onnx

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)
@@ -111,7 +111,7 @@ class ASRModelExportParaformer:
             dummy_input,
             os.path.join(path, f'{model.model_name}.onnx'),
             verbose=verbose,
-            opset_version=12,
+            opset_version=14,
             input_names=model.get_input_names(),
             output_names=model.get_output_names(),
             dynamic_axes=model.get_dynamic_axes()

funasr/export/models/predictor/cif.py

@@ -76,108 +76,6 @@ 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)
-# 	# 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
-#
-# 		integrate += alpha
-# 		list_fires.append(integrate)
-#
-# 		fire_place = integrate >= threshold
-# 		integrate = torch.where(fire_place,
-# 		                        integrate - torch.ones([batch_size], device=hidden.device),
-# 		                        integrate)
-# 		cur = torch.where(fire_place,
-# 		                  distribution_completion,
-# 		                  alpha)
-# 		remainds = alpha - cur
-#
-# 		frame += cur[:, None] * hidden[:, t, :]
-# 		list_frames.append(frame)
-# 		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
-# 		                    remainds[:, None] * hidden[:, t, :],
-# 		                    frame)
-#
-# 	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().item()
-# 	# print("type: {}".format(type(max_label_len)))
-# 	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)], dtype=l.dtype, device=hidden.device)
-# 		list_ls.append(torch.cat([l, pad_l], 0))
-# 	return torch.stack(list_ls, 0), fires
-
-# @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], 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], 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], dtype=alphas.dtype, device=hidden.device),
-# 		                        integrate)
-# 		cur = torch.where(fire_place,
-# 		                  distribution_completion,
-# 		                  alpha)
-# 		remainds = alpha - cur
-#
-# 		frame += cur[:, None] * hidden[:, t, :]
-# 		list_frames.append(frame)
-# 		frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
-# 		                    remainds[:, None] * hidden[:, t, :],
-# 		                    frame)
-#
-# 	fires = torch.stack(list_fires, 1)
-# 	frames = torch.stack(list_frames, 1)
-# 	len_labels = torch.floor(torch.sum(alphas, dim=1)).int()
-# 	max_label_len = torch.max(len_labels)
-# 	pad_num = max_label_len - len_labels
-# 	pad_num_max = torch.max(pad_num).item()
-# 	frames_pad_tensor = torch.zeros([int(batch_size), int(pad_num_max), int(hidden_size)], dtype=frames.dtype,
-# 	                                device=frames.device)
-# 	fires_pad_tensor = torch.ones([int(batch_size), int(pad_num_max)], dtype=fires.dtype, device=fires.device)
-# 	fires_pad_tensor_mask = sequence_mask_scripts(pad_num, maxlen=int(pad_num_max))
-# 	fires_pad_tensor *= fires_pad_tensor_mask
-# 	frames_pad = torch.cat([frames, frames_pad_tensor], dim=1)
-# 	fires_pad = torch.cat([fires, fires_pad_tensor], dim=1)
-# 	index_bool = fires_pad >= threshold
-# 	frames_fire = frames_pad[index_bool]
-# 	frames_fire = torch.reshape(frames_fire, (int(batch_size), -1, int(hidden_size)))
-# 	frames_fire_mask = sequence_mask_scripts(len_labels, maxlen=int(max_label_len))
-# 	frames_fire *= frames_fire_mask[:, :, None]
-#
-# 	return frames_fire, fires
-
 
 @torch.jit.script
 def cif(hidden, alphas, threshold: float):