From 81acb17544a05424dff0ef74f3aeb2ce9866ba6a Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 06 十二月 2023 19:54:37 +0800
Subject: [PATCH] update with main (#1152)
---
funasr/bin/asr_inference_launch.py | 34 +++++++++++++++++++++++++---------
1 files changed, 25 insertions(+), 9 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index f61c085..f34bfb2 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -48,13 +48,13 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils.vad_utils import slice_padding_fbank
-from funasr.utils.speaker_utils import (check_audio_list,
- sv_preprocess,
- sv_chunk,
- CAMPPlus,
- extract_feature,
+from funasr.utils.speaker_utils import (check_audio_list,
+ sv_preprocess,
+ sv_chunk,
+ extract_feature,
postprocess,
distribute_spk)
+import funasr.modules.cnn as sv_module
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.utils.cluster_backend import ClusterBackend
from funasr.utils.modelscope_utils import get_cache_dir
@@ -818,7 +818,15 @@
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
- sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
+ sv_model_config_path = asr_model_file.replace("model.pb", "sv_model_config.yaml")
+ if not os.path.exists(sv_model_config_path):
+ sv_model_config = {'sv_model_class': 'CAMPPlus','sv_model_file': 'campplus_cn_common.bin', 'models_config': {}}
+ else:
+ with open(sv_model_config_path, 'r') as f:
+ sv_model_config = yaml.load(f, Loader=yaml.FullLoader)
+ if sv_model_config['models_config'] is None:
+ sv_model_config['models_config'] = {}
+ sv_model_file = asr_model_file.replace("model.pb", sv_model_config['sv_model_file'])
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
@@ -944,9 +952,15 @@
##### speaker_verification #####
##################################
# load sv model
- sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
- sv_model = CAMPPlus()
+ if ngpu > 0:
+ sv_model_dict = torch.load(sv_model_file)
+ sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
+ sv_model.cuda()
+ else:
+ sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
+ sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
sv_model.load_state_dict(sv_model_dict)
+ print(f'load sv model params: {sv_model_file}')
sv_model.eval()
cb_model = ClusterBackend()
vad_segments = []
@@ -969,9 +983,11 @@
embs = []
for x in wavs:
x = extract_feature([x])
+ if ngpu > 0:
+ x = x.cuda()
embs.append(sv_model(x))
embs = torch.cat(embs)
- embeddings.append(embs.detach().numpy())
+ embeddings.append(embs.cpu().detach().numpy())
embeddings = np.concatenate(embeddings)
labels = cb_model(embeddings)
sv_output = postprocess(segments, vad_segments, labels, embeddings)
--
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