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@@ -171,6 +171,7 @@ def inference_modelscope(
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streaming: bool = False,
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embedding_node: str = "resnet1_dense",
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sv_threshold: float = 0.9465,
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+ param_dict: Optional[dict] = None,
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**kwargs,
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):
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assert check_argument_types()
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@@ -183,6 +184,7 @@ def inference_modelscope(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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+ logging.info("param_dict: {}".format(param_dict))
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if ngpu >= 1 and torch.cuda.is_available():
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device = "cuda"
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@@ -212,7 +214,9 @@ def inference_modelscope(
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data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
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raw_inputs: Union[np.ndarray, torch.Tensor] = None,
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output_dir_v2: Optional[str] = None,
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+ param_dict: Optional[dict] = None,
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):
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+ logging.info("param_dict: {}".format(param_dict))
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if data_path_and_name_and_type is None and raw_inputs is not None:
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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@@ -233,11 +237,10 @@ def inference_modelscope(
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# 7 .Start for-loop
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output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
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- embd_fd, ref_emb_fd, score_fd = None, None, None
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+ embd_writer, ref_embd_writer, score_writer = None, None, None
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if output_path is not None:
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os.makedirs(output_path, exist_ok=True)
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- embd_writer = WriteHelper("ark:{}/xvector.ark".format(output_path))
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- # embd_fd = open(os.path.join(output_path, "xvector.ark"), "wb")
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+ embd_writer = WriteHelper("ark,scp:{}/xvector.ark,{}/xvector.scp".format(output_path, output_path))
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sv_result_list = []
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for keys, batch in loader:
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assert isinstance(batch, dict), type(batch)
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@@ -249,6 +252,7 @@ def inference_modelscope(
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embedding, ref_embedding, score = speech2xvector(**batch)
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# Only supporting batch_size==1
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key = keys[0]
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+ normalized_score = 0.0
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if score is not None:
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score = score.item()
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normalized_score = max(score - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
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@@ -257,23 +261,21 @@ def inference_modelscope(
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item = {"key": key, "value": embedding.squeeze(0).cpu().numpy()}
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sv_result_list.append(item)
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if output_path is not None:
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- # kaldiio.save_mat(embd_fd, embedding[0].cpu().numpy(), key)
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embd_writer(key, embedding[0].cpu().numpy())
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if ref_embedding is not None:
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- if ref_emb_fd is None:
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- # ref_emb_fd = open(os.path.join(output_path, "ref_xvector.ark"), "wb")
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- ref_embd_writer = WriteHelper("ark:{}/ref_xvector.ark".format(output_path))
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- score_fd = open(os.path.join(output_path, "score.txt"), "w")
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- # kaldiio.save_mat(ref_emb_fd, ref_embedding[0].cpu().numpy(), key)
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+ if ref_embd_writer is None:
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+ ref_embd_writer = WriteHelper(
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+ "ark,scp:{}/ref_xvector.ark,{}/ref_xvector.scp".format(output_path, output_path)
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+ )
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+ score_writer = open(os.path.join(output_path, "score.txt"), "w")
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ref_embd_writer(key, ref_embedding[0].cpu().numpy())
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- score_fd.write("{:.6f}\n".format(score.item()))
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+ score_writer.write("{} {:.6f}\n".format(key, normalized_score))
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+
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if output_path is not None:
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- # embd_fd.close()
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embd_writer.close()
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- if ref_emb_fd is not None:
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- # ref_emb_fd.close()
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- ref_emb_fd.close()
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- score_fd.close()
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+ if ref_embd_writer is not None:
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+ ref_embd_writer.close()
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+ score_writer.close()
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return sv_result_list
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