Upload perch_v2.onnx

ONNX Model Information:
Inputs:
- Name: inputs, Shape: ['batch', 160000], Type: tensor(float)
Outputs:
- Name: embedding, Shape: ['batch', 1536], Type: tensor(float)
- Name: spatial_embedding, Shape: ['batch', 16, 4, 1536], Type: tensor(float)
- Name: spectrogram, Shape: ['batch', 500, 128], Type: tensor(float)
- Name: label, Shape: ['batch', 14795], Type: tensor(float)

TFLite Model Information:
Inputs:
- Name: serving_default_inputs:0, Shape: [ 1 160000], Type: <class 'numpy.float32'>
Outputs:
- Name: StatefulPartitionedCall:0, Shape: [ 1 1536], Type: <class 'numpy.float32'>
- Name: StatefulPartitionedCall:2, Shape: [ 1 16 4 1536], Type: <class 'numpy.float32'>
- Name: StatefulPartitionedCall:3, Shape: [ 1 500 128], Type: <class 'numpy.float32'>
- Name: StatefulPartitionedCall:1, Shape: [ 1 14795], Type: <class 'numpy.float32'>

Generating random inputs:
- inputs: shape=(1, 160000), dtype=float32

Running ONNX model inference...
Running TFLite model inference...

================================================================================
COMPARISON RESULTS
================================================================================

Output 0:
ONNX Runtime shape: (1, 1536), dtype: float32
TFLite shape: (1, 1536), dtype: float32

ONNX Runtime vs TFLite:
Max difference: 0.0000007208
Mean difference: 0.0000001543
Relative tolerance: 1e-05
Absolute tolerance: 1e-05
✅ Outputs match within tolerance

Output 1:
ONNX Runtime shape: (1, 16, 4, 1536), dtype: float32
TFLite shape: (1, 16, 4, 1536), dtype: float32

ONNX Runtime vs TFLite:
Max difference: 0.0000131130
Mean difference: 0.0000005482
Relative tolerance: 1e-05
Absolute tolerance: 1e-05
✅ Outputs match within tolerance

Output 2:
ONNX Runtime shape: (1, 500, 128), dtype: float32
TFLite shape: (1, 500, 128), dtype: float32

ONNX Runtime vs TFLite:
Max difference: 0.0000005960
Mean difference: 0.0000000100
Relative tolerance: 1e-05
Absolute tolerance: 1e-05
✅ Outputs match within tolerance

Output 3:
ONNX Runtime shape: (1, 14795), dtype: float32
TFLite shape: (1, 14795), dtype: float32

ONNX Runtime vs TFLite:
Max difference: 0.0000152588
Mean difference: 0.0000014861
Relative tolerance: 1e-05
Absolute tolerance: 1e-05
✅ Outputs match within tolerance

================================================================================
✅ ALL OUTPUTS MATCH!
================================================================================

Benchmarking ONNX model (10 warmup + 100 test runs)...
Benchmarking TFLite model (10 warmup + 100 test runs)...

================================================================================
BENCHMARK RESULTS
================================================================================

ONNX Model:
Mean: 66.350 ms
Median: 66.339 ms
Std: 2.160 ms
Min: 61.801 ms
Max: 74.614 ms

TFLite Model:
Mean: 608.777 ms
Median: 606.753 ms
Std: 11.304 ms
Min: 602.735 ms
Max: 684.807 ms

Comparison:
ONNX Runtime is 9.18x faster than TFLite
Difference: 542.427 ms
================================================================================

perch_v2.onnx

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