update scripts

examples/industrial_data_pretraining/bicif_paraformer/export.py

@@ -12,17 +12,17 @@ model = AutoModel(
     device="cpu",
 )
 
-res = model.export(type="onnx", quantize=False)
+res = model.export(type="torchscripts", quantize=False)
 print(res)
 
 
-# method2, inference from local path
-from funasr import AutoModel
+# # method2, inference from local path
+# from funasr import AutoModel
 
-model = AutoModel(
-    model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
-    device="cpu",
-)
+# model = AutoModel(
+#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+#     device="cpu",
+# )
 
-res = model.export(type="onnx", quantize=False)
-print(res)
+# res = model.export(type="onnx", quantize=False)
+# print(res)

funasr/utils/export_utils.py

@@ -22,6 +22,7 @@ def export(model, data_in=None, quantize: bool = False, opset_version: int = 14,
             )
         elif type == 'torchscripts':
             device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            print("Exporting torchscripts on device {}".format(device))
             _torchscripts(
                 m,
                 path=export_dir,

runtime/python/libtorch/funasr_torch/utils/timestamp_utils.py

@@ -7,7 +7,7 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0, total_offset=-1
     START_END_THRESHOLD = 5
     MAX_TOKEN_DURATION = 30
     TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
-    cif_peak = us_cif_peak.reshape(-1)
+    cif_peak = us_cif_peak.reshape(-1).cpu()
     num_frames = cif_peak.shape[-1]
     if char_list[-1] == "</s>":
         char_list = char_list[:-1]