
@@ -80,7 +79,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@@ -240,7 +238,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
@@ -258,9 +255,10 @@
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
export_mode = param_dict.get("export_mode", False)
+ clas_scale = param_dict.get('clas_scale', 1.0)
else:
hotword_list_or_file = None
- clas_scale = param_dict.get('clas_scale', 1.0)
+ clas_scale = 1.0
if kwargs.get("device", None) == "cpu":
ngpu = 0
@@ -293,7 +291,7 @@
penalty=penalty,
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
- clas_sacle=clas_scale,
+ clas_scale=clas_scale,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -483,7 +481,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
@@ -623,6 +620,22 @@
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
results_sorted = []
+ if not len(sorted_data):
+ key = keys[0]
+ # no active segments after VAD
+ if writer is not None:
+ # Write empty results
+ ibest_writer["token"][key] = ""
+ ibest_writer["token_int"][key] = ""
+ ibest_writer["vad"][key] = ""
+ ibest_writer["text"][key] = ""
+ ibest_writer["text_with_punc"][key] = ""
+ if use_timestamp:
+ ibest_writer["time_stamp"][key] = ""
+
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
batch_size_token_ms = batch_size_token*60
if speech2text.device == "cpu":
batch_size_token_ms = 0
@@ -751,7 +764,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
if word_lm_train_config is not None:
raise NotImplementedError("Word LM is not implemented")
@@ -959,7 +971,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@@ -1128,7 +1139,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
ncpu = kwargs.get("ncpu", 1)
torch.set_num_threads(ncpu)
if batch_size > 1:
@@ -1316,7 +1326,6 @@
right_context: Number of frames in right context AFTER subsampling.
display_partial_hypotheses: Whether to display partial hypotheses.
"""
- assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
@@ -1359,10 +1368,7 @@
left_context=left_context,
right_context=right_context,
)
- speech2text = Speech2TextTransducer.from_pretrained(
- model_tag=model_tag,
- **speech2text_kwargs,
- )
+ speech2text = Speech2TextTransducer(**speech2text_kwargs)
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@@ -1466,7 +1472,6 @@
param_dict: dict = None,
**kwargs,
):
- assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
if word_lm_train_config is not None:
@@ -1615,6 +1620,8 @@
return inference_mfcca(**kwargs)
elif mode == "rnnt":
return inference_transducer(**kwargs)
+ elif mode == "bat":
+ return inference_transducer(**kwargs)
elif mode == "sa_asr":
return inference_sa_asr(**kwargs)
else:
--
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