| | |
| | | def build_model(self, **kwargs): |
| | | assert "model" in kwargs |
| | | if "model_conf" not in kwargs: |
| | | logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms"))) |
| | | logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms"))) |
| | | kwargs = download_model(**kwargs) |
| | | |
| | | set_all_random_seed(kwargs.get("seed", 0)) |
| | |
| | | |
| | | # build model |
| | | model_class = tables.model_classes.get(kwargs["model"]) |
| | | model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=vocab_size) |
| | | model = model_class(**kwargs, **kwargs.get("model_conf", {}), vocab_size=vocab_size) |
| | | model.to(device) |
| | | |
| | | # init_param |
| | |
| | | |
| | | time1 = time.perf_counter() |
| | | with torch.no_grad(): |
| | | results, meta_data = model.inference(**batch, **kwargs) |
| | | res = model.inference(**batch, **kwargs) |
| | | if isinstance(res, (list, tuple)): |
| | | results = res[0] |
| | | meta_data = res[1] if len(res) > 1 else {} |
| | | time2 = time.perf_counter() |
| | | |
| | | asr_result_list.extend(results) |