From 8dab6d184a034ca86eafa644ea0d2100aadfe27d Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 09 五月 2023 10:58:33 +0800
Subject: [PATCH] Merge pull request #473 from alibaba-damo-academy/dev_smohan
---
funasr/bin/asr_inference.py | 27 +++++++++++++++++++++------
1 files changed, 21 insertions(+), 6 deletions(-)
diff --git a/funasr/bin/asr_inference.py b/funasr/bin/asr_inference.py
index 4722602..a52e94a 100644
--- a/funasr/bin/asr_inference.py
+++ b/funasr/bin/asr_inference.py
@@ -41,6 +41,7 @@
from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
+from funasr.tasks.asr import frontend_choices
header_colors = '\033[95m'
@@ -92,7 +93,11 @@
)
frontend = None
if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None:
- frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
+ if asr_train_args.frontend=='wav_frontend':
+ frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf)
+ else:
+ frontend_class=frontend_choices.get_class(asr_train_args.frontend)
+ frontend = frontend_class(**asr_train_args.frontend_conf).eval()
logging.info("asr_model: {}".format(asr_model))
logging.info("asr_train_args: {}".format(asr_train_args))
@@ -111,7 +116,7 @@
# 2. Build Language model
if lm_train_config is not None:
lm, lm_train_args = LMTask.build_model_from_file(
- lm_train_config, lm_file, device
+ lm_train_config, lm_file, None, device
)
scorers["lm"] = lm.lm
@@ -193,7 +198,7 @@
"""
assert check_argument_types()
-
+
# Input as audio signal
if isinstance(speech, np.ndarray):
speech = torch.tensor(speech)
@@ -280,6 +285,7 @@
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
+ mc: bool = False,
**kwargs,
):
inference_pipeline = inference_modelscope(
@@ -310,6 +316,7 @@
ngram_weight=ngram_weight,
nbest=nbest,
num_workers=num_workers,
+ mc=mc,
**kwargs,
)
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
@@ -342,6 +349,7 @@
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
+ mc: bool = False,
param_dict: dict = None,
**kwargs,
):
@@ -355,6 +363,9 @@
if ngpu > 1:
raise NotImplementedError("only single GPU decoding is supported")
+ for handler in logging.root.handlers[:]:
+ logging.root.removeHandler(handler)
+
logging.basicConfig(
level=log_level,
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
@@ -408,6 +419,7 @@
data_path_and_name_and_type,
dtype=dtype,
fs=fs,
+ mc=mc,
batch_size=batch_size,
key_file=key_file,
num_workers=num_workers,
@@ -416,7 +428,7 @@
allow_variable_data_keys=allow_variable_data_keys,
inference=True,
)
-
+
finish_count = 0
file_count = 1
# 7 .Start for-loop
@@ -452,7 +464,7 @@
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
- # ibest_writer["token_int"][key] = " ".join(map(str, token_int))
+ ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
if text is not None:
@@ -463,6 +475,9 @@
asr_utils.print_progress(finish_count / file_count)
if writer is not None:
ibest_writer["text"][key] = text
+
+ logging.info("uttid: {}".format(key))
+ logging.info("text predictions: {}\n".format(text))
return asr_result_list
return _forward
@@ -637,4 +652,4 @@
if __name__ == "__main__":
- main()
+ main()
\ No newline at end of file
--
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