SpeakerKit Pro v1
Browse files- .gitignore +1 -0
- LICENSE_NOTICE.txt +7 -0
- README.md +24 -0
- speaker_embedder/pyannote-v3/LICENSE_NOTICE.txt +7 -0
- speaker_embedder/pyannote-v3/README.txt +6 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin +3 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/coremldata.bin +3 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/metadata.json +86 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/model.mil +473 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/weights/weight.bin +3 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin +3 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin +3 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json +77 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/model.mil +90 -0
- speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin +3 -0
- speaker_segmenter/pyannote-v3/LICENSE_NOTICE.txt +7 -0
- speaker_segmenter/pyannote-v3/README.txt +6 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt +7 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin +3 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/coremldata.bin +3 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/metadata.json +132 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/model.mil +0 -0
- speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/weights/weight.bin +3 -0
.gitignore
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.DS_Store
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LICENSE_NOTICE.txt
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Argmax proprietary and confidential. Under NDA.
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Copyright 2024 Argmax, Inc. All rights reserved.
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Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
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Please contact Argmax for licensing information at [email protected].
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README.md
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license: other
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license_name: argmax-fmod-license
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license_link: https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt
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---
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license: other
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license_name: argmax-fmod-license
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license_link: https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt
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pretty_name: SpeakerKit
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viewer: false
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library_name: speakerkit
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tags:
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- speakerkit
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- pyannote
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- diarization
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- speaker-diarization
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- whisper
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- whisperkit
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- coreml
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- asr
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- quantized
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- automatic-speech-recognition
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extra_gated_heading: SpeakerKit Pro is now in early access!
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extra_gated_description: >-
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SpeakerKit Pro is Argmax's state-of-the-art on-device framework for speaker recognition tasks such as speaker diarization. Please submit your
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information below to join the waitlist for early access or directly send an
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email to [[email protected]](mailto:[email protected]).
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extra_gated_fields:
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Company: text
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Work email: text
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I acknowledge the license notice: checkbox
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extra_gated_button_content: Submit
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---
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speaker_embedder/pyannote-v3/LICENSE_NOTICE.txt
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Argmax proprietary and confidential. Under NDA.
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+
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Copyright 2024 Argmax, Inc. All rights reserved.
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+
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| 5 |
+
Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
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| 6 |
+
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| 7 |
+
Please contact Argmax for licensing information at [email protected].
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speaker_embedder/pyannote-v3/README.txt
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# License
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Original model weights: https://github.com/wenet-e2e/wespeaker/blob/master/docs/pretrained.md#model-license
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Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt
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Please contact [email protected] for licensing SpeakerKit Pro assets
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speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/analytics/coremldata.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bcf0cbfb93bef444abf2bc2646b56c0e8975e85ecf36e756ce865398c3b3f2e6
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size 243
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speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/coremldata.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:804911154844b88b7bb76e70448c6d8206dbf30664eded18ae6b60be5c81b4e4
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size 370
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speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/metadata.json
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[
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"type" : "MultiArray"
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"modelParameters" : [
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"computePrecision" : "Mixed (Float16, Float32, Int32)",
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speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/model.mil
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|
| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.1"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios16>(tensor<fp16, [1, 998, 80]> preprocessor_output_1, tensor<fp16, [1, 3, 589]> speaker_masks) {
|
| 5 |
+
tensor<int32, []> var_12 = const()[name = tensor<string, []>("op_12"), val = tensor<int32, []>(1)];
|
| 6 |
+
tensor<int32, [3]> var_22 = const()[name = tensor<string, []>("op_22"), val = tensor<int32, [3]>([0, 2, 1])];
|
| 7 |
+
tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([1])];
|
| 8 |
+
tensor<fp16, [1, 80, 998]> fbank_cast_fp16 = transpose(perm = var_22, x = preprocessor_output_1)[name = tensor<string, []>("transpose_0")];
|
| 9 |
+
tensor<fp16, [1, 1, 80, 998]> input_1_cast_fp16 = expand_dims(axes = input_1_axes_0, x = fbank_cast_fp16)[name = tensor<string, []>("input_1_cast_fp16")];
|
| 10 |
+
tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
|
| 11 |
+
tensor<int32, [4]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 12 |
+
tensor<int32, [2]> input_3_strides_0 = const()[name = tensor<string, []>("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 13 |
+
tensor<int32, [2]> input_3_dilations_0 = const()[name = tensor<string, []>("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 14 |
+
tensor<int32, []> input_3_groups_0 = const()[name = tensor<string, []>("input_3_groups_0"), val = tensor<int32, []>(1)];
|
| 15 |
+
tensor<fp16, [32, 1, 3, 3]> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 16 |
+
tensor<fp16, [32]> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(704)))];
|
| 17 |
+
tensor<fp16, [1, 32, 80, 998]> input_5_cast_fp16 = conv(bias = const_6_to_fp16, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_5_to_fp16, x = input_1_cast_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
|
| 18 |
+
tensor<fp16, [1, 32, 80, 998]> input_7_cast_fp16 = relu(x = input_5_cast_fp16)[name = tensor<string, []>("input_7_cast_fp16")];
|
| 19 |
+
tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("custom")];
|
| 20 |
+
tensor<int32, [4]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 21 |
+
tensor<int32, [2]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 22 |
+
tensor<int32, [2]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 23 |
+
tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
|
| 24 |
+
tensor<fp16, [32, 32, 3, 3]> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
|
| 25 |
+
tensor<fp16, [32]> const_8_to_fp16 = const()[name = tensor<string, []>("const_8_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19328)))];
|
| 26 |
+
tensor<fp16, [1, 32, 80, 998]> input_11_cast_fp16 = conv(bias = const_8_to_fp16, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_7_to_fp16, x = input_7_cast_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
|
| 27 |
+
tensor<fp16, [1, 32, 80, 998]> input_13_cast_fp16 = relu(x = input_11_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
|
| 28 |
+
tensor<string, []> input_15_pad_type_0 = const()[name = tensor<string, []>("input_15_pad_type_0"), val = tensor<string, []>("custom")];
|
| 29 |
+
tensor<int32, [4]> input_15_pad_0 = const()[name = tensor<string, []>("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 30 |
+
tensor<int32, [2]> input_15_strides_0 = const()[name = tensor<string, []>("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 31 |
+
tensor<int32, [2]> input_15_dilations_0 = const()[name = tensor<string, []>("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 32 |
+
tensor<int32, []> input_15_groups_0 = const()[name = tensor<string, []>("input_15_groups_0"), val = tensor<int32, []>(1)];
|
| 33 |
+
tensor<fp16, [32, 32, 3, 3]> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19456)))];
|
| 34 |
+
tensor<fp16, [32]> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37952)))];
|
| 35 |
+
tensor<fp16, [1, 32, 80, 998]> out_1_cast_fp16 = conv(bias = const_10_to_fp16, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_9_to_fp16, x = input_13_cast_fp16)[name = tensor<string, []>("out_1_cast_fp16")];
|
| 36 |
+
tensor<fp16, [1, 32, 80, 998]> input_17_cast_fp16 = add(x = out_1_cast_fp16, y = input_7_cast_fp16)[name = tensor<string, []>("input_17_cast_fp16")];
|
| 37 |
+
tensor<fp16, [1, 32, 80, 998]> input_19_cast_fp16 = relu(x = input_17_cast_fp16)[name = tensor<string, []>("input_19_cast_fp16")];
|
| 38 |
+
tensor<string, []> input_21_pad_type_0 = const()[name = tensor<string, []>("input_21_pad_type_0"), val = tensor<string, []>("custom")];
|
| 39 |
+
tensor<int32, [4]> input_21_pad_0 = const()[name = tensor<string, []>("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 40 |
+
tensor<int32, [2]> input_21_strides_0 = const()[name = tensor<string, []>("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 41 |
+
tensor<int32, [2]> input_21_dilations_0 = const()[name = tensor<string, []>("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 42 |
+
tensor<int32, []> input_21_groups_0 = const()[name = tensor<string, []>("input_21_groups_0"), val = tensor<int32, []>(1)];
|
| 43 |
+
tensor<fp16, [32, 32, 3, 3]> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(38080)))];
|
| 44 |
+
tensor<fp16, [32]> const_12_to_fp16 = const()[name = tensor<string, []>("const_12_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56576)))];
|
| 45 |
+
tensor<fp16, [1, 32, 80, 998]> input_23_cast_fp16 = conv(bias = const_12_to_fp16, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_11_to_fp16, x = input_19_cast_fp16)[name = tensor<string, []>("input_23_cast_fp16")];
|
| 46 |
+
tensor<fp16, [1, 32, 80, 998]> input_25_cast_fp16 = relu(x = input_23_cast_fp16)[name = tensor<string, []>("input_25_cast_fp16")];
|
| 47 |
+
tensor<string, []> input_27_pad_type_0 = const()[name = tensor<string, []>("input_27_pad_type_0"), val = tensor<string, []>("custom")];
|
| 48 |
+
tensor<int32, [4]> input_27_pad_0 = const()[name = tensor<string, []>("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 49 |
+
tensor<int32, [2]> input_27_strides_0 = const()[name = tensor<string, []>("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 50 |
+
tensor<int32, [2]> input_27_dilations_0 = const()[name = tensor<string, []>("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 51 |
+
tensor<int32, []> input_27_groups_0 = const()[name = tensor<string, []>("input_27_groups_0"), val = tensor<int32, []>(1)];
|
| 52 |
+
tensor<fp16, [32, 32, 3, 3]> const_13_to_fp16 = const()[name = tensor<string, []>("const_13_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(56704)))];
|
| 53 |
+
tensor<fp16, [32]> const_14_to_fp16 = const()[name = tensor<string, []>("const_14_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75200)))];
|
| 54 |
+
tensor<fp16, [1, 32, 80, 998]> out_3_cast_fp16 = conv(bias = const_14_to_fp16, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_13_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("out_3_cast_fp16")];
|
| 55 |
+
tensor<fp16, [1, 32, 80, 998]> input_29_cast_fp16 = add(x = out_3_cast_fp16, y = input_19_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
|
| 56 |
+
tensor<fp16, [1, 32, 80, 998]> input_31_cast_fp16 = relu(x = input_29_cast_fp16)[name = tensor<string, []>("input_31_cast_fp16")];
|
| 57 |
+
tensor<string, []> input_33_pad_type_0 = const()[name = tensor<string, []>("input_33_pad_type_0"), val = tensor<string, []>("custom")];
|
| 58 |
+
tensor<int32, [4]> input_33_pad_0 = const()[name = tensor<string, []>("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 59 |
+
tensor<int32, [2]> input_33_strides_0 = const()[name = tensor<string, []>("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 60 |
+
tensor<int32, [2]> input_33_dilations_0 = const()[name = tensor<string, []>("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 61 |
+
tensor<int32, []> input_33_groups_0 = const()[name = tensor<string, []>("input_33_groups_0"), val = tensor<int32, []>(1)];
|
| 62 |
+
tensor<fp16, [32, 32, 3, 3]> const_15_to_fp16 = const()[name = tensor<string, []>("const_15_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(75328)))];
|
| 63 |
+
tensor<fp16, [32]> const_16_to_fp16 = const()[name = tensor<string, []>("const_16_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93824)))];
|
| 64 |
+
tensor<fp16, [1, 32, 80, 998]> input_35_cast_fp16 = conv(bias = const_16_to_fp16, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_15_to_fp16, x = input_31_cast_fp16)[name = tensor<string, []>("input_35_cast_fp16")];
|
| 65 |
+
tensor<fp16, [1, 32, 80, 998]> input_37_cast_fp16 = relu(x = input_35_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
|
| 66 |
+
tensor<string, []> input_39_pad_type_0 = const()[name = tensor<string, []>("input_39_pad_type_0"), val = tensor<string, []>("custom")];
|
| 67 |
+
tensor<int32, [4]> input_39_pad_0 = const()[name = tensor<string, []>("input_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 68 |
+
tensor<int32, [2]> input_39_strides_0 = const()[name = tensor<string, []>("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 69 |
+
tensor<int32, [2]> input_39_dilations_0 = const()[name = tensor<string, []>("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 70 |
+
tensor<int32, []> input_39_groups_0 = const()[name = tensor<string, []>("input_39_groups_0"), val = tensor<int32, []>(1)];
|
| 71 |
+
tensor<fp16, [32, 32, 3, 3]> const_17_to_fp16 = const()[name = tensor<string, []>("const_17_to_fp16"), val = tensor<fp16, [32, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(93952)))];
|
| 72 |
+
tensor<fp16, [32]> const_18_to_fp16 = const()[name = tensor<string, []>("const_18_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112448)))];
|
| 73 |
+
tensor<fp16, [1, 32, 80, 998]> out_5_cast_fp16 = conv(bias = const_18_to_fp16, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_17_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("out_5_cast_fp16")];
|
| 74 |
+
tensor<fp16, [1, 32, 80, 998]> input_41_cast_fp16 = add(x = out_5_cast_fp16, y = input_31_cast_fp16)[name = tensor<string, []>("input_41_cast_fp16")];
|
| 75 |
+
tensor<fp16, [1, 32, 80, 998]> input_43_cast_fp16 = relu(x = input_41_cast_fp16)[name = tensor<string, []>("input_43_cast_fp16")];
|
| 76 |
+
tensor<string, []> input_45_pad_type_0 = const()[name = tensor<string, []>("input_45_pad_type_0"), val = tensor<string, []>("custom")];
|
| 77 |
+
tensor<int32, [4]> input_45_pad_0 = const()[name = tensor<string, []>("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 78 |
+
tensor<int32, [2]> input_45_strides_0 = const()[name = tensor<string, []>("input_45_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 79 |
+
tensor<int32, [2]> input_45_dilations_0 = const()[name = tensor<string, []>("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 80 |
+
tensor<int32, []> input_45_groups_0 = const()[name = tensor<string, []>("input_45_groups_0"), val = tensor<int32, []>(1)];
|
| 81 |
+
tensor<fp16, [64, 32, 3, 3]> const_19_to_fp16 = const()[name = tensor<string, []>("const_19_to_fp16"), val = tensor<fp16, [64, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(112576)))];
|
| 82 |
+
tensor<fp16, [64]> const_20_to_fp16 = const()[name = tensor<string, []>("const_20_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149504)))];
|
| 83 |
+
tensor<fp16, [1, 64, 40, 499]> input_47_cast_fp16 = conv(bias = const_20_to_fp16, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_19_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("input_47_cast_fp16")];
|
| 84 |
+
tensor<fp16, [1, 64, 40, 499]> input_49_cast_fp16 = relu(x = input_47_cast_fp16)[name = tensor<string, []>("input_49_cast_fp16")];
|
| 85 |
+
tensor<string, []> input_51_pad_type_0 = const()[name = tensor<string, []>("input_51_pad_type_0"), val = tensor<string, []>("custom")];
|
| 86 |
+
tensor<int32, [4]> input_51_pad_0 = const()[name = tensor<string, []>("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 87 |
+
tensor<int32, [2]> input_51_strides_0 = const()[name = tensor<string, []>("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 88 |
+
tensor<int32, [2]> input_51_dilations_0 = const()[name = tensor<string, []>("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 89 |
+
tensor<int32, []> input_51_groups_0 = const()[name = tensor<string, []>("input_51_groups_0"), val = tensor<int32, []>(1)];
|
| 90 |
+
tensor<fp16, [64, 64, 3, 3]> const_21_to_fp16 = const()[name = tensor<string, []>("const_21_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(149696)))];
|
| 91 |
+
tensor<fp16, [64]> const_22_to_fp16 = const()[name = tensor<string, []>("const_22_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223488)))];
|
| 92 |
+
tensor<fp16, [1, 64, 40, 499]> out_7_cast_fp16 = conv(bias = const_22_to_fp16, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_21_to_fp16, x = input_49_cast_fp16)[name = tensor<string, []>("out_7_cast_fp16")];
|
| 93 |
+
tensor<string, []> input_53_pad_type_0 = const()[name = tensor<string, []>("input_53_pad_type_0"), val = tensor<string, []>("valid")];
|
| 94 |
+
tensor<int32, [2]> input_53_strides_0 = const()[name = tensor<string, []>("input_53_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 95 |
+
tensor<int32, [4]> input_53_pad_0 = const()[name = tensor<string, []>("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 96 |
+
tensor<int32, [2]> input_53_dilations_0 = const()[name = tensor<string, []>("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 97 |
+
tensor<int32, []> input_53_groups_0 = const()[name = tensor<string, []>("input_53_groups_0"), val = tensor<int32, []>(1)];
|
| 98 |
+
tensor<fp16, [64, 32, 1, 1]> const_23_to_fp16 = const()[name = tensor<string, []>("const_23_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(223680)))];
|
| 99 |
+
tensor<fp16, [64]> const_24_to_fp16 = const()[name = tensor<string, []>("const_24_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(227840)))];
|
| 100 |
+
tensor<fp16, [1, 64, 40, 499]> var_171_cast_fp16 = conv(bias = const_24_to_fp16, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_23_to_fp16, x = input_43_cast_fp16)[name = tensor<string, []>("op_171_cast_fp16")];
|
| 101 |
+
tensor<fp16, [1, 64, 40, 499]> input_55_cast_fp16 = add(x = out_7_cast_fp16, y = var_171_cast_fp16)[name = tensor<string, []>("input_55_cast_fp16")];
|
| 102 |
+
tensor<fp16, [1, 64, 40, 499]> input_57_cast_fp16 = relu(x = input_55_cast_fp16)[name = tensor<string, []>("input_57_cast_fp16")];
|
| 103 |
+
tensor<string, []> input_59_pad_type_0 = const()[name = tensor<string, []>("input_59_pad_type_0"), val = tensor<string, []>("custom")];
|
| 104 |
+
tensor<int32, [4]> input_59_pad_0 = const()[name = tensor<string, []>("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 105 |
+
tensor<int32, [2]> input_59_strides_0 = const()[name = tensor<string, []>("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 106 |
+
tensor<int32, [2]> input_59_dilations_0 = const()[name = tensor<string, []>("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 107 |
+
tensor<int32, []> input_59_groups_0 = const()[name = tensor<string, []>("input_59_groups_0"), val = tensor<int32, []>(1)];
|
| 108 |
+
tensor<fp16, [64, 64, 3, 3]> const_25_to_fp16 = const()[name = tensor<string, []>("const_25_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(228032)))];
|
| 109 |
+
tensor<fp16, [64]> const_26_to_fp16 = const()[name = tensor<string, []>("const_26_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(301824)))];
|
| 110 |
+
tensor<fp16, [1, 64, 40, 499]> input_61_cast_fp16 = conv(bias = const_26_to_fp16, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_25_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
|
| 111 |
+
tensor<fp16, [1, 64, 40, 499]> input_63_cast_fp16 = relu(x = input_61_cast_fp16)[name = tensor<string, []>("input_63_cast_fp16")];
|
| 112 |
+
tensor<string, []> input_65_pad_type_0 = const()[name = tensor<string, []>("input_65_pad_type_0"), val = tensor<string, []>("custom")];
|
| 113 |
+
tensor<int32, [4]> input_65_pad_0 = const()[name = tensor<string, []>("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 114 |
+
tensor<int32, [2]> input_65_strides_0 = const()[name = tensor<string, []>("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 115 |
+
tensor<int32, [2]> input_65_dilations_0 = const()[name = tensor<string, []>("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 116 |
+
tensor<int32, []> input_65_groups_0 = const()[name = tensor<string, []>("input_65_groups_0"), val = tensor<int32, []>(1)];
|
| 117 |
+
tensor<fp16, [64, 64, 3, 3]> const_27_to_fp16 = const()[name = tensor<string, []>("const_27_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302016)))];
|
| 118 |
+
tensor<fp16, [64]> const_28_to_fp16 = const()[name = tensor<string, []>("const_28_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(375808)))];
|
| 119 |
+
tensor<fp16, [1, 64, 40, 499]> out_9_cast_fp16 = conv(bias = const_28_to_fp16, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_27_to_fp16, x = input_63_cast_fp16)[name = tensor<string, []>("out_9_cast_fp16")];
|
| 120 |
+
tensor<fp16, [1, 64, 40, 499]> input_67_cast_fp16 = add(x = out_9_cast_fp16, y = input_57_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
|
| 121 |
+
tensor<fp16, [1, 64, 40, 499]> input_69_cast_fp16 = relu(x = input_67_cast_fp16)[name = tensor<string, []>("input_69_cast_fp16")];
|
| 122 |
+
tensor<string, []> input_71_pad_type_0 = const()[name = tensor<string, []>("input_71_pad_type_0"), val = tensor<string, []>("custom")];
|
| 123 |
+
tensor<int32, [4]> input_71_pad_0 = const()[name = tensor<string, []>("input_71_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 124 |
+
tensor<int32, [2]> input_71_strides_0 = const()[name = tensor<string, []>("input_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 125 |
+
tensor<int32, [2]> input_71_dilations_0 = const()[name = tensor<string, []>("input_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 126 |
+
tensor<int32, []> input_71_groups_0 = const()[name = tensor<string, []>("input_71_groups_0"), val = tensor<int32, []>(1)];
|
| 127 |
+
tensor<fp16, [64, 64, 3, 3]> const_29_to_fp16 = const()[name = tensor<string, []>("const_29_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(376000)))];
|
| 128 |
+
tensor<fp16, [64]> const_30_to_fp16 = const()[name = tensor<string, []>("const_30_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449792)))];
|
| 129 |
+
tensor<fp16, [1, 64, 40, 499]> input_73_cast_fp16 = conv(bias = const_30_to_fp16, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_29_to_fp16, x = input_69_cast_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
|
| 130 |
+
tensor<fp16, [1, 64, 40, 499]> input_75_cast_fp16 = relu(x = input_73_cast_fp16)[name = tensor<string, []>("input_75_cast_fp16")];
|
| 131 |
+
tensor<string, []> input_77_pad_type_0 = const()[name = tensor<string, []>("input_77_pad_type_0"), val = tensor<string, []>("custom")];
|
| 132 |
+
tensor<int32, [4]> input_77_pad_0 = const()[name = tensor<string, []>("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 133 |
+
tensor<int32, [2]> input_77_strides_0 = const()[name = tensor<string, []>("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 134 |
+
tensor<int32, [2]> input_77_dilations_0 = const()[name = tensor<string, []>("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 135 |
+
tensor<int32, []> input_77_groups_0 = const()[name = tensor<string, []>("input_77_groups_0"), val = tensor<int32, []>(1)];
|
| 136 |
+
tensor<fp16, [64, 64, 3, 3]> const_31_to_fp16 = const()[name = tensor<string, []>("const_31_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(449984)))];
|
| 137 |
+
tensor<fp16, [64]> const_32_to_fp16 = const()[name = tensor<string, []>("const_32_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523776)))];
|
| 138 |
+
tensor<fp16, [1, 64, 40, 499]> out_11_cast_fp16 = conv(bias = const_32_to_fp16, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_31_to_fp16, x = input_75_cast_fp16)[name = tensor<string, []>("out_11_cast_fp16")];
|
| 139 |
+
tensor<fp16, [1, 64, 40, 499]> input_79_cast_fp16 = add(x = out_11_cast_fp16, y = input_69_cast_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
|
| 140 |
+
tensor<fp16, [1, 64, 40, 499]> input_81_cast_fp16 = relu(x = input_79_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
|
| 141 |
+
tensor<string, []> input_83_pad_type_0 = const()[name = tensor<string, []>("input_83_pad_type_0"), val = tensor<string, []>("custom")];
|
| 142 |
+
tensor<int32, [4]> input_83_pad_0 = const()[name = tensor<string, []>("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 143 |
+
tensor<int32, [2]> input_83_strides_0 = const()[name = tensor<string, []>("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 144 |
+
tensor<int32, [2]> input_83_dilations_0 = const()[name = tensor<string, []>("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 145 |
+
tensor<int32, []> input_83_groups_0 = const()[name = tensor<string, []>("input_83_groups_0"), val = tensor<int32, []>(1)];
|
| 146 |
+
tensor<fp16, [64, 64, 3, 3]> const_33_to_fp16 = const()[name = tensor<string, []>("const_33_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(523968)))];
|
| 147 |
+
tensor<fp16, [64]> const_34_to_fp16 = const()[name = tensor<string, []>("const_34_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597760)))];
|
| 148 |
+
tensor<fp16, [1, 64, 40, 499]> input_85_cast_fp16 = conv(bias = const_34_to_fp16, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_33_to_fp16, x = input_81_cast_fp16)[name = tensor<string, []>("input_85_cast_fp16")];
|
| 149 |
+
tensor<fp16, [1, 64, 40, 499]> input_87_cast_fp16 = relu(x = input_85_cast_fp16)[name = tensor<string, []>("input_87_cast_fp16")];
|
| 150 |
+
tensor<string, []> input_89_pad_type_0 = const()[name = tensor<string, []>("input_89_pad_type_0"), val = tensor<string, []>("custom")];
|
| 151 |
+
tensor<int32, [4]> input_89_pad_0 = const()[name = tensor<string, []>("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 152 |
+
tensor<int32, [2]> input_89_strides_0 = const()[name = tensor<string, []>("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 153 |
+
tensor<int32, [2]> input_89_dilations_0 = const()[name = tensor<string, []>("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 154 |
+
tensor<int32, []> input_89_groups_0 = const()[name = tensor<string, []>("input_89_groups_0"), val = tensor<int32, []>(1)];
|
| 155 |
+
tensor<fp16, [64, 64, 3, 3]> const_35_to_fp16 = const()[name = tensor<string, []>("const_35_to_fp16"), val = tensor<fp16, [64, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(597952)))];
|
| 156 |
+
tensor<fp16, [64]> const_36_to_fp16 = const()[name = tensor<string, []>("const_36_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671744)))];
|
| 157 |
+
tensor<fp16, [1, 64, 40, 499]> out_13_cast_fp16 = conv(bias = const_36_to_fp16, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_35_to_fp16, x = input_87_cast_fp16)[name = tensor<string, []>("out_13_cast_fp16")];
|
| 158 |
+
tensor<fp16, [1, 64, 40, 499]> input_91_cast_fp16 = add(x = out_13_cast_fp16, y = input_81_cast_fp16)[name = tensor<string, []>("input_91_cast_fp16")];
|
| 159 |
+
tensor<fp16, [1, 64, 40, 499]> input_93_cast_fp16 = relu(x = input_91_cast_fp16)[name = tensor<string, []>("input_93_cast_fp16")];
|
| 160 |
+
tensor<string, []> input_95_pad_type_0 = const()[name = tensor<string, []>("input_95_pad_type_0"), val = tensor<string, []>("custom")];
|
| 161 |
+
tensor<int32, [4]> input_95_pad_0 = const()[name = tensor<string, []>("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 162 |
+
tensor<int32, [2]> input_95_strides_0 = const()[name = tensor<string, []>("input_95_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 163 |
+
tensor<int32, [2]> input_95_dilations_0 = const()[name = tensor<string, []>("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 164 |
+
tensor<int32, []> input_95_groups_0 = const()[name = tensor<string, []>("input_95_groups_0"), val = tensor<int32, []>(1)];
|
| 165 |
+
tensor<fp16, [128, 64, 3, 3]> const_37_to_fp16 = const()[name = tensor<string, []>("const_37_to_fp16"), val = tensor<fp16, [128, 64, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(671936)))];
|
| 166 |
+
tensor<fp16, [128]> const_38_to_fp16 = const()[name = tensor<string, []>("const_38_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819456)))];
|
| 167 |
+
tensor<fp16, [1, 128, 20, 250]> input_97_cast_fp16 = conv(bias = const_38_to_fp16, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_37_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
|
| 168 |
+
tensor<fp16, [1, 128, 20, 250]> input_99_cast_fp16 = relu(x = input_97_cast_fp16)[name = tensor<string, []>("input_99_cast_fp16")];
|
| 169 |
+
tensor<string, []> input_101_pad_type_0 = const()[name = tensor<string, []>("input_101_pad_type_0"), val = tensor<string, []>("custom")];
|
| 170 |
+
tensor<int32, [4]> input_101_pad_0 = const()[name = tensor<string, []>("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 171 |
+
tensor<int32, [2]> input_101_strides_0 = const()[name = tensor<string, []>("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 172 |
+
tensor<int32, [2]> input_101_dilations_0 = const()[name = tensor<string, []>("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 173 |
+
tensor<int32, []> input_101_groups_0 = const()[name = tensor<string, []>("input_101_groups_0"), val = tensor<int32, []>(1)];
|
| 174 |
+
tensor<fp16, [128, 128, 3, 3]> const_39_to_fp16 = const()[name = tensor<string, []>("const_39_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(819776)))];
|
| 175 |
+
tensor<fp16, [128]> const_40_to_fp16 = const()[name = tensor<string, []>("const_40_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1114752)))];
|
| 176 |
+
tensor<fp16, [1, 128, 20, 250]> out_15_cast_fp16 = conv(bias = const_40_to_fp16, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_39_to_fp16, x = input_99_cast_fp16)[name = tensor<string, []>("out_15_cast_fp16")];
|
| 177 |
+
tensor<string, []> input_103_pad_type_0 = const()[name = tensor<string, []>("input_103_pad_type_0"), val = tensor<string, []>("valid")];
|
| 178 |
+
tensor<int32, [2]> input_103_strides_0 = const()[name = tensor<string, []>("input_103_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 179 |
+
tensor<int32, [4]> input_103_pad_0 = const()[name = tensor<string, []>("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 180 |
+
tensor<int32, [2]> input_103_dilations_0 = const()[name = tensor<string, []>("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 181 |
+
tensor<int32, []> input_103_groups_0 = const()[name = tensor<string, []>("input_103_groups_0"), val = tensor<int32, []>(1)];
|
| 182 |
+
tensor<fp16, [128, 64, 1, 1]> const_41_to_fp16 = const()[name = tensor<string, []>("const_41_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1115072)))];
|
| 183 |
+
tensor<fp16, [128]> const_42_to_fp16 = const()[name = tensor<string, []>("const_42_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1131520)))];
|
| 184 |
+
tensor<fp16, [1, 128, 20, 250]> var_307_cast_fp16 = conv(bias = const_42_to_fp16, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_41_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("op_307_cast_fp16")];
|
| 185 |
+
tensor<fp16, [1, 128, 20, 250]> input_105_cast_fp16 = add(x = out_15_cast_fp16, y = var_307_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
|
| 186 |
+
tensor<fp16, [1, 128, 20, 250]> input_107_cast_fp16 = relu(x = input_105_cast_fp16)[name = tensor<string, []>("input_107_cast_fp16")];
|
| 187 |
+
tensor<string, []> input_109_pad_type_0 = const()[name = tensor<string, []>("input_109_pad_type_0"), val = tensor<string, []>("custom")];
|
| 188 |
+
tensor<int32, [4]> input_109_pad_0 = const()[name = tensor<string, []>("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 189 |
+
tensor<int32, [2]> input_109_strides_0 = const()[name = tensor<string, []>("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 190 |
+
tensor<int32, [2]> input_109_dilations_0 = const()[name = tensor<string, []>("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 191 |
+
tensor<int32, []> input_109_groups_0 = const()[name = tensor<string, []>("input_109_groups_0"), val = tensor<int32, []>(1)];
|
| 192 |
+
tensor<fp16, [128, 128, 3, 3]> const_43_to_fp16 = const()[name = tensor<string, []>("const_43_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1131840)))];
|
| 193 |
+
tensor<fp16, [128]> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1426816)))];
|
| 194 |
+
tensor<fp16, [1, 128, 20, 250]> input_111_cast_fp16 = conv(bias = const_44_to_fp16, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_43_to_fp16, x = input_107_cast_fp16)[name = tensor<string, []>("input_111_cast_fp16")];
|
| 195 |
+
tensor<fp16, [1, 128, 20, 250]> input_113_cast_fp16 = relu(x = input_111_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
|
| 196 |
+
tensor<string, []> input_115_pad_type_0 = const()[name = tensor<string, []>("input_115_pad_type_0"), val = tensor<string, []>("custom")];
|
| 197 |
+
tensor<int32, [4]> input_115_pad_0 = const()[name = tensor<string, []>("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 198 |
+
tensor<int32, [2]> input_115_strides_0 = const()[name = tensor<string, []>("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 199 |
+
tensor<int32, [2]> input_115_dilations_0 = const()[name = tensor<string, []>("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 200 |
+
tensor<int32, []> input_115_groups_0 = const()[name = tensor<string, []>("input_115_groups_0"), val = tensor<int32, []>(1)];
|
| 201 |
+
tensor<fp16, [128, 128, 3, 3]> const_45_to_fp16 = const()[name = tensor<string, []>("const_45_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1427136)))];
|
| 202 |
+
tensor<fp16, [128]> const_46_to_fp16 = const()[name = tensor<string, []>("const_46_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1722112)))];
|
| 203 |
+
tensor<fp16, [1, 128, 20, 250]> out_17_cast_fp16 = conv(bias = const_46_to_fp16, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_45_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("out_17_cast_fp16")];
|
| 204 |
+
tensor<fp16, [1, 128, 20, 250]> input_117_cast_fp16 = add(x = out_17_cast_fp16, y = input_107_cast_fp16)[name = tensor<string, []>("input_117_cast_fp16")];
|
| 205 |
+
tensor<fp16, [1, 128, 20, 250]> input_119_cast_fp16 = relu(x = input_117_cast_fp16)[name = tensor<string, []>("input_119_cast_fp16")];
|
| 206 |
+
tensor<string, []> input_121_pad_type_0 = const()[name = tensor<string, []>("input_121_pad_type_0"), val = tensor<string, []>("custom")];
|
| 207 |
+
tensor<int32, [4]> input_121_pad_0 = const()[name = tensor<string, []>("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 208 |
+
tensor<int32, [2]> input_121_strides_0 = const()[name = tensor<string, []>("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 209 |
+
tensor<int32, [2]> input_121_dilations_0 = const()[name = tensor<string, []>("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 210 |
+
tensor<int32, []> input_121_groups_0 = const()[name = tensor<string, []>("input_121_groups_0"), val = tensor<int32, []>(1)];
|
| 211 |
+
tensor<fp16, [128, 128, 3, 3]> const_47_to_fp16 = const()[name = tensor<string, []>("const_47_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1722432)))];
|
| 212 |
+
tensor<fp16, [128]> const_48_to_fp16 = const()[name = tensor<string, []>("const_48_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2017408)))];
|
| 213 |
+
tensor<fp16, [1, 128, 20, 250]> input_123_cast_fp16 = conv(bias = const_48_to_fp16, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_47_to_fp16, x = input_119_cast_fp16)[name = tensor<string, []>("input_123_cast_fp16")];
|
| 214 |
+
tensor<fp16, [1, 128, 20, 250]> input_125_cast_fp16 = relu(x = input_123_cast_fp16)[name = tensor<string, []>("input_125_cast_fp16")];
|
| 215 |
+
tensor<string, []> input_127_pad_type_0 = const()[name = tensor<string, []>("input_127_pad_type_0"), val = tensor<string, []>("custom")];
|
| 216 |
+
tensor<int32, [4]> input_127_pad_0 = const()[name = tensor<string, []>("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 217 |
+
tensor<int32, [2]> input_127_strides_0 = const()[name = tensor<string, []>("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 218 |
+
tensor<int32, [2]> input_127_dilations_0 = const()[name = tensor<string, []>("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 219 |
+
tensor<int32, []> input_127_groups_0 = const()[name = tensor<string, []>("input_127_groups_0"), val = tensor<int32, []>(1)];
|
| 220 |
+
tensor<fp16, [128, 128, 3, 3]> const_49_to_fp16 = const()[name = tensor<string, []>("const_49_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2017728)))];
|
| 221 |
+
tensor<fp16, [128]> const_50_to_fp16 = const()[name = tensor<string, []>("const_50_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2312704)))];
|
| 222 |
+
tensor<fp16, [1, 128, 20, 250]> out_19_cast_fp16 = conv(bias = const_50_to_fp16, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_49_to_fp16, x = input_125_cast_fp16)[name = tensor<string, []>("out_19_cast_fp16")];
|
| 223 |
+
tensor<fp16, [1, 128, 20, 250]> input_129_cast_fp16 = add(x = out_19_cast_fp16, y = input_119_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
|
| 224 |
+
tensor<fp16, [1, 128, 20, 250]> input_131_cast_fp16 = relu(x = input_129_cast_fp16)[name = tensor<string, []>("input_131_cast_fp16")];
|
| 225 |
+
tensor<string, []> input_133_pad_type_0 = const()[name = tensor<string, []>("input_133_pad_type_0"), val = tensor<string, []>("custom")];
|
| 226 |
+
tensor<int32, [4]> input_133_pad_0 = const()[name = tensor<string, []>("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 227 |
+
tensor<int32, [2]> input_133_strides_0 = const()[name = tensor<string, []>("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 228 |
+
tensor<int32, [2]> input_133_dilations_0 = const()[name = tensor<string, []>("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 229 |
+
tensor<int32, []> input_133_groups_0 = const()[name = tensor<string, []>("input_133_groups_0"), val = tensor<int32, []>(1)];
|
| 230 |
+
tensor<fp16, [128, 128, 3, 3]> const_51_to_fp16 = const()[name = tensor<string, []>("const_51_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2313024)))];
|
| 231 |
+
tensor<fp16, [128]> const_52_to_fp16 = const()[name = tensor<string, []>("const_52_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2608000)))];
|
| 232 |
+
tensor<fp16, [1, 128, 20, 250]> input_135_cast_fp16 = conv(bias = const_52_to_fp16, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_51_to_fp16, x = input_131_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
|
| 233 |
+
tensor<fp16, [1, 128, 20, 250]> input_137_cast_fp16 = relu(x = input_135_cast_fp16)[name = tensor<string, []>("input_137_cast_fp16")];
|
| 234 |
+
tensor<string, []> input_139_pad_type_0 = const()[name = tensor<string, []>("input_139_pad_type_0"), val = tensor<string, []>("custom")];
|
| 235 |
+
tensor<int32, [4]> input_139_pad_0 = const()[name = tensor<string, []>("input_139_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 236 |
+
tensor<int32, [2]> input_139_strides_0 = const()[name = tensor<string, []>("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 237 |
+
tensor<int32, [2]> input_139_dilations_0 = const()[name = tensor<string, []>("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 238 |
+
tensor<int32, []> input_139_groups_0 = const()[name = tensor<string, []>("input_139_groups_0"), val = tensor<int32, []>(1)];
|
| 239 |
+
tensor<fp16, [128, 128, 3, 3]> const_53_to_fp16 = const()[name = tensor<string, []>("const_53_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2608320)))];
|
| 240 |
+
tensor<fp16, [128]> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2903296)))];
|
| 241 |
+
tensor<fp16, [1, 128, 20, 250]> out_21_cast_fp16 = conv(bias = const_54_to_fp16, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_53_to_fp16, x = input_137_cast_fp16)[name = tensor<string, []>("out_21_cast_fp16")];
|
| 242 |
+
tensor<fp16, [1, 128, 20, 250]> input_141_cast_fp16 = add(x = out_21_cast_fp16, y = input_131_cast_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
|
| 243 |
+
tensor<fp16, [1, 128, 20, 250]> input_143_cast_fp16 = relu(x = input_141_cast_fp16)[name = tensor<string, []>("input_143_cast_fp16")];
|
| 244 |
+
tensor<string, []> input_145_pad_type_0 = const()[name = tensor<string, []>("input_145_pad_type_0"), val = tensor<string, []>("custom")];
|
| 245 |
+
tensor<int32, [4]> input_145_pad_0 = const()[name = tensor<string, []>("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 246 |
+
tensor<int32, [2]> input_145_strides_0 = const()[name = tensor<string, []>("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 247 |
+
tensor<int32, [2]> input_145_dilations_0 = const()[name = tensor<string, []>("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 248 |
+
tensor<int32, []> input_145_groups_0 = const()[name = tensor<string, []>("input_145_groups_0"), val = tensor<int32, []>(1)];
|
| 249 |
+
tensor<fp16, [128, 128, 3, 3]> const_55_to_fp16 = const()[name = tensor<string, []>("const_55_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2903616)))];
|
| 250 |
+
tensor<fp16, [128]> const_56_to_fp16 = const()[name = tensor<string, []>("const_56_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3198592)))];
|
| 251 |
+
tensor<fp16, [1, 128, 20, 250]> input_147_cast_fp16 = conv(bias = const_56_to_fp16, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_55_to_fp16, x = input_143_cast_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
|
| 252 |
+
tensor<fp16, [1, 128, 20, 250]> input_149_cast_fp16 = relu(x = input_147_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")];
|
| 253 |
+
tensor<string, []> input_151_pad_type_0 = const()[name = tensor<string, []>("input_151_pad_type_0"), val = tensor<string, []>("custom")];
|
| 254 |
+
tensor<int32, [4]> input_151_pad_0 = const()[name = tensor<string, []>("input_151_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 255 |
+
tensor<int32, [2]> input_151_strides_0 = const()[name = tensor<string, []>("input_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 256 |
+
tensor<int32, [2]> input_151_dilations_0 = const()[name = tensor<string, []>("input_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 257 |
+
tensor<int32, []> input_151_groups_0 = const()[name = tensor<string, []>("input_151_groups_0"), val = tensor<int32, []>(1)];
|
| 258 |
+
tensor<fp16, [128, 128, 3, 3]> const_57_to_fp16 = const()[name = tensor<string, []>("const_57_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3198912)))];
|
| 259 |
+
tensor<fp16, [128]> const_58_to_fp16 = const()[name = tensor<string, []>("const_58_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3493888)))];
|
| 260 |
+
tensor<fp16, [1, 128, 20, 250]> out_23_cast_fp16 = conv(bias = const_58_to_fp16, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_57_to_fp16, x = input_149_cast_fp16)[name = tensor<string, []>("out_23_cast_fp16")];
|
| 261 |
+
tensor<fp16, [1, 128, 20, 250]> input_153_cast_fp16 = add(x = out_23_cast_fp16, y = input_143_cast_fp16)[name = tensor<string, []>("input_153_cast_fp16")];
|
| 262 |
+
tensor<fp16, [1, 128, 20, 250]> input_155_cast_fp16 = relu(x = input_153_cast_fp16)[name = tensor<string, []>("input_155_cast_fp16")];
|
| 263 |
+
tensor<string, []> input_157_pad_type_0 = const()[name = tensor<string, []>("input_157_pad_type_0"), val = tensor<string, []>("custom")];
|
| 264 |
+
tensor<int32, [4]> input_157_pad_0 = const()[name = tensor<string, []>("input_157_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 265 |
+
tensor<int32, [2]> input_157_strides_0 = const()[name = tensor<string, []>("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 266 |
+
tensor<int32, [2]> input_157_dilations_0 = const()[name = tensor<string, []>("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 267 |
+
tensor<int32, []> input_157_groups_0 = const()[name = tensor<string, []>("input_157_groups_0"), val = tensor<int32, []>(1)];
|
| 268 |
+
tensor<fp16, [128, 128, 3, 3]> const_59_to_fp16 = const()[name = tensor<string, []>("const_59_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3494208)))];
|
| 269 |
+
tensor<fp16, [128]> const_60_to_fp16 = const()[name = tensor<string, []>("const_60_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3789184)))];
|
| 270 |
+
tensor<fp16, [1, 128, 20, 250]> input_159_cast_fp16 = conv(bias = const_60_to_fp16, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_59_to_fp16, x = input_155_cast_fp16)[name = tensor<string, []>("input_159_cast_fp16")];
|
| 271 |
+
tensor<fp16, [1, 128, 20, 250]> input_161_cast_fp16 = relu(x = input_159_cast_fp16)[name = tensor<string, []>("input_161_cast_fp16")];
|
| 272 |
+
tensor<string, []> input_163_pad_type_0 = const()[name = tensor<string, []>("input_163_pad_type_0"), val = tensor<string, []>("custom")];
|
| 273 |
+
tensor<int32, [4]> input_163_pad_0 = const()[name = tensor<string, []>("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 274 |
+
tensor<int32, [2]> input_163_strides_0 = const()[name = tensor<string, []>("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 275 |
+
tensor<int32, [2]> input_163_dilations_0 = const()[name = tensor<string, []>("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 276 |
+
tensor<int32, []> input_163_groups_0 = const()[name = tensor<string, []>("input_163_groups_0"), val = tensor<int32, []>(1)];
|
| 277 |
+
tensor<fp16, [128, 128, 3, 3]> const_61_to_fp16 = const()[name = tensor<string, []>("const_61_to_fp16"), val = tensor<fp16, [128, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3789504)))];
|
| 278 |
+
tensor<fp16, [128]> const_62_to_fp16 = const()[name = tensor<string, []>("const_62_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4084480)))];
|
| 279 |
+
tensor<fp16, [1, 128, 20, 250]> out_25_cast_fp16 = conv(bias = const_62_to_fp16, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_61_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("out_25_cast_fp16")];
|
| 280 |
+
tensor<fp16, [1, 128, 20, 250]> input_165_cast_fp16 = add(x = out_25_cast_fp16, y = input_155_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
|
| 281 |
+
tensor<fp16, [1, 128, 20, 250]> input_167_cast_fp16 = relu(x = input_165_cast_fp16)[name = tensor<string, []>("input_167_cast_fp16")];
|
| 282 |
+
tensor<string, []> input_169_pad_type_0 = const()[name = tensor<string, []>("input_169_pad_type_0"), val = tensor<string, []>("custom")];
|
| 283 |
+
tensor<int32, [4]> input_169_pad_0 = const()[name = tensor<string, []>("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 284 |
+
tensor<int32, [2]> input_169_strides_0 = const()[name = tensor<string, []>("input_169_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 285 |
+
tensor<int32, [2]> input_169_dilations_0 = const()[name = tensor<string, []>("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 286 |
+
tensor<int32, []> input_169_groups_0 = const()[name = tensor<string, []>("input_169_groups_0"), val = tensor<int32, []>(1)];
|
| 287 |
+
tensor<fp16, [256, 128, 3, 3]> const_63_to_fp16 = const()[name = tensor<string, []>("const_63_to_fp16"), val = tensor<fp16, [256, 128, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4084800)))];
|
| 288 |
+
tensor<fp16, [256]> const_64_to_fp16 = const()[name = tensor<string, []>("const_64_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4674688)))];
|
| 289 |
+
tensor<fp16, [1, 256, 10, 125]> input_171_cast_fp16 = conv(bias = const_64_to_fp16, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_63_to_fp16, x = input_167_cast_fp16)[name = tensor<string, []>("input_171_cast_fp16")];
|
| 290 |
+
tensor<fp16, [1, 256, 10, 125]> input_173_cast_fp16 = relu(x = input_171_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
|
| 291 |
+
tensor<string, []> input_175_pad_type_0 = const()[name = tensor<string, []>("input_175_pad_type_0"), val = tensor<string, []>("custom")];
|
| 292 |
+
tensor<int32, [4]> input_175_pad_0 = const()[name = tensor<string, []>("input_175_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 293 |
+
tensor<int32, [2]> input_175_strides_0 = const()[name = tensor<string, []>("input_175_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 294 |
+
tensor<int32, [2]> input_175_dilations_0 = const()[name = tensor<string, []>("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 295 |
+
tensor<int32, []> input_175_groups_0 = const()[name = tensor<string, []>("input_175_groups_0"), val = tensor<int32, []>(1)];
|
| 296 |
+
tensor<fp16, [256, 256, 3, 3]> const_65_to_fp16 = const()[name = tensor<string, []>("const_65_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4675264)))];
|
| 297 |
+
tensor<fp16, [256]> const_66_to_fp16 = const()[name = tensor<string, []>("const_66_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5854976)))];
|
| 298 |
+
tensor<fp16, [1, 256, 10, 125]> out_27_cast_fp16 = conv(bias = const_66_to_fp16, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_65_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("out_27_cast_fp16")];
|
| 299 |
+
tensor<string, []> input_177_pad_type_0 = const()[name = tensor<string, []>("input_177_pad_type_0"), val = tensor<string, []>("valid")];
|
| 300 |
+
tensor<int32, [2]> input_177_strides_0 = const()[name = tensor<string, []>("input_177_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 301 |
+
tensor<int32, [4]> input_177_pad_0 = const()[name = tensor<string, []>("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 302 |
+
tensor<int32, [2]> input_177_dilations_0 = const()[name = tensor<string, []>("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 303 |
+
tensor<int32, []> input_177_groups_0 = const()[name = tensor<string, []>("input_177_groups_0"), val = tensor<int32, []>(1)];
|
| 304 |
+
tensor<fp16, [256, 128, 1, 1]> const_67_to_fp16 = const()[name = tensor<string, []>("const_67_to_fp16"), val = tensor<fp16, [256, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5855552)))];
|
| 305 |
+
tensor<fp16, [256]> const_68_to_fp16 = const()[name = tensor<string, []>("const_68_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5921152)))];
|
| 306 |
+
tensor<fp16, [1, 256, 10, 125]> var_498_cast_fp16 = conv(bias = const_68_to_fp16, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_67_to_fp16, x = input_167_cast_fp16)[name = tensor<string, []>("op_498_cast_fp16")];
|
| 307 |
+
tensor<fp16, [1, 256, 10, 125]> input_179_cast_fp16 = add(x = out_27_cast_fp16, y = var_498_cast_fp16)[name = tensor<string, []>("input_179_cast_fp16")];
|
| 308 |
+
tensor<fp16, [1, 256, 10, 125]> input_181_cast_fp16 = relu(x = input_179_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
|
| 309 |
+
tensor<string, []> input_183_pad_type_0 = const()[name = tensor<string, []>("input_183_pad_type_0"), val = tensor<string, []>("custom")];
|
| 310 |
+
tensor<int32, [4]> input_183_pad_0 = const()[name = tensor<string, []>("input_183_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 311 |
+
tensor<int32, [2]> input_183_strides_0 = const()[name = tensor<string, []>("input_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 312 |
+
tensor<int32, [2]> input_183_dilations_0 = const()[name = tensor<string, []>("input_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 313 |
+
tensor<int32, []> input_183_groups_0 = const()[name = tensor<string, []>("input_183_groups_0"), val = tensor<int32, []>(1)];
|
| 314 |
+
tensor<fp16, [256, 256, 3, 3]> const_69_to_fp16 = const()[name = tensor<string, []>("const_69_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5921728)))];
|
| 315 |
+
tensor<fp16, [256]> const_70_to_fp16 = const()[name = tensor<string, []>("const_70_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7101440)))];
|
| 316 |
+
tensor<fp16, [1, 256, 10, 125]> input_185_cast_fp16 = conv(bias = const_70_to_fp16, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_69_to_fp16, x = input_181_cast_fp16)[name = tensor<string, []>("input_185_cast_fp16")];
|
| 317 |
+
tensor<fp16, [1, 256, 10, 125]> input_187_cast_fp16 = relu(x = input_185_cast_fp16)[name = tensor<string, []>("input_187_cast_fp16")];
|
| 318 |
+
tensor<string, []> input_189_pad_type_0 = const()[name = tensor<string, []>("input_189_pad_type_0"), val = tensor<string, []>("custom")];
|
| 319 |
+
tensor<int32, [4]> input_189_pad_0 = const()[name = tensor<string, []>("input_189_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 320 |
+
tensor<int32, [2]> input_189_strides_0 = const()[name = tensor<string, []>("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 321 |
+
tensor<int32, [2]> input_189_dilations_0 = const()[name = tensor<string, []>("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 322 |
+
tensor<int32, []> input_189_groups_0 = const()[name = tensor<string, []>("input_189_groups_0"), val = tensor<int32, []>(1)];
|
| 323 |
+
tensor<fp16, [256, 256, 3, 3]> const_71_to_fp16 = const()[name = tensor<string, []>("const_71_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7102016)))];
|
| 324 |
+
tensor<fp16, [256]> const_72_to_fp16 = const()[name = tensor<string, []>("const_72_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8281728)))];
|
| 325 |
+
tensor<fp16, [1, 256, 10, 125]> out_29_cast_fp16 = conv(bias = const_72_to_fp16, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_71_to_fp16, x = input_187_cast_fp16)[name = tensor<string, []>("out_29_cast_fp16")];
|
| 326 |
+
tensor<fp16, [1, 256, 10, 125]> input_191_cast_fp16 = add(x = out_29_cast_fp16, y = input_181_cast_fp16)[name = tensor<string, []>("input_191_cast_fp16")];
|
| 327 |
+
tensor<fp16, [1, 256, 10, 125]> input_193_cast_fp16 = relu(x = input_191_cast_fp16)[name = tensor<string, []>("input_193_cast_fp16")];
|
| 328 |
+
tensor<string, []> input_195_pad_type_0 = const()[name = tensor<string, []>("input_195_pad_type_0"), val = tensor<string, []>("custom")];
|
| 329 |
+
tensor<int32, [4]> input_195_pad_0 = const()[name = tensor<string, []>("input_195_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 330 |
+
tensor<int32, [2]> input_195_strides_0 = const()[name = tensor<string, []>("input_195_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 331 |
+
tensor<int32, [2]> input_195_dilations_0 = const()[name = tensor<string, []>("input_195_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 332 |
+
tensor<int32, []> input_195_groups_0 = const()[name = tensor<string, []>("input_195_groups_0"), val = tensor<int32, []>(1)];
|
| 333 |
+
tensor<fp16, [256, 256, 3, 3]> const_73_to_fp16 = const()[name = tensor<string, []>("const_73_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8282304)))];
|
| 334 |
+
tensor<fp16, [256]> const_74_to_fp16 = const()[name = tensor<string, []>("const_74_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9462016)))];
|
| 335 |
+
tensor<fp16, [1, 256, 10, 125]> input_197_cast_fp16 = conv(bias = const_74_to_fp16, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_73_to_fp16, x = input_193_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
|
| 336 |
+
tensor<fp16, [1, 256, 10, 125]> input_199_cast_fp16 = relu(x = input_197_cast_fp16)[name = tensor<string, []>("input_199_cast_fp16")];
|
| 337 |
+
tensor<string, []> input_201_pad_type_0 = const()[name = tensor<string, []>("input_201_pad_type_0"), val = tensor<string, []>("custom")];
|
| 338 |
+
tensor<int32, [4]> input_201_pad_0 = const()[name = tensor<string, []>("input_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 339 |
+
tensor<int32, [2]> input_201_strides_0 = const()[name = tensor<string, []>("input_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 340 |
+
tensor<int32, [2]> input_201_dilations_0 = const()[name = tensor<string, []>("input_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 341 |
+
tensor<int32, []> input_201_groups_0 = const()[name = tensor<string, []>("input_201_groups_0"), val = tensor<int32, []>(1)];
|
| 342 |
+
tensor<fp16, [256, 256, 3, 3]> const_75_to_fp16 = const()[name = tensor<string, []>("const_75_to_fp16"), val = tensor<fp16, [256, 256, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9462592)))];
|
| 343 |
+
tensor<fp16, [256]> const_76_to_fp16 = const()[name = tensor<string, []>("const_76_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10642304)))];
|
| 344 |
+
tensor<fp16, [1, 256, 10, 125]> out_cast_fp16 = conv(bias = const_76_to_fp16, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_75_to_fp16, x = input_199_cast_fp16)[name = tensor<string, []>("out_cast_fp16")];
|
| 345 |
+
tensor<fp16, [1, 256, 10, 125]> input_203_cast_fp16 = add(x = out_cast_fp16, y = input_193_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
|
| 346 |
+
tensor<fp16, [1, 256, 10, 125]> x_cast_fp16 = relu(x = input_203_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
|
| 347 |
+
tensor<int32, [3]> var_577 = const()[name = tensor<string, []>("op_577"), val = tensor<int32, [3]>([1, 2560, 125])];
|
| 348 |
+
tensor<fp16, [1, 2560, 125]> sequences_cast_fp16 = reshape(shape = var_577, x = x_cast_fp16)[name = tensor<string, []>("sequences_cast_fp16")];
|
| 349 |
+
tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = tensor<string, []>("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
|
| 350 |
+
tensor<fp16, [1, 3, 589, 1]> expand_dims_0_cast_fp16 = expand_dims(axes = expand_dims_0_axes_0, x = speaker_masks)[name = tensor<string, []>("expand_dims_0_cast_fp16")];
|
| 351 |
+
tensor<fp32, []> upsample_nearest_neighbor_0_scale_factor_height_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_height_0"), val = tensor<fp32, []>(0x1.b2a2a4p-3)];
|
| 352 |
+
tensor<fp32, []> upsample_nearest_neighbor_0_scale_factor_width_0 = const()[name = tensor<string, []>("upsample_nearest_neighbor_0_scale_factor_width_0"), val = tensor<fp32, []>(0x1p+0)];
|
| 353 |
+
tensor<fp16, [1, 3, 125, 1]> upsample_nearest_neighbor_0_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0_cast_fp16)[name = tensor<string, []>("upsample_nearest_neighbor_0_cast_fp16")];
|
| 354 |
+
tensor<int32, [1]> weights_1_axes_0 = const()[name = tensor<string, []>("weights_1_axes_0"), val = tensor<int32, [1]>([3])];
|
| 355 |
+
tensor<fp16, [1, 3, 125]> weights_1_cast_fp16 = squeeze(axes = weights_1_axes_0, x = upsample_nearest_neighbor_0_cast_fp16)[name = tensor<string, []>("weights_1_cast_fp16")];
|
| 356 |
+
tensor<int32, [3]> var_583_begin_0 = const()[name = tensor<string, []>("op_583_begin_0"), val = tensor<int32, [3]>([0, 0, 0])];
|
| 357 |
+
tensor<int32, [3]> var_583_end_0 = const()[name = tensor<string, []>("op_583_end_0"), val = tensor<int32, [3]>([1, 1, 125])];
|
| 358 |
+
tensor<bool, [3]> var_583_end_mask_0 = const()[name = tensor<string, []>("op_583_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
|
| 359 |
+
tensor<bool, [3]> var_583_squeeze_mask_0 = const()[name = tensor<string, []>("op_583_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])];
|
| 360 |
+
tensor<fp16, [1, 125]> var_583_cast_fp16 = slice_by_index(begin = var_583_begin_0, end = var_583_end_0, end_mask = var_583_end_mask_0, squeeze_mask = var_583_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor<string, []>("op_583_cast_fp16")];
|
| 361 |
+
tensor<int32, [1]> weights_5_axes_0 = const()[name = tensor<string, []>("weights_5_axes_0"), val = tensor<int32, [1]>([1])];
|
| 362 |
+
tensor<fp16, [1, 1, 125]> weights_5_cast_fp16 = expand_dims(axes = weights_5_axes_0, x = var_583_cast_fp16)[name = tensor<string, []>("weights_5_cast_fp16")];
|
| 363 |
+
tensor<int32, [1]> var_587_axes_0 = const()[name = tensor<string, []>("op_587_axes_0"), val = tensor<int32, [1]>([2])];
|
| 364 |
+
tensor<bool, []> var_587_keep_dims_0 = const()[name = tensor<string, []>("op_587_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 365 |
+
tensor<fp16, [1, 1]> var_587_cast_fp16 = reduce_sum(axes = var_587_axes_0, keep_dims = var_587_keep_dims_0, x = weights_5_cast_fp16)[name = tensor<string, []>("op_587_cast_fp16")];
|
| 366 |
+
tensor<fp16, []> var_588_to_fp16 = const()[name = tensor<string, []>("op_588_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 367 |
+
tensor<fp16, [1, 1]> v1_1_cast_fp16 = add(x = var_587_cast_fp16, y = var_588_to_fp16)[name = tensor<string, []>("v1_1_cast_fp16")];
|
| 368 |
+
tensor<fp16, [1, 2560, 125]> var_590_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_5_cast_fp16)[name = tensor<string, []>("op_590_cast_fp16")];
|
| 369 |
+
tensor<int32, [1]> var_592_axes_0 = const()[name = tensor<string, []>("op_592_axes_0"), val = tensor<int32, [1]>([2])];
|
| 370 |
+
tensor<bool, []> var_592_keep_dims_0 = const()[name = tensor<string, []>("op_592_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 371 |
+
tensor<fp16, [1, 2560]> var_592_cast_fp16 = reduce_sum(axes = var_592_axes_0, keep_dims = var_592_keep_dims_0, x = var_590_cast_fp16)[name = tensor<string, []>("op_592_cast_fp16")];
|
| 372 |
+
tensor<fp16, [1, 2560]> mean_1_cast_fp16 = real_div(x = var_592_cast_fp16, y = v1_1_cast_fp16)[name = tensor<string, []>("mean_1_cast_fp16")];
|
| 373 |
+
tensor<int32, [1]> var_594_axes_0 = const()[name = tensor<string, []>("op_594_axes_0"), val = tensor<int32, [1]>([2])];
|
| 374 |
+
tensor<fp16, [1, 2560, 1]> var_594_cast_fp16 = expand_dims(axes = var_594_axes_0, x = mean_1_cast_fp16)[name = tensor<string, []>("op_594_cast_fp16")];
|
| 375 |
+
tensor<fp16, [1, 2560, 125]> var_595_cast_fp16 = sub(x = sequences_cast_fp16, y = var_594_cast_fp16)[name = tensor<string, []>("op_595_cast_fp16")];
|
| 376 |
+
tensor<fp16, [1, 2560, 125]> dx2_1_cast_fp16 = mul(x = var_595_cast_fp16, y = var_595_cast_fp16)[name = tensor<string, []>("dx2_1_cast_fp16")];
|
| 377 |
+
tensor<fp16, [1, 1, 125]> var_597_cast_fp16 = mul(x = weights_5_cast_fp16, y = weights_5_cast_fp16)[name = tensor<string, []>("op_597_cast_fp16")];
|
| 378 |
+
tensor<int32, [1]> v2_1_axes_0 = const()[name = tensor<string, []>("v2_1_axes_0"), val = tensor<int32, [1]>([2])];
|
| 379 |
+
tensor<bool, []> v2_1_keep_dims_0 = const()[name = tensor<string, []>("v2_1_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 380 |
+
tensor<fp16, [1, 1]> v2_1_cast_fp16 = reduce_sum(axes = v2_1_axes_0, keep_dims = v2_1_keep_dims_0, x = var_597_cast_fp16)[name = tensor<string, []>("v2_1_cast_fp16")];
|
| 381 |
+
tensor<fp16, [1, 2560, 125]> var_600_cast_fp16 = mul(x = dx2_1_cast_fp16, y = weights_5_cast_fp16)[name = tensor<string, []>("op_600_cast_fp16")];
|
| 382 |
+
tensor<int32, [1]> var_602_axes_0 = const()[name = tensor<string, []>("op_602_axes_0"), val = tensor<int32, [1]>([2])];
|
| 383 |
+
tensor<bool, []> var_602_keep_dims_0 = const()[name = tensor<string, []>("op_602_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 384 |
+
tensor<fp16, [1, 2560]> var_602_cast_fp16 = reduce_sum(axes = var_602_axes_0, keep_dims = var_602_keep_dims_0, x = var_600_cast_fp16)[name = tensor<string, []>("op_602_cast_fp16")];
|
| 385 |
+
tensor<fp16, [1, 1]> var_603_cast_fp16 = real_div(x = v2_1_cast_fp16, y = v1_1_cast_fp16)[name = tensor<string, []>("op_603_cast_fp16")];
|
| 386 |
+
tensor<fp16, [1, 1]> var_604_cast_fp16 = sub(x = v1_1_cast_fp16, y = var_603_cast_fp16)[name = tensor<string, []>("op_604_cast_fp16")];
|
| 387 |
+
tensor<fp16, []> var_605_to_fp16 = const()[name = tensor<string, []>("op_605_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 388 |
+
tensor<fp16, [1, 1]> var_606_cast_fp16 = add(x = var_604_cast_fp16, y = var_605_to_fp16)[name = tensor<string, []>("op_606_cast_fp16")];
|
| 389 |
+
tensor<fp16, [1, 2560]> var_1_cast_fp16 = real_div(x = var_602_cast_fp16, y = var_606_cast_fp16)[name = tensor<string, []>("var_1_cast_fp16")];
|
| 390 |
+
tensor<fp16, [1, 2560]> std_1_cast_fp16 = sqrt(x = var_1_cast_fp16)[name = tensor<string, []>("std_1_cast_fp16")];
|
| 391 |
+
tensor<bool, []> var_610_interleave_0 = const()[name = tensor<string, []>("op_610_interleave_0"), val = tensor<bool, []>(false)];
|
| 392 |
+
tensor<fp16, [1, 5120]> var_610_cast_fp16 = concat(axis = var_12, interleave = var_610_interleave_0, values = (mean_1_cast_fp16, std_1_cast_fp16))[name = tensor<string, []>("op_610_cast_fp16")];
|
| 393 |
+
tensor<int32, [3]> var_612_begin_0 = const()[name = tensor<string, []>("op_612_begin_0"), val = tensor<int32, [3]>([0, 1, 0])];
|
| 394 |
+
tensor<int32, [3]> var_612_end_0 = const()[name = tensor<string, []>("op_612_end_0"), val = tensor<int32, [3]>([1, 2, 125])];
|
| 395 |
+
tensor<bool, [3]> var_612_end_mask_0 = const()[name = tensor<string, []>("op_612_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
|
| 396 |
+
tensor<bool, [3]> var_612_squeeze_mask_0 = const()[name = tensor<string, []>("op_612_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])];
|
| 397 |
+
tensor<fp16, [1, 125]> var_612_cast_fp16 = slice_by_index(begin = var_612_begin_0, end = var_612_end_0, end_mask = var_612_end_mask_0, squeeze_mask = var_612_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor<string, []>("op_612_cast_fp16")];
|
| 398 |
+
tensor<int32, [1]> weights_9_axes_0 = const()[name = tensor<string, []>("weights_9_axes_0"), val = tensor<int32, [1]>([1])];
|
| 399 |
+
tensor<fp16, [1, 1, 125]> weights_9_cast_fp16 = expand_dims(axes = weights_9_axes_0, x = var_612_cast_fp16)[name = tensor<string, []>("weights_9_cast_fp16")];
|
| 400 |
+
tensor<int32, [1]> var_616_axes_0 = const()[name = tensor<string, []>("op_616_axes_0"), val = tensor<int32, [1]>([2])];
|
| 401 |
+
tensor<bool, []> var_616_keep_dims_0 = const()[name = tensor<string, []>("op_616_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 402 |
+
tensor<fp16, [1, 1]> var_616_cast_fp16 = reduce_sum(axes = var_616_axes_0, keep_dims = var_616_keep_dims_0, x = weights_9_cast_fp16)[name = tensor<string, []>("op_616_cast_fp16")];
|
| 403 |
+
tensor<fp16, []> var_617_to_fp16 = const()[name = tensor<string, []>("op_617_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 404 |
+
tensor<fp16, [1, 1]> v1_3_cast_fp16 = add(x = var_616_cast_fp16, y = var_617_to_fp16)[name = tensor<string, []>("v1_3_cast_fp16")];
|
| 405 |
+
tensor<fp16, [1, 2560, 125]> var_619_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_9_cast_fp16)[name = tensor<string, []>("op_619_cast_fp16")];
|
| 406 |
+
tensor<int32, [1]> var_621_axes_0 = const()[name = tensor<string, []>("op_621_axes_0"), val = tensor<int32, [1]>([2])];
|
| 407 |
+
tensor<bool, []> var_621_keep_dims_0 = const()[name = tensor<string, []>("op_621_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 408 |
+
tensor<fp16, [1, 2560]> var_621_cast_fp16 = reduce_sum(axes = var_621_axes_0, keep_dims = var_621_keep_dims_0, x = var_619_cast_fp16)[name = tensor<string, []>("op_621_cast_fp16")];
|
| 409 |
+
tensor<fp16, [1, 2560]> mean_3_cast_fp16 = real_div(x = var_621_cast_fp16, y = v1_3_cast_fp16)[name = tensor<string, []>("mean_3_cast_fp16")];
|
| 410 |
+
tensor<int32, [1]> var_623_axes_0 = const()[name = tensor<string, []>("op_623_axes_0"), val = tensor<int32, [1]>([2])];
|
| 411 |
+
tensor<fp16, [1, 2560, 1]> var_623_cast_fp16 = expand_dims(axes = var_623_axes_0, x = mean_3_cast_fp16)[name = tensor<string, []>("op_623_cast_fp16")];
|
| 412 |
+
tensor<fp16, [1, 2560, 125]> var_624_cast_fp16 = sub(x = sequences_cast_fp16, y = var_623_cast_fp16)[name = tensor<string, []>("op_624_cast_fp16")];
|
| 413 |
+
tensor<fp16, [1, 2560, 125]> dx2_3_cast_fp16 = mul(x = var_624_cast_fp16, y = var_624_cast_fp16)[name = tensor<string, []>("dx2_3_cast_fp16")];
|
| 414 |
+
tensor<fp16, [1, 1, 125]> var_626_cast_fp16 = mul(x = weights_9_cast_fp16, y = weights_9_cast_fp16)[name = tensor<string, []>("op_626_cast_fp16")];
|
| 415 |
+
tensor<int32, [1]> v2_3_axes_0 = const()[name = tensor<string, []>("v2_3_axes_0"), val = tensor<int32, [1]>([2])];
|
| 416 |
+
tensor<bool, []> v2_3_keep_dims_0 = const()[name = tensor<string, []>("v2_3_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 417 |
+
tensor<fp16, [1, 1]> v2_3_cast_fp16 = reduce_sum(axes = v2_3_axes_0, keep_dims = v2_3_keep_dims_0, x = var_626_cast_fp16)[name = tensor<string, []>("v2_3_cast_fp16")];
|
| 418 |
+
tensor<fp16, [1, 2560, 125]> var_629_cast_fp16 = mul(x = dx2_3_cast_fp16, y = weights_9_cast_fp16)[name = tensor<string, []>("op_629_cast_fp16")];
|
| 419 |
+
tensor<int32, [1]> var_631_axes_0 = const()[name = tensor<string, []>("op_631_axes_0"), val = tensor<int32, [1]>([2])];
|
| 420 |
+
tensor<bool, []> var_631_keep_dims_0 = const()[name = tensor<string, []>("op_631_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 421 |
+
tensor<fp16, [1, 2560]> var_631_cast_fp16 = reduce_sum(axes = var_631_axes_0, keep_dims = var_631_keep_dims_0, x = var_629_cast_fp16)[name = tensor<string, []>("op_631_cast_fp16")];
|
| 422 |
+
tensor<fp16, [1, 1]> var_632_cast_fp16 = real_div(x = v2_3_cast_fp16, y = v1_3_cast_fp16)[name = tensor<string, []>("op_632_cast_fp16")];
|
| 423 |
+
tensor<fp16, [1, 1]> var_633_cast_fp16 = sub(x = v1_3_cast_fp16, y = var_632_cast_fp16)[name = tensor<string, []>("op_633_cast_fp16")];
|
| 424 |
+
tensor<fp16, []> var_634_to_fp16 = const()[name = tensor<string, []>("op_634_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 425 |
+
tensor<fp16, [1, 1]> var_635_cast_fp16 = add(x = var_633_cast_fp16, y = var_634_to_fp16)[name = tensor<string, []>("op_635_cast_fp16")];
|
| 426 |
+
tensor<fp16, [1, 2560]> var_3_cast_fp16 = real_div(x = var_631_cast_fp16, y = var_635_cast_fp16)[name = tensor<string, []>("var_3_cast_fp16")];
|
| 427 |
+
tensor<fp16, [1, 2560]> std_3_cast_fp16 = sqrt(x = var_3_cast_fp16)[name = tensor<string, []>("std_3_cast_fp16")];
|
| 428 |
+
tensor<bool, []> var_639_interleave_0 = const()[name = tensor<string, []>("op_639_interleave_0"), val = tensor<bool, []>(false)];
|
| 429 |
+
tensor<fp16, [1, 5120]> var_639_cast_fp16 = concat(axis = var_12, interleave = var_639_interleave_0, values = (mean_3_cast_fp16, std_3_cast_fp16))[name = tensor<string, []>("op_639_cast_fp16")];
|
| 430 |
+
tensor<int32, [3]> var_641_begin_0 = const()[name = tensor<string, []>("op_641_begin_0"), val = tensor<int32, [3]>([0, 2, 0])];
|
| 431 |
+
tensor<int32, [3]> var_641_end_0 = const()[name = tensor<string, []>("op_641_end_0"), val = tensor<int32, [3]>([1, 3, 125])];
|
| 432 |
+
tensor<bool, [3]> var_641_end_mask_0 = const()[name = tensor<string, []>("op_641_end_mask_0"), val = tensor<bool, [3]>([true, false, true])];
|
| 433 |
+
tensor<bool, [3]> var_641_squeeze_mask_0 = const()[name = tensor<string, []>("op_641_squeeze_mask_0"), val = tensor<bool, [3]>([false, true, false])];
|
| 434 |
+
tensor<fp16, [1, 125]> var_641_cast_fp16 = slice_by_index(begin = var_641_begin_0, end = var_641_end_0, end_mask = var_641_end_mask_0, squeeze_mask = var_641_squeeze_mask_0, x = weights_1_cast_fp16)[name = tensor<string, []>("op_641_cast_fp16")];
|
| 435 |
+
tensor<int32, [1]> weights_axes_0 = const()[name = tensor<string, []>("weights_axes_0"), val = tensor<int32, [1]>([1])];
|
| 436 |
+
tensor<fp16, [1, 1, 125]> weights_cast_fp16 = expand_dims(axes = weights_axes_0, x = var_641_cast_fp16)[name = tensor<string, []>("weights_cast_fp16")];
|
| 437 |
+
tensor<int32, [1]> var_645_axes_0 = const()[name = tensor<string, []>("op_645_axes_0"), val = tensor<int32, [1]>([2])];
|
| 438 |
+
tensor<bool, []> var_645_keep_dims_0 = const()[name = tensor<string, []>("op_645_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 439 |
+
tensor<fp16, [1, 1]> var_645_cast_fp16 = reduce_sum(axes = var_645_axes_0, keep_dims = var_645_keep_dims_0, x = weights_cast_fp16)[name = tensor<string, []>("op_645_cast_fp16")];
|
| 440 |
+
tensor<fp16, []> var_646_to_fp16 = const()[name = tensor<string, []>("op_646_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 441 |
+
tensor<fp16, [1, 1]> v1_cast_fp16 = add(x = var_645_cast_fp16, y = var_646_to_fp16)[name = tensor<string, []>("v1_cast_fp16")];
|
| 442 |
+
tensor<fp16, [1, 2560, 125]> var_648_cast_fp16 = mul(x = sequences_cast_fp16, y = weights_cast_fp16)[name = tensor<string, []>("op_648_cast_fp16")];
|
| 443 |
+
tensor<int32, [1]> var_650_axes_0 = const()[name = tensor<string, []>("op_650_axes_0"), val = tensor<int32, [1]>([2])];
|
| 444 |
+
tensor<bool, []> var_650_keep_dims_0 = const()[name = tensor<string, []>("op_650_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 445 |
+
tensor<fp16, [1, 2560]> var_650_cast_fp16 = reduce_sum(axes = var_650_axes_0, keep_dims = var_650_keep_dims_0, x = var_648_cast_fp16)[name = tensor<string, []>("op_650_cast_fp16")];
|
| 446 |
+
tensor<fp16, [1, 2560]> mean_cast_fp16 = real_div(x = var_650_cast_fp16, y = v1_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
|
| 447 |
+
tensor<int32, [1]> var_652_axes_0 = const()[name = tensor<string, []>("op_652_axes_0"), val = tensor<int32, [1]>([2])];
|
| 448 |
+
tensor<fp16, [1, 2560, 1]> var_652_cast_fp16 = expand_dims(axes = var_652_axes_0, x = mean_cast_fp16)[name = tensor<string, []>("op_652_cast_fp16")];
|
| 449 |
+
tensor<fp16, [1, 2560, 125]> var_653_cast_fp16 = sub(x = sequences_cast_fp16, y = var_652_cast_fp16)[name = tensor<string, []>("op_653_cast_fp16")];
|
| 450 |
+
tensor<fp16, [1, 2560, 125]> dx2_cast_fp16 = mul(x = var_653_cast_fp16, y = var_653_cast_fp16)[name = tensor<string, []>("dx2_cast_fp16")];
|
| 451 |
+
tensor<fp16, [1, 1, 125]> var_655_cast_fp16 = mul(x = weights_cast_fp16, y = weights_cast_fp16)[name = tensor<string, []>("op_655_cast_fp16")];
|
| 452 |
+
tensor<int32, [1]> v2_axes_0 = const()[name = tensor<string, []>("v2_axes_0"), val = tensor<int32, [1]>([2])];
|
| 453 |
+
tensor<bool, []> v2_keep_dims_0 = const()[name = tensor<string, []>("v2_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 454 |
+
tensor<fp16, [1, 1]> v2_cast_fp16 = reduce_sum(axes = v2_axes_0, keep_dims = v2_keep_dims_0, x = var_655_cast_fp16)[name = tensor<string, []>("v2_cast_fp16")];
|
| 455 |
+
tensor<fp16, [1, 2560, 125]> var_658_cast_fp16 = mul(x = dx2_cast_fp16, y = weights_cast_fp16)[name = tensor<string, []>("op_658_cast_fp16")];
|
| 456 |
+
tensor<int32, [1]> var_660_axes_0 = const()[name = tensor<string, []>("op_660_axes_0"), val = tensor<int32, [1]>([2])];
|
| 457 |
+
tensor<bool, []> var_660_keep_dims_0 = const()[name = tensor<string, []>("op_660_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 458 |
+
tensor<fp16, [1, 2560]> var_660_cast_fp16 = reduce_sum(axes = var_660_axes_0, keep_dims = var_660_keep_dims_0, x = var_658_cast_fp16)[name = tensor<string, []>("op_660_cast_fp16")];
|
| 459 |
+
tensor<fp16, [1, 1]> var_661_cast_fp16 = real_div(x = v2_cast_fp16, y = v1_cast_fp16)[name = tensor<string, []>("op_661_cast_fp16")];
|
| 460 |
+
tensor<fp16, [1, 1]> var_662_cast_fp16 = sub(x = v1_cast_fp16, y = var_661_cast_fp16)[name = tensor<string, []>("op_662_cast_fp16")];
|
| 461 |
+
tensor<fp16, []> var_663_to_fp16 = const()[name = tensor<string, []>("op_663_to_fp16"), val = tensor<fp16, []>(0x1p-24)];
|
| 462 |
+
tensor<fp16, [1, 1]> var_664_cast_fp16 = add(x = var_662_cast_fp16, y = var_663_to_fp16)[name = tensor<string, []>("op_664_cast_fp16")];
|
| 463 |
+
tensor<fp16, [1, 2560]> var_cast_fp16 = real_div(x = var_660_cast_fp16, y = var_664_cast_fp16)[name = tensor<string, []>("var_cast_fp16")];
|
| 464 |
+
tensor<fp16, [1, 2560]> std_cast_fp16 = sqrt(x = var_cast_fp16)[name = tensor<string, []>("std_cast_fp16")];
|
| 465 |
+
tensor<bool, []> var_668_interleave_0 = const()[name = tensor<string, []>("op_668_interleave_0"), val = tensor<bool, []>(false)];
|
| 466 |
+
tensor<fp16, [1, 5120]> var_668_cast_fp16 = concat(axis = var_12, interleave = var_668_interleave_0, values = (mean_cast_fp16, std_cast_fp16))[name = tensor<string, []>("op_668_cast_fp16")];
|
| 467 |
+
tensor<int32, []> input_axis_0 = const()[name = tensor<string, []>("input_axis_0"), val = tensor<int32, []>(1)];
|
| 468 |
+
tensor<fp16, [1, 3, 5120]> input_cast_fp16 = stack(axis = input_axis_0, values = (var_610_cast_fp16, var_639_cast_fp16, var_668_cast_fp16))[name = tensor<string, []>("input_cast_fp16")];
|
| 469 |
+
tensor<fp16, [256, 5120]> model_resnet_seg_1_weight_to_fp16 = const()[name = tensor<string, []>("model_resnet_seg_1_weight_to_fp16"), val = tensor<fp16, [256, 5120]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10642880)))];
|
| 470 |
+
tensor<fp16, [256]> model_resnet_seg_1_bias_to_fp16 = const()[name = tensor<string, []>("model_resnet_seg_1_bias_to_fp16"), val = tensor<fp16, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13264384)))];
|
| 471 |
+
tensor<fp16, [1, 3, 256]> speaker_embeddings = linear(bias = model_resnet_seg_1_bias_to_fp16, weight = model_resnet_seg_1_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
|
| 472 |
+
} -> (speaker_embeddings);
|
| 473 |
+
}
|
speaker_embedder/pyannote-v3/SpeakerEmbedder.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6dba18a57a81b1e872802ca4def29541bb7900ccff430d9b2040092cadd7d688
|
| 3 |
+
size 13264960
|
speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:af0ef58ba31419e0e4d2f9714a3b2f382955fb3ee0472f2e76987934b88cebc2
|
| 3 |
+
size 243
|
speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f252f1834b495a132333af500f573a7218c2d3d1f7bfb0faaad89c51a989dac7
|
| 3 |
+
size 330
|
speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/metadata.json
ADDED
|
@@ -0,0 +1,77 @@
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"metadataOutputVersion" : "3.0",
|
| 4 |
+
"storagePrecision" : "Float32",
|
| 5 |
+
"outputSchema" : [
|
| 6 |
+
{
|
| 7 |
+
"hasShapeFlexibility" : "0",
|
| 8 |
+
"isOptional" : "0",
|
| 9 |
+
"dataType" : "Float16",
|
| 10 |
+
"formattedType" : "MultiArray (Float16 1 × 998 × 80)",
|
| 11 |
+
"shortDescription" : "",
|
| 12 |
+
"shape" : "[1, 998, 80]",
|
| 13 |
+
"name" : "preprocessor_output_1",
|
| 14 |
+
"type" : "MultiArray"
|
| 15 |
+
}
|
| 16 |
+
],
|
| 17 |
+
"modelParameters" : [
|
| 18 |
+
|
| 19 |
+
],
|
| 20 |
+
"specificationVersion" : 7,
|
| 21 |
+
"mlProgramOperationTypeHistogram" : {
|
| 22 |
+
"Ios16.cast" : 2,
|
| 23 |
+
"Ios16.mul" : 4,
|
| 24 |
+
"SliceByIndex" : 2,
|
| 25 |
+
"Transpose" : 2,
|
| 26 |
+
"SlidingWindows" : 1,
|
| 27 |
+
"Ios16.sub" : 3,
|
| 28 |
+
"Ios16.log" : 1,
|
| 29 |
+
"Ios16.reduceMean" : 2,
|
| 30 |
+
"Ios16.square" : 2,
|
| 31 |
+
"Squeeze" : 2,
|
| 32 |
+
"Ios16.matmul" : 2,
|
| 33 |
+
"Ios16.add" : 1,
|
| 34 |
+
"Ios16.linear" : 1,
|
| 35 |
+
"ExpandDims" : 4,
|
| 36 |
+
"Ios16.gather" : 2,
|
| 37 |
+
"Ios16.maximum" : 1,
|
| 38 |
+
"Identity" : 1,
|
| 39 |
+
"Pad" : 2
|
| 40 |
+
},
|
| 41 |
+
"computePrecision" : "Mixed (Float16, Float32, Int32)",
|
| 42 |
+
"isUpdatable" : "0",
|
| 43 |
+
"stateSchema" : [
|
| 44 |
+
|
| 45 |
+
],
|
| 46 |
+
"availability" : {
|
| 47 |
+
"macOS" : "13.0",
|
| 48 |
+
"tvOS" : "16.0",
|
| 49 |
+
"visionOS" : "1.0",
|
| 50 |
+
"watchOS" : "9.0",
|
| 51 |
+
"iOS" : "16.0",
|
| 52 |
+
"macCatalyst" : "16.0"
|
| 53 |
+
},
|
| 54 |
+
"modelType" : {
|
| 55 |
+
"name" : "MLModelType_mlProgram"
|
| 56 |
+
},
|
| 57 |
+
"userDefinedMetadata" : {
|
| 58 |
+
"com.github.apple.coremltools.source_dialect" : "TorchScript",
|
| 59 |
+
"com.github.apple.coremltools.source" : "torch==2.5.1",
|
| 60 |
+
"com.github.apple.coremltools.version" : "8.1"
|
| 61 |
+
},
|
| 62 |
+
"inputSchema" : [
|
| 63 |
+
{
|
| 64 |
+
"hasShapeFlexibility" : "0",
|
| 65 |
+
"isOptional" : "0",
|
| 66 |
+
"dataType" : "Float16",
|
| 67 |
+
"formattedType" : "MultiArray (Float16 1 × 160000)",
|
| 68 |
+
"shortDescription" : "",
|
| 69 |
+
"shape" : "[1, 160000]",
|
| 70 |
+
"name" : "waveforms",
|
| 71 |
+
"type" : "MultiArray"
|
| 72 |
+
}
|
| 73 |
+
],
|
| 74 |
+
"generatedClassName" : "SpeakerEmbeddingPreprocessor",
|
| 75 |
+
"method" : "predict"
|
| 76 |
+
}
|
| 77 |
+
]
|
speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/model.mil
ADDED
|
@@ -0,0 +1,90 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3401.3.1"}, {"coremlc-version", "3401.4.1"}, {"coremltools-component-torch", "2.5.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "8.1"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios16>(tensor<fp16, [1, 160000]> waveforms) {
|
| 5 |
+
tensor<string, []> cast_0_dtype_0 = const()[name = tensor<string, []>("cast_0_dtype_0"), val = tensor<string, []>("fp32")];
|
| 6 |
+
tensor<fp32, []> var_2_promoted = const()[name = tensor<string, []>("op_2_promoted"), val = tensor<fp32, []>(0x1p+15)];
|
| 7 |
+
tensor<fp32, [1, 160000]> cast_0 = cast(dtype = cast_0_dtype_0, x = waveforms)[name = tensor<string, []>("cast_11")];
|
| 8 |
+
tensor<fp32, [1, 160000]> waveform_1 = mul(x = cast_0, y = var_2_promoted)[name = tensor<string, []>("waveform_1")];
|
| 9 |
+
tensor<int32, [2]> var_6_begin_0 = const()[name = tensor<string, []>("op_6_begin_0"), val = tensor<int32, [2]>([0, 0])];
|
| 10 |
+
tensor<int32, [2]> var_6_end_0 = const()[name = tensor<string, []>("op_6_end_0"), val = tensor<int32, [2]>([1, 160000])];
|
| 11 |
+
tensor<bool, [2]> var_6_end_mask_0 = const()[name = tensor<string, []>("op_6_end_mask_0"), val = tensor<bool, [2]>([false, true])];
|
| 12 |
+
tensor<bool, [2]> var_6_squeeze_mask_0 = const()[name = tensor<string, []>("op_6_squeeze_mask_0"), val = tensor<bool, [2]>([true, false])];
|
| 13 |
+
tensor<fp32, [160000]> var_6 = slice_by_index(begin = var_6_begin_0, end = var_6_end_0, end_mask = var_6_end_mask_0, squeeze_mask = var_6_squeeze_mask_0, x = waveform_1)[name = tensor<string, []>("op_6")];
|
| 14 |
+
tensor<int32, []> sliding_windows_0_axis_0 = const()[name = tensor<string, []>("sliding_windows_0_axis_0"), val = tensor<int32, []>(0)];
|
| 15 |
+
tensor<int32, []> sliding_windows_0_size_0 = const()[name = tensor<string, []>("sliding_windows_0_size_0"), val = tensor<int32, []>(400)];
|
| 16 |
+
tensor<int32, []> sliding_windows_0_stride_0 = const()[name = tensor<string, []>("sliding_windows_0_stride_0"), val = tensor<int32, []>(160)];
|
| 17 |
+
tensor<fp32, [998, 400]> sliding_windows_0 = sliding_windows(axis = sliding_windows_0_axis_0, size = sliding_windows_0_size_0, stride = sliding_windows_0_stride_0, x = var_6)[name = tensor<string, []>("sliding_windows_0")];
|
| 18 |
+
tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([1])];
|
| 19 |
+
tensor<bool, []> var_42_keep_dims_0 = const()[name = tensor<string, []>("op_42_keep_dims_0"), val = tensor<bool, []>(false)];
|
| 20 |
+
tensor<fp32, [998]> var_42 = reduce_mean(axes = var_42_axes_0, keep_dims = var_42_keep_dims_0, x = sliding_windows_0)[name = tensor<string, []>("op_42")];
|
| 21 |
+
tensor<int32, [1]> row_means_axes_0 = const()[name = tensor<string, []>("row_means_axes_0"), val = tensor<int32, [1]>([1])];
|
| 22 |
+
tensor<fp32, [998, 1]> row_means = expand_dims(axes = row_means_axes_0, x = var_42)[name = tensor<string, []>("row_means")];
|
| 23 |
+
tensor<fp32, [998, 400]> strided_input_3 = sub(x = sliding_windows_0, y = row_means)[name = tensor<string, []>("strided_input_3")];
|
| 24 |
+
tensor<int32, [1]> input_1_axes_0 = const()[name = tensor<string, []>("input_1_axes_0"), val = tensor<int32, [1]>([0])];
|
| 25 |
+
tensor<fp32, [1, 998, 400]> input_1 = expand_dims(axes = input_1_axes_0, x = strided_input_3)[name = tensor<string, []>("input_1")];
|
| 26 |
+
tensor<fp32, []> const_2 = const()[name = tensor<string, []>("const_2"), val = tensor<fp32, []>(0x0p+0)];
|
| 27 |
+
tensor<int32, [6]> var_54_pad_0 = const()[name = tensor<string, []>("op_54_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 1, 0])];
|
| 28 |
+
tensor<string, []> var_54_mode_0 = const()[name = tensor<string, []>("op_54_mode_0"), val = tensor<string, []>("replicate")];
|
| 29 |
+
tensor<fp32, [1, 998, 401]> var_54 = pad(constant_val = const_2, mode = var_54_mode_0, pad = var_54_pad_0, x = input_1)[name = tensor<string, []>("op_54")];
|
| 30 |
+
tensor<int32, [1]> offset_strided_input_axes_0 = const()[name = tensor<string, []>("offset_strided_input_axes_0"), val = tensor<int32, [1]>([0])];
|
| 31 |
+
tensor<fp32, [998, 401]> offset_strided_input = squeeze(axes = offset_strided_input_axes_0, x = var_54)[name = tensor<string, []>("offset_strided_input")];
|
| 32 |
+
tensor<int32, [2]> var_66_begin_0 = const()[name = tensor<string, []>("op_66_begin_0"), val = tensor<int32, [2]>([0, 0])];
|
| 33 |
+
tensor<int32, [2]> var_66_end_0 = const()[name = tensor<string, []>("op_66_end_0"), val = tensor<int32, [2]>([998, 400])];
|
| 34 |
+
tensor<bool, [2]> var_66_end_mask_0 = const()[name = tensor<string, []>("op_66_end_mask_0"), val = tensor<bool, [2]>([true, false])];
|
| 35 |
+
tensor<fp32, [998, 400]> var_66 = slice_by_index(begin = var_66_begin_0, end = var_66_end_0, end_mask = var_66_end_mask_0, x = offset_strided_input)[name = tensor<string, []>("op_66")];
|
| 36 |
+
tensor<fp32, []> var_67 = const()[name = tensor<string, []>("op_67"), val = tensor<fp32, []>(0x1.f0a3d8p-1)];
|
| 37 |
+
tensor<fp32, [998, 400]> var_68 = mul(x = var_66, y = var_67)[name = tensor<string, []>("op_68")];
|
| 38 |
+
tensor<fp32, [998, 400]> strided_input_5 = sub(x = strided_input_3, y = var_68)[name = tensor<string, []>("strided_input_5")];
|
| 39 |
+
tensor<fp32, [1, 400]> window_function = const()[name = tensor<string, []>("window_function"), val = tensor<fp32, [1, 400]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 40 |
+
tensor<fp32, [998, 400]> strided_input_7 = mul(x = strided_input_5, y = window_function)[name = tensor<string, []>("strided_input_7")];
|
| 41 |
+
tensor<int32, [1]> input_3_axes_0 = const()[name = tensor<string, []>("input_3_axes_0"), val = tensor<int32, [1]>([0])];
|
| 42 |
+
tensor<fp32, [1, 998, 400]> input_3 = expand_dims(axes = input_3_axes_0, x = strided_input_7)[name = tensor<string, []>("input_3")];
|
| 43 |
+
tensor<fp32, []> const_3 = const()[name = tensor<string, []>("const_3"), val = tensor<fp32, []>(0x0p+0)];
|
| 44 |
+
tensor<int32, [6]> var_90_pad_0 = const()[name = tensor<string, []>("op_90_pad_0"), val = tensor<int32, [6]>([0, 0, 0, 0, 0, 112])];
|
| 45 |
+
tensor<string, []> var_90_mode_0 = const()[name = tensor<string, []>("op_90_mode_0"), val = tensor<string, []>("constant")];
|
| 46 |
+
tensor<fp32, [1, 998, 512]> var_90 = pad(constant_val = const_3, mode = var_90_mode_0, pad = var_90_pad_0, x = input_3)[name = tensor<string, []>("op_90")];
|
| 47 |
+
tensor<int32, [1]> strided_input_axes_0 = const()[name = tensor<string, []>("strided_input_axes_0"), val = tensor<int32, [1]>([0])];
|
| 48 |
+
tensor<fp32, [998, 512]> strided_input = squeeze(axes = strided_input_axes_0, x = var_90)[name = tensor<string, []>("strided_input")];
|
| 49 |
+
tensor<fp32, [512, 512]> cos_0 = const()[name = tensor<string, []>("cos_0"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1728)))];
|
| 50 |
+
tensor<fp32, [512, 512]> sin_0 = const()[name = tensor<string, []>("sin_0"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1050368)))];
|
| 51 |
+
tensor<bool, []> matmul_1_transpose_x_1 = const()[name = tensor<string, []>("matmul_1_transpose_x_1"), val = tensor<bool, []>(false)];
|
| 52 |
+
tensor<bool, []> matmul_1_transpose_y_1 = const()[name = tensor<string, []>("matmul_1_transpose_y_1"), val = tensor<bool, []>(true)];
|
| 53 |
+
tensor<fp32, [512, 998]> matmul_1 = matmul(transpose_x = matmul_1_transpose_x_1, transpose_y = matmul_1_transpose_y_1, x = cos_0, y = strided_input)[name = tensor<string, []>("matmul_1")];
|
| 54 |
+
tensor<bool, []> matmul_3_transpose_x_1 = const()[name = tensor<string, []>("matmul_3_transpose_x_1"), val = tensor<bool, []>(false)];
|
| 55 |
+
tensor<bool, []> matmul_3_transpose_y_1 = const()[name = tensor<string, []>("matmul_3_transpose_y_1"), val = tensor<bool, []>(true)];
|
| 56 |
+
tensor<fp32, [512, 998]> matmul_3 = matmul(transpose_x = matmul_3_transpose_x_1, transpose_y = matmul_3_transpose_y_1, x = sin_0, y = strided_input)[name = tensor<string, []>("matmul_3")];
|
| 57 |
+
tensor<fp32, []> mul_1_y_0 = const()[name = tensor<string, []>("mul_1_y_0"), val = tensor<fp32, []>(-0x1p+0)];
|
| 58 |
+
tensor<fp32, [512, 998]> mul_1 = mul(x = matmul_3, y = mul_1_y_0)[name = tensor<string, []>("mul_1")];
|
| 59 |
+
tensor<int32, [2]> transpose_3_perm_0 = const()[name = tensor<string, []>("transpose_3_perm_0"), val = tensor<int32, [2]>([-1, 0])];
|
| 60 |
+
tensor<int32, [2]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [2]>([-1, 0])];
|
| 61 |
+
tensor<int32, [257]> range_1d_2 = const()[name = tensor<string, []>("range_1d_2"), val = tensor<int32, [257]>([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256])];
|
| 62 |
+
tensor<int32, []> gather_0_axis_0 = const()[name = tensor<string, []>("gather_0_axis_0"), val = tensor<int32, []>(-1)];
|
| 63 |
+
tensor<int32, []> gather_0_batch_dims_0 = const()[name = tensor<string, []>("gather_0_batch_dims_0"), val = tensor<int32, []>(0)];
|
| 64 |
+
tensor<fp32, [998, 512]> transpose_3 = transpose(perm = transpose_3_perm_0, x = matmul_1)[name = tensor<string, []>("transpose_6")];
|
| 65 |
+
tensor<fp32, [998, 257]> gather_0 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = range_1d_2, x = transpose_3)[name = tensor<string, []>("gather_0")];
|
| 66 |
+
tensor<int32, []> gather_1_axis_0 = const()[name = tensor<string, []>("gather_1_axis_0"), val = tensor<int32, []>(-1)];
|
| 67 |
+
tensor<int32, []> gather_1_batch_dims_0 = const()[name = tensor<string, []>("gather_1_batch_dims_0"), val = tensor<int32, []>(0)];
|
| 68 |
+
tensor<fp32, [998, 512]> transpose_4 = transpose(perm = transpose_4_perm_0, x = mul_1)[name = tensor<string, []>("transpose_5")];
|
| 69 |
+
tensor<fp32, [998, 257]> gather_1 = gather(axis = gather_1_axis_0, batch_dims = gather_1_batch_dims_0, indices = range_1d_2, x = transpose_4)[name = tensor<string, []>("gather_1")];
|
| 70 |
+
tensor<fp32, [998, 257]> square_0 = square(x = gather_0)[name = tensor<string, []>("square_0")];
|
| 71 |
+
tensor<fp32, [998, 257]> square_1 = square(x = gather_1)[name = tensor<string, []>("square_1")];
|
| 72 |
+
tensor<fp32, [998, 257]> add_1 = add(x = square_0, y = square_1)[name = tensor<string, []>("add_1")];
|
| 73 |
+
tensor<fp32, [998, 257]> spectrum = identity(x = add_1)[name = tensor<string, []>("spectrum")];
|
| 74 |
+
tensor<fp32, [80, 257]> mel_energies_3 = const()[name = tensor<string, []>("mel_energies_3"), val = tensor<fp32, [80, 257]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2099008)))];
|
| 75 |
+
tensor<fp32, [80]> mel_energies_bias_0 = const()[name = tensor<string, []>("mel_energies_bias_0"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2181312)))];
|
| 76 |
+
tensor<fp32, [998, 80]> mel_energies = linear(bias = mel_energies_bias_0, weight = mel_energies_3, x = spectrum)[name = tensor<string, []>("mel_energies")];
|
| 77 |
+
tensor<fp32, []> const_10 = const()[name = tensor<string, []>("const_10"), val = tensor<fp32, []>(0x1p-23)];
|
| 78 |
+
tensor<fp32, [998, 80]> var_186 = maximum(x = mel_energies, y = const_10)[name = tensor<string, []>("op_186")];
|
| 79 |
+
tensor<fp32, []> filter_banks_epsilon_0 = const()[name = tensor<string, []>("filter_banks_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
|
| 80 |
+
tensor<fp32, [998, 80]> filter_banks = log(epsilon = filter_banks_epsilon_0, x = var_186)[name = tensor<string, []>("filter_banks")];
|
| 81 |
+
tensor<int32, [1]> var_192_axes_0 = const()[name = tensor<string, []>("op_192_axes_0"), val = tensor<int32, [1]>([0])];
|
| 82 |
+
tensor<bool, []> var_192_keep_dims_0 = const()[name = tensor<string, []>("op_192_keep_dims_0"), val = tensor<bool, []>(true)];
|
| 83 |
+
tensor<fp32, [1, 80]> var_192 = reduce_mean(axes = var_192_axes_0, keep_dims = var_192_keep_dims_0, x = filter_banks)[name = tensor<string, []>("op_192")];
|
| 84 |
+
tensor<fp32, [998, 80]> var_194 = sub(x = filter_banks, y = var_192)[name = tensor<string, []>("op_194")];
|
| 85 |
+
tensor<int32, [1]> var_196_axes_0 = const()[name = tensor<string, []>("op_196_axes_0"), val = tensor<int32, [1]>([0])];
|
| 86 |
+
tensor<fp32, [1, 998, 80]> preprocessor_output_1_type_fp32 = expand_dims(axes = var_196_axes_0, x = var_194)[name = tensor<string, []>("op_196")];
|
| 87 |
+
tensor<string, []> cast_9_dtype_0 = const()[name = tensor<string, []>("cast_9_dtype_0"), val = tensor<string, []>("fp16")];
|
| 88 |
+
tensor<fp16, [1, 998, 80]> preprocessor_output_1 = cast(dtype = cast_9_dtype_0, x = preprocessor_output_1_type_fp32)[name = tensor<string, []>("cast_10")];
|
| 89 |
+
} -> (preprocessor_output_1);
|
| 90 |
+
}
|
speaker_embedder/pyannote-v3/SpeakerEmbedderPreprocessor.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5f2c284bd22f1f7ab76901c1c6e57f82d4ebbf057fa0b924aad057f124f77a89
|
| 3 |
+
size 2181696
|
speaker_segmenter/pyannote-v3/LICENSE_NOTICE.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Argmax proprietary and confidential. Under NDA.
|
| 2 |
+
|
| 3 |
+
Copyright 2024 Argmax, Inc. All rights reserved.
|
| 4 |
+
|
| 5 |
+
Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
|
| 6 |
+
|
| 7 |
+
Please contact Argmax for licensing information at [email protected].
|
speaker_segmenter/pyannote-v3/README.txt
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# License
|
| 2 |
+
|
| 3 |
+
Original model weights: https://huggingface.co/pyannote/segmentation-3.0/blob/main/LICENSE
|
| 4 |
+
Argmax-optimized model asset (Assets with `.mlmodelc` extension): https://huggingface.co/argmaxinc/speakerkit-pro/blob/main/LICENSE_NOTICE.txt
|
| 5 |
+
|
| 6 |
+
Please contact [email protected] for licensing SpeakerKit Pro assets
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/LICENSE_NOTICE.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
Argmax proprietary and confidential. Under NDA.
|
| 2 |
+
|
| 3 |
+
Copyright 2024 Argmax, Inc. All rights reserved.
|
| 4 |
+
|
| 5 |
+
Unauthorized access, copying, use, distribution, and or commercialization of this file, via any medium or means is strictly prohibited.
|
| 6 |
+
|
| 7 |
+
Please contact Argmax for licensing information at [email protected].
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:42e2434809899abce6a3947f4f3b1365af7c8d3762e4c4bfc0df886f1dca8347
|
| 3 |
+
size 243
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ed53832ecc7af1c0eb6bc1bc8a475c369d6103ea08f21a40302a57f06966c6c8
|
| 3 |
+
size 497
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/metadata.json
ADDED
|
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"metadataOutputVersion" : "3.0",
|
| 4 |
+
"storagePrecision" : "Mixed (Float16, Float32)",
|
| 5 |
+
"outputSchema" : [
|
| 6 |
+
{
|
| 7 |
+
"hasShapeFlexibility" : "0",
|
| 8 |
+
"isOptional" : "0",
|
| 9 |
+
"dataType" : "Float16",
|
| 10 |
+
"formattedType" : "MultiArray (Float16 21 × 589 × 3)",
|
| 11 |
+
"shortDescription" : "",
|
| 12 |
+
"shape" : "[21, 589, 3]",
|
| 13 |
+
"name" : "speaker_probs",
|
| 14 |
+
"type" : "MultiArray"
|
| 15 |
+
},
|
| 16 |
+
{
|
| 17 |
+
"hasShapeFlexibility" : "0",
|
| 18 |
+
"isOptional" : "0",
|
| 19 |
+
"dataType" : "Float16",
|
| 20 |
+
"formattedType" : "MultiArray (Float16 21 × 589 × 3)",
|
| 21 |
+
"shortDescription" : "",
|
| 22 |
+
"shape" : "[21, 589, 3]",
|
| 23 |
+
"name" : "speaker_ids",
|
| 24 |
+
"type" : "MultiArray"
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"hasShapeFlexibility" : "0",
|
| 28 |
+
"isOptional" : "0",
|
| 29 |
+
"dataType" : "Float16",
|
| 30 |
+
"formattedType" : "MultiArray (Float16 21 × 3)",
|
| 31 |
+
"shortDescription" : "",
|
| 32 |
+
"shape" : "[21, 3]",
|
| 33 |
+
"name" : "speaker_activity",
|
| 34 |
+
"type" : "MultiArray"
|
| 35 |
+
},
|
| 36 |
+
{
|
| 37 |
+
"hasShapeFlexibility" : "0",
|
| 38 |
+
"isOptional" : "0",
|
| 39 |
+
"dataType" : "Float16",
|
| 40 |
+
"formattedType" : "MultiArray (Float16 21 × 589)",
|
| 41 |
+
"shortDescription" : "",
|
| 42 |
+
"shape" : "[21, 589]",
|
| 43 |
+
"name" : "overlapped_speaker_activity",
|
| 44 |
+
"type" : "MultiArray"
|
| 45 |
+
},
|
| 46 |
+
{
|
| 47 |
+
"hasShapeFlexibility" : "0",
|
| 48 |
+
"isOptional" : "0",
|
| 49 |
+
"dataType" : "Float16",
|
| 50 |
+
"formattedType" : "MultiArray (Float16 1767)",
|
| 51 |
+
"shortDescription" : "",
|
| 52 |
+
"shape" : "[1767]",
|
| 53 |
+
"name" : "voice_activity",
|
| 54 |
+
"type" : "MultiArray"
|
| 55 |
+
},
|
| 56 |
+
{
|
| 57 |
+
"hasShapeFlexibility" : "0",
|
| 58 |
+
"isOptional" : "0",
|
| 59 |
+
"dataType" : "Float16",
|
| 60 |
+
"formattedType" : "MultiArray (Float16 21 × 1 × 160000)",
|
| 61 |
+
"shortDescription" : "",
|
| 62 |
+
"shape" : "[21, 1, 160000]",
|
| 63 |
+
"name" : "sliding_window_waveform",
|
| 64 |
+
"type" : "MultiArray"
|
| 65 |
+
}
|
| 66 |
+
],
|
| 67 |
+
"modelParameters" : [
|
| 68 |
+
|
| 69 |
+
],
|
| 70 |
+
"specificationVersion" : 7,
|
| 71 |
+
"mlProgramOperationTypeHistogram" : {
|
| 72 |
+
"Transpose" : 2,
|
| 73 |
+
"Ios16.maxPool" : 3,
|
| 74 |
+
"Ios16.exp" : 1,
|
| 75 |
+
"Ios16.softmax" : 1,
|
| 76 |
+
"SlidingWindows" : 1,
|
| 77 |
+
"Ios16.linear" : 5,
|
| 78 |
+
"Ios16.add" : 40,
|
| 79 |
+
"Ios16.realDiv" : 1,
|
| 80 |
+
"Ios16.reduceMax" : 1,
|
| 81 |
+
"Ios16.reduceSum" : 2,
|
| 82 |
+
"Ios16.reduceArgmax" : 1,
|
| 83 |
+
"Ios16.greater" : 1,
|
| 84 |
+
"Ios16.log" : 1,
|
| 85 |
+
"ExpandDims" : 1,
|
| 86 |
+
"Ios16.instanceNorm" : 4,
|
| 87 |
+
"Ios16.cast" : 4,
|
| 88 |
+
"Ios16.conv" : 3,
|
| 89 |
+
"Ios16.lstm" : 4,
|
| 90 |
+
"OneHot" : 1,
|
| 91 |
+
"Ios16.scatter" : 42,
|
| 92 |
+
"SliceByIndex" : 61,
|
| 93 |
+
"Ios16.abs" : 1,
|
| 94 |
+
"Ios16.leakyRelu" : 5
|
| 95 |
+
},
|
| 96 |
+
"computePrecision" : "Mixed (Float16, Float32, Int32)",
|
| 97 |
+
"isUpdatable" : "0",
|
| 98 |
+
"stateSchema" : [
|
| 99 |
+
|
| 100 |
+
],
|
| 101 |
+
"availability" : {
|
| 102 |
+
"macOS" : "13.0",
|
| 103 |
+
"tvOS" : "16.0",
|
| 104 |
+
"visionOS" : "1.0",
|
| 105 |
+
"watchOS" : "9.0",
|
| 106 |
+
"iOS" : "16.0",
|
| 107 |
+
"macCatalyst" : "16.0"
|
| 108 |
+
},
|
| 109 |
+
"modelType" : {
|
| 110 |
+
"name" : "MLModelType_mlProgram"
|
| 111 |
+
},
|
| 112 |
+
"userDefinedMetadata" : {
|
| 113 |
+
"com.github.apple.coremltools.source_dialect" : "TorchScript",
|
| 114 |
+
"com.github.apple.coremltools.source" : "torch==2.5.1",
|
| 115 |
+
"com.github.apple.coremltools.version" : "8.0"
|
| 116 |
+
},
|
| 117 |
+
"inputSchema" : [
|
| 118 |
+
{
|
| 119 |
+
"hasShapeFlexibility" : "0",
|
| 120 |
+
"isOptional" : "0",
|
| 121 |
+
"dataType" : "Float16",
|
| 122 |
+
"formattedType" : "MultiArray (Float16 480000)",
|
| 123 |
+
"shortDescription" : "",
|
| 124 |
+
"shape" : "[480000]",
|
| 125 |
+
"name" : "waveform",
|
| 126 |
+
"type" : "MultiArray"
|
| 127 |
+
}
|
| 128 |
+
],
|
| 129 |
+
"generatedClassName" : "SpeakerSegmenter",
|
| 130 |
+
"method" : "predict"
|
| 131 |
+
}
|
| 132 |
+
]
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
speaker_segmenter/pyannote-v3/SpeakerSegmenter.mlmodelc/weights/weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1584619d1180ef89807b66c2c96605720365c02d4fbdcc9be02bbad91d188128
|
| 3 |
+
size 5760346
|