From bc723ea200144bd6fa8a5dff4b9a780feda144fc Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 29 六月 2023 18:55:01 +0800
Subject: [PATCH] dcos
---
funasr/export/export_model.py | 124 ++++++++++++++++++++++++++--------------
1 files changed, 80 insertions(+), 44 deletions(-)
diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index b1161cb..a68724a 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -1,7 +1,6 @@
import json
from typing import Union, Dict
from pathlib import Path
-from typeguard import check_argument_types
import os
import logging
@@ -19,28 +18,29 @@
self,
cache_dir: Union[Path, str] = None,
onnx: bool = True,
+ device: str = "cpu",
quant: bool = True,
fallback_num: int = 0,
audio_in: str = None,
calib_num: int = 200,
+ model_revision: str = None,
):
- assert check_argument_types()
self.set_all_random_seed(0)
- if cache_dir is None:
- cache_dir = Path.home() / ".cache" / "export"
- self.cache_dir = Path(cache_dir)
+ self.cache_dir = cache_dir
self.export_config = dict(
feats_dim=560,
onnx=False,
)
- print("output dir: {}".format(self.cache_dir))
+
self.onnx = onnx
+ self.device = device
self.quant = quant
self.fallback_num = fallback_num
self.frontend = None
self.audio_in = audio_in
self.calib_num = calib_num
+ self.model_revision = model_revision
def _export(
@@ -50,7 +50,7 @@
verbose: bool = False,
):
- export_dir = self.cache_dir / tag_name.replace(' ', '-')
+ export_dir = self.cache_dir
os.makedirs(export_dir, exist_ok=True)
# export encoder1
@@ -112,6 +112,10 @@
else:
dummy_input = model.get_dummy_inputs()
+ if self.device == 'cuda':
+ model = model.cuda()
+ dummy_input = tuple([i.cuda() for i in dummy_input])
+
# model_script = torch.jit.script(model)
model_script = torch.jit.trace(model, dummy_input)
model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
@@ -161,31 +165,58 @@
def export(self,
tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
- mode: str = 'paraformer',
+ mode: str = None,
):
model_dir = tag_name
- if model_dir.startswith('damo/'):
+ if model_dir.startswith('damo'):
from modelscope.hub.snapshot_download import snapshot_download
- model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
- asr_train_config = os.path.join(model_dir, 'config.yaml')
- asr_model_file = os.path.join(model_dir, 'model.pb')
- cmvn_file = os.path.join(model_dir, 'am.mvn')
- json_file = os.path.join(model_dir, 'configuration.json')
+ model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir, revision=self.model_revision)
+ self.cache_dir = model_dir
+
if mode is None:
import json
+ json_file = os.path.join(model_dir, 'configuration.json')
with open(json_file, 'r') as f:
config_data = json.load(f)
- mode = config_data['model']['model_config']['mode']
+ if config_data['task'] == "punctuation":
+ mode = config_data['model']['punc_model_config']['mode']
+ else:
+ mode = config_data['model']['model_config']['mode']
if mode.startswith('paraformer'):
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
- elif mode.startswith('uniasr'):
- from funasr.tasks.asr import ASRTaskUniASR as ASRTask
+ config = os.path.join(model_dir, 'config.yaml')
+ model_file = os.path.join(model_dir, 'model.pb')
+ cmvn_file = os.path.join(model_dir, 'am.mvn')
+ model, asr_train_args = ASRTask.build_model_from_file(
+ config, model_file, cmvn_file, 'cpu'
+ )
+ self.frontend = model.frontend
+ elif mode.startswith('offline'):
+ from funasr.tasks.vad import VADTask
+ config = os.path.join(model_dir, 'vad.yaml')
+ model_file = os.path.join(model_dir, 'vad.pb')
+ cmvn_file = os.path.join(model_dir, 'vad.mvn')
- model, asr_train_args = ASRTask.build_model_from_file(
- asr_train_config, asr_model_file, cmvn_file, 'cpu'
- )
- self.frontend = model.frontend
+ model, vad_infer_args = VADTask.build_model_from_file(
+ config, model_file, cmvn_file=cmvn_file, device='cpu'
+ )
+ self.export_config["feats_dim"] = 400
+ self.frontend = model.frontend
+ elif mode.startswith('punc'):
+ from funasr.tasks.punctuation import PunctuationTask as PUNCTask
+ punc_train_config = os.path.join(model_dir, 'config.yaml')
+ punc_model_file = os.path.join(model_dir, 'punc.pb')
+ model, punc_train_args = PUNCTask.build_model_from_file(
+ punc_train_config, punc_model_file, 'cpu'
+ )
+ elif mode.startswith('punc_VadRealtime'):
+ from funasr.tasks.punctuation import PunctuationTask as PUNCTask
+ punc_train_config = os.path.join(model_dir, 'config.yaml')
+ punc_model_file = os.path.join(model_dir, 'punc.pb')
+ model, punc_train_args = PUNCTask.build_model_from_file(
+ punc_train_config, punc_model_file, 'cpu'
+ )
self._export(model, tag_name)
@@ -198,34 +229,35 @@
# model_script = torch.jit.script(model)
model_script = model #torch.jit.trace(model)
model_path = os.path.join(path, f'{model.model_name}.onnx')
-
- torch.onnx.export(
- model_script,
- dummy_input,
- model_path,
- verbose=verbose,
- opset_version=14,
- input_names=model.get_input_names(),
- output_names=model.get_output_names(),
- dynamic_axes=model.get_dynamic_axes()
- )
+ if not os.path.exists(model_path):
+ torch.onnx.export(
+ model_script,
+ dummy_input,
+ model_path,
+ verbose=verbose,
+ opset_version=14,
+ input_names=model.get_input_names(),
+ output_names=model.get_output_names(),
+ dynamic_axes=model.get_dynamic_axes()
+ )
if self.quant:
from onnxruntime.quantization import QuantType, quantize_dynamic
import onnx
quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
- onnx_model = onnx.load(model_path)
- nodes = [n.name for n in onnx_model.graph.node]
- nodes_to_exclude = [m for m in nodes if 'output' in m]
- quantize_dynamic(
- model_input=model_path,
- model_output=quant_model_path,
- op_types_to_quantize=['MatMul'],
- per_channel=True,
- reduce_range=False,
- weight_type=QuantType.QUInt8,
- nodes_to_exclude=nodes_to_exclude,
- )
+ if not os.path.exists(quant_model_path):
+ onnx_model = onnx.load(model_path)
+ nodes = [n.name for n in onnx_model.graph.node]
+ nodes_to_exclude = [m for m in nodes if 'output' in m]
+ quantize_dynamic(
+ model_input=model_path,
+ model_output=quant_model_path,
+ op_types_to_quantize=['MatMul'],
+ per_channel=True,
+ reduce_range=False,
+ weight_type=QuantType.QUInt8,
+ nodes_to_exclude=nodes_to_exclude,
+ )
if __name__ == '__main__':
@@ -234,18 +266,22 @@
parser.add_argument('--model-name', type=str, required=True)
parser.add_argument('--export-dir', type=str, required=True)
parser.add_argument('--type', type=str, default='onnx', help='["onnx", "torch"]')
+ parser.add_argument('--device', type=str, default='cpu', help='["cpu", "cuda"]')
parser.add_argument('--quantize', type=str2bool, default=False, help='export quantized model')
parser.add_argument('--fallback-num', type=int, default=0, help='amp fallback number')
parser.add_argument('--audio_in', type=str, default=None, help='["wav", "wav.scp"]')
parser.add_argument('--calib_num', type=int, default=200, help='calib max num')
+ parser.add_argument('--model_revision', type=str, default=None, help='model_revision')
args = parser.parse_args()
export_model = ModelExport(
cache_dir=args.export_dir,
onnx=args.type == 'onnx',
+ device=args.device,
quant=args.quantize,
fallback_num=args.fallback_num,
audio_in=args.audio_in,
calib_num=args.calib_num,
+ model_revision=args.model_revision,
)
export_model.export(args.model_name)
--
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