update preset_spk_num

examples/industrial_data_pretraining/seaco_paraformer/demo.py

@@ -11,18 +11,22 @@
  vad_model_revision="v2.0.4",
  punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
  punc_model_revision="v2.0.4",
- # spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
- # spk_model_revision="v2.0.2",
+ spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
+ spk_model_revision="v2.0.2",
  )
 
 
 # example1
 res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
  hotword='达摩院 魔搭',
+ # preset_spk_num=2,
  # sentence_timestamp=True, # return sentence level information when spk_model is not given
  )
 print(res)
 
+
+'''
+# tensor or numpy as input
 # example2
 import torchaudio
 import os
@@ -38,4 +42,4 @@
 wav_file = os.path.join(model.model_path, "example/asr_example.wav")
 speech, sample_rate = soundfile.read(wav_file)
 res = model.generate(input=[speech], batch_size_s=300, is_final=True)
-
+'''

funasr/auto/auto_model.py

@@ -121,9 +121,6 @@ def __init__(self, **kwargs):
  if spk_mode not in ["default", "vad_segment", "punc_segment"]:
  logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
  self.spk_mode = spk_mode
- self.preset_spk_num = kwargs.get("preset_spk_num", None)
- if self.preset_spk_num:
- logging.warning("Using preset speaker number: {}".format(self.preset_spk_num))
 
  self.kwargs = kwargs
  self.model = model
@@ -391,7 +388,7 @@ def inference_with_vad(self, input, input_len=None, **cfg):
  if self.spk_model is not None:
  all_segments = sorted(all_segments, key=lambda x: x[0])
  spk_embedding = result['spk_embedding']
- labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
+ labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs['preset_spk_num'])
  del result['spk_embedding']
  sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
  if self.spk_mode == 'vad_segment': # recover sentence_list