From f98c4bf6d2bb5202488cd4243efdbca65288c313 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 27 二月 2023 14:26:32 +0800
Subject: [PATCH] onnx export

---
 funasr/export/export_model.py |  110 ++-----------------------------------------------------
 1 files changed, 4 insertions(+), 106 deletions(-)

diff --git a/funasr/export/export_model.py b/funasr/export/export_model.py
index 933a927..8d41462 100644
--- a/funasr/export/export_model.py
+++ b/funasr/export/export_model.py
@@ -7,10 +7,12 @@
 import logging
 import torch
 
-from funasr.bin.asr_inference_paraformer import Speech2Text
 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):
@@ -30,7 +32,7 @@
 
     def _export(
         self,
-        model: Speech2Text,
+        model,
         tag_name: str = None,
         verbose: bool = False,
     ):
@@ -112,110 +114,6 @@
             os.path.join(path, f'{model.model_name}.onnx'),
             verbose=verbose,
             opset_version=14,
-            input_names=model.get_input_names(),
-            output_names=model.get_output_names(),
-            dynamic_axes=model.get_dynamic_axes()
-        )
-
-
-class ASRModelExport:
-    def __init__(self, cache_dir: Union[Path, str] = None, onnx: 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
-    
-    def _export(
-        self,
-        model: Speech2Text,
-        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 _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_txt()
-        
-        # 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'))
-    
-    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)
-        
-        torch.onnx.export(
-            model_script,
-            dummy_input,
-            os.path.join(path, f'{model.model_name}.onnx'),
-            verbose=verbose,
-            opset_version=12,
             input_names=model.get_input_names(),
             output_names=model.get_output_names(),
             dynamic_axes=model.get_dynamic_axes()

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