Update whisper model test cases and e2e example (#496)

* Update whisper model test cases and e2e example

* fix unit test on windows

* more refinement

* utest fix

java/README.md

@@ -12,9 +12,6 @@ This java and Android API and packaging principles were inspired by the https://
 1. install visual studio 2022 (with cmake, git, desktop C++)
 2. OpenJDK: https://docs.microsoft.com/en-us/java/openjdk/download
 		(OpenJDK 11.0.15 LTS)
-3. Gradle: https://gradle.org/releases/
-		(v6.9.2)
-
 
 ### Build command
 ./build.sh **-DOCOS_BUILD_JAVA=ON**

onnxruntime_extensions/_torch_cvt.py

@@ -66,7 +66,7 @@ def _mel_filterbank(
         # intersect them with each other and zero
         fbank[i] = np.maximum(0, np.minimum(left, right))
 
-    energy_norm = 2.0 / (mel_bins[2 : n_mels + 2] - mel_bins[:n_mels])
+    energy_norm = 2.0 / (mel_bins[2: n_mels + 2] - mel_bins[:n_mels])
     fbank *= energy_norm[:, np.newaxis]
     return fbank
 
@@ -109,9 +109,9 @@ class WhisperPrePipeline(torch.nn.Module):
         log_spec = torch.maximum(log_spec, spec_min)
         spec_shape = log_spec.shape
         padding_spec = torch.ones(spec_shape[0],
-                                  spec_shape[1], (
+                                  spec_shape[1],
-                                    _WhisperHParams.N_SAMPLES // _WhisperHParams.HOP_LENGTH -
+                                  _WhisperHParams.N_SAMPLES // _WhisperHParams.HOP_LENGTH - spec_shape[2],
-                                        spec_shape[2]), dtype=torch.float)
+                                  dtype=torch.float)
         padding_spec *= spec_min
         log_spec = torch.cat((log_spec, padding_spec), dim=2)
         log_spec = (log_spec + 4.0) / 4.0
@@ -138,7 +138,7 @@ def _to_onnx_stft(onnx_model):
                   value=numpy_helper.from_array(np.array([0,
                                                           _WhisperHParams.N_FFT // 2, 0,
                                                           _WhisperHParams.N_FFT // 2], dtype='int64'),
-                  name='const_14')),
+                                                name='const_14')),
         make_node('Pad',
                   inputs=[stft_norm_node.input[0], 'const_14_output_0'],
                   outputs=['pad_1_output_0'], mode='reflect'),
@@ -225,9 +225,22 @@ class WhisperDataProcGraph:
         return pre_full
 
     def post_processing(self, **kwargs):
+        skip_special_tokens = kwargs.get('skip_special_tokens', True)
         g = SingleOpGraph.build_graph(
             "BpeDecoder",
             cvt=HFTokenizerConverter(self.hf_processor.tokenizer).bpe_decoder,
-            skip_special_tokens=True,
+            skip_special_tokens=skip_special_tokens)
-            cpu_only=True)
+
-        return make_onnx_model(g)
+        bpenode = g.node[0]
+        bpenode.input[0] = "generated_ids"
+        nodes = [onnx.helper.make_node('Cast', ['sequences'], ["generated_ids"], to=onnx.TensorProto.INT64),
+                 bpenode]
+        del g.node[:]
+        g.node.extend(nodes)
+
+        inputs = [onnx.helper.make_tensor_value_info("sequences", onnx.TensorProto.INT32, ['N', 'seq_len', 'ids'])]
+        del g.input[:]
+        g.input.extend(inputs)
+        g.output[0].type.CopyFrom(onnx.helper.make_tensor_type_proto(onnx.TensorProto.STRING, ['N', 'seq_len', 'text']))
+
+        return make_onnx_model(g, opset_version=self.opset_version)

onnxruntime_extensions/util.py

@@ -60,7 +60,7 @@ def mel_filterbank(
         # intersect them with each other and zero
         fbank[i] = np.maximum(0, np.minimum(left, right))
 
-    energy_norm = 2.0 / (mel_bins[2 : n_mels + 2] - mel_bins[:n_mels])
+    energy_norm = 2.0 / (mel_bins[2: n_mels + 2] - mel_bins[:n_mels])
     fbank *= energy_norm[:, np.newaxis]
     return fbank
 
@@ -126,3 +126,55 @@ def remove_unused_initializers(subgraph, top_level_initializers=None):
             elif attr.type == onnx.AttributeProto.GRAPHS:
                 for subgraph in attr.graphs:
                     remove_unused_initializers(subgraph, top_level_initializers)
+
+
+def quick_merge(*models, connection_indices=None):
+    """
+    This function merges multiple ONNX models into a single model, without performing any ONNX format checks.
+
+    Parameters:
+    *models (onnx.ModelProto): Varargs parameter representing the ONNX models to be merged.
+    connection_indices (List[List[int]], optional): A nested list specifying which outputs in one model should connect 
+                                                    to which inputs in the next model, based on their indices. 
+                                                    If not provided, it's assumed that the sequence of outputs in 
+                                                    one model exactly matches the sequence of inputs in the next model.
+
+    Returns:
+    merged_model (onnx.ModelProto): The merged ONNX model.
+
+    Raises:
+    ValueError: If there is any conflict in tensor names, either in initializers or in nodes, including subgraphs.
+                If there is any conflict in opset versions for the same domain.
+    """
+
+    merged_graph = models[0].graph
+
+    # Dictionary to store unique opsets
+    opset_imports = {opset.domain if opset.domain else "ai.onnx": opset for opset in models[0].opset_import}
+
+    # Iterate over all other models and merge
+    for model_idx, model in enumerate(models[1:], start=1):
+        if connection_indices is None:
+            io_map = [(out.name, in_.name) for out, in_ in zip(models[model_idx - 1].graph.output, model.graph.input)]
+        else:
+            io_map = [(models[model_idx - 1].graph.output[out_idx].name, model.graph.input[in_idx].name)
+                      for out_idx, in_idx in connection_indices[model_idx - 1]]
+
+        merged_graph = onnx.compose.merge_graphs(merged_graph, model.graph, io_map)
+
+        for opset in model.opset_import:
+            if not opset.domain:
+                opset.domain = "ai.onnx"
+            if opset.domain in opset_imports and opset_imports[opset.domain].version != opset.version:
+                raise ValueError(f"Conflict in opset versions for domain '{opset.domain}': " +
+                                 f"model {model_idx} has version {opset.version}, while previous model has version " +
+                                 f"{opset_imports[opset.domain].version}.")
+            else:
+                opset_imports[opset.domain] = opset
+
+    default_opset = opset_imports.pop("ai.onnx", None)
+    merged_model = onnx.helper.make_model_gen_version(merged_graph,
+                                                      opset_imports=[default_opset],
+                                                      producer_name='ONNX Model Merger')
+    merged_model.opset_import.extend(opset_imports.values())
+    return merged_model

operators/audio/audio.cc

@@ -8,7 +8,9 @@
 
 FxLoadCustomOpFactory LoadCustomOpClasses_Audio = []()-> CustomOpArray& {
   static OrtOpLoader op_loader(
+    []() { return nullptr; }
 #ifdef ENABLE_DR_LIBS
+    ,
     CustomCpuStruct("AudioDecoder", AudioDecoder)
 #endif
   );

test/test_audio_signal.py

@@ -3,7 +3,7 @@
 import wave
 import unittest
 import numpy as np
-from onnxruntime_extensions import PyOrtFunction, util, make_onnx_model
+from onnxruntime_extensions import OrtPyFunction, util, make_onnx_model
 
 import onnx
 from onnx import onnx_pb as onnx_proto
@@ -12,6 +12,7 @@ from onnx import onnx_pb as onnx_proto
 _is_torch_available = False
 try:
     import torch
+
     _is_torch_available = True
 except ImportError:
     pass
@@ -19,6 +20,7 @@ except ImportError:
 _is_librosa_avaliable = False
 try:
     import librosa
+
     _is_librosa_avaliable = True
 except ImportError:
     pass
@@ -57,7 +59,7 @@ class TestAudio(unittest.TestCase):
         audio = wavefile.readframes(samples)
         audio_as_np_int16 = np.frombuffer(audio, dtype=np.int16)
         audio_as_np_float32 = audio_as_np_int16.astype(np.float32)
-        max_int16 = 2**15
+        max_int16 = 2 ** 15
         cls.test_pcm = audio_as_np_float32 / max_int16
 
     @staticmethod
@@ -99,7 +101,7 @@ class TestAudio(unittest.TestCase):
         audio_pcm = self.test_pcm
         expected = self.stft(audio_pcm, 400, 160, np.hanning(400).astype(np.float32))
 
-        ortx_stft = PyOrtFunction.from_model(_create_test_model(), cpu_only=True)
+        ortx_stft = OrtPyFunction.from_model(_create_test_model(), cpu_only=True)
         actual = ortx_stft(np.expand_dims(audio_pcm, axis=0), 400, 160, np.hanning(400).astype(np.float32), 400)
         actual = actual[0]
         actual = actual[:, :, 0] ** 2 + actual[:, :, 1] ** 2
@@ -109,7 +111,7 @@ class TestAudio(unittest.TestCase):
         audio_pcm = self.test_pcm
         expected = self.stft(audio_pcm, 400, 160, np.hanning(400).astype(np.float32))
 
-        ortx_stft = PyOrtFunction.from_customop("StftNorm", cpu_only=True)
+        ortx_stft = OrtPyFunction.from_customop("StftNorm", cpu_only=True)
         actual = ortx_stft(np.expand_dims(audio_pcm, axis=0), 400, 160, np.hanning(400).astype(np.float32), 400)
         actual = actual[0]
         np.testing.assert_allclose(expected[:, 1:], actual[:, 1:], rtol=1e-3, atol=1e-3)
@@ -125,7 +127,7 @@ class TestAudio(unittest.TestCase):
                               center=True,
                               return_complex=True).abs().pow(2).numpy()
         audio_pcm = np.expand_dims(self.test_pcm, axis=0)
-        ortx_stft = PyOrtFunction.from_customop("StftNorm")
+        ortx_stft = OrtPyFunction.from_customop("StftNorm")
         actual = ortx_stft(audio_pcm, 400, 160, np.hanning(wlen).astype(np.float32), 400)
         actual = actual[0]
         np.testing.assert_allclose(expected[:, 1:], actual[:, 1:], rtol=1e-3, atol=1e-3)

test/test_autotokenizer.py

@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
+import sys
 import unittest
 import transformers as _hfts
 
@@ -9,6 +10,7 @@ from packaging import version
 from onnxruntime_extensions import OrtPyFunction, util, gen_processing_models
 
 
+@unittest.skipIf(version.parse(_ort.__version__) < version.parse("1.14.0"), "skip for onnxruntime < 1.14.0")
 class TestAutoTokenizer(unittest.TestCase):
     def test_t5_tokenizer(self):
         tokenizer = _hfts.AutoTokenizer.from_pretrained("t5-base", model_max_length=512)
@@ -31,37 +33,77 @@ class TestAutoTokenizer(unittest.TestCase):
         actual_ids = ort_tok(["best hotel in bay area."], *t5_default_inputs)[0]
         np.testing.assert_array_equal(ids[0][:-1], actual_ids)
 
-    @unittest.skipIf(version.parse(_ort.__version__) < version.parse("1.14.0"), "skip for onnxruntime < 1.14.0")
+    def test_whisper_overall(self):
-    def test_whisper(self):
         processor = _hfts.WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
         pre_m, post_m = gen_processing_models(processor,
-                              pre_kwargs={"USE_AUDIO_DECODER": False, "USE_ONNX_STFT": False},
+                                              pre_kwargs={"USE_AUDIO_DECODER": False, "USE_ONNX_STFT": False},
-                              post_kwargs={})
+                                              post_kwargs={})
 
         fn_pre = OrtPyFunction.from_model(pre_m, session_options={"graph_optimization_level": 0})
-        t = np.linspace(0, 2*np.pi, 480000).astype(np.float32)
+        t = np.linspace(0, 2 * np.pi, 480000).astype(np.float32)
         simaudio = np.expand_dims(np.sin(2 * np.pi * 100 * t), axis=0)
         log_mel = fn_pre(simaudio)
 
         self.assertEqual(log_mel.shape, (1, 80, 3000))
 
         fn_post = OrtPyFunction.from_model(post_m)
-        rel = fn_post(np.asarray([3, 4, 5], dtype=np.int32))
+        rel = fn_post(np.asarray([3, 4, 5], dtype=np.int32)[np.newaxis, np.newaxis, :])
         self.assertEqual(rel[0], "$%&")
 
-    @unittest.skipIf(version.parse(_ort.__version__) < version.parse("1.14.0"), "skip for onnxruntime < 1.14.0")
     def test_whisper_audio_decoder(self):
         processor = _hfts.WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
         pre_m, _ = gen_processing_models(processor,
-                              pre_kwargs={"USE_AUDIO_DECODER": True, "USE_ONNX_STFT": True})
+                                         pre_kwargs={"USE_AUDIO_DECODER": True, "USE_ONNX_STFT": True})
 
         fn_pre = OrtPyFunction.from_model(pre_m, session_options={"graph_optimization_level": 0})
         test_flac_file = util.get_test_data_file('data', '1272-141231-0002.flac')
-        raw_audio = np.fromfile(test_flac_file, dtype=np.uint8)
+        audio_data = np.fromfile(test_flac_file, dtype=np.uint8)
-        log_mel = fn_pre(np.expand_dims(raw_audio, axis=0))
+        log_mel = fn_pre(np.expand_dims(audio_data, axis=0))
 
         self.assertEqual(log_mel.shape, (1, 80, 3000))
 
+    @unittest.skipIf(sys.platform.startswith('win'), "Huggingface Processor crashed on Windows.")
+    def test_ort_stft_consistency(self):
+        processor = _hfts.WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        pre_m, _ = gen_processing_models(processor,
+                                         pre_kwargs={"USE_AUDIO_DECODER": False, "USE_ONNX_STFT": True})
+
+        test_mp3_file = util.get_test_data_file('data', '1272-141231-0002.mp3')
+        test_data = np.expand_dims(np.fromfile(test_mp3_file, dtype=np.uint8), axis=0)
+        raw_audio = OrtPyFunction.from_customop(
+            "AudioDecoder", cpu_only=True, downsampling_rate=16000, stereo_to_mono=1)(test_data)
+
+        input_features = processor([raw_audio[0]], sampling_rate=16000)
+        expected = input_features['input_features'][0]
+
+        log_mel = OrtPyFunction.from_model(pre_m)(raw_audio)
+        actual = log_mel[0]
+
+        num_mismatched = np.sum(~np.isclose(expected, actual, rtol=1e-03, atol=1e-05))
+        # ORT STFT has a few more mismatched values than HuggingFace's WhisperProcessor, around 1.5%.
+        self.assertTrue(num_mismatched / np.size(expected) < 0.02)
+        self.assertAlmostEqual(expected.min(), actual.min(), delta=1e-05)
+
+    @unittest.skipIf(sys.platform.startswith('win'), "Huggingface Processor crashed on Windows.")
+    def test_stft_norm_consistency(self):
+        processor = _hfts.WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        pre_m, _ = gen_processing_models(processor,
+                                         pre_kwargs={"USE_AUDIO_DECODER": False, "USE_ONNX_STFT": False})
+
+        test_mp3_file = util.get_test_data_file('data', '1272-141231-0002.mp3')
+        test_data = np.expand_dims(np.fromfile(test_mp3_file, dtype=np.uint8), axis=0)
+        raw_audio = OrtPyFunction.from_customop(
+            "AudioDecoder", cpu_only=True, downsampling_rate=16000, stereo_to_mono=1)(test_data)
+
+        input_features = processor([raw_audio[0]], sampling_rate=16000)
+        expected = input_features['input_features'][0]
+
+        log_mel = OrtPyFunction.from_model(pre_m)(raw_audio)
+        actual = log_mel[0]
+
+        np.testing.assert_allclose(expected, actual, rtol=1e-03, atol=1e-05)
+        self.assertAlmostEqual(expected.min(), actual.min(), delta=1e-05)
+
 
 if __name__ == '__main__':
     unittest.main()

tutorials/whisper_e2e.py

@@ -1,291 +1,99 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
-import argparse
+
-import io
+# Run the whisper end-to-end inference with ONNXRuntime-Extensions for pre/post processing.
+# THIS SCRIPT IS USED TO DEMO ONLY, WHICH IS NOT A PART OF THE PACKAGE.
+# TO GENERATE THE FULL-FUNCTION MODEL, PLEASE USE https://github.com/microsoft/Olive
 import os
 import onnx
-import re
+import subprocess
-import torch
 import numpy as np
+import onnxruntime as ort
 
-from onnx import numpy_helper
+from packaging import version
 from transformers import WhisperProcessor
+from onnxruntime_extensions import OrtPyFunction, util
+from onnxruntime_extensions.cvt import gen_processing_models
 
-from onnxruntime_extensions import PyOrtFunction, util
+# Constants
-from onnxruntime_extensions.cvt import HFTokenizerConverter
+MODEL_NAME = "openai/whisper-tiny.en"
+CACHE_DIR = 'temp_caches_onnx'
+OUTPUT_DIR = 'temp_model_onnx'
+FINAL_MODEL = "whisper_onnx_tiny_en_fp32_e2e.onnx"
+TEST_AUDIO_FILE = util.get_test_data_file('../test/data', "1272-141231-0002.mp3")
 
 
-# the flags for pre-processing
+def check_onnx_version():
-USE_ONNX_STFT = True
+    if version.parse(ort.__version__) < version.parse("1.16.0"):
-USE_AUDIO_DECODER = True
+        raise RuntimeError("ONNXRuntime version must >= 1.16.0")
 
 
-if not USE_AUDIO_DECODER:
+def export_onnx_model():
-    try:
+    print("Exporting Whisper ONNX model from Huggingface model hub...")
-        import librosa
+    command = ['python', '-m',
-    except ImportError:
+               'onnxruntime.transformers.models.whisper.convert_to_onnx',
-        raise ImportError("Please pip3 install librosa without ort-extensions audio codec support.")
+               '-m', MODEL_NAME,
+               '--cache_dir', CACHE_DIR,
+               '--output', OUTPUT_DIR,
+               '--precision', 'fp32']
+    process = subprocess.run(command)
+    if process.returncode != 0:
+        raise RuntimeError("Failed to export the core ONNX models.")
 
 
-# hard-coded audio hyperparameters
+def process_test_file():
-# copied from https://github.com/openai/whisper/blob/main/whisper/audio.py#L12
+    if not os.path.exists(TEST_AUDIO_FILE):
-SAMPLE_RATE = 16000
+        raise FileNotFoundError(f"Test audio path {TEST_AUDIO_FILE} does not exist.")
-N_FFT = 400
+
-N_MELS = 80
+    raw_audio = np.fromfile(TEST_AUDIO_FILE, dtype=np.uint8)
-HOP_LENGTH = 160
+    _processor = WhisperProcessor.from_pretrained(MODEL_NAME, cache_dir=CACHE_DIR)
-CHUNK_LENGTH = 30
+    pre_m, post_m = gen_processing_models(_processor,
-N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000 samples in a 30-second chunk
+                                          pre_kwargs={"USE_AUDIO_DECODER": True, "USE_ONNX_STFT": True},
-N_FRAMES = N_SAMPLES // HOP_LENGTH
+                                          post_kwargs={},
+                                          opset=17)
+
+    fn_pre = OrtPyFunction.from_model(pre_m, session_options={"graph_optimization_level": 0})
+    return fn_pre(np.expand_dims(raw_audio, axis=0)), pre_m, post_m
 
 
-class CustomOpStftNorm(torch.autograd.Function):
+def main():
-    @staticmethod
+    check_onnx_version()
-    def symbolic(g, self, n_fft, hop_length, window):
+    export_onnx_model()
-        t_n_fft = g.op('Constant', value_t=torch.tensor(n_fft, dtype=torch.int64))
+    log_mel, pre_m, post_m = process_test_file()
-        t_hop_length = g.op('Constant', value_t=torch.tensor(hop_length, dtype=torch.int64))
-        t_frame_size = g.op('Constant', value_t=torch.tensor(n_fft, dtype=torch.int64))
-        return g.op("ai.onnx.contrib::StftNorm", self, t_n_fft, t_hop_length, window, t_frame_size)
 
-    @staticmethod
+    # Apply core ONNX model
-    def forward(ctx, audio, n_fft, hop_length, window):
+    fn_core = OrtPyFunction.from_model(os.path.join(OUTPUT_DIR, "whisper-tiny.en_beamsearch.onnx"), cpu_only=True)
-        win_length = window.shape[0]
+    token_seq = fn_core(log_mel,
-        stft = torch.stft(audio, n_fft, hop_length, win_length, window,
-                          center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=True)
-        return stft.abs() ** 2
-
-
-class WhisperPrePipeline(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.window = torch.hann_window(N_FFT)
-        self.mel_filters = torch.from_numpy(util.mel_filterbank(sr=SAMPLE_RATE, n_fft=N_FFT, n_mels=N_MELS))
-
-    def forward(self, audio_pcm: torch.Tensor):
-        stft_norm = CustomOpStftNorm.apply(audio_pcm, N_FFT, HOP_LENGTH, self.window)
-        magnitudes = stft_norm[:, :, :-1]
-        mel_spec = self.mel_filters @ magnitudes
-        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
-        spec_min = log_spec.max() - 8.0
-        log_spec = torch.maximum(log_spec, spec_min)
-        spec_shape = log_spec.shape
-        padding_spec = torch.ones(spec_shape[0],
-                                  spec_shape[1], (N_SAMPLES // HOP_LENGTH - spec_shape[2]), dtype=torch.float)
-        padding_spec *= spec_min
-        log_spec = torch.cat((log_spec, padding_spec), dim=2)
-        log_spec = (log_spec + 4.0) / 4.0
-        return log_spec
-
-
-def _to_onnx_stft(onnx_model):
-    """Convert custom-op STFT-Norm to ONNX STFT"""
-    node_idx = 0
-    new_stft_nodes = []
-    stft_norm_node = None
-    for node in onnx_model.graph.node:
-        if node.op_type == "StftNorm":
-            stft_norm_node = node
-            break
-        node_idx += 1
-
-    if stft_norm_node is None:
-        raise RuntimeError("Cannot find STFTNorm node in the graph")
-
-    make_node = onnx.helper.make_node
-    replaced_nodes = [
-        make_node('Constant', inputs=[], outputs=['const_14_output_0'], name='const_14',
-                  value=numpy_helper.from_array(np.array([0, N_FFT // 2, 0, N_FFT // 2], dtype='int64'),
-                  name='const_14')),
-        make_node('Pad',
-                  inputs=[stft_norm_node.input[0], 'const_14_output_0'],
-                  outputs=['pad_1_output_0'], mode='reflect'),
-        make_node('STFT',
-                  inputs=['pad_1_output_0', stft_norm_node.input[2], stft_norm_node.input[3], stft_norm_node.input[4]],
-                  outputs=['stft_output_0'], name='stft', domain='', onesided=1),
-        make_node('Transpose', inputs=['stft_output_0'], outputs=['transpose_1_output_0'], name='transpose_1',
-                  perm=[0, 2, 1, 3]),
-        make_node('Constant', inputs=[], outputs=['const_17_output_0'], name='const_17',
-                  value=numpy_helper.from_array(np.array([2], dtype='int64'), name='')),
-        make_node('Constant', inputs=[], outputs=['const_18_output_0'], name='const_18',
-                  value=numpy_helper.from_array(np.array([0], dtype='int64'), name='')),
-        make_node('Constant', inputs=[], outputs=['const_19_output_0'], name='const_19',
-                  value=numpy_helper.from_array(np.array([-1], dtype='int64'), name='')),
-        make_node('Constant', inputs=[], outputs=['const_20_output_0'], name='const_20',
-                  value=numpy_helper.from_array(np.array([1], dtype='int64'), name='')),
-        make_node('Slice', inputs=['transpose_1_output_0', 'const_18_output_0', 'const_19_output_0',
-                                   'const_17_output_0', 'const_20_output_0'], outputs=['slice_1_output_0'],
-                  name='slice_1'),
-        make_node('Constant', inputs=[], outputs=['const0_output_0'], name='const0', value_int=0),
-        make_node('Constant', inputs=[], outputs=['const1_output_0'], name='const1', value_int=1),
-        make_node('Gather', inputs=['slice_1_output_0', 'const0_output_0'], outputs=['gather_4_output_0'],
-                  name='gather_4', axis=3),
-        make_node('Gather', inputs=['slice_1_output_0', 'const1_output_0'], outputs=['gather_5_output_0'],
-                  name='gather_5', axis=3),
-        make_node('Mul', inputs=['gather_4_output_0', 'gather_4_output_0'], outputs=['mul_output_0'], name='mul0'),
-        make_node('Mul', inputs=['gather_5_output_0', 'gather_5_output_0'], outputs=['mul_1_output_0'], name='mul1'),
-        make_node('Add', inputs=['mul_output_0', 'mul_1_output_0'], outputs=[stft_norm_node.output[0]], name='add0'),
-    ]
-    new_stft_nodes.extend(onnx_model.graph.node[:node_idx])
-    new_stft_nodes.extend(replaced_nodes)
-    new_stft_nodes.extend(onnx_model.graph.node[node_idx + 1:])
-    del onnx_model.graph.node[:]
-    onnx_model.graph.node.extend(new_stft_nodes)
-    onnx.checker.check_model(onnx_model)
-    return onnx_model
-
-
-def _torch_export(*arg, **kwargs):
-    with io.BytesIO() as f:
-        torch.onnx.export(*arg, f, **kwargs)
-        return onnx.load_from_string(f.getvalue())
-
-
-def preprocessing(audio_data):
-    if USE_AUDIO_DECODER:
-        decoder = PyOrtFunction.from_customop(
-            "AudioDecoder", cpu_only=True, downsampling_rate=SAMPLE_RATE, stereo_to_mono=1)
-        audio_pcm = torch.from_numpy(decoder(audio_data))
-    else:
-        audio_pcm = torch.from_numpy(audio_data)
-
-    prep_model_name = 'whisper_pre.onnx'
-    whisper_processing = WhisperPrePipeline()
-
-    model_args = (audio_pcm,)
-    pre_model = _torch_export(
-        whisper_processing,
-        model_args,
-        input_names=["audio_pcm"],
-        output_names=["log_mel"],
-        do_constant_folding=True,
-        export_params=True,
-        opset_version=17,
-        dynamic_axes={
-            "audio_pcm": {1: "sample_len"},
-        }
-    )
-    onnx.save_model(pre_model, os.path.join(root_dir, prep_model_name))
-    if USE_ONNX_STFT:
-        pre_model = _to_onnx_stft(pre_model)
-        util.remove_unused_initializers(pre_model.graph)
-
-    pre_f = PyOrtFunction.from_model(pre_model, cpu_only=True)
-    if not USE_AUDIO_DECODER:
-        return pre_f(audio_data)
-    else:
-        pre_full = onnx.compose.merge_models(
-            decoder.onnx_model,
-            pre_model,
-            io_map=[("floatPCM", "audio_pcm")])
-        pre_f = PyOrtFunction.from_model(pre_full, cpu_only=True)
-
-        onnx.save_model(pre_f.onnx_model, os.path.join(root_dir, "whisper_codec_pre.onnx"))
-        result = pre_f(audio_data)
-        return result
-
-
-def merge_models(core: str, output_model: str, audio_data):
-    m_pre_path = os.path.join(root_dir, "whisper_codec_pre.onnx" if USE_AUDIO_DECODER else "whisper_pre.onnx")
-    m_pre = onnx.load_model(m_pre_path)
-    m_core = onnx.load_model(core)
-    m1 = onnx.compose.merge_models(m_pre, m_core, io_map=[("log_mel", "input_features")])
-    m2 = onnx.load_model(os.path.join(root_dir, "whisper_post.onnx"))
-
-    m_all = onnx.compose.merge_models(m1, m2, io_map=[("sequences", "ids")])
-    bpe_decoder_node = m_all.graph.node.pop(-1)
-    make_node = onnx.helper.make_node
-    bpe_decoder_node.input.pop(0)
-    bpe_decoder_node.input.extend(["generated_ids"])
-    m_all.graph.node.extend([
-        make_node('Cast', ['sequences'], ["generated_ids"], to=onnx.TensorProto.INT64),
-        bpe_decoder_node
-        ])
-    try:
-        onnx.save_model(m_all, output_model)
-    except ValueError:
-        onnx.save_model(m_all, output_model,
-                        save_as_external_data=True,
-                        all_tensors_to_one_file=True,
-                        location=f"{os.path.basename(output_model)}.data",
-                        convert_attribute=True)
-    print(f"The final merged model was saved as: {output_model}")
-
-    print("Verify the final model...")
-    m_final = PyOrtFunction.from_model(output_model, cpu_only=True)
-    output_text = m_final(audio_data,
-                          np.asarray([200], dtype=np.int32),
-                          np.asarray([0], dtype=np.int32),
-                          np.asarray([2], dtype=np.int32),
-                          np.asarray([1], dtype=np.int32),
-                          np.asarray([1.0], dtype=np.float32), np.asarray([1.0], dtype=np.float32),
-                          np.zeros((1, N_MELS, N_FRAMES)).astype(np.int32))
-    print(output_text)
-
-
-def postprocessing(token_ids, hf_processor):
-    fn_decoder = PyOrtFunction.from_customop(
-        "BpeDecoder",
-        cvt=HFTokenizerConverter(hf_processor.tokenizer).bpe_decoder,
-        skip_special_tokens=True,
-        cpu_only=True)
-
-    onnx.save_model(fn_decoder.onnx_model, os.path.join(root_dir, "whisper_post.onnx"))
-    return fn_decoder(token_ids)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-a", "--audio", required=True, help="Path to audio file")
-    parser.add_argument("-m", "--model", required=True, help="Path to custom export of Whisper with beam search")
-    args = parser.parse_args()
-
-    print("Looking for the exported model...", end='')
-    onnx_model_name = os.path.basename(args.model)
-    if not re.search("whisper-.*_beamsearch\.onnx", onnx_model_name):
-        print("None")
-        print("Cannot find the whisper beamsearch ONNX models. "
-              "Please run this script from where Whisper ONNX model was exported. like */onnx_models/openai")
-        exit(-1)
-    else:
-        print(f"{onnx_model_name}")
-
-    model_name = "openai/" + onnx_model_name[:-len("_beamsearch.onnx")]
-    root_dir = os.path.dirname(args.model)
-
-    _processor = WhisperProcessor.from_pretrained(model_name)
-    # The model similar to Huggingface model like:
-    # model = WhisperForConditionalGeneration.from_pretrained(model_name)
-
-    # The onnx model can be generated by the following command:
-    #  python -m onnxruntime.transformers.models.whisper.convert_to_onnx -m "openai/whisper-base.en" -e
-    # !!! only be valid after onnxruntime 1.15 or nightly build after 05/05/2023
-    model = PyOrtFunction.from_model(args.model, cpu_only=True)
-
-    test_file = util.get_test_data_file(args.audio)
-    if USE_AUDIO_DECODER:
-        with open(test_file, "rb") as _f:
-            audio_blob = np.asarray(list(_f.read()), dtype=np.uint8)
-    else:
-        audio_blob, _ = librosa.load(test_file)
-    audio_blob = np.expand_dims(audio_blob, axis=0)  # add a batch_size dimension
-
-    log_mel = preprocessing(audio_blob)
-    print(log_mel.shape)
-
-    input_features = log_mel
-    # similar to:
-    # generated_ids = model.generate(torch.from_numpy(input_features)).numpy()
-    ort_outputs = model(input_features,
                         np.asarray([200], dtype=np.int32),
                         np.asarray([0], dtype=np.int32),
                         np.asarray([2], dtype=np.int32),
                         np.asarray([1], dtype=np.int32),
                         np.asarray([1.0], dtype=np.float32),
-                        np.asarray([1.0], dtype=np.float32),
+                        np.asarray([1.0], dtype=np.float32))
-                        np.zeros(input_features.shape).astype(np.int32))
+    print(token_seq.shape)
-    generated_ids = ort_outputs[0]
 
-    text = postprocessing(generated_ids[0], _processor)
+    # Apply post processing
+    fn_post = OrtPyFunction.from_model(post_m, cpu_only=True)
+    output_text = fn_post(token_seq)
+    print(output_text)
+
+    # Merge models and save final model
+    print("Combine the data processing graphs into the ONNX model...")
+    final_m = util.quick_merge(pre_m, fn_core.onnx_model, post_m)
+    onnx.save(final_m, FINAL_MODEL)
+
+    # Test the final model
+    raw_audio = np.fromfile(TEST_AUDIO_FILE, dtype=np.uint8)
+    text = OrtPyFunction.from_model(final_m, cpu_only=True)(
+        np.expand_dims(raw_audio, axis=0),
+        np.asarray([200], dtype=np.int32),
+        np.asarray([0], dtype=np.int32),
+        np.asarray([2], dtype=np.int32),
+        np.asarray([1], dtype=np.int32),
+        np.asarray([1.0], dtype=np.float32),
+        np.asarray([1.0], dtype=np.float32))
     print(text)
 
-    print("build the final model...")
+
-    merge_models(args.model, args.model.replace("beamsearch", "all"), audio_blob)
+if __name__ == "__main__":
+    main()