import json
|
from typing import Union, Dict
|
from pathlib import Path
|
from typeguard import check_argument_types
|
|
import os
|
import logging
|
import torch
|
|
from funasr.export.models import get_model
|
import numpy as np
|
import random
|
|
# torch_version = float(".".join(torch.__version__.split(".")[:2]))
|
# assert torch_version > 1.9
|
|
class ASRModelExportParaformer:
|
def __init__(
|
self, cache_dir: Union[Path, str] = None, onnx: bool = True, quant: bool = True
|
):
|
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.export_config = dict(
|
feats_dim=560,
|
onnx=False,
|
)
|
print("output dir: {}".format(self.cache_dir))
|
self.onnx = onnx
|
self.quant = quant
|
|
|
def _export(
|
self,
|
model,
|
tag_name: str = None,
|
verbose: bool = False,
|
):
|
|
export_dir = self.cache_dir / tag_name.replace(' ', '-')
|
os.makedirs(export_dir, exist_ok=True)
|
|
# export encoder1
|
self.export_config["model_name"] = "model"
|
model = get_model(
|
model,
|
self.export_config,
|
)
|
model.eval()
|
# self._export_onnx(model, verbose, export_dir)
|
if self.onnx:
|
self._export_onnx(model, verbose, export_dir)
|
else:
|
self._export_torchscripts(model, verbose, export_dir)
|
|
print("output dir: {}".format(export_dir))
|
|
|
def _torch_quantize(self, model):
|
from torch_quant.module import ModuleFilter
|
from torch_quant.observer import HistogramObserver
|
from torch_quant.quantizer import Backend, Quantizer
|
from funasr.export.models.modules.decoder_layer import DecoderLayerSANM
|
from funasr.export.models.modules.encoder_layer import EncoderLayerSANM
|
module_filter = ModuleFilter(include_classes=[EncoderLayerSANM, DecoderLayerSANM])
|
module_filter.exclude_op_types = [torch.nn.Conv1d]
|
quantizer = Quantizer(
|
module_filter=module_filter,
|
backend=Backend.FBGEMM,
|
act_ob_ctr=HistogramObserver,
|
)
|
model.eval()
|
calib_model = quantizer.calib(model)
|
# run calibration data
|
# using dummy inputs for a example
|
dummy_input = model.get_dummy_inputs()
|
_ = calib_model(*dummy_input)
|
quant_model = quantizer.quantize(model)
|
return quant_model
|
|
def _export_torchscripts(self, model, verbose, path, enc_size=None):
|
if enc_size:
|
dummy_input = model.get_dummy_inputs(enc_size)
|
else:
|
dummy_input = model.get_dummy_inputs()
|
|
# 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'))
|
|
if self.quant:
|
quant_model = self._torch_quantize(model)
|
model_script = torch.jit.trace(quant_model, dummy_input)
|
model_script.save(os.path.join(path, f'{model.model_name}_quant.torchscripts'))
|
|
|
def set_all_random_seed(self, seed: int):
|
random.seed(seed)
|
np.random.seed(seed)
|
torch.random.manual_seed(seed)
|
def export(self,
|
tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
|
mode: str = 'paraformer',
|
):
|
|
model_dir = tag_name
|
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')
|
if mode is None:
|
import json
|
with open(json_file, 'r') as f:
|
config_data = json.load(f)
|
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
|
|
model, asr_train_args = ASRTask.build_model_from_file(
|
asr_train_config, asr_model_file, cmvn_file, 'cpu'
|
)
|
self._export(model, tag_name)
|
|
|
def _export_onnx(self, model, verbose, path, enc_size=None):
|
if enc_size:
|
dummy_input = model.get_dummy_inputs(enc_size)
|
else:
|
dummy_input = model.get_dummy_inputs()
|
|
# 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 self.quant:
|
from onnxruntime.quantization import QuantType, quantize_dynamic
|
quant_model_path = os.path.join(path, f'{model.model_name}_quant.onnx')
|
quantize_dynamic(
|
model_input=model_path,
|
model_output=quant_model_path,
|
weight_type=QuantType.QUInt8,
|
)
|
|
|
if __name__ == '__main__':
|
import sys
|
|
model_path = sys.argv[1]
|
output_dir = sys.argv[2]
|
onnx = sys.argv[3]
|
quant = sys.argv[4]
|
onnx = onnx.lower()
|
onnx = onnx == 'true'
|
quant = quant == 'true'
|
# model_path = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
|
# output_dir = "../export"
|
export_model = ASRModelExportParaformer(cache_dir=output_dir, onnx=onnx, quant=quant)
|
export_model.export(model_path)
|
# export_model.export('/root/cache/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
|
