Update load_pretrained_model.py

zhifu gao

2024-01-17 e67ed1d45d5a9d7fb7bb22d15fd2bfef17e9076f

 funasr/auto/auto_model.py

@@ -146,7 +146,7 @@
        device = kwargs.get("device", "cuda")
        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
            device = "cpu"
            # kwargs["batch_size"] = 1
            kwargs["batch_size"] = 1
        kwargs["device"] = device
        
        if kwargs.get("ncpu", None):
@@ -183,9 +183,11 @@
            logging.info(f"Loading pretrained params from {init_param}")
            load_pretrained_model(
                model=model,
                init_param=init_param,
                path=init_param,
                ignore_init_mismatch=kwargs.get("ignore_init_mismatch", False),
                oss_bucket=kwargs.get("oss_bucket", None),
                scope_map=kwargs.get("scope_map", None),
                excludes=kwargs.get("excludes", None),
            )
        
        return model, kwargs
@@ -264,7 +266,7 @@
        # step.1: compute the vad model
        self.vad_kwargs.update(cfg)
        beg_vad = time.time()
        res = self.generate(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
        res = self.inference(input, input_len=input_len, model=self.vad_model, kwargs=self.vad_kwargs, **cfg)
        end_vad = time.time()
        print(f"time cost vad: {end_vad - beg_vad:0.3f}")

@@ -316,7 +318,7 @@
                batch_size_ms_cum = 0
                end_idx = j + 1
                speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])       
                results = self.generate(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
                results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
                if self.spk_model is not None:
                    all_segments = []
                    # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
@@ -327,7 +329,7 @@
                        segments = sv_chunk(vad_segments)
                        all_segments.extend(segments)
                        speech_b = [i[2] for i in segments]
                        spk_res = self.generate(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
                        spk_res = self.inference(speech_b, input_len=None, model=self.spk_model, kwargs=kwargs, **cfg)
                        results[_b]['spk_embedding'] = spk_res[0]['spk_embedding']
                beg_idx = end_idx
                if len(results) < 1:
@@ -378,7 +380,7 @@
            # step.3 compute punc model
            if self.punc_model is not None:
                self.punc_kwargs.update(cfg)
                punc_res = self.generate(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
                result["text_with_punc"] = punc_res[0]["text"]
                     
            # speaker embedding cluster after resorted