Merge pull request #153 from alibaba-damo-academy/dev_gzf

Dev gzf

funasr/export/export_model.py

@@ -58,7 +58,7 @@ class ASRModelExportParaformer:
         if enc_size:
             dummy_input = model.get_dummy_inputs(enc_size)
         else:
-            dummy_input = model.get_dummy_inputs_txt()
+            dummy_input = model.get_dummy_inputs()
 
         # model_script = torch.jit.script(model)
         model_script = torch.jit.trace(model, dummy_input)
@@ -106,6 +106,110 @@ class ASRModelExportParaformer:
         # 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=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,
@@ -117,6 +221,7 @@ class ASRModelExportParaformer:
             dynamic_axes=model.get_dynamic_axes()
         )
 
+
 if __name__ == '__main__':
     import sys
     

funasr/export/models/__init__.py

@@ -1,5 +1,6 @@
 from funasr.models.e2e_asr_paraformer import Paraformer
 from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
+from funasr.models.e2e_uni_asr import UniASR
 
 def get_model(model, export_config=None):
 

funasr/export/models/e2e_asr_paraformer.py

@@ -59,7 +59,7 @@ class Paraformer(nn.Module):
         enc, enc_len = self.encoder(**batch)
         mask = self.make_pad_mask(enc_len)[:, None, :]
         pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
-        pre_token_length = pre_token_length.round().type(torch.int32)
+        pre_token_length = pre_token_length.floor().type(torch.int32)
 
         decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
         decoder_out = torch.log_softmax(decoder_out, dim=-1)

funasr/export/models/predictor/cif.py

@@ -16,6 +16,11 @@ def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
 	
 	return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
 
+def sequence_mask_scripts(lengths, maxlen:int):
+	row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
+	matrix = torch.unsqueeze(lengths, dim=-1)
+	mask = row_vector < matrix
+	return mask.type(torch.float32).to(lengths.device)
 
 class CifPredictorV2(nn.Module):
 	def __init__(self, model):
@@ -71,28 +76,29 @@ class CifPredictorV2(nn.Module):
 		
 		return hidden, alphas, token_num_floor
 
+
 @torch.jit.script
 def cif(hidden, alphas, threshold: float):
 	batch_size, len_time, hidden_size = hidden.size()
 	threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
 	
 	# loop varss
-	integrate = torch.zeros([batch_size], device=hidden.device)
-	frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
+	integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
+	frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
 	# intermediate vars along time
 	list_fires = []
 	list_frames = []
 	
 	for t in range(len_time):
 		alpha = alphas[:, t]
-		distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
+		distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
 		
 		integrate += alpha
 		list_fires.append(integrate)
 		
 		fire_place = integrate >= threshold
 		integrate = torch.where(fire_place,
-		                        integrate - torch.ones([batch_size], device=hidden.device),
+		                        integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
 		                        integrate)
 		cur = torch.where(fire_place,
 		                  distribution_completion,
@@ -107,12 +113,20 @@ def cif(hidden, alphas, threshold: float):
 	
 	fires = torch.stack(list_fires, 1)
 	frames = torch.stack(list_frames, 1)
-	list_ls = []
-	len_labels = torch.round(alphas.sum(-1)).int()
-	max_label_len = len_labels.max()
+	# list_ls = []
+	len_labels = torch.round(alphas.sum(-1)).type(torch.int32)
+	# max_label_len = int(torch.max(len_labels).item())
+	# print("type: {}".format(type(max_label_len)))
+	fire_idxs = fires >= threshold
+	frame_fires = torch.zeros_like(hidden)
+	max_label_len = frames[0, fire_idxs[0]].size(0)
 	for b in range(batch_size):
-		fire = fires[b, :]
-		l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
-		pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
-		list_ls.append(torch.cat([l, pad_l], 0))
-	return torch.stack(list_ls, 0), fires
+		# fire = fires[b, :]
+		frame_fire = frames[b, fire_idxs[b]]
+		frame_len = frame_fire.size(0)
+		frame_fires[b, :frame_len, :] = frame_fire
+	
+		if frame_len >= max_label_len:
+			max_label_len = frame_len
+	frame_fires = frame_fires[:, :max_label_len, :]
+	return frame_fires, fires