update preset_spk_num

shixian.shi

2024-02-05 94f7bd079c78d6b7f9ab1961e991caa2a929b858

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 @@
            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 @@
            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