From 54931dd4e1a099d7d6f144c4e12e5453deb3aa26 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期三, 28 六月 2023 10:41:57 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
funasr/build_utils/build_model_from_file.py | 36 ++++++++++++++++++++++++++++++++----
1 files changed, 32 insertions(+), 4 deletions(-)
diff --git a/funasr/build_utils/build_model_from_file.py b/funasr/build_utils/build_model_from_file.py
index 2eadae4..8fd4e46 100644
--- a/funasr/build_utils/build_model_from_file.py
+++ b/funasr/build_utils/build_model_from_file.py
@@ -74,7 +74,10 @@
model_dict = torch.load(model_file, map_location=device)
if task_name == "diar" and mode == "sond":
model_dict = fileter_model_dict(model_dict, model.state_dict())
- model.load_state_dict(model_dict)
+ if task_name == "vad":
+ model.encoder.load_state_dict(model_dict)
+ else:
+ model.load_state_dict(model_dict)
if model_name_pth is not None and not os.path.exists(model_name_pth):
torch.save(model_dict, model_name_pth)
logging.info("model_file is saved to pth: {}".format(model_name_pth))
@@ -87,7 +90,7 @@
ckpt,
mode,
):
- assert mode == "paraformer" or mode == "uniasr" or mode == "sond"
+ assert mode == "paraformer" or mode == "uniasr" or mode == "sond" or mode == "sv" or mode == "tp"
logging.info("start convert tf model to torch model")
from funasr.modules.streaming_utils.load_fr_tf import load_tf_dict
var_dict_tf = load_tf_dict(ckpt)
@@ -128,7 +131,7 @@
# bias_encoder
var_dict_torch_update_local = model.clas_convert_tf2torch(var_dict_tf, var_dict_torch)
var_dict_torch_update.update(var_dict_torch_update_local)
- else:
+ elif "mode" == "sond":
if model.encoder is not None:
var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
var_dict_torch_update.update(var_dict_torch_update_local)
@@ -148,8 +151,33 @@
if model.decoder is not None:
var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
var_dict_torch_update.update(var_dict_torch_update_local)
+ elif "mode" == "sv":
+ # speech encoder
+ var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # pooling layer
+ var_dict_torch_update_local = model.pooling_layer.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # decoder
+ var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ else:
+ # encoder
+ var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # predictor
+ var_dict_torch_update_local = model.predictor.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # decoder
+ var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # bias_encoder
+ var_dict_torch_update_local = model.clas_convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ return var_dict_torch_update
return var_dict_torch_update
+
def fileter_model_dict(src_dict: dict, dest_dict: dict):
from collections import OrderedDict
@@ -162,4 +190,4 @@
for key, value in dest_dict.items():
if key not in new_dict:
logging.warning("{} is missed in checkpoint.".format(key))
- return new_dict
\ No newline at end of file
+ return new_dict
--
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