From f6a1cdaf3488c9ec572e1f753b50cb58a0f8fd79 Mon Sep 17 00:00:00 2001
From: 志浩 <neo.dzh@alibaba-inc.com>
Date: 星期五, 10 二月 2023 18:56:14 +0800
Subject: [PATCH] add sond model
---
funasr/bin/sv_inference.py | 29 ++++++++++++++++-------------
1 files changed, 16 insertions(+), 13 deletions(-)
diff --git a/funasr/bin/sv_inference.py b/funasr/bin/sv_inference.py
index 57ce91d..a78bccd 100755
--- a/funasr/bin/sv_inference.py
+++ b/funasr/bin/sv_inference.py
@@ -1,4 +1,7 @@
#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
import argparse
import logging
import os
@@ -26,7 +29,7 @@
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
-
+from funasr.utils.misc import statistic_model_parameters
class Speech2Xvector:
"""Speech2Xvector class
@@ -59,6 +62,7 @@
device=device
)
logging.info("sv_model: {}".format(sv_model))
+ logging.info("model parameter number: {}".format(statistic_model_parameters(sv_model)))
logging.info("sv_train_args: {}".format(sv_train_args))
sv_model.to(dtype=getattr(torch, dtype)).eval()
@@ -156,17 +160,17 @@
def inference_modelscope(
- output_dir: Optional[str],
- batch_size: int,
- dtype: str,
- ngpu: int,
- seed: int,
- num_workers: int,
- log_level: Union[int, str],
- key_file: Optional[str],
- sv_train_config: Optional[str],
- sv_model_file: Optional[str],
- model_tag: Optional[str],
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 1,
+ seed: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ sv_train_config: Optional[str] = "sv.yaml",
+ sv_model_file: Optional[str] = "sv.pth",
+ model_tag: Optional[str] = None,
allow_variable_data_keys: bool = True,
streaming: bool = False,
embedding_node: str = "resnet1_dense",
@@ -214,7 +218,6 @@
data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
- fs: dict = None,
param_dict: Optional[dict] = None,
):
logging.info("param_dict: {}".format(param_dict))
--
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