diff --git a/nemo/collections/tts/modules/common.py b/nemo/collections/tts/modules/common.py
index 63c28f12a4a7..0765d0499bda 100644
--- a/nemo/collections/tts/modules/common.py
+++ b/nemo/collections/tts/modules/common.py
@@ -122,22 +122,30 @@ def lstm_tensor(self, context: Tensor, lens: Tensor, enforce_sorted: bool = Fals
         seq = nn.utils.rnn.pack_padded_sequence(
             context, lens.long().cpu(), batch_first=True, enforce_sorted=enforce_sorted
         )
-        return self.lstm_sequence(seq)
+        if not (torch.jit.is_scripting() or torch.jit.is_tracing()):
+            self.bilstm.flatten_parameters()
+        if hasattr(self.bilstm, 'forward'):
+            ret, _ = self.bilstm.forward(seq)
+        else:
+            ret, _ = self.bilstm.forward_1(seq)
+        return nn.utils.rnn.pad_packed_sequence(ret, batch_first=True)
 
     def lstm_sequence(self, seq: PackedSequence) -> Tuple[Tensor, Tensor]:
         if not (torch.jit.is_scripting() or torch.jit.is_tracing()):
             self.bilstm.flatten_parameters()
-        ret, _ = self.bilstm(seq)
+        if hasattr(self.bilstm, 'forward'):
+            ret, _ = self.bilstm.forward(seq)
+        elif hasattr(self.bilstm, 'forward_1'):
+            ret, _ = self.bilstm.forward_1(seq)
         return nn.utils.rnn.pad_packed_sequence(ret, batch_first=True)
 
-    def forward(self, context: Tensor, lens: Tensor) -> Tensor:
+    @torch.jit.export
+    def sort_and_lstm_tensor(self, context: Tensor, lens: Tensor) -> Tensor:
         context, lens_sorted, unsort_ids = sort_tensor(context, lens)
-        dtype = context.dtype
-        # this is only needed for Torchscript to run in Triton
-        # (https://github.com/pytorch/pytorch/issues/89241)
-        with torch.cuda.amp.autocast(enabled=False):
-            ret = self.lstm_tensor(context.to(dtype=torch.float32), lens_sorted, enforce_sorted=True)
-        return ret[0].to(dtype=dtype)[unsort_ids]
+        seq = nn.utils.rnn.pack_padded_sequence(
+            context, lens_sorted.long().cpu(), batch_first=True, enforce_sorted=True
+        )
+        return self.lstm_sequence(seq)[0][unsort_ids]
 
 
 class ConvLSTMLinear(nn.Module):
@@ -152,8 +160,7 @@ def __init__(
         use_partial_padding=False,
         norm_fn=None,
     ):
-        super(ConvLSTMLinear, self).__init__()
-        self.bilstm = BiLSTM(n_channels, int(n_channels // 2), 1)
+        super(ConvLSTMLinear, self).__init__(n_channels, int(n_channels // 2), 1)
         self.convolutions = nn.ModuleList()
 
         if n_layers > 0:
@@ -184,14 +191,24 @@ def __init__(
         if out_dim is not None:
             self.dense = nn.Linear(n_channels, out_dim)
 
-    def forward(self, context: Tensor, lens: Tensor) -> Tensor:
+    def masked_conv_to_sequence(self, context: Tensor, lens: Tensor, enforce_sorted: bool = False) -> PackedSequence:
         mask = get_mask_from_lengths_and_val(lens, context)
         mask = mask.to(dtype=context.dtype).unsqueeze(1)
         for conv in self.convolutions:
             context = self.dropout(F.relu(conv(context, mask)))
+
         context = context.transpose(1, 2)
-        # Apply Bidirectional LSTM
-        context = self.bilstm(context, lens)
+        seq = torch.nn.utils.rnn.pack_padded_sequence(
+            context, lens.long().cpu(), batch_first=True, enforce_sorted=enforce_sorted
+        )
+        return seq
+
+    def forward(self, context: Tensor, lens: Tensor) -> Tensor:
+        context, lens, unsort_ids = sort_tensor(context, lens)
+        seq = self.masked_conv_to_sequence(context, lens, enforce_sorted=True)
+        context, _ = self.lstm_sequence(seq)
+        context = context[unsort_ids]
+
         if self.dense is not None:
             context = self.dense(context).permute(0, 2, 1)
         return context
diff --git a/nemo/collections/tts/modules/radtts.py b/nemo/collections/tts/modules/radtts.py
index 9f360a4e5a33..dca0f0ede62c 100644
--- a/nemo/collections/tts/modules/radtts.py
+++ b/nemo/collections/tts/modules/radtts.py
@@ -345,7 +345,9 @@ def preprocess_context(self, context, speaker_vecs, out_lens, f0, energy_avg):
                     context_w_spkvec = torch.cat((context_w_spkvec, energy_avg), 1)
 
             unfolded_out_lens = out_lens // self.n_group_size
-            context_lstm_padded_output = self.context_lstm(context_w_spkvec.transpose(1, 2), unfolded_out_lens)
+            context_lstm_padded_output = self.context_lstm.sort_and_lstm_tensor(
+                context_w_spkvec.transpose(1, 2), unfolded_out_lens
+            )
             context_w_spkvec = context_lstm_padded_output.transpose(1, 2)
 
         if not self.context_lstm_w_f0_and_energy:
@@ -770,8 +772,8 @@ def input_example(self, max_batch=1, max_dim=256):
         """
         par = next(self.parameters())
         sz = (max_batch, max_dim)
-        inp = torch.randint(16, 32, sz, device=par.device, dtype=torch.int64)
-        lens = torch.randint(max_dim // 4, max_dim // 2, (max_batch,), device=par.device, dtype=torch.int)
+        inp = torch.randint(0, 16, sz, device=par.device, dtype=torch.int64)
+        lens = torch.randint(16, max_dim, (max_batch,), device=par.device, dtype=torch.int)
         speaker = torch.randint(0, 1, (max_batch,), device=par.device, dtype=torch.int64)
         inputs = {
             'text': inp,
diff --git a/nemo/core/classes/exportable.py b/nemo/core/classes/exportable.py
index 50266dab3dbe..b3f0b2fdd642 100644
--- a/nemo/core/classes/exportable.py
+++ b/nemo/core/classes/exportable.py
@@ -128,7 +128,7 @@ def _export(
             # Set module mode
             with torch.onnx.select_model_mode_for_export(
                 self, training
-            ), torch.inference_mode(), torch.no_grad(), torch.jit.optimized_execution(True), _jit_is_scripting():
+            ), torch.inference_mode(), torch.no_grad(), torch.jit.optimized_execution(True):
 
                 if input_example is None:
                     input_example = self.input_module.input_example()
diff --git a/nemo/utils/cast_utils.py b/nemo/utils/cast_utils.py
index 9eb064936ea5..f973a4719e24 100644
--- a/nemo/utils/cast_utils.py
+++ b/nemo/utils/cast_utils.py
@@ -70,6 +70,6 @@ def __init__(self, mod):
         self.mod = mod
 
     def forward(self, x):
-        with avoid_float16_autocast_context():
+        with torch.cuda.amp.autocast(enabled=False):
             ret = self.mod.forward(x.to(torch.float32)).to(x.dtype)
         return ret
diff --git a/nemo/utils/export_utils.py b/nemo/utils/export_utils.py
index c7a45649daa2..0fbe2999bffe 100644
--- a/nemo/utils/export_utils.py
+++ b/nemo/utils/export_utils.py
@@ -15,7 +15,7 @@
 import os
 from contextlib import nullcontext
 from enum import Enum
-from typing import Callable, Dict, Optional, Type
+from typing import Callable, Dict, List, Optional, Type
 
 import onnx
 import torch
@@ -158,12 +158,8 @@ def verify_torchscript(model, output, input_examples, input_names, check_toleran
     for input_example in input_examples:
         input_list, input_dict = parse_input_example(input_example)
         output_example = model.forward(*input_list, **input_dict)
-        # We disable autocast here to make sure exported TS will run under Triton or other C++ env
-        with torch.cuda.amp.autocast(enabled=False):
-            ts_model = torch.jit.load(output)
-            all_good = all_good and run_ts_and_compare(
-                ts_model, input_list, input_dict, output_example, check_tolerance
-            )
+
+        all_good = all_good and run_ts_and_compare(ts_model, input_list, input_dict, output_example, check_tolerance)
     status = "SUCCESS" if all_good else "FAIL"
     logging.info(f"Torchscript generated at {output} verified with torchscript forward : " + status)
     return all_good
@@ -205,15 +201,8 @@ def run_ts_and_compare(ts_model, ts_input_list, ts_input_dict, output_example, c
 
         if torch.is_tensor(expected):
             tout = out.to('cpu')
-            logging.debug(f"Checking output {i}, shape: {expected.shape}:\n")
-            this_good = True
-            try:
-                if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=check_tolerance):
-                    this_good = False
-            except Exception:  # there may ne size mismatch and it may be OK
-                this_good = False
-            if not this_good:
-                logging.info(f"Results mismatch! PyTorch(expected):\n{expected}\nTorchScript:\n{tout}")
+            logging.debug(f"Checking output {i}, shape: {expected.shape}:\n{expected}\n{tout}")
+            if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=check_tolerance):
                 all_good = False
     return all_good
 
@@ -227,14 +216,9 @@ def run_ort_and_compare(sess, ort_input, output_example, check_tolerance=0.01):
 
         if torch.is_tensor(expected):
             tout = torch.from_numpy(out)
-            logging.debug(f"Checking output {i}, shape: {expected.shape}:\n")
-            this_good = True
-            try:
-                if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=100 * check_tolerance):
-                    this_good = False
-            except Exception:  # there may ne size mismatch and it may be OK
-                this_good = False
-            if not this_good:
+            logging.debug(f"Checking output {i}, shape: {expected.shape}:\n{expected}\n{tout}")
+            if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=100 * check_tolerance):
+                all_good = False
                 logging.info(f"onnxruntime results mismatch! PyTorch(expected):\n{expected}\nONNXruntime:\n{tout}")
                 all_good = False
     return all_good
@@ -433,7 +417,8 @@ def replace_modules(
 
 
 def script_module(m: nn.Module):
-    return torch.jit.script(m)
+    m1 = torch.jit.script(m)
+    return m1
 
 
 default_replacements = {
@@ -443,6 +428,11 @@ def script_module(m: nn.Module):
     "MatchedScaleMaskSoftmax": wrap_module(nn.Softmax, ExportableMatchedScaleMaskSoftmax),
 }
 
+script_replacements = {
+    "BiLSTM": script_module,
+    "ConvLSTMLinear": script_module,
+}
+
 
 def replace_for_export(model: nn.Module) -> nn.Module:
     """
diff --git a/scripts/export.py b/scripts/export.py
index b3d6317e936c..2e100e446e72 100644
--- a/scripts/export.py
+++ b/scripts/export.py
@@ -143,10 +143,11 @@ def nemo_export(argv):
             if check_trace and len(in_args) > 0:
                 input_example = model.input_module.input_example(**in_args)
                 check_trace = [input_example]
-                for key, arg in in_args:
+                for key, arg in in_args.items():
                     in_args[key] = (arg + 1) // 2
                 input_example2 = model.input_module.input_example(**in_args)
                 check_trace.append(input_example2)
+                logging.info(f"Using additional check args: {in_args}")
 
             _, descriptions = model.export(
                 out,
diff --git a/tests/collections/tts/test_tts_exportables.py b/tests/collections/tts/test_tts_exportables.py
index d7684de732e5..e3e496373271 100644
--- a/tests/collections/tts/test_tts_exportables.py
+++ b/tests/collections/tts/test_tts_exportables.py
@@ -15,7 +15,6 @@
 import tempfile
 
 import pytest
-import torch
 from omegaconf import OmegaConf
 
 from nemo.collections.tts.models import FastPitchModel, HifiGanModel, RadTTSModel
@@ -74,12 +73,10 @@ def test_HifiGanModel_export_to_onnx(self, hifigan_model):
             filename = os.path.join(tmpdir, 'hfg.pt')
             model.export(output=filename, verbose=True, check_trace=True)
 
-    @pytest.mark.pleasefixme
     @pytest.mark.run_only_on('GPU')
     @pytest.mark.unit
     def test_RadTTSModel_export_to_torchscript(self, radtts_model):
         model = radtts_model.cuda()
         with tempfile.TemporaryDirectory() as tmpdir:
             filename = os.path.join(tmpdir, 'rad.ts')
-            with torch.cuda.amp.autocast(enabled=True):
-                model.export(output=filename, verbose=True, check_trace=True)
+            model.export(output=filename, verbose=True, check_trace=True)