From 9b4e9cc8a0311e5243d69b73ed073e7ea441982e Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 三月 2024 16:05:29 +0800
Subject: [PATCH] train update
---
runtime/python/onnxruntime/funasr_onnx/vad_bin.py | 47 +++++++++++++++++++++--------------------------
1 files changed, 21 insertions(+), 26 deletions(-)
diff --git a/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 5892995..a2f443a 100644
--- a/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -31,7 +31,8 @@
quantize: bool = False,
intra_op_num_threads: int = 4,
max_end_sil: int = None,
- cache_dir: str = None
+ cache_dir: str = None,
+ **kwargs
):
if not Path(model_dir).exists():
@@ -54,21 +55,17 @@
if not os.path.exists(model_file):
print(".onnx is not exist, begin to export onnx")
try:
- from funasr.export.export_model import ModelExport
+ from funasr import AutoModel
except:
raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
"\npip3 install -U funasr\n" \
"For the users in China, you could install with the command:\n" \
"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
- export_model = ModelExport(
- cache_dir=cache_dir,
- onnx=True,
- device="cpu",
- quant=quantize,
- )
- export_model.export(model_dir)
- config_file = os.path.join(model_dir, 'vad.yaml')
- cmvn_file = os.path.join(model_dir, 'vad.mvn')
+
+ model = AutoModel(model=model_dir)
+ model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
+ config_file = os.path.join(model_dir, 'config.yaml')
+ cmvn_file = os.path.join(model_dir, 'am.mvn')
config = read_yaml(config_file)
self.frontend = WavFrontend(
@@ -77,8 +74,8 @@
)
self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
self.batch_size = batch_size
- self.vad_scorer = E2EVadModel(config["vad_post_conf"])
- self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
+ self.vad_scorer = E2EVadModel(config["model_conf"])
+ self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
self.encoder_conf = config["encoder_conf"]
def prepare_cache(self, in_cache: list = []):
@@ -200,7 +197,8 @@
quantize: bool = False,
intra_op_num_threads: int = 4,
max_end_sil: int = None,
- cache_dir: str = None
+ cache_dir: str = None,
+ **kwargs
):
if not Path(model_dir).exists():
try:
@@ -222,21 +220,18 @@
if not os.path.exists(model_file):
print(".onnx is not exist, begin to export onnx")
try:
- from funasr.export.export_model import ModelExport
+ from funasr import AutoModel
except:
raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
"\npip3 install -U funasr\n" \
"For the users in China, you could install with the command:\n" \
"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
- export_model = ModelExport(
- cache_dir=cache_dir,
- onnx=True,
- device="cpu",
- quant=quantize,
- )
- export_model.export(model_dir)
- config_file = os.path.join(model_dir, 'vad.yaml')
- cmvn_file = os.path.join(model_dir, 'vad.mvn')
+
+ model = AutoModel(model=model_dir)
+ model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
+
+ config_file = os.path.join(model_dir, 'config.yaml')
+ cmvn_file = os.path.join(model_dir, 'am.mvn')
config = read_yaml(config_file)
self.frontend = WavFrontendOnline(
@@ -245,8 +240,8 @@
)
self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
self.batch_size = batch_size
- self.vad_scorer = E2EVadModel(config["vad_post_conf"])
- self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
+ self.vad_scorer = E2EVadModel(config["model_conf"])
+ self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
self.encoder_conf = config["encoder_conf"]
def prepare_cache(self, in_cache: list = []):
--
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