Duplicate from FluidInference/diar-streaming-sortformer-coreml
Browse filesCo-authored-by: Alex Weng <alexwengg@users.noreply.huggingface.co>
This view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +35 -0
- README.md +155 -0
- Sortformer.mlmodelc/analytics/coremldata.bin +3 -0
- Sortformer.mlmodelc/coremldata.bin +3 -0
- Sortformer.mlmodelc/metadata.json +176 -0
- Sortformer.mlmodelc/model0/analytics/coremldata.bin +3 -0
- Sortformer.mlmodelc/model0/coremldata.bin +3 -0
- Sortformer.mlmodelc/model0/model.mil +201 -0
- Sortformer.mlmodelc/model0/weights/0-weight.bin +3 -0
- Sortformer.mlmodelc/model1/analytics/coremldata.bin +3 -0
- Sortformer.mlmodelc/model1/coremldata.bin +3 -0
- Sortformer.mlmodelc/model1/model.mil +0 -0
- Sortformer.mlmodelc/model1/weights/1-weight.bin +3 -0
- Sortformer.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- Sortformer.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin +3 -0
- Sortformer.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin +3 -0
- Sortformer.mlpackage/Manifest.json +18 -0
- SortformerNvidiaHigh.mlmodelc/analytics/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/metadata.json +177 -0
- SortformerNvidiaHigh.mlmodelc/model0/analytics/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/model0/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/model0/model.mil +201 -0
- SortformerNvidiaHigh.mlmodelc/model0/weights/0-weight.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/model1/analytics/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/model1/coremldata.bin +3 -0
- SortformerNvidiaHigh.mlmodelc/model1/model.mil +0 -0
- SortformerNvidiaHigh.mlmodelc/model1/weights/1-weight.bin +3 -0
- SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin +3 -0
- SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin +3 -0
- SortformerNvidiaHigh.mlpackage/Manifest.json +18 -0
- SortformerNvidiaHighV2.mlmodelc/analytics/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/metadata.json +176 -0
- SortformerNvidiaHighV2.mlmodelc/model0/analytics/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/model0/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/model0/model.mil +201 -0
- SortformerNvidiaHighV2.mlmodelc/model0/weights/0-weight.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/model1/analytics/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/model1/coremldata.bin +3 -0
- SortformerNvidiaHighV2.mlmodelc/model1/model.mil +0 -0
- SortformerNvidiaHighV2.mlmodelc/model1/weights/1-weight.bin +3 -0
- SortformerNvidiaHighV2.mlpackage/Data/com.apple.CoreML/model.mlmodel +3 -0
- SortformerNvidiaHighV2.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin +3 -0
- SortformerNvidiaHighV2.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin +3 -0
- SortformerNvidiaHighV2.mlpackage/Manifest.json +18 -0
- SortformerNvidiaHigh_v2.1.mlmodelc/analytics/coremldata.bin +3 -0
- SortformerNvidiaHigh_v2.1.mlmodelc/coremldata.bin +3 -0
- SortformerNvidiaHigh_v2.1.mlmodelc/metadata.json +177 -0
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---
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license: cc-by-4.0
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library_name: coreml
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base_model:
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- nvidia/diar_streaming_sortformer_4spk-v2.1
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base_model_relation: finetune
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tags:
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- speaker-diarization
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- speech
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- audio
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- coreml
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- apple
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- ios
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- macos
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- sortformer
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- streaming
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pipeline_tag: automatic-speech-recognition
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---
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# Sortformer CoreML Models
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Streaming speaker diarization models converted from NVIDIA's Sortformer to CoreML for Apple Silicon.
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## Model Variants
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| Variant | File | Latency | Use Case |
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| 26 |
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| -------------------- | ------------------------------------ | ------- | --------------------- |
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| **Fastest v2** | `Sortformer_v2.mlmodelc` | ~1.04s | Low latency streaming |
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| **Fastest v2.1** | `Sortformer_v2.1.mlmodelc` | ~1.04s | Low latency streaming |
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| 29 |
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| **NVIDIA Low v2** | `SortformerNvidiaLow_v2.mlmodelc` | ~1.04s | Low latency streaming |
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| 30 |
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| **NVIDIA Low v2.1** | `SortformerNvidiaLow_v2.1.mlmodelc` | ~1.04s | Low latency streaming |
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| 31 |
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| **NVIDIA High v2** | `SortformerNvidiaHigh_v2.mlmodelc` | ~30.4s | Best quality, offline |
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| **NVIDIA High v2.1** | `SortformerNvidiaHigh_v2.1.mlmodelc` | ~30.4s | Best quality, offline |
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The `v2` and `v2.1` refer to the version of the model weights to use. According to NVIDIA, `v2.1` is more robust in meeting scenarios.
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## Configuration Parameters
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| Parameter | Default | NVIDIA Low | NVIDIA High |
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| ------------------- | ------- | ---------- | ----------- |
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| chunk_len | 6 | 6 | 340 |
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| chunk_right_context | 7 | 7 | 40 |
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| chunk_left_context | 1 | 1 | 1 |
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| fifo_len | 40 | 188 | 40 |
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| spkcache_len | 188 | 188 | 188 |
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## Model Input/Output Shapes
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**General**:
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| Input | Shape | Description |
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| 51 |
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| ---------------- | --------------------- | ------------------------ |
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| chunk | `[1, 8*(C+L+R), 128]` | Mel spectrogram features |
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| 53 |
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| chunk_lengths | `[1]` | Actual chunk length |
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| 54 |
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| spkcache | `[1, S, 512]` | Speaker cache embeddings |
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| 55 |
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| spkcache_lengths | `[1]` | Actual cache length |
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| 56 |
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| fifo | `[1, F, 512]` | FIFO queue embeddings |
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| 57 |
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| fifo_lengths | `[1]` | Actual FIFO length |
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| 58 |
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| 59 |
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| Output | Shape | Description |
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| 60 |
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| ------------------------- | ------------------ | ------------------------------------- |
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| 61 |
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| speaker_preds | `[C+L+R+S+F, 4]` | Speaker probabilities (4 speakers) |
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| 62 |
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| chunk_pre_encoder_embs | `[C+L+R, 512]` | Embeddings for state update |
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| 63 |
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| chunk_pre_encoder_lengths | `[1]` | Actual embedding count |
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| 64 |
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| nest_encoder_embs | `[C+L+R+S+F, 192]` | Embeddings for speaker discrimination |
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| 65 |
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| nest_encoder_lengths | `[1]` | Actual speaker embedding count |
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| 66 |
+
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| 67 |
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Note: `C = chunk_len`, `L = chunk_left_context`, `R = chunk_right_context`, `S = spkcache_len`, `F = fifo_len`.
|
| 68 |
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|
| 69 |
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**Configuration-Specific Shapes**:
|
| 70 |
+
|
| 71 |
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| Input | Default | NVIDIA Low | NVIDIA High |
|
| 72 |
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| ---------------- | --------------- | --------------- | ---------------- |
|
| 73 |
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| chunk | `[1, 112, 128]` | `[1, 112, 128]` | `[1, 3048, 128]` |
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| 74 |
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| chunk_lengths | `[1]` | `[1]` | `[1]` |
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| 75 |
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| spkcache | `[1, 188, 512]` | `[1, 188, 512]` | `[1, 188, 512]` |
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| 76 |
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| spkcache_lengths | `[1]` | `[1]` | `[1]` |
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| 77 |
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| fifo | `[1, 40, 512]` | `[1, 188, 512]` | `[1, 40, 512]` |
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| 78 |
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| fifo_lengths | `[1]` | `[1]` | `[1]` |
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| 79 |
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|
| 80 |
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| Output | Default | NVIDIA Low | NVIDIA High |
|
| 81 |
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| ------------------------- | --------------- | --------------- | --------------- |
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| 82 |
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| speaker_preds | `[1, 242, 128]` | `[1, 390, 128]` | `[1, 609, 128]` |
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| 83 |
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| chunk_pre_encoder_embs | `[1, 14, 512]` | `[1, 14, 512]` | `[1, 381, 512]` |
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| 84 |
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| chunk_pre_encoder_lengths | `[1]` | `[1]` | `[1]` |
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| 85 |
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| nest_encoder_embs | `[1, 242, 192]` | `[1, 390, 192]` | `[1, 609, 192]` |
|
| 86 |
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| nest_encoder_lengths | `[1]` | `[1]` | `[1]` |
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| 87 |
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|
| 88 |
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|
| 89 |
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| Metric | Default | NVIDIA High |
|
| 90 |
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| ------------- | ------- | ----------- |
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| 91 |
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| Latency | ~1.12s | ~30.4s |
|
| 92 |
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| RTFx (M4 Max) | ~5.7x | ~125.3x |
|
| 93 |
+
|
| 94 |
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## Usage with FluidAudio (Swift)
|
| 95 |
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|
| 96 |
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```swift
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| 97 |
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import FluidAudio
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| 98 |
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|
| 99 |
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// Initialize with default config (auto-downloads from HuggingFace)
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| 100 |
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let diarizer = SortformerDiarizer(config: .default)
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| 101 |
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let models = try await SortformerModels.loadFromHuggingFace(config: .default)
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| 102 |
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diarizer.initialize(models: models)
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| 103 |
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|
| 104 |
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// Streaming processing
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| 105 |
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for audioChunk in audioStream {
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| 106 |
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if let result = try diarizer.processSamples(audioChunk) {
|
| 107 |
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for frame in 0..<result.frameCount {
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| 108 |
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for speaker in 0..<4 {
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| 109 |
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let prob = result.getSpeakerPrediction(speaker: speaker, frame: frame)
|
| 110 |
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}
|
| 111 |
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}
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| 112 |
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}
|
| 113 |
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}
|
| 114 |
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| 115 |
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// Or batch processing
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| 116 |
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let timeline = try diarizer.processComplete(audioSamples)
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| 117 |
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for (speakerIndex, segments) in timeline.segments.enumerated() {
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| 118 |
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for segment in segments {
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| 119 |
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print("Speaker \(speakerIndex): \(segment.startTime)s - \(segment.endTime)s")
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| 120 |
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}
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| 121 |
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}
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| 122 |
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```
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| 123 |
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Performance
|
| 124 |
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|
| 125 |
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https://github.com/FluidInference/FluidAudio/blob/main/Documentation/Benchmarks.md
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| 126 |
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Files
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Models
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| 130 |
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- Sortformer.mlpackage / .mlmodelc - Default config (low latency)
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- SortformerNvidiaLow.mlpackage / .mlmodelc - NVIDIA low latency config
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| 133 |
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- SortformerNvidiaHigh.mlpackage / .mlmodelc - NVIDIA high latency config
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| 134 |
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| 135 |
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Scripts
|
| 136 |
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| 137 |
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- convert_to_coreml.py - PyTorch to CoreML conversion
|
| 138 |
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- streaming_inference.py - Python streaming inference example
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| 139 |
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- mic_inference.py - Real-time microphone demo
|
| 140 |
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| 141 |
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Source
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| 142 |
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| 143 |
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Original model: https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1
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| 144 |
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| 145 |
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Credits & Acknowledgements
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| 146 |
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| 147 |
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This project would not have been possible without the significant technical contributions of https://huggingface.co/GradientDescent2718.
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| 148 |
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| 149 |
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Their work was instrumental in:
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| 150 |
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- Architecture Conversion: Developing the complex PyTorch-to-CoreML conversion pipeline for the 17-layer Fast-Conformer and 18-layer Transformer heads.
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- Build & Optimization: Engineering the static shape configurations that allow the model to achieve ~120x RTF on Apple Silicon.
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- Logic Implementation: Porting the critical streaming state logic (speaker cache and FIFO management) to ensure consistent speaker identity tracking.
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This project was built upon the foundational work of the NVIDIA NeMo team.
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version https://git-lfs.github.com/spec/v1
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oid sha256:b1336dfc84d1084140347209f4c2cd1d972ba998d418734a24c0a437dbbcab75
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size 202
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version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1d69415aa509e8c407dfa64136402232dd02610c41d8c5729446830639373376
|
| 3 |
+
size 1078
|
Sortformer.mlmodelc/metadata.json
ADDED
|
@@ -0,0 +1,176 @@
|
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|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"metadataOutputVersion" : "3.0",
|
| 4 |
+
"shortDescription" : "CoreML port of Nvidia's Streaming Sortformer diarization model",
|
| 5 |
+
"outputSchema" : [
|
| 6 |
+
{
|
| 7 |
+
"hasShapeFlexibility" : "0",
|
| 8 |
+
"isOptional" : "0",
|
| 9 |
+
"dataType" : "Float32",
|
| 10 |
+
"formattedType" : "MultiArray (Float32 1 × 242 × 4)",
|
| 11 |
+
"shortDescription" : "Combined speaker probabilities for the speaker cache, FIFO queue, and chunk",
|
| 12 |
+
"shape" : "[1, 242, 4]",
|
| 13 |
+
"name" : "speaker_preds",
|
| 14 |
+
"type" : "MultiArray"
|
| 15 |
+
},
|
| 16 |
+
{
|
| 17 |
+
"hasShapeFlexibility" : "0",
|
| 18 |
+
"isOptional" : "0",
|
| 19 |
+
"dataType" : "Float32",
|
| 20 |
+
"formattedType" : "MultiArray (Float32 1 × 14 × 512)",
|
| 21 |
+
"shortDescription" : "Speaker embeddings for the new chunk",
|
| 22 |
+
"shape" : "[1, 14, 512]",
|
| 23 |
+
"name" : "chunk_pre_encoder_embs",
|
| 24 |
+
"type" : "MultiArray"
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"hasShapeFlexibility" : "0",
|
| 28 |
+
"isOptional" : "0",
|
| 29 |
+
"dataType" : "Int32",
|
| 30 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 31 |
+
"shortDescription" : "Number of frames for the new chunk",
|
| 32 |
+
"shape" : "[1]",
|
| 33 |
+
"name" : "chunk_pre_encoder_lengths",
|
| 34 |
+
"type" : "MultiArray"
|
| 35 |
+
}
|
| 36 |
+
],
|
| 37 |
+
"storagePrecision" : "Mixed (Float16, Float32)",
|
| 38 |
+
"modelParameters" : [
|
| 39 |
+
|
| 40 |
+
],
|
| 41 |
+
"author" : "Benjamin Lee",
|
| 42 |
+
"specificationVersion" : 7,
|
| 43 |
+
"license" : "MIT",
|
| 44 |
+
"mlProgramOperationTypeHistogram" : {
|
| 45 |
+
"Ios16.floorDiv" : 3,
|
| 46 |
+
"Transpose" : 193,
|
| 47 |
+
"Identity" : 2,
|
| 48 |
+
"Ios16.softmax" : 35,
|
| 49 |
+
"Ios16.gatherAlongAxis" : 1,
|
| 50 |
+
"Split" : 17,
|
| 51 |
+
"Ios16.linear" : 248,
|
| 52 |
+
"Ios16.add" : 186,
|
| 53 |
+
"Concat" : 1,
|
| 54 |
+
"Ios16.greaterEqual" : 2,
|
| 55 |
+
"Tile" : 9,
|
| 56 |
+
"Select" : 51,
|
| 57 |
+
"Ios16.minimum" : 1,
|
| 58 |
+
"Ios16.sigmoid" : 18,
|
| 59 |
+
"Ios16.logicalAnd" : 2,
|
| 60 |
+
"Pad" : 34,
|
| 61 |
+
"ExpandDims" : 25,
|
| 62 |
+
"Ios16.sub" : 6,
|
| 63 |
+
"Ios16.cast" : 16,
|
| 64 |
+
"Ios16.less" : 7,
|
| 65 |
+
"Ios16.conv" : 56,
|
| 66 |
+
"Ios16.relu" : 23,
|
| 67 |
+
"Ios16.reshape" : 175,
|
| 68 |
+
"Ios16.matmul" : 87,
|
| 69 |
+
"Ios16.maximum" : 1,
|
| 70 |
+
"Ios16.layerNorm" : 121,
|
| 71 |
+
"SliceByIndex" : 34,
|
| 72 |
+
"Ios16.silu" : 51,
|
| 73 |
+
"Ios16.mul" : 119,
|
| 74 |
+
"Ios16.logicalNot" : 2
|
| 75 |
+
},
|
| 76 |
+
"computePrecision" : "Mixed (Float16, Float32, Int32)",
|
| 77 |
+
"stateSchema" : [
|
| 78 |
+
|
| 79 |
+
],
|
| 80 |
+
"isUpdatable" : "0",
|
| 81 |
+
"availability" : {
|
| 82 |
+
"macOS" : "13.0",
|
| 83 |
+
"tvOS" : "16.0",
|
| 84 |
+
"visionOS" : "1.0",
|
| 85 |
+
"watchOS" : "9.0",
|
| 86 |
+
"iOS" : "16.0",
|
| 87 |
+
"macCatalyst" : "16.0"
|
| 88 |
+
},
|
| 89 |
+
"modelType" : {
|
| 90 |
+
"name" : "MLModelType_pipeline",
|
| 91 |
+
"structure" : [
|
| 92 |
+
{
|
| 93 |
+
"name" : "MLModelType_mlProgram"
|
| 94 |
+
},
|
| 95 |
+
{
|
| 96 |
+
"name" : "MLModelType_mlProgram"
|
| 97 |
+
}
|
| 98 |
+
]
|
| 99 |
+
},
|
| 100 |
+
"inputSchema" : [
|
| 101 |
+
{
|
| 102 |
+
"hasShapeFlexibility" : "0",
|
| 103 |
+
"isOptional" : "0",
|
| 104 |
+
"dataType" : "Float32",
|
| 105 |
+
"formattedType" : "MultiArray (Float32 1 × 112 × 128)",
|
| 106 |
+
"shortDescription" : "Mel spectrogram features for the new chunk",
|
| 107 |
+
"shape" : "[1, 112, 128]",
|
| 108 |
+
"name" : "chunk",
|
| 109 |
+
"type" : "MultiArray"
|
| 110 |
+
},
|
| 111 |
+
{
|
| 112 |
+
"hasShapeFlexibility" : "0",
|
| 113 |
+
"isOptional" : "0",
|
| 114 |
+
"dataType" : "Int32",
|
| 115 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 116 |
+
"shortDescription" : "Length of the new chunk",
|
| 117 |
+
"shape" : "[1]",
|
| 118 |
+
"name" : "chunk_lengths",
|
| 119 |
+
"type" : "MultiArray"
|
| 120 |
+
},
|
| 121 |
+
{
|
| 122 |
+
"hasShapeFlexibility" : "0",
|
| 123 |
+
"isOptional" : "0",
|
| 124 |
+
"dataType" : "Float32",
|
| 125 |
+
"formattedType" : "MultiArray (Float32 1 × 188 × 512)",
|
| 126 |
+
"shortDescription" : "Order of Arrival Speaker Cache",
|
| 127 |
+
"shape" : "[1, 188, 512]",
|
| 128 |
+
"name" : "spkcache",
|
| 129 |
+
"type" : "MultiArray"
|
| 130 |
+
},
|
| 131 |
+
{
|
| 132 |
+
"hasShapeFlexibility" : "0",
|
| 133 |
+
"isOptional" : "0",
|
| 134 |
+
"dataType" : "Int32",
|
| 135 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 136 |
+
"shortDescription" : "Length of the speaker cache (in frames)",
|
| 137 |
+
"shape" : "[1]",
|
| 138 |
+
"name" : "spkcache_lengths",
|
| 139 |
+
"type" : "MultiArray"
|
| 140 |
+
},
|
| 141 |
+
{
|
| 142 |
+
"hasShapeFlexibility" : "0",
|
| 143 |
+
"isOptional" : "0",
|
| 144 |
+
"dataType" : "Float32",
|
| 145 |
+
"formattedType" : "MultiArray (Float32 1 × 40 × 512)",
|
| 146 |
+
"shortDescription" : "First-In-First-Out speech queue",
|
| 147 |
+
"shape" : "[1, 40, 512]",
|
| 148 |
+
"name" : "fifo",
|
| 149 |
+
"type" : "MultiArray"
|
| 150 |
+
},
|
| 151 |
+
{
|
| 152 |
+
"hasShapeFlexibility" : "0",
|
| 153 |
+
"isOptional" : "0",
|
| 154 |
+
"dataType" : "Int32",
|
| 155 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 156 |
+
"shortDescription" : "Length of the FIFO queue (in frames)",
|
| 157 |
+
"shape" : "[1]",
|
| 158 |
+
"name" : "fifo_lengths",
|
| 159 |
+
"type" : "MultiArray"
|
| 160 |
+
}
|
| 161 |
+
],
|
| 162 |
+
"userDefinedMetadata" : {
|
| 163 |
+
"frame_duration" : "0.08",
|
| 164 |
+
"spkcache_update_period" : "31",
|
| 165 |
+
"chunk_len" : "6",
|
| 166 |
+
"subsampling_factor" : "8",
|
| 167 |
+
"chunk_right_context" : "7",
|
| 168 |
+
"mel_feature_frames" : "48",
|
| 169 |
+
"fifo_len" : "40",
|
| 170 |
+
"chunk_left_context" : "1",
|
| 171 |
+
"spkcache_len" : "188"
|
| 172 |
+
},
|
| 173 |
+
"generatedClassName" : "Sortformer",
|
| 174 |
+
"method" : "predict"
|
| 175 |
+
}
|
| 176 |
+
]
|
Sortformer.mlmodelc/model0/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
|
| 3 |
+
size 108
|
Sortformer.mlmodelc/model0/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:94cc859be2d5514e057506ea9bed05ac455fb72ebf05a52e4aeddf5dea80ceb1
|
| 3 |
+
size 632
|
Sortformer.mlmodelc/model0/model.mil
ADDED
|
@@ -0,0 +1,201 @@
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|
| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios16>(tensor<fp32, [1, 112, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
|
| 5 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 6 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(1152)))];
|
| 7 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10432)))];
|
| 8 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(11520)))];
|
| 9 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(20800)))];
|
| 10 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(21888)))];
|
| 11 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(284096)))];
|
| 12 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(285184)))];
|
| 13 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(294464)))];
|
| 14 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(295552)))];
|
| 15 |
+
tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(557760)))];
|
| 16 |
+
tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(559872)))];
|
| 17 |
+
tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
|
| 18 |
+
tensor<fp32, [1, 1, 112, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
|
| 19 |
+
tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 20 |
+
tensor<int32, [1, 112]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 112]>([[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]])];
|
| 21 |
+
tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
|
| 22 |
+
tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
|
| 23 |
+
tensor<bool, [1, 112]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
|
| 24 |
+
tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 25 |
+
tensor<bool, [1, 112, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
|
| 26 |
+
tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
|
| 27 |
+
tensor<bool, [1, 112, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
|
| 28 |
+
tensor<string, []> mask_1_dtype_0 = const()[name = tensor<string, []>("mask_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 29 |
+
tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
|
| 30 |
+
tensor<fp32, [1, 112, 128]> mask_1 = cast(dtype = mask_1_dtype_0, x = var_44)[name = tensor<string, []>("cast_11")];
|
| 31 |
+
tensor<fp32, [1, 1, 112, 128]> var_50 = expand_dims(axes = var_50_axes_0, x = mask_1)[name = tensor<string, []>("op_50")];
|
| 32 |
+
tensor<fp32, [1, 1, 112, 128]> input_1 = mul(x = tensor_1, y = var_50)[name = tensor<string, []>("input_1")];
|
| 33 |
+
tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
|
| 34 |
+
tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 35 |
+
tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 36 |
+
tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 37 |
+
tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
|
| 38 |
+
tensor<fp32, [1, 256, 56, 64]> tensor_3 = conv(bias = model_encoder_pre_encode_conv_0_bias, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = model_encoder_pre_encode_conv_0_weight, x = input_1)[name = tensor<string, []>("tensor_3")];
|
| 39 |
+
tensor<fp32, []> var_61_promoted = const()[name = tensor<string, []>("op_61_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 40 |
+
tensor<fp32, [1]> current_lengths_1 = cast(dtype = current_lengths_1_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_12")];
|
| 41 |
+
tensor<fp32, [1]> var_62 = add(x = current_lengths_1, y = var_61_promoted)[name = tensor<string, []>("op_62")];
|
| 42 |
+
tensor<fp32, []> var_63_promoted = const()[name = tensor<string, []>("op_63_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 43 |
+
tensor<fp32, [1]> var_64 = add(x = var_62, y = var_63_promoted)[name = tensor<string, []>("op_64")];
|
| 44 |
+
tensor<fp32, []> var_65_promoted = const()[name = tensor<string, []>("op_65_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 45 |
+
tensor<fp32, [1]> var_66 = sub(x = var_64, y = var_65_promoted)[name = tensor<string, []>("op_66")];
|
| 46 |
+
tensor<fp32, []> var_21_promoted = const()[name = tensor<string, []>("op_21_promoted"), val = tensor<fp32, []>(0x1p+1)];
|
| 47 |
+
tensor<fp32, [1]> floor_div_0 = floor_div(x = var_66, y = var_21_promoted)[name = tensor<string, []>("floor_div_0")];
|
| 48 |
+
tensor<fp32, []> var_68_promoted = const()[name = tensor<string, []>("op_68_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 49 |
+
tensor<fp32, [1]> current_lengths_3 = add(x = floor_div_0, y = var_68_promoted)[name = tensor<string, []>("current_lengths_3")];
|
| 50 |
+
tensor<string, []> lengths_21_dtype_0 = const()[name = tensor<string, []>("lengths_21_dtype_0"), val = tensor<string, []>("int32")];
|
| 51 |
+
tensor<int32, [1, 56]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 56]>([[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]])];
|
| 52 |
+
tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
|
| 53 |
+
tensor<int32, [1]> lengths_21 = cast(dtype = lengths_21_dtype_0, x = current_lengths_3)[name = tensor<string, []>("cast_10")];
|
| 54 |
+
tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = lengths_21)[name = tensor<string, []>("op_77")];
|
| 55 |
+
tensor<bool, [1, 56]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
|
| 56 |
+
tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 57 |
+
tensor<bool, [1, 56, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
|
| 58 |
+
tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
|
| 59 |
+
tensor<bool, [1, 56, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
|
| 60 |
+
tensor<string, []> mask_3_dtype_0 = const()[name = tensor<string, []>("mask_3_dtype_0"), val = tensor<string, []>("fp32")];
|
| 61 |
+
tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
|
| 62 |
+
tensor<fp32, [1, 56, 64]> mask_3 = cast(dtype = mask_3_dtype_0, x = var_81)[name = tensor<string, []>("cast_9")];
|
| 63 |
+
tensor<fp32, [1, 1, 56, 64]> var_87 = expand_dims(axes = var_87_axes_0, x = mask_3)[name = tensor<string, []>("op_87")];
|
| 64 |
+
tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 65 |
+
tensor<fp32, [1, 256, 56, 64]> expanded_mask_3 = tile(reps = expanded_mask_3_reps_0, x = var_87)[name = tensor<string, []>("expanded_mask_3")];
|
| 66 |
+
tensor<fp32, [1, 256, 56, 64]> input_3 = mul(x = tensor_3, y = expanded_mask_3)[name = tensor<string, []>("input_3")];
|
| 67 |
+
tensor<fp32, [1, 256, 56, 64]> tensor_5 = relu(x = input_3)[name = tensor<string, []>("tensor_5")];
|
| 68 |
+
tensor<fp32, [1, 256, 56, 64]> input_5 = mul(x = tensor_5, y = expanded_mask_3)[name = tensor<string, []>("input_5")];
|
| 69 |
+
tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
|
| 70 |
+
tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 71 |
+
tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 72 |
+
tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
|
| 73 |
+
tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 74 |
+
tensor<fp32, [1, 256, 28, 32]> tensor_7 = conv(bias = model_encoder_pre_encode_conv_2_bias, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = model_encoder_pre_encode_conv_2_weight, x = input_5)[name = tensor<string, []>("tensor_7")];
|
| 75 |
+
tensor<fp32, []> var_107_promoted = const()[name = tensor<string, []>("op_107_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 76 |
+
tensor<fp32, [1]> var_108 = add(x = current_lengths_3, y = var_107_promoted)[name = tensor<string, []>("op_108")];
|
| 77 |
+
tensor<fp32, []> var_109_promoted = const()[name = tensor<string, []>("op_109_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 78 |
+
tensor<fp32, [1]> var_110 = add(x = var_108, y = var_109_promoted)[name = tensor<string, []>("op_110")];
|
| 79 |
+
tensor<fp32, []> var_111_promoted = const()[name = tensor<string, []>("op_111_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 80 |
+
tensor<fp32, [1]> var_112 = sub(x = var_110, y = var_111_promoted)[name = tensor<string, []>("op_112")];
|
| 81 |
+
tensor<fp32, []> var_21_promoted_1 = const()[name = tensor<string, []>("op_21_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
|
| 82 |
+
tensor<fp32, [1]> floor_div_1 = floor_div(x = var_112, y = var_21_promoted_1)[name = tensor<string, []>("floor_div_1")];
|
| 83 |
+
tensor<fp32, []> var_114_promoted = const()[name = tensor<string, []>("op_114_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 84 |
+
tensor<fp32, [1]> current_lengths_5 = add(x = floor_div_1, y = var_114_promoted)[name = tensor<string, []>("current_lengths_5")];
|
| 85 |
+
tensor<string, []> lengths_23_dtype_0 = const()[name = tensor<string, []>("lengths_23_dtype_0"), val = tensor<string, []>("int32")];
|
| 86 |
+
tensor<int32, [1, 28]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 28]>([[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]])];
|
| 87 |
+
tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
|
| 88 |
+
tensor<int32, [1]> lengths_23 = cast(dtype = lengths_23_dtype_0, x = current_lengths_5)[name = tensor<string, []>("cast_8")];
|
| 89 |
+
tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = lengths_23)[name = tensor<string, []>("op_123")];
|
| 90 |
+
tensor<bool, [1, 28]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
|
| 91 |
+
tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 92 |
+
tensor<bool, [1, 28, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
|
| 93 |
+
tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
|
| 94 |
+
tensor<bool, [1, 28, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
|
| 95 |
+
tensor<string, []> mask_5_dtype_0 = const()[name = tensor<string, []>("mask_5_dtype_0"), val = tensor<string, []>("fp32")];
|
| 96 |
+
tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
|
| 97 |
+
tensor<fp32, [1, 28, 32]> mask_5 = cast(dtype = mask_5_dtype_0, x = var_127)[name = tensor<string, []>("cast_7")];
|
| 98 |
+
tensor<fp32, [1, 1, 28, 32]> var_133 = expand_dims(axes = var_133_axes_0, x = mask_5)[name = tensor<string, []>("op_133")];
|
| 99 |
+
tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 100 |
+
tensor<fp32, [1, 256, 28, 32]> expanded_mask_7 = tile(reps = expanded_mask_7_reps_0, x = var_133)[name = tensor<string, []>("expanded_mask_7")];
|
| 101 |
+
tensor<fp32, [1, 256, 28, 32]> input_7 = mul(x = tensor_7, y = expanded_mask_7)[name = tensor<string, []>("input_7")];
|
| 102 |
+
tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 103 |
+
tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 104 |
+
tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 105 |
+
tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 106 |
+
tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
|
| 107 |
+
tensor<fp32, [1, 256, 28, 32]> tensor_9 = conv(bias = model_encoder_pre_encode_conv_3_bias, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = model_encoder_pre_encode_conv_3_weight, x = input_7)[name = tensor<string, []>("tensor_9")];
|
| 108 |
+
tensor<fp32, [1, 256, 28, 32]> input_9 = mul(x = tensor_9, y = expanded_mask_7)[name = tensor<string, []>("input_9")];
|
| 109 |
+
tensor<fp32, [1, 256, 28, 32]> tensor_11 = relu(x = input_9)[name = tensor<string, []>("tensor_11")];
|
| 110 |
+
tensor<fp32, [1, 256, 28, 32]> input_11 = mul(x = tensor_11, y = expanded_mask_7)[name = tensor<string, []>("input_11")];
|
| 111 |
+
tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
|
| 112 |
+
tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 113 |
+
tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 114 |
+
tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
|
| 115 |
+
tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 116 |
+
tensor<fp32, [1, 256, 14, 16]> tensor_13 = conv(bias = model_encoder_pre_encode_conv_5_bias, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = model_encoder_pre_encode_conv_5_weight, x = input_11)[name = tensor<string, []>("tensor_13")];
|
| 117 |
+
tensor<fp32, []> var_168_promoted = const()[name = tensor<string, []>("op_168_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 118 |
+
tensor<fp32, [1]> var_169 = add(x = current_lengths_5, y = var_168_promoted)[name = tensor<string, []>("op_169")];
|
| 119 |
+
tensor<fp32, []> var_170_promoted = const()[name = tensor<string, []>("op_170_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 120 |
+
tensor<fp32, [1]> var_171 = add(x = var_169, y = var_170_promoted)[name = tensor<string, []>("op_171")];
|
| 121 |
+
tensor<fp32, []> var_172_promoted = const()[name = tensor<string, []>("op_172_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 122 |
+
tensor<fp32, [1]> var_173 = sub(x = var_171, y = var_172_promoted)[name = tensor<string, []>("op_173")];
|
| 123 |
+
tensor<fp32, []> var_21_promoted_2 = const()[name = tensor<string, []>("op_21_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
|
| 124 |
+
tensor<fp32, [1]> floor_div_2 = floor_div(x = var_173, y = var_21_promoted_2)[name = tensor<string, []>("floor_div_2")];
|
| 125 |
+
tensor<fp32, []> var_175_promoted = const()[name = tensor<string, []>("op_175_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 126 |
+
tensor<fp32, [1]> current_lengths = add(x = floor_div_2, y = var_175_promoted)[name = tensor<string, []>("current_lengths")];
|
| 127 |
+
tensor<string, []> lengths_dtype_0 = const()[name = tensor<string, []>("lengths_dtype_0"), val = tensor<string, []>("int32")];
|
| 128 |
+
tensor<int32, [1, 14]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 14]>([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]])];
|
| 129 |
+
tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
|
| 130 |
+
tensor<int32, [1]> lengths = cast(dtype = lengths_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_6")];
|
| 131 |
+
tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = lengths)[name = tensor<string, []>("op_184")];
|
| 132 |
+
tensor<bool, [1, 14]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
|
| 133 |
+
tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 134 |
+
tensor<bool, [1, 14, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
|
| 135 |
+
tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
|
| 136 |
+
tensor<bool, [1, 14, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
|
| 137 |
+
tensor<string, []> mask_dtype_0 = const()[name = tensor<string, []>("mask_dtype_0"), val = tensor<string, []>("fp32")];
|
| 138 |
+
tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
|
| 139 |
+
tensor<fp32, [1, 14, 16]> mask = cast(dtype = mask_dtype_0, x = var_188)[name = tensor<string, []>("cast_5")];
|
| 140 |
+
tensor<fp32, [1, 1, 14, 16]> var_194 = expand_dims(axes = var_194_axes_0, x = mask)[name = tensor<string, []>("op_194")];
|
| 141 |
+
tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 142 |
+
tensor<fp32, [1, 256, 14, 16]> expanded_mask_13 = tile(reps = expanded_mask_13_reps_0, x = var_194)[name = tensor<string, []>("expanded_mask_13")];
|
| 143 |
+
tensor<fp32, [1, 256, 14, 16]> input_13 = mul(x = tensor_13, y = expanded_mask_13)[name = tensor<string, []>("input_13")];
|
| 144 |
+
tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
|
| 145 |
+
tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 146 |
+
tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 147 |
+
tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 148 |
+
tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
|
| 149 |
+
tensor<fp32, [1, 256, 14, 16]> tensor_15 = conv(bias = model_encoder_pre_encode_conv_6_bias, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = model_encoder_pre_encode_conv_6_weight, x = input_13)[name = tensor<string, []>("tensor_15")];
|
| 150 |
+
tensor<fp32, [1, 256, 14, 16]> input_15 = mul(x = tensor_15, y = expanded_mask_13)[name = tensor<string, []>("input_15")];
|
| 151 |
+
tensor<fp32, [1, 256, 14, 16]> tensor_workaround = relu(x = input_15)[name = tensor<string, []>("tensor_workaround")];
|
| 152 |
+
tensor<fp32, [1, 256, 14, 16]> x = mul(x = tensor_workaround, y = expanded_mask_13)[name = tensor<string, []>("x")];
|
| 153 |
+
tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
|
| 154 |
+
tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 14, -1])];
|
| 155 |
+
tensor<fp32, [1, 14, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
|
| 156 |
+
tensor<fp32, [1, 14, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
|
| 157 |
+
tensor<fp32, [1, 14, 512]> chunk_pre_encoder_embs = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
|
| 158 |
+
tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
|
| 159 |
+
tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
|
| 160 |
+
tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
|
| 161 |
+
tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
|
| 162 |
+
tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
|
| 163 |
+
tensor<fp32, [1, 242, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full_concat")];
|
| 164 |
+
tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
|
| 165 |
+
tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
|
| 166 |
+
tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
|
| 167 |
+
tensor<int32, [242]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [242]>([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])];
|
| 168 |
+
tensor<bool, [242]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
|
| 169 |
+
tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
|
| 170 |
+
tensor<bool, [242]> var_290 = greater_equal(x = out_pos, y = var_273)[name = tensor<string, []>("op_290")];
|
| 171 |
+
tensor<string, []> in_seg2_dtype_0 = const()[name = tensor<string, []>("in_seg2_dtype_0"), val = tensor<string, []>("int32")];
|
| 172 |
+
tensor<int32, [1]> var_297 = sub(x = size0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
|
| 173 |
+
tensor<int32, [242]> in_seg1_or_2 = cast(dtype = in_seg1_or_2_dtype_0, x = var_284)[name = tensor<string, []>("cast_3")];
|
| 174 |
+
tensor<int32, [242]> var_298 = mul(x = in_seg1_or_2, y = var_297)[name = tensor<string, []>("op_298")];
|
| 175 |
+
tensor<int32, [1]> var_300 = sub(x = size1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
|
| 176 |
+
tensor<int32, [242]> in_seg2 = cast(dtype = in_seg2_dtype_0, x = var_290)[name = tensor<string, []>("cast_2")];
|
| 177 |
+
tensor<int32, [242]> var_301 = mul(x = in_seg2, y = var_300)[name = tensor<string, []>("op_301")];
|
| 178 |
+
tensor<int32, [242]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
|
| 179 |
+
tensor<int32, [242]> var_305 = add(x = out_pos, y = offset)[name = tensor<string, []>("op_305")];
|
| 180 |
+
tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(241)];
|
| 181 |
+
tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
|
| 182 |
+
tensor<int32, [242]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
|
| 183 |
+
tensor<int32, [242]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
|
| 184 |
+
tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
|
| 185 |
+
tensor<int32, [1, 242]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
|
| 186 |
+
tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 187 |
+
tensor<int32, [1, 242, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
|
| 188 |
+
tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
|
| 189 |
+
tensor<int32, [1, 242, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
|
| 190 |
+
tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
|
| 191 |
+
tensor<fp32, [1, 242, 512]> output = gather_along_axis(axis = var_320, indices = gather_idx, x = full_concat)[name = tensor<string, []>("output")];
|
| 192 |
+
tensor<bool, [242]> var_323 = less(x = out_pos, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
|
| 193 |
+
tensor<string, []> var_328_dtype_0 = const()[name = tensor<string, []>("op_328_dtype_0"), val = tensor<string, []>("fp32")];
|
| 194 |
+
tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
|
| 195 |
+
tensor<fp32, [242]> var_328 = cast(dtype = var_328_dtype_0, x = var_323)[name = tensor<string, []>("cast_1")];
|
| 196 |
+
tensor<fp32, [1, 242]> var_330 = expand_dims(axes = var_330_axes_0, x = var_328)[name = tensor<string, []>("op_330")];
|
| 197 |
+
tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 198 |
+
tensor<fp32, [1, 242, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
|
| 199 |
+
tensor<fp32, [1, 242, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
|
| 200 |
+
} -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
|
| 201 |
+
}
|
Sortformer.mlmodelc/model0/weights/0-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:88a98803e35186b1dfb41d7f748f7cee5093bb6efeb117f56953c17549792fa4
|
| 3 |
+
size 8948544
|
Sortformer.mlmodelc/model1/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
|
| 3 |
+
size 108
|
Sortformer.mlmodelc/model1/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e613306cf96ba390ac0ebb449947c8b4dc344a6a9e3be69b8220ad1caa74056b
|
| 3 |
+
size 585
|
Sortformer.mlmodelc/model1/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Sortformer.mlmodelc/model1/weights/1-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c85926af77684bce762b355a2b162df557d832444fbeb79ee195113a4bbf1db
|
| 3 |
+
size 230428224
|
Sortformer.mlpackage/Data/com.apple.CoreML/model.mlmodel
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d4f5a6e1ce4615929239b6a59a4a91378483c95335e468d02bf57ff2d3e615e2
|
| 3 |
+
size 762968
|
Sortformer.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:88a98803e35186b1dfb41d7f748f7cee5093bb6efeb117f56953c17549792fa4
|
| 3 |
+
size 8948544
|
Sortformer.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4c85926af77684bce762b355a2b162df557d832444fbeb79ee195113a4bbf1db
|
| 3 |
+
size 230428224
|
Sortformer.mlpackage/Manifest.json
ADDED
|
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"fileFormatVersion": "1.0.0",
|
| 3 |
+
"itemInfoEntries": {
|
| 4 |
+
"91957EE3-C4C0-4E0F-95BC-72E14851707C": {
|
| 5 |
+
"author": "com.apple.CoreML",
|
| 6 |
+
"description": "CoreML Model Weights",
|
| 7 |
+
"name": "weights",
|
| 8 |
+
"path": "com.apple.CoreML/weights"
|
| 9 |
+
},
|
| 10 |
+
"FC8642BA-1EC2-4646-874C-1A4912A3591C": {
|
| 11 |
+
"author": "com.apple.CoreML",
|
| 12 |
+
"description": "CoreML Model Specification",
|
| 13 |
+
"name": "model.mlmodel",
|
| 14 |
+
"path": "com.apple.CoreML/model.mlmodel"
|
| 15 |
+
}
|
| 16 |
+
},
|
| 17 |
+
"rootModelIdentifier": "FC8642BA-1EC2-4646-874C-1A4912A3591C"
|
| 18 |
+
}
|
SortformerNvidiaHigh.mlmodelc/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9f122bb10c4ec1add16a3440c5dd497b96b77e3ac9863183a06f78a18773e53b
|
| 3 |
+
size 202
|
SortformerNvidiaHigh.mlmodelc/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:6b04dcb554e85010612799da011422017ff4db0bba30b755296dac6ed4821799
|
| 3 |
+
size 1091
|
SortformerNvidiaHigh.mlmodelc/metadata.json
ADDED
|
@@ -0,0 +1,177 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[
|
| 2 |
+
{
|
| 3 |
+
"shortDescription" : "CoreML port of Nvidia's Streaming Sortformer diarization model",
|
| 4 |
+
"metadataOutputVersion" : "3.0",
|
| 5 |
+
"outputSchema" : [
|
| 6 |
+
{
|
| 7 |
+
"hasShapeFlexibility" : "0",
|
| 8 |
+
"isOptional" : "0",
|
| 9 |
+
"dataType" : "Float32",
|
| 10 |
+
"formattedType" : "MultiArray (Float32 1 × 609 × 4)",
|
| 11 |
+
"shortDescription" : "Combined speaker probabilities for the speaker cache, FIFO queue, and chunk",
|
| 12 |
+
"shape" : "[1, 609, 4]",
|
| 13 |
+
"name" : "speaker_preds",
|
| 14 |
+
"type" : "MultiArray"
|
| 15 |
+
},
|
| 16 |
+
{
|
| 17 |
+
"hasShapeFlexibility" : "0",
|
| 18 |
+
"isOptional" : "0",
|
| 19 |
+
"dataType" : "Float32",
|
| 20 |
+
"formattedType" : "MultiArray (Float32 1 × 381 × 512)",
|
| 21 |
+
"shortDescription" : "Speaker embeddings for the new chunk",
|
| 22 |
+
"shape" : "[1, 381, 512]",
|
| 23 |
+
"name" : "chunk_pre_encoder_embs",
|
| 24 |
+
"type" : "MultiArray"
|
| 25 |
+
},
|
| 26 |
+
{
|
| 27 |
+
"hasShapeFlexibility" : "0",
|
| 28 |
+
"isOptional" : "0",
|
| 29 |
+
"dataType" : "Int32",
|
| 30 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 31 |
+
"shortDescription" : "Number of frames for the new chunk",
|
| 32 |
+
"shape" : "[1]",
|
| 33 |
+
"name" : "chunk_pre_encoder_lengths",
|
| 34 |
+
"type" : "MultiArray"
|
| 35 |
+
}
|
| 36 |
+
],
|
| 37 |
+
"version" : "2.1",
|
| 38 |
+
"modelParameters" : [
|
| 39 |
+
|
| 40 |
+
],
|
| 41 |
+
"author" : "Benjamin Lee",
|
| 42 |
+
"specificationVersion" : 7,
|
| 43 |
+
"storagePrecision" : "Mixed (Float16, Float32)",
|
| 44 |
+
"license" : "MIT",
|
| 45 |
+
"mlProgramOperationTypeHistogram" : {
|
| 46 |
+
"Ios16.floorDiv" : 3,
|
| 47 |
+
"Transpose" : 193,
|
| 48 |
+
"Identity" : 2,
|
| 49 |
+
"Ios16.softmax" : 35,
|
| 50 |
+
"Ios16.gatherAlongAxis" : 1,
|
| 51 |
+
"Split" : 17,
|
| 52 |
+
"Ios16.linear" : 248,
|
| 53 |
+
"Ios16.add" : 186,
|
| 54 |
+
"Concat" : 1,
|
| 55 |
+
"Ios16.greaterEqual" : 2,
|
| 56 |
+
"Tile" : 9,
|
| 57 |
+
"Select" : 51,
|
| 58 |
+
"Ios16.minimum" : 1,
|
| 59 |
+
"Ios16.sigmoid" : 18,
|
| 60 |
+
"Ios16.logicalAnd" : 2,
|
| 61 |
+
"Pad" : 34,
|
| 62 |
+
"ExpandDims" : 25,
|
| 63 |
+
"Ios16.sub" : 6,
|
| 64 |
+
"Ios16.cast" : 16,
|
| 65 |
+
"Ios16.less" : 7,
|
| 66 |
+
"Ios16.conv" : 56,
|
| 67 |
+
"Ios16.relu" : 23,
|
| 68 |
+
"Ios16.reshape" : 175,
|
| 69 |
+
"Ios16.matmul" : 87,
|
| 70 |
+
"Ios16.maximum" : 1,
|
| 71 |
+
"Ios16.layerNorm" : 121,
|
| 72 |
+
"SliceByIndex" : 34,
|
| 73 |
+
"Ios16.silu" : 51,
|
| 74 |
+
"Ios16.mul" : 119,
|
| 75 |
+
"Ios16.logicalNot" : 2
|
| 76 |
+
},
|
| 77 |
+
"computePrecision" : "Mixed (Float16, Float32, Int32)",
|
| 78 |
+
"stateSchema" : [
|
| 79 |
+
|
| 80 |
+
],
|
| 81 |
+
"isUpdatable" : "0",
|
| 82 |
+
"availability" : {
|
| 83 |
+
"macOS" : "13.0",
|
| 84 |
+
"tvOS" : "16.0",
|
| 85 |
+
"visionOS" : "1.0",
|
| 86 |
+
"watchOS" : "9.0",
|
| 87 |
+
"iOS" : "16.0",
|
| 88 |
+
"macCatalyst" : "16.0"
|
| 89 |
+
},
|
| 90 |
+
"modelType" : {
|
| 91 |
+
"name" : "MLModelType_pipeline",
|
| 92 |
+
"structure" : [
|
| 93 |
+
{
|
| 94 |
+
"name" : "MLModelType_mlProgram"
|
| 95 |
+
},
|
| 96 |
+
{
|
| 97 |
+
"name" : "MLModelType_mlProgram"
|
| 98 |
+
}
|
| 99 |
+
]
|
| 100 |
+
},
|
| 101 |
+
"inputSchema" : [
|
| 102 |
+
{
|
| 103 |
+
"hasShapeFlexibility" : "0",
|
| 104 |
+
"isOptional" : "0",
|
| 105 |
+
"dataType" : "Float32",
|
| 106 |
+
"formattedType" : "MultiArray (Float32 1 × 3048 × 128)",
|
| 107 |
+
"shortDescription" : "Mel spectrogram features for the new chunk",
|
| 108 |
+
"shape" : "[1, 3048, 128]",
|
| 109 |
+
"name" : "chunk",
|
| 110 |
+
"type" : "MultiArray"
|
| 111 |
+
},
|
| 112 |
+
{
|
| 113 |
+
"hasShapeFlexibility" : "0",
|
| 114 |
+
"isOptional" : "0",
|
| 115 |
+
"dataType" : "Int32",
|
| 116 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 117 |
+
"shortDescription" : "Length of the new chunk",
|
| 118 |
+
"shape" : "[1]",
|
| 119 |
+
"name" : "chunk_lengths",
|
| 120 |
+
"type" : "MultiArray"
|
| 121 |
+
},
|
| 122 |
+
{
|
| 123 |
+
"hasShapeFlexibility" : "0",
|
| 124 |
+
"isOptional" : "0",
|
| 125 |
+
"dataType" : "Float32",
|
| 126 |
+
"formattedType" : "MultiArray (Float32 1 × 188 × 512)",
|
| 127 |
+
"shortDescription" : "Order of Arrival Speaker Cache",
|
| 128 |
+
"shape" : "[1, 188, 512]",
|
| 129 |
+
"name" : "spkcache",
|
| 130 |
+
"type" : "MultiArray"
|
| 131 |
+
},
|
| 132 |
+
{
|
| 133 |
+
"hasShapeFlexibility" : "0",
|
| 134 |
+
"isOptional" : "0",
|
| 135 |
+
"dataType" : "Int32",
|
| 136 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 137 |
+
"shortDescription" : "Length of the speaker cache (in frames)",
|
| 138 |
+
"shape" : "[1]",
|
| 139 |
+
"name" : "spkcache_lengths",
|
| 140 |
+
"type" : "MultiArray"
|
| 141 |
+
},
|
| 142 |
+
{
|
| 143 |
+
"hasShapeFlexibility" : "0",
|
| 144 |
+
"isOptional" : "0",
|
| 145 |
+
"dataType" : "Float32",
|
| 146 |
+
"formattedType" : "MultiArray (Float32 1 × 40 × 512)",
|
| 147 |
+
"shortDescription" : "First-In-First-Out speech queue",
|
| 148 |
+
"shape" : "[1, 40, 512]",
|
| 149 |
+
"name" : "fifo",
|
| 150 |
+
"type" : "MultiArray"
|
| 151 |
+
},
|
| 152 |
+
{
|
| 153 |
+
"hasShapeFlexibility" : "0",
|
| 154 |
+
"isOptional" : "0",
|
| 155 |
+
"dataType" : "Int32",
|
| 156 |
+
"formattedType" : "MultiArray (Int32 1)",
|
| 157 |
+
"shortDescription" : "Length of the FIFO queue (in frames)",
|
| 158 |
+
"shape" : "[1]",
|
| 159 |
+
"name" : "fifo_lengths",
|
| 160 |
+
"type" : "MultiArray"
|
| 161 |
+
}
|
| 162 |
+
],
|
| 163 |
+
"userDefinedMetadata" : {
|
| 164 |
+
"frame_duration" : "0.08",
|
| 165 |
+
"spkcache_update_period" : "300",
|
| 166 |
+
"chunk_len" : "340",
|
| 167 |
+
"mel_feature_frames" : "2720",
|
| 168 |
+
"chunk_right_context" : "40",
|
| 169 |
+
"subsampling_factor" : "8",
|
| 170 |
+
"fifo_len" : "40",
|
| 171 |
+
"chunk_left_context" : "1",
|
| 172 |
+
"spkcache_len" : "188"
|
| 173 |
+
},
|
| 174 |
+
"generatedClassName" : "SortformerNvidiaHigh",
|
| 175 |
+
"method" : "predict"
|
| 176 |
+
}
|
| 177 |
+
]
|
SortformerNvidiaHigh.mlmodelc/model0/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
|
| 3 |
+
size 108
|
SortformerNvidiaHigh.mlmodelc/model0/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:330c631d7b8d6100ea7cb1f93616512b083fef39ca4ffb6c8f9e6fba756f4fcd
|
| 3 |
+
size 634
|
SortformerNvidiaHigh.mlmodelc/model0/model.mil
ADDED
|
@@ -0,0 +1,201 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.9.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios16>(tensor<fp32, [1, 3048, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
|
| 5 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 6 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(1152)))];
|
| 7 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10432)))];
|
| 8 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(11520)))];
|
| 9 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(20800)))];
|
| 10 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(21888)))];
|
| 11 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(284096)))];
|
| 12 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(285184)))];
|
| 13 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(294464)))];
|
| 14 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(295552)))];
|
| 15 |
+
tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(557760)))];
|
| 16 |
+
tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(559872)))];
|
| 17 |
+
tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
|
| 18 |
+
tensor<fp32, [1, 1, 3048, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
|
| 19 |
+
tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 20 |
+
tensor<int32, [1, 3048]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 3048]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2250, 2251, 2252, 2253, 2254, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299, 2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047]])];
|
| 21 |
+
tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
|
| 22 |
+
tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
|
| 23 |
+
tensor<bool, [1, 3048]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
|
| 24 |
+
tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 25 |
+
tensor<bool, [1, 3048, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
|
| 26 |
+
tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
|
| 27 |
+
tensor<bool, [1, 3048, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
|
| 28 |
+
tensor<string, []> mask_1_dtype_0 = const()[name = tensor<string, []>("mask_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 29 |
+
tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
|
| 30 |
+
tensor<fp32, [1, 3048, 128]> mask_1 = cast(dtype = mask_1_dtype_0, x = var_44)[name = tensor<string, []>("cast_11")];
|
| 31 |
+
tensor<fp32, [1, 1, 3048, 128]> var_50 = expand_dims(axes = var_50_axes_0, x = mask_1)[name = tensor<string, []>("op_50")];
|
| 32 |
+
tensor<fp32, [1, 1, 3048, 128]> input_1 = mul(x = tensor_1, y = var_50)[name = tensor<string, []>("input_1")];
|
| 33 |
+
tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
|
| 34 |
+
tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 35 |
+
tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 36 |
+
tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 37 |
+
tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
|
| 38 |
+
tensor<fp32, [1, 256, 1524, 64]> tensor_3 = conv(bias = model_encoder_pre_encode_conv_0_bias, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = model_encoder_pre_encode_conv_0_weight, x = input_1)[name = tensor<string, []>("tensor_3")];
|
| 39 |
+
tensor<fp32, []> var_61_promoted = const()[name = tensor<string, []>("op_61_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 40 |
+
tensor<fp32, [1]> current_lengths_1 = cast(dtype = current_lengths_1_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_12")];
|
| 41 |
+
tensor<fp32, [1]> var_62 = add(x = current_lengths_1, y = var_61_promoted)[name = tensor<string, []>("op_62")];
|
| 42 |
+
tensor<fp32, []> var_63_promoted = const()[name = tensor<string, []>("op_63_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 43 |
+
tensor<fp32, [1]> var_64 = add(x = var_62, y = var_63_promoted)[name = tensor<string, []>("op_64")];
|
| 44 |
+
tensor<fp32, []> var_65_promoted = const()[name = tensor<string, []>("op_65_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 45 |
+
tensor<fp32, [1]> var_66 = sub(x = var_64, y = var_65_promoted)[name = tensor<string, []>("op_66")];
|
| 46 |
+
tensor<fp32, []> var_21_promoted = const()[name = tensor<string, []>("op_21_promoted"), val = tensor<fp32, []>(0x1p+1)];
|
| 47 |
+
tensor<fp32, [1]> floor_div_0 = floor_div(x = var_66, y = var_21_promoted)[name = tensor<string, []>("floor_div_0")];
|
| 48 |
+
tensor<fp32, []> var_68_promoted = const()[name = tensor<string, []>("op_68_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 49 |
+
tensor<fp32, [1]> current_lengths_3 = add(x = floor_div_0, y = var_68_promoted)[name = tensor<string, []>("current_lengths_3")];
|
| 50 |
+
tensor<string, []> lengths_21_dtype_0 = const()[name = tensor<string, []>("lengths_21_dtype_0"), val = tensor<string, []>("int32")];
|
| 51 |
+
tensor<int32, [1, 1524]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 1524]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523]])];
|
| 52 |
+
tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
|
| 53 |
+
tensor<int32, [1]> lengths_21 = cast(dtype = lengths_21_dtype_0, x = current_lengths_3)[name = tensor<string, []>("cast_10")];
|
| 54 |
+
tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = lengths_21)[name = tensor<string, []>("op_77")];
|
| 55 |
+
tensor<bool, [1, 1524]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
|
| 56 |
+
tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 57 |
+
tensor<bool, [1, 1524, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
|
| 58 |
+
tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
|
| 59 |
+
tensor<bool, [1, 1524, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
|
| 60 |
+
tensor<string, []> mask_3_dtype_0 = const()[name = tensor<string, []>("mask_3_dtype_0"), val = tensor<string, []>("fp32")];
|
| 61 |
+
tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
|
| 62 |
+
tensor<fp32, [1, 1524, 64]> mask_3 = cast(dtype = mask_3_dtype_0, x = var_81)[name = tensor<string, []>("cast_9")];
|
| 63 |
+
tensor<fp32, [1, 1, 1524, 64]> var_87 = expand_dims(axes = var_87_axes_0, x = mask_3)[name = tensor<string, []>("op_87")];
|
| 64 |
+
tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 65 |
+
tensor<fp32, [1, 256, 1524, 64]> expanded_mask_3 = tile(reps = expanded_mask_3_reps_0, x = var_87)[name = tensor<string, []>("expanded_mask_3")];
|
| 66 |
+
tensor<fp32, [1, 256, 1524, 64]> input_3 = mul(x = tensor_3, y = expanded_mask_3)[name = tensor<string, []>("input_3")];
|
| 67 |
+
tensor<fp32, [1, 256, 1524, 64]> tensor_5 = relu(x = input_3)[name = tensor<string, []>("tensor_5")];
|
| 68 |
+
tensor<fp32, [1, 256, 1524, 64]> input_5 = mul(x = tensor_5, y = expanded_mask_3)[name = tensor<string, []>("input_5")];
|
| 69 |
+
tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
|
| 70 |
+
tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 71 |
+
tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 72 |
+
tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
|
| 73 |
+
tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 74 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_7 = conv(bias = model_encoder_pre_encode_conv_2_bias, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = model_encoder_pre_encode_conv_2_weight, x = input_5)[name = tensor<string, []>("tensor_7")];
|
| 75 |
+
tensor<fp32, []> var_107_promoted = const()[name = tensor<string, []>("op_107_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 76 |
+
tensor<fp32, [1]> var_108 = add(x = current_lengths_3, y = var_107_promoted)[name = tensor<string, []>("op_108")];
|
| 77 |
+
tensor<fp32, []> var_109_promoted = const()[name = tensor<string, []>("op_109_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 78 |
+
tensor<fp32, [1]> var_110 = add(x = var_108, y = var_109_promoted)[name = tensor<string, []>("op_110")];
|
| 79 |
+
tensor<fp32, []> var_111_promoted = const()[name = tensor<string, []>("op_111_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 80 |
+
tensor<fp32, [1]> var_112 = sub(x = var_110, y = var_111_promoted)[name = tensor<string, []>("op_112")];
|
| 81 |
+
tensor<fp32, []> var_21_promoted_1 = const()[name = tensor<string, []>("op_21_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
|
| 82 |
+
tensor<fp32, [1]> floor_div_1 = floor_div(x = var_112, y = var_21_promoted_1)[name = tensor<string, []>("floor_div_1")];
|
| 83 |
+
tensor<fp32, []> var_114_promoted = const()[name = tensor<string, []>("op_114_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 84 |
+
tensor<fp32, [1]> current_lengths_5 = add(x = floor_div_1, y = var_114_promoted)[name = tensor<string, []>("current_lengths_5")];
|
| 85 |
+
tensor<string, []> lengths_23_dtype_0 = const()[name = tensor<string, []>("lengths_23_dtype_0"), val = tensor<string, []>("int32")];
|
| 86 |
+
tensor<int32, [1, 762]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 762]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761]])];
|
| 87 |
+
tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
|
| 88 |
+
tensor<int32, [1]> lengths_23 = cast(dtype = lengths_23_dtype_0, x = current_lengths_5)[name = tensor<string, []>("cast_8")];
|
| 89 |
+
tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = lengths_23)[name = tensor<string, []>("op_123")];
|
| 90 |
+
tensor<bool, [1, 762]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
|
| 91 |
+
tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 92 |
+
tensor<bool, [1, 762, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
|
| 93 |
+
tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
|
| 94 |
+
tensor<bool, [1, 762, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
|
| 95 |
+
tensor<string, []> mask_5_dtype_0 = const()[name = tensor<string, []>("mask_5_dtype_0"), val = tensor<string, []>("fp32")];
|
| 96 |
+
tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
|
| 97 |
+
tensor<fp32, [1, 762, 32]> mask_5 = cast(dtype = mask_5_dtype_0, x = var_127)[name = tensor<string, []>("cast_7")];
|
| 98 |
+
tensor<fp32, [1, 1, 762, 32]> var_133 = expand_dims(axes = var_133_axes_0, x = mask_5)[name = tensor<string, []>("op_133")];
|
| 99 |
+
tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 100 |
+
tensor<fp32, [1, 256, 762, 32]> expanded_mask_7 = tile(reps = expanded_mask_7_reps_0, x = var_133)[name = tensor<string, []>("expanded_mask_7")];
|
| 101 |
+
tensor<fp32, [1, 256, 762, 32]> input_7 = mul(x = tensor_7, y = expanded_mask_7)[name = tensor<string, []>("input_7")];
|
| 102 |
+
tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 103 |
+
tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 104 |
+
tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 105 |
+
tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 106 |
+
tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
|
| 107 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_9 = conv(bias = model_encoder_pre_encode_conv_3_bias, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = model_encoder_pre_encode_conv_3_weight, x = input_7)[name = tensor<string, []>("tensor_9")];
|
| 108 |
+
tensor<fp32, [1, 256, 762, 32]> input_9 = mul(x = tensor_9, y = expanded_mask_7)[name = tensor<string, []>("input_9")];
|
| 109 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_11 = relu(x = input_9)[name = tensor<string, []>("tensor_11")];
|
| 110 |
+
tensor<fp32, [1, 256, 762, 32]> input_11 = mul(x = tensor_11, y = expanded_mask_7)[name = tensor<string, []>("input_11")];
|
| 111 |
+
tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
|
| 112 |
+
tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 113 |
+
tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 114 |
+
tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
|
| 115 |
+
tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 116 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_13 = conv(bias = model_encoder_pre_encode_conv_5_bias, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = model_encoder_pre_encode_conv_5_weight, x = input_11)[name = tensor<string, []>("tensor_13")];
|
| 117 |
+
tensor<fp32, []> var_168_promoted = const()[name = tensor<string, []>("op_168_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 118 |
+
tensor<fp32, [1]> var_169 = add(x = current_lengths_5, y = var_168_promoted)[name = tensor<string, []>("op_169")];
|
| 119 |
+
tensor<fp32, []> var_170_promoted = const()[name = tensor<string, []>("op_170_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 120 |
+
tensor<fp32, [1]> var_171 = add(x = var_169, y = var_170_promoted)[name = tensor<string, []>("op_171")];
|
| 121 |
+
tensor<fp32, []> var_172_promoted = const()[name = tensor<string, []>("op_172_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 122 |
+
tensor<fp32, [1]> var_173 = sub(x = var_171, y = var_172_promoted)[name = tensor<string, []>("op_173")];
|
| 123 |
+
tensor<fp32, []> var_21_promoted_2 = const()[name = tensor<string, []>("op_21_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
|
| 124 |
+
tensor<fp32, [1]> floor_div_2 = floor_div(x = var_173, y = var_21_promoted_2)[name = tensor<string, []>("floor_div_2")];
|
| 125 |
+
tensor<fp32, []> var_175_promoted = const()[name = tensor<string, []>("op_175_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 126 |
+
tensor<fp32, [1]> current_lengths = add(x = floor_div_2, y = var_175_promoted)[name = tensor<string, []>("current_lengths")];
|
| 127 |
+
tensor<string, []> lengths_dtype_0 = const()[name = tensor<string, []>("lengths_dtype_0"), val = tensor<string, []>("int32")];
|
| 128 |
+
tensor<int32, [1, 381]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 381]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380]])];
|
| 129 |
+
tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
|
| 130 |
+
tensor<int32, [1]> lengths = cast(dtype = lengths_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_6")];
|
| 131 |
+
tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = lengths)[name = tensor<string, []>("op_184")];
|
| 132 |
+
tensor<bool, [1, 381]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
|
| 133 |
+
tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 134 |
+
tensor<bool, [1, 381, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
|
| 135 |
+
tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
|
| 136 |
+
tensor<bool, [1, 381, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
|
| 137 |
+
tensor<string, []> mask_dtype_0 = const()[name = tensor<string, []>("mask_dtype_0"), val = tensor<string, []>("fp32")];
|
| 138 |
+
tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
|
| 139 |
+
tensor<fp32, [1, 381, 16]> mask = cast(dtype = mask_dtype_0, x = var_188)[name = tensor<string, []>("cast_5")];
|
| 140 |
+
tensor<fp32, [1, 1, 381, 16]> var_194 = expand_dims(axes = var_194_axes_0, x = mask)[name = tensor<string, []>("op_194")];
|
| 141 |
+
tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 142 |
+
tensor<fp32, [1, 256, 381, 16]> expanded_mask_13 = tile(reps = expanded_mask_13_reps_0, x = var_194)[name = tensor<string, []>("expanded_mask_13")];
|
| 143 |
+
tensor<fp32, [1, 256, 381, 16]> input_13 = mul(x = tensor_13, y = expanded_mask_13)[name = tensor<string, []>("input_13")];
|
| 144 |
+
tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
|
| 145 |
+
tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 146 |
+
tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 147 |
+
tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 148 |
+
tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
|
| 149 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_15 = conv(bias = model_encoder_pre_encode_conv_6_bias, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = model_encoder_pre_encode_conv_6_weight, x = input_13)[name = tensor<string, []>("tensor_15")];
|
| 150 |
+
tensor<fp32, [1, 256, 381, 16]> input_15 = mul(x = tensor_15, y = expanded_mask_13)[name = tensor<string, []>("input_15")];
|
| 151 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_workaround = relu(x = input_15)[name = tensor<string, []>("tensor_workaround")];
|
| 152 |
+
tensor<fp32, [1, 256, 381, 16]> x = mul(x = tensor_workaround, y = expanded_mask_13)[name = tensor<string, []>("x")];
|
| 153 |
+
tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
|
| 154 |
+
tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 381, -1])];
|
| 155 |
+
tensor<fp32, [1, 381, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
|
| 156 |
+
tensor<fp32, [1, 381, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
|
| 157 |
+
tensor<fp32, [1, 381, 512]> chunk_pre_encoder_embs = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
|
| 158 |
+
tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
|
| 159 |
+
tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
|
| 160 |
+
tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
|
| 161 |
+
tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
|
| 162 |
+
tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
|
| 163 |
+
tensor<fp32, [1, 609, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full_concat")];
|
| 164 |
+
tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
|
| 165 |
+
tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
|
| 166 |
+
tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
|
| 167 |
+
tensor<int32, [609]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [609]>([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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608])];
|
| 168 |
+
tensor<bool, [609]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
|
| 169 |
+
tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
|
| 170 |
+
tensor<bool, [609]> var_290 = greater_equal(x = out_pos, y = var_273)[name = tensor<string, []>("op_290")];
|
| 171 |
+
tensor<string, []> in_seg2_dtype_0 = const()[name = tensor<string, []>("in_seg2_dtype_0"), val = tensor<string, []>("int32")];
|
| 172 |
+
tensor<int32, [1]> var_297 = sub(x = size0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
|
| 173 |
+
tensor<int32, [609]> in_seg1_or_2 = cast(dtype = in_seg1_or_2_dtype_0, x = var_284)[name = tensor<string, []>("cast_3")];
|
| 174 |
+
tensor<int32, [609]> var_298 = mul(x = in_seg1_or_2, y = var_297)[name = tensor<string, []>("op_298")];
|
| 175 |
+
tensor<int32, [1]> var_300 = sub(x = size1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
|
| 176 |
+
tensor<int32, [609]> in_seg2 = cast(dtype = in_seg2_dtype_0, x = var_290)[name = tensor<string, []>("cast_2")];
|
| 177 |
+
tensor<int32, [609]> var_301 = mul(x = in_seg2, y = var_300)[name = tensor<string, []>("op_301")];
|
| 178 |
+
tensor<int32, [609]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
|
| 179 |
+
tensor<int32, [609]> var_305 = add(x = out_pos, y = offset)[name = tensor<string, []>("op_305")];
|
| 180 |
+
tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(608)];
|
| 181 |
+
tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
|
| 182 |
+
tensor<int32, [609]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
|
| 183 |
+
tensor<int32, [609]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
|
| 184 |
+
tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
|
| 185 |
+
tensor<int32, [1, 609]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
|
| 186 |
+
tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 187 |
+
tensor<int32, [1, 609, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
|
| 188 |
+
tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
|
| 189 |
+
tensor<int32, [1, 609, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
|
| 190 |
+
tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
|
| 191 |
+
tensor<fp32, [1, 609, 512]> output = gather_along_axis(axis = var_320, indices = gather_idx, x = full_concat)[name = tensor<string, []>("output")];
|
| 192 |
+
tensor<bool, [609]> var_323 = less(x = out_pos, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
|
| 193 |
+
tensor<string, []> var_328_dtype_0 = const()[name = tensor<string, []>("op_328_dtype_0"), val = tensor<string, []>("fp32")];
|
| 194 |
+
tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
|
| 195 |
+
tensor<fp32, [609]> var_328 = cast(dtype = var_328_dtype_0, x = var_323)[name = tensor<string, []>("cast_1")];
|
| 196 |
+
tensor<fp32, [1, 609]> var_330 = expand_dims(axes = var_330_axes_0, x = var_328)[name = tensor<string, []>("op_330")];
|
| 197 |
+
tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 198 |
+
tensor<fp32, [1, 609, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
|
| 199 |
+
tensor<fp32, [1, 609, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
|
| 200 |
+
} -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
|
| 201 |
+
}
|
SortformerNvidiaHigh.mlmodelc/model0/weights/0-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:88a98803e35186b1dfb41d7f748f7cee5093bb6efeb117f56953c17549792fa4
|
| 3 |
+
size 8948544
|
SortformerNvidiaHigh.mlmodelc/model1/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
|
| 3 |
+
size 108
|
SortformerNvidiaHigh.mlmodelc/model1/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:44face253d8b2c35339a1a54a9f709c369a877b3f673c9e2c1ce4db485d562ea
|
| 3 |
+
size 587
|
SortformerNvidiaHigh.mlmodelc/model1/model.mil
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
SortformerNvidiaHigh.mlmodelc/model1/weights/1-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ebc87598641cbf2235145dd6d7bf314eeda17341e78b1d0cc9d15bcddad7b3fb
|
| 3 |
+
size 243205696
|
SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/model.mlmodel
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:06b732c6ce6334a05528abd80bcc45db2cbebaf84bf8f3f11f1a64951b94f2b2
|
| 3 |
+
size 1087553
|
SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/0-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:88a98803e35186b1dfb41d7f748f7cee5093bb6efeb117f56953c17549792fa4
|
| 3 |
+
size 8948544
|
SortformerNvidiaHigh.mlpackage/Data/com.apple.CoreML/weights/1-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ebc87598641cbf2235145dd6d7bf314eeda17341e78b1d0cc9d15bcddad7b3fb
|
| 3 |
+
size 243205696
|
SortformerNvidiaHigh.mlpackage/Manifest.json
ADDED
|
@@ -0,0 +1,18 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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|
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
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| 10 |
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|
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|
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|
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| 16 |
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|
| 17 |
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|
| 18 |
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SortformerNvidiaHighV2.mlmodelc/analytics/coremldata.bin
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SortformerNvidiaHighV2.mlmodelc/coremldata.bin
ADDED
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SortformerNvidiaHighV2.mlmodelc/metadata.json
ADDED
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| 1 |
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[
|
| 2 |
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{
|
| 3 |
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|
| 4 |
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|
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
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|
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|
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|
| 34 |
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|
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|
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|
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|
| 40 |
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],
|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
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|
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|
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|
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|
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|
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|
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|
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},
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],
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| 88 |
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},
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| 89 |
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|
| 90 |
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|
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| 93 |
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| 96 |
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| 105 |
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|
| 106 |
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| 107 |
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|
| 108 |
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|
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| 125 |
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| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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| 131 |
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| 132 |
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| 133 |
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| 134 |
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| 135 |
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| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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{
|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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| 151 |
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| 152 |
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|
| 153 |
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| 154 |
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|
| 155 |
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|
| 156 |
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|
| 157 |
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|
| 158 |
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|
| 159 |
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|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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]
|
SortformerNvidiaHighV2.mlmodelc/model0/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
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| 3 |
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size 108
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SortformerNvidiaHighV2.mlmodelc/model0/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 635
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SortformerNvidiaHighV2.mlmodelc/model0/model.mil
ADDED
|
@@ -0,0 +1,201 @@
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| 1 |
+
program(1.0)
|
| 2 |
+
[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.10.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
|
| 3 |
+
{
|
| 4 |
+
func main<ios16>(tensor<fp32, [1, 3048, 128]> chunk, tensor<int32, [1]> chunk_lengths, tensor<fp32, [1, 40, 512]> fifo, tensor<int32, [1]> fifo_lengths, tensor<fp32, [1, 188, 512]> spkcache, tensor<int32, [1]> spkcache_lengths) {
|
| 5 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_0_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(64)))];
|
| 6 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_0_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_0_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(1152)))];
|
| 7 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_2_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(10432)))];
|
| 8 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_2_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_2_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(11520)))];
|
| 9 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_3_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(20800)))];
|
| 10 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_3_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_3_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(21888)))];
|
| 11 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_5_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(284096)))];
|
| 12 |
+
tensor<fp32, [256, 1, 3, 3]> model_encoder_pre_encode_conv_5_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_5_weight"), val = tensor<fp32, [256, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(285184)))];
|
| 13 |
+
tensor<fp32, [256]> model_encoder_pre_encode_conv_6_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(294464)))];
|
| 14 |
+
tensor<fp32, [256, 256, 1, 1]> model_encoder_pre_encode_conv_6_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_conv_6_weight"), val = tensor<fp32, [256, 256, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(295552)))];
|
| 15 |
+
tensor<fp32, [512]> model_encoder_pre_encode_out_bias = const()[name = tensor<string, []>("model_encoder_pre_encode_out_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(557760)))];
|
| 16 |
+
tensor<fp32, [512, 4096]> model_encoder_pre_encode_out_weight = const()[name = tensor<string, []>("model_encoder_pre_encode_out_weight"), val = tensor<fp32, [512, 4096]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/0-weight.bin"), offset = tensor<uint64, []>(559872)))];
|
| 17 |
+
tensor<int32, [1]> tensor_1_axes_0 = const()[name = tensor<string, []>("tensor_1_axes_0"), val = tensor<int32, [1]>([1])];
|
| 18 |
+
tensor<fp32, [1, 1, 3048, 128]> tensor_1 = expand_dims(axes = tensor_1_axes_0, x = chunk)[name = tensor<string, []>("tensor_1")];
|
| 19 |
+
tensor<string, []> current_lengths_1_dtype_0 = const()[name = tensor<string, []>("current_lengths_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 20 |
+
tensor<int32, [1, 3048]> expand_dims_0 = const()[name = tensor<string, []>("expand_dims_0"), val = tensor<int32, [1, 3048]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 1579, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779, 1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, 2107, 2108, 2109, 2110, 2111, 2112, 2113, 2114, 2115, 2116, 2117, 2118, 2119, 2120, 2121, 2122, 2123, 2124, 2125, 2126, 2127, 2128, 2129, 2130, 2131, 2132, 2133, 2134, 2135, 2136, 2137, 2138, 2139, 2140, 2141, 2142, 2143, 2144, 2145, 2146, 2147, 2148, 2149, 2150, 2151, 2152, 2153, 2154, 2155, 2156, 2157, 2158, 2159, 2160, 2161, 2162, 2163, 2164, 2165, 2166, 2167, 2168, 2169, 2170, 2171, 2172, 2173, 2174, 2175, 2176, 2177, 2178, 2179, 2180, 2181, 2182, 2183, 2184, 2185, 2186, 2187, 2188, 2189, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2198, 2199, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2207, 2208, 2209, 2210, 2211, 2212, 2213, 2214, 2215, 2216, 2217, 2218, 2219, 2220, 2221, 2222, 2223, 2224, 2225, 2226, 2227, 2228, 2229, 2230, 2231, 2232, 2233, 2234, 2235, 2236, 2237, 2238, 2239, 2240, 2241, 2242, 2243, 2244, 2245, 2246, 2247, 2248, 2249, 2250, 2251, 2252, 2253, 2254, 2255, 2256, 2257, 2258, 2259, 2260, 2261, 2262, 2263, 2264, 2265, 2266, 2267, 2268, 2269, 2270, 2271, 2272, 2273, 2274, 2275, 2276, 2277, 2278, 2279, 2280, 2281, 2282, 2283, 2284, 2285, 2286, 2287, 2288, 2289, 2290, 2291, 2292, 2293, 2294, 2295, 2296, 2297, 2298, 2299, 2300, 2301, 2302, 2303, 2304, 2305, 2306, 2307, 2308, 2309, 2310, 2311, 2312, 2313, 2314, 2315, 2316, 2317, 2318, 2319, 2320, 2321, 2322, 2323, 2324, 2325, 2326, 2327, 2328, 2329, 2330, 2331, 2332, 2333, 2334, 2335, 2336, 2337, 2338, 2339, 2340, 2341, 2342, 2343, 2344, 2345, 2346, 2347, 2348, 2349, 2350, 2351, 2352, 2353, 2354, 2355, 2356, 2357, 2358, 2359, 2360, 2361, 2362, 2363, 2364, 2365, 2366, 2367, 2368, 2369, 2370, 2371, 2372, 2373, 2374, 2375, 2376, 2377, 2378, 2379, 2380, 2381, 2382, 2383, 2384, 2385, 2386, 2387, 2388, 2389, 2390, 2391, 2392, 2393, 2394, 2395, 2396, 2397, 2398, 2399, 2400, 2401, 2402, 2403, 2404, 2405, 2406, 2407, 2408, 2409, 2410, 2411, 2412, 2413, 2414, 2415, 2416, 2417, 2418, 2419, 2420, 2421, 2422, 2423, 2424, 2425, 2426, 2427, 2428, 2429, 2430, 2431, 2432, 2433, 2434, 2435, 2436, 2437, 2438, 2439, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453, 2454, 2455, 2456, 2457, 2458, 2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2476, 2477, 2478, 2479, 2480, 2481, 2482, 2483, 2484, 2485, 2486, 2487, 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502, 2503, 2504, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2515, 2516, 2517, 2518, 2519, 2520, 2521, 2522, 2523, 2524, 2525, 2526, 2527, 2528, 2529, 2530, 2531, 2532, 2533, 2534, 2535, 2536, 2537, 2538, 2539, 2540, 2541, 2542, 2543, 2544, 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559, 2560, 2561, 2562, 2563, 2564, 2565, 2566, 2567, 2568, 2569, 2570, 2571, 2572, 2573, 2574, 2575, 2576, 2577, 2578, 2579, 2580, 2581, 2582, 2583, 2584, 2585, 2586, 2587, 2588, 2589, 2590, 2591, 2592, 2593, 2594, 2595, 2596, 2597, 2598, 2599, 2600, 2601, 2602, 2603, 2604, 2605, 2606, 2607, 2608, 2609, 2610, 2611, 2612, 2613, 2614, 2615, 2616, 2617, 2618, 2619, 2620, 2621, 2622, 2623, 2624, 2625, 2626, 2627, 2628, 2629, 2630, 2631, 2632, 2633, 2634, 2635, 2636, 2637, 2638, 2639, 2640, 2641, 2642, 2643, 2644, 2645, 2646, 2647, 2648, 2649, 2650, 2651, 2652, 2653, 2654, 2655, 2656, 2657, 2658, 2659, 2660, 2661, 2662, 2663, 2664, 2665, 2666, 2667, 2668, 2669, 2670, 2671, 2672, 2673, 2674, 2675, 2676, 2677, 2678, 2679, 2680, 2681, 2682, 2683, 2684, 2685, 2686, 2687, 2688, 2689, 2690, 2691, 2692, 2693, 2694, 2695, 2696, 2697, 2698, 2699, 2700, 2701, 2702, 2703, 2704, 2705, 2706, 2707, 2708, 2709, 2710, 2711, 2712, 2713, 2714, 2715, 2716, 2717, 2718, 2719, 2720, 2721, 2722, 2723, 2724, 2725, 2726, 2727, 2728, 2729, 2730, 2731, 2732, 2733, 2734, 2735, 2736, 2737, 2738, 2739, 2740, 2741, 2742, 2743, 2744, 2745, 2746, 2747, 2748, 2749, 2750, 2751, 2752, 2753, 2754, 2755, 2756, 2757, 2758, 2759, 2760, 2761, 2762, 2763, 2764, 2765, 2766, 2767, 2768, 2769, 2770, 2771, 2772, 2773, 2774, 2775, 2776, 2777, 2778, 2779, 2780, 2781, 2782, 2783, 2784, 2785, 2786, 2787, 2788, 2789, 2790, 2791, 2792, 2793, 2794, 2795, 2796, 2797, 2798, 2799, 2800, 2801, 2802, 2803, 2804, 2805, 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820, 2821, 2822, 2823, 2824, 2825, 2826, 2827, 2828, 2829, 2830, 2831, 2832, 2833, 2834, 2835, 2836, 2837, 2838, 2839, 2840, 2841, 2842, 2843, 2844, 2845, 2846, 2847, 2848, 2849, 2850, 2851, 2852, 2853, 2854, 2855, 2856, 2857, 2858, 2859, 2860, 2861, 2862, 2863, 2864, 2865, 2866, 2867, 2868, 2869, 2870, 2871, 2872, 2873, 2874, 2875, 2876, 2877, 2878, 2879, 2880, 2881, 2882, 2883, 2884, 2885, 2886, 2887, 2888, 2889, 2890, 2891, 2892, 2893, 2894, 2895, 2896, 2897, 2898, 2899, 2900, 2901, 2902, 2903, 2904, 2905, 2906, 2907, 2908, 2909, 2910, 2911, 2912, 2913, 2914, 2915, 2916, 2917, 2918, 2919, 2920, 2921, 2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047]])];
|
| 21 |
+
tensor<int32, [1]> var_40_axes_0 = const()[name = tensor<string, []>("op_40_axes_0"), val = tensor<int32, [1]>([1])];
|
| 22 |
+
tensor<int32, [1, 1]> var_40 = expand_dims(axes = var_40_axes_0, x = chunk_lengths)[name = tensor<string, []>("op_40")];
|
| 23 |
+
tensor<bool, [1, 3048]> time_mask_1 = less(x = expand_dims_0, y = var_40)[name = tensor<string, []>("time_mask_1")];
|
| 24 |
+
tensor<int32, [1]> var_42_axes_0 = const()[name = tensor<string, []>("op_42_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 25 |
+
tensor<bool, [1, 3048, 1]> var_42 = expand_dims(axes = var_42_axes_0, x = time_mask_1)[name = tensor<string, []>("op_42")];
|
| 26 |
+
tensor<int32, [3]> var_44_reps_0 = const()[name = tensor<string, []>("op_44_reps_0"), val = tensor<int32, [3]>([1, 1, 128])];
|
| 27 |
+
tensor<bool, [1, 3048, 128]> var_44 = tile(reps = var_44_reps_0, x = var_42)[name = tensor<string, []>("op_44")];
|
| 28 |
+
tensor<string, []> mask_1_dtype_0 = const()[name = tensor<string, []>("mask_1_dtype_0"), val = tensor<string, []>("fp32")];
|
| 29 |
+
tensor<int32, [1]> var_50_axes_0 = const()[name = tensor<string, []>("op_50_axes_0"), val = tensor<int32, [1]>([1])];
|
| 30 |
+
tensor<fp32, [1, 3048, 128]> mask_1 = cast(dtype = mask_1_dtype_0, x = var_44)[name = tensor<string, []>("cast_11")];
|
| 31 |
+
tensor<fp32, [1, 1, 3048, 128]> var_50 = expand_dims(axes = var_50_axes_0, x = mask_1)[name = tensor<string, []>("op_50")];
|
| 32 |
+
tensor<fp32, [1, 1, 3048, 128]> input_1 = mul(x = tensor_1, y = var_50)[name = tensor<string, []>("input_1")];
|
| 33 |
+
tensor<string, []> tensor_3_pad_type_0 = const()[name = tensor<string, []>("tensor_3_pad_type_0"), val = tensor<string, []>("custom")];
|
| 34 |
+
tensor<int32, [4]> tensor_3_pad_0 = const()[name = tensor<string, []>("tensor_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 35 |
+
tensor<int32, [2]> tensor_3_strides_0 = const()[name = tensor<string, []>("tensor_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 36 |
+
tensor<int32, [2]> tensor_3_dilations_0 = const()[name = tensor<string, []>("tensor_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 37 |
+
tensor<int32, []> tensor_3_groups_0 = const()[name = tensor<string, []>("tensor_3_groups_0"), val = tensor<int32, []>(1)];
|
| 38 |
+
tensor<fp32, [1, 256, 1524, 64]> tensor_3 = conv(bias = model_encoder_pre_encode_conv_0_bias, dilations = tensor_3_dilations_0, groups = tensor_3_groups_0, pad = tensor_3_pad_0, pad_type = tensor_3_pad_type_0, strides = tensor_3_strides_0, weight = model_encoder_pre_encode_conv_0_weight, x = input_1)[name = tensor<string, []>("tensor_3")];
|
| 39 |
+
tensor<fp32, []> var_61_promoted = const()[name = tensor<string, []>("op_61_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 40 |
+
tensor<fp32, [1]> current_lengths_1 = cast(dtype = current_lengths_1_dtype_0, x = chunk_lengths)[name = tensor<string, []>("cast_12")];
|
| 41 |
+
tensor<fp32, [1]> var_62 = add(x = current_lengths_1, y = var_61_promoted)[name = tensor<string, []>("op_62")];
|
| 42 |
+
tensor<fp32, []> var_63_promoted = const()[name = tensor<string, []>("op_63_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 43 |
+
tensor<fp32, [1]> var_64 = add(x = var_62, y = var_63_promoted)[name = tensor<string, []>("op_64")];
|
| 44 |
+
tensor<fp32, []> var_65_promoted = const()[name = tensor<string, []>("op_65_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 45 |
+
tensor<fp32, [1]> var_66 = sub(x = var_64, y = var_65_promoted)[name = tensor<string, []>("op_66")];
|
| 46 |
+
tensor<fp32, []> var_21_promoted = const()[name = tensor<string, []>("op_21_promoted"), val = tensor<fp32, []>(0x1p+1)];
|
| 47 |
+
tensor<fp32, [1]> floor_div_0 = floor_div(x = var_66, y = var_21_promoted)[name = tensor<string, []>("floor_div_0")];
|
| 48 |
+
tensor<fp32, []> var_68_promoted = const()[name = tensor<string, []>("op_68_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 49 |
+
tensor<fp32, [1]> current_lengths_3 = add(x = floor_div_0, y = var_68_promoted)[name = tensor<string, []>("current_lengths_3")];
|
| 50 |
+
tensor<string, []> lengths_21_dtype_0 = const()[name = tensor<string, []>("lengths_21_dtype_0"), val = tensor<string, []>("int32")];
|
| 51 |
+
tensor<int32, [1, 1524]> expand_dims_1 = const()[name = tensor<string, []>("expand_dims_1"), val = tensor<int32, [1, 1524]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1203, 1204, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 1301, 1302, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 1473, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1482, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523]])];
|
| 52 |
+
tensor<int32, [1]> var_77_axes_0 = const()[name = tensor<string, []>("op_77_axes_0"), val = tensor<int32, [1]>([1])];
|
| 53 |
+
tensor<int32, [1]> lengths_21 = cast(dtype = lengths_21_dtype_0, x = current_lengths_3)[name = tensor<string, []>("cast_10")];
|
| 54 |
+
tensor<int32, [1, 1]> var_77 = expand_dims(axes = var_77_axes_0, x = lengths_21)[name = tensor<string, []>("op_77")];
|
| 55 |
+
tensor<bool, [1, 1524]> time_mask_3 = less(x = expand_dims_1, y = var_77)[name = tensor<string, []>("time_mask_3")];
|
| 56 |
+
tensor<int32, [1]> var_79_axes_0 = const()[name = tensor<string, []>("op_79_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 57 |
+
tensor<bool, [1, 1524, 1]> var_79 = expand_dims(axes = var_79_axes_0, x = time_mask_3)[name = tensor<string, []>("op_79")];
|
| 58 |
+
tensor<int32, [3]> var_81_reps_0 = const()[name = tensor<string, []>("op_81_reps_0"), val = tensor<int32, [3]>([1, 1, 64])];
|
| 59 |
+
tensor<bool, [1, 1524, 64]> var_81 = tile(reps = var_81_reps_0, x = var_79)[name = tensor<string, []>("op_81")];
|
| 60 |
+
tensor<string, []> mask_3_dtype_0 = const()[name = tensor<string, []>("mask_3_dtype_0"), val = tensor<string, []>("fp32")];
|
| 61 |
+
tensor<int32, [1]> var_87_axes_0 = const()[name = tensor<string, []>("op_87_axes_0"), val = tensor<int32, [1]>([1])];
|
| 62 |
+
tensor<fp32, [1, 1524, 64]> mask_3 = cast(dtype = mask_3_dtype_0, x = var_81)[name = tensor<string, []>("cast_9")];
|
| 63 |
+
tensor<fp32, [1, 1, 1524, 64]> var_87 = expand_dims(axes = var_87_axes_0, x = mask_3)[name = tensor<string, []>("op_87")];
|
| 64 |
+
tensor<int32, [4]> expanded_mask_3_reps_0 = const()[name = tensor<string, []>("expanded_mask_3_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 65 |
+
tensor<fp32, [1, 256, 1524, 64]> expanded_mask_3 = tile(reps = expanded_mask_3_reps_0, x = var_87)[name = tensor<string, []>("expanded_mask_3")];
|
| 66 |
+
tensor<fp32, [1, 256, 1524, 64]> input_3 = mul(x = tensor_3, y = expanded_mask_3)[name = tensor<string, []>("input_3")];
|
| 67 |
+
tensor<fp32, [1, 256, 1524, 64]> tensor_5 = relu(x = input_3)[name = tensor<string, []>("tensor_5")];
|
| 68 |
+
tensor<fp32, [1, 256, 1524, 64]> input_5 = mul(x = tensor_5, y = expanded_mask_3)[name = tensor<string, []>("input_5")];
|
| 69 |
+
tensor<string, []> tensor_7_pad_type_0 = const()[name = tensor<string, []>("tensor_7_pad_type_0"), val = tensor<string, []>("custom")];
|
| 70 |
+
tensor<int32, [4]> tensor_7_pad_0 = const()[name = tensor<string, []>("tensor_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 71 |
+
tensor<int32, [2]> tensor_7_strides_0 = const()[name = tensor<string, []>("tensor_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 72 |
+
tensor<int32, []> tensor_7_groups_0 = const()[name = tensor<string, []>("tensor_7_groups_0"), val = tensor<int32, []>(256)];
|
| 73 |
+
tensor<int32, [2]> tensor_7_dilations_0 = const()[name = tensor<string, []>("tensor_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 74 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_7 = conv(bias = model_encoder_pre_encode_conv_2_bias, dilations = tensor_7_dilations_0, groups = tensor_7_groups_0, pad = tensor_7_pad_0, pad_type = tensor_7_pad_type_0, strides = tensor_7_strides_0, weight = model_encoder_pre_encode_conv_2_weight, x = input_5)[name = tensor<string, []>("tensor_7")];
|
| 75 |
+
tensor<fp32, []> var_107_promoted = const()[name = tensor<string, []>("op_107_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 76 |
+
tensor<fp32, [1]> var_108 = add(x = current_lengths_3, y = var_107_promoted)[name = tensor<string, []>("op_108")];
|
| 77 |
+
tensor<fp32, []> var_109_promoted = const()[name = tensor<string, []>("op_109_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 78 |
+
tensor<fp32, [1]> var_110 = add(x = var_108, y = var_109_promoted)[name = tensor<string, []>("op_110")];
|
| 79 |
+
tensor<fp32, []> var_111_promoted = const()[name = tensor<string, []>("op_111_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 80 |
+
tensor<fp32, [1]> var_112 = sub(x = var_110, y = var_111_promoted)[name = tensor<string, []>("op_112")];
|
| 81 |
+
tensor<fp32, []> var_21_promoted_1 = const()[name = tensor<string, []>("op_21_promoted_1"), val = tensor<fp32, []>(0x1p+1)];
|
| 82 |
+
tensor<fp32, [1]> floor_div_1 = floor_div(x = var_112, y = var_21_promoted_1)[name = tensor<string, []>("floor_div_1")];
|
| 83 |
+
tensor<fp32, []> var_114_promoted = const()[name = tensor<string, []>("op_114_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 84 |
+
tensor<fp32, [1]> current_lengths_5 = add(x = floor_div_1, y = var_114_promoted)[name = tensor<string, []>("current_lengths_5")];
|
| 85 |
+
tensor<string, []> lengths_23_dtype_0 = const()[name = tensor<string, []>("lengths_23_dtype_0"), val = tensor<string, []>("int32")];
|
| 86 |
+
tensor<int32, [1, 762]> expand_dims_2 = const()[name = tensor<string, []>("expand_dims_2"), val = tensor<int32, [1, 762]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761]])];
|
| 87 |
+
tensor<int32, [1]> var_123_axes_0 = const()[name = tensor<string, []>("op_123_axes_0"), val = tensor<int32, [1]>([1])];
|
| 88 |
+
tensor<int32, [1]> lengths_23 = cast(dtype = lengths_23_dtype_0, x = current_lengths_5)[name = tensor<string, []>("cast_8")];
|
| 89 |
+
tensor<int32, [1, 1]> var_123 = expand_dims(axes = var_123_axes_0, x = lengths_23)[name = tensor<string, []>("op_123")];
|
| 90 |
+
tensor<bool, [1, 762]> time_mask_5 = less(x = expand_dims_2, y = var_123)[name = tensor<string, []>("time_mask_5")];
|
| 91 |
+
tensor<int32, [1]> var_125_axes_0 = const()[name = tensor<string, []>("op_125_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 92 |
+
tensor<bool, [1, 762, 1]> var_125 = expand_dims(axes = var_125_axes_0, x = time_mask_5)[name = tensor<string, []>("op_125")];
|
| 93 |
+
tensor<int32, [3]> var_127_reps_0 = const()[name = tensor<string, []>("op_127_reps_0"), val = tensor<int32, [3]>([1, 1, 32])];
|
| 94 |
+
tensor<bool, [1, 762, 32]> var_127 = tile(reps = var_127_reps_0, x = var_125)[name = tensor<string, []>("op_127")];
|
| 95 |
+
tensor<string, []> mask_5_dtype_0 = const()[name = tensor<string, []>("mask_5_dtype_0"), val = tensor<string, []>("fp32")];
|
| 96 |
+
tensor<int32, [1]> var_133_axes_0 = const()[name = tensor<string, []>("op_133_axes_0"), val = tensor<int32, [1]>([1])];
|
| 97 |
+
tensor<fp32, [1, 762, 32]> mask_5 = cast(dtype = mask_5_dtype_0, x = var_127)[name = tensor<string, []>("cast_7")];
|
| 98 |
+
tensor<fp32, [1, 1, 762, 32]> var_133 = expand_dims(axes = var_133_axes_0, x = mask_5)[name = tensor<string, []>("op_133")];
|
| 99 |
+
tensor<int32, [4]> expanded_mask_7_reps_0 = const()[name = tensor<string, []>("expanded_mask_7_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 100 |
+
tensor<fp32, [1, 256, 762, 32]> expanded_mask_7 = tile(reps = expanded_mask_7_reps_0, x = var_133)[name = tensor<string, []>("expanded_mask_7")];
|
| 101 |
+
tensor<fp32, [1, 256, 762, 32]> input_7 = mul(x = tensor_7, y = expanded_mask_7)[name = tensor<string, []>("input_7")];
|
| 102 |
+
tensor<string, []> tensor_9_pad_type_0 = const()[name = tensor<string, []>("tensor_9_pad_type_0"), val = tensor<string, []>("valid")];
|
| 103 |
+
tensor<int32, [2]> tensor_9_strides_0 = const()[name = tensor<string, []>("tensor_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 104 |
+
tensor<int32, [4]> tensor_9_pad_0 = const()[name = tensor<string, []>("tensor_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 105 |
+
tensor<int32, [2]> tensor_9_dilations_0 = const()[name = tensor<string, []>("tensor_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 106 |
+
tensor<int32, []> tensor_9_groups_0 = const()[name = tensor<string, []>("tensor_9_groups_0"), val = tensor<int32, []>(1)];
|
| 107 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_9 = conv(bias = model_encoder_pre_encode_conv_3_bias, dilations = tensor_9_dilations_0, groups = tensor_9_groups_0, pad = tensor_9_pad_0, pad_type = tensor_9_pad_type_0, strides = tensor_9_strides_0, weight = model_encoder_pre_encode_conv_3_weight, x = input_7)[name = tensor<string, []>("tensor_9")];
|
| 108 |
+
tensor<fp32, [1, 256, 762, 32]> input_9 = mul(x = tensor_9, y = expanded_mask_7)[name = tensor<string, []>("input_9")];
|
| 109 |
+
tensor<fp32, [1, 256, 762, 32]> tensor_11 = relu(x = input_9)[name = tensor<string, []>("tensor_11")];
|
| 110 |
+
tensor<fp32, [1, 256, 762, 32]> input_11 = mul(x = tensor_11, y = expanded_mask_7)[name = tensor<string, []>("input_11")];
|
| 111 |
+
tensor<string, []> tensor_13_pad_type_0 = const()[name = tensor<string, []>("tensor_13_pad_type_0"), val = tensor<string, []>("custom")];
|
| 112 |
+
tensor<int32, [4]> tensor_13_pad_0 = const()[name = tensor<string, []>("tensor_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
|
| 113 |
+
tensor<int32, [2]> tensor_13_strides_0 = const()[name = tensor<string, []>("tensor_13_strides_0"), val = tensor<int32, [2]>([2, 2])];
|
| 114 |
+
tensor<int32, []> tensor_13_groups_0 = const()[name = tensor<string, []>("tensor_13_groups_0"), val = tensor<int32, []>(256)];
|
| 115 |
+
tensor<int32, [2]> tensor_13_dilations_0 = const()[name = tensor<string, []>("tensor_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 116 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_13 = conv(bias = model_encoder_pre_encode_conv_5_bias, dilations = tensor_13_dilations_0, groups = tensor_13_groups_0, pad = tensor_13_pad_0, pad_type = tensor_13_pad_type_0, strides = tensor_13_strides_0, weight = model_encoder_pre_encode_conv_5_weight, x = input_11)[name = tensor<string, []>("tensor_13")];
|
| 117 |
+
tensor<fp32, []> var_168_promoted = const()[name = tensor<string, []>("op_168_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 118 |
+
tensor<fp32, [1]> var_169 = add(x = current_lengths_5, y = var_168_promoted)[name = tensor<string, []>("op_169")];
|
| 119 |
+
tensor<fp32, []> var_170_promoted = const()[name = tensor<string, []>("op_170_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 120 |
+
tensor<fp32, [1]> var_171 = add(x = var_169, y = var_170_promoted)[name = tensor<string, []>("op_171")];
|
| 121 |
+
tensor<fp32, []> var_172_promoted = const()[name = tensor<string, []>("op_172_promoted"), val = tensor<fp32, []>(0x1.8p+1)];
|
| 122 |
+
tensor<fp32, [1]> var_173 = sub(x = var_171, y = var_172_promoted)[name = tensor<string, []>("op_173")];
|
| 123 |
+
tensor<fp32, []> var_21_promoted_2 = const()[name = tensor<string, []>("op_21_promoted_2"), val = tensor<fp32, []>(0x1p+1)];
|
| 124 |
+
tensor<fp32, [1]> floor_div_2 = floor_div(x = var_173, y = var_21_promoted_2)[name = tensor<string, []>("floor_div_2")];
|
| 125 |
+
tensor<fp32, []> var_175_promoted = const()[name = tensor<string, []>("op_175_promoted"), val = tensor<fp32, []>(0x1p+0)];
|
| 126 |
+
tensor<fp32, [1]> current_lengths = add(x = floor_div_2, y = var_175_promoted)[name = tensor<string, []>("current_lengths")];
|
| 127 |
+
tensor<string, []> lengths_dtype_0 = const()[name = tensor<string, []>("lengths_dtype_0"), val = tensor<string, []>("int32")];
|
| 128 |
+
tensor<int32, [1, 381]> expand_dims_3 = const()[name = tensor<string, []>("expand_dims_3"), val = tensor<int32, [1, 381]>([[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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380]])];
|
| 129 |
+
tensor<int32, [1]> var_184_axes_0 = const()[name = tensor<string, []>("op_184_axes_0"), val = tensor<int32, [1]>([1])];
|
| 130 |
+
tensor<int32, [1]> lengths = cast(dtype = lengths_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_6")];
|
| 131 |
+
tensor<int32, [1, 1]> var_184 = expand_dims(axes = var_184_axes_0, x = lengths)[name = tensor<string, []>("op_184")];
|
| 132 |
+
tensor<bool, [1, 381]> time_mask = less(x = expand_dims_3, y = var_184)[name = tensor<string, []>("time_mask")];
|
| 133 |
+
tensor<int32, [1]> var_186_axes_0 = const()[name = tensor<string, []>("op_186_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 134 |
+
tensor<bool, [1, 381, 1]> var_186 = expand_dims(axes = var_186_axes_0, x = time_mask)[name = tensor<string, []>("op_186")];
|
| 135 |
+
tensor<int32, [3]> var_188_reps_0 = const()[name = tensor<string, []>("op_188_reps_0"), val = tensor<int32, [3]>([1, 1, 16])];
|
| 136 |
+
tensor<bool, [1, 381, 16]> var_188 = tile(reps = var_188_reps_0, x = var_186)[name = tensor<string, []>("op_188")];
|
| 137 |
+
tensor<string, []> mask_dtype_0 = const()[name = tensor<string, []>("mask_dtype_0"), val = tensor<string, []>("fp32")];
|
| 138 |
+
tensor<int32, [1]> var_194_axes_0 = const()[name = tensor<string, []>("op_194_axes_0"), val = tensor<int32, [1]>([1])];
|
| 139 |
+
tensor<fp32, [1, 381, 16]> mask = cast(dtype = mask_dtype_0, x = var_188)[name = tensor<string, []>("cast_5")];
|
| 140 |
+
tensor<fp32, [1, 1, 381, 16]> var_194 = expand_dims(axes = var_194_axes_0, x = mask)[name = tensor<string, []>("op_194")];
|
| 141 |
+
tensor<int32, [4]> expanded_mask_13_reps_0 = const()[name = tensor<string, []>("expanded_mask_13_reps_0"), val = tensor<int32, [4]>([1, 256, 1, 1])];
|
| 142 |
+
tensor<fp32, [1, 256, 381, 16]> expanded_mask_13 = tile(reps = expanded_mask_13_reps_0, x = var_194)[name = tensor<string, []>("expanded_mask_13")];
|
| 143 |
+
tensor<fp32, [1, 256, 381, 16]> input_13 = mul(x = tensor_13, y = expanded_mask_13)[name = tensor<string, []>("input_13")];
|
| 144 |
+
tensor<string, []> tensor_15_pad_type_0 = const()[name = tensor<string, []>("tensor_15_pad_type_0"), val = tensor<string, []>("valid")];
|
| 145 |
+
tensor<int32, [2]> tensor_15_strides_0 = const()[name = tensor<string, []>("tensor_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
|
| 146 |
+
tensor<int32, [4]> tensor_15_pad_0 = const()[name = tensor<string, []>("tensor_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
|
| 147 |
+
tensor<int32, [2]> tensor_15_dilations_0 = const()[name = tensor<string, []>("tensor_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
|
| 148 |
+
tensor<int32, []> tensor_15_groups_0 = const()[name = tensor<string, []>("tensor_15_groups_0"), val = tensor<int32, []>(1)];
|
| 149 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_15 = conv(bias = model_encoder_pre_encode_conv_6_bias, dilations = tensor_15_dilations_0, groups = tensor_15_groups_0, pad = tensor_15_pad_0, pad_type = tensor_15_pad_type_0, strides = tensor_15_strides_0, weight = model_encoder_pre_encode_conv_6_weight, x = input_13)[name = tensor<string, []>("tensor_15")];
|
| 150 |
+
tensor<fp32, [1, 256, 381, 16]> input_15 = mul(x = tensor_15, y = expanded_mask_13)[name = tensor<string, []>("input_15")];
|
| 151 |
+
tensor<fp32, [1, 256, 381, 16]> tensor_workaround = relu(x = input_15)[name = tensor<string, []>("tensor_workaround")];
|
| 152 |
+
tensor<fp32, [1, 256, 381, 16]> x = mul(x = tensor_workaround, y = expanded_mask_13)[name = tensor<string, []>("x")];
|
| 153 |
+
tensor<int32, [4]> var_228_perm_0 = const()[name = tensor<string, []>("op_228_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])];
|
| 154 |
+
tensor<int32, [3]> var_229 = const()[name = tensor<string, []>("op_229"), val = tensor<int32, [3]>([1, 381, -1])];
|
| 155 |
+
tensor<fp32, [1, 381, 256, 16]> var_228 = transpose(perm = var_228_perm_0, x = x)[name = tensor<string, []>("transpose_0")];
|
| 156 |
+
tensor<fp32, [1, 381, 4096]> input = reshape(shape = var_229, x = var_228)[name = tensor<string, []>("input")];
|
| 157 |
+
tensor<fp32, [1, 381, 512]> chunk_pre_encoder_embs = linear(bias = model_encoder_pre_encode_out_bias, weight = model_encoder_pre_encode_out_weight, x = input)[name = tensor<string, []>("linear_0")];
|
| 158 |
+
tensor<string, []> var_241_dtype_0 = const()[name = tensor<string, []>("op_241_dtype_0"), val = tensor<string, []>("int32")];
|
| 159 |
+
tensor<int32, [1]> size0 = const()[name = tensor<string, []>("size0"), val = tensor<int32, [1]>([188])];
|
| 160 |
+
tensor<int32, [1]> size1 = const()[name = tensor<string, []>("size1"), val = tensor<int32, [1]>([40])];
|
| 161 |
+
tensor<int32, []> var_264 = const()[name = tensor<string, []>("op_264"), val = tensor<int32, []>(1)];
|
| 162 |
+
tensor<bool, []> full_concat_interleave_0 = const()[name = tensor<string, []>("full_concat_interleave_0"), val = tensor<bool, []>(false)];
|
| 163 |
+
tensor<fp32, [1, 609, 512]> full_concat = concat(axis = var_264, interleave = full_concat_interleave_0, values = (spkcache, fifo, chunk_pre_encoder_embs))[name = tensor<string, []>("full_concat")];
|
| 164 |
+
tensor<int32, [1]> var_273 = add(x = spkcache_lengths, y = fifo_lengths)[name = tensor<string, []>("op_273")];
|
| 165 |
+
tensor<int32, [1]> chunk_pre_encoder_lengths = cast(dtype = var_241_dtype_0, x = current_lengths)[name = tensor<string, []>("cast_4")];
|
| 166 |
+
tensor<int32, [1]> pre_encoder_lengths = add(x = var_273, y = chunk_pre_encoder_lengths)[name = tensor<string, []>("total_length")];
|
| 167 |
+
tensor<int32, [609]> out_pos = const()[name = tensor<string, []>("out_pos"), val = tensor<int32, [609]>([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, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608])];
|
| 168 |
+
tensor<bool, [609]> var_284 = greater_equal(x = out_pos, y = spkcache_lengths)[name = tensor<string, []>("op_284")];
|
| 169 |
+
tensor<string, []> in_seg1_or_2_dtype_0 = const()[name = tensor<string, []>("in_seg1_or_2_dtype_0"), val = tensor<string, []>("int32")];
|
| 170 |
+
tensor<bool, [609]> var_290 = greater_equal(x = out_pos, y = var_273)[name = tensor<string, []>("op_290")];
|
| 171 |
+
tensor<string, []> in_seg2_dtype_0 = const()[name = tensor<string, []>("in_seg2_dtype_0"), val = tensor<string, []>("int32")];
|
| 172 |
+
tensor<int32, [1]> var_297 = sub(x = size0, y = spkcache_lengths)[name = tensor<string, []>("op_297")];
|
| 173 |
+
tensor<int32, [609]> in_seg1_or_2 = cast(dtype = in_seg1_or_2_dtype_0, x = var_284)[name = tensor<string, []>("cast_3")];
|
| 174 |
+
tensor<int32, [609]> var_298 = mul(x = in_seg1_or_2, y = var_297)[name = tensor<string, []>("op_298")];
|
| 175 |
+
tensor<int32, [1]> var_300 = sub(x = size1, y = fifo_lengths)[name = tensor<string, []>("op_300")];
|
| 176 |
+
tensor<int32, [609]> in_seg2 = cast(dtype = in_seg2_dtype_0, x = var_290)[name = tensor<string, []>("cast_2")];
|
| 177 |
+
tensor<int32, [609]> var_301 = mul(x = in_seg2, y = var_300)[name = tensor<string, []>("op_301")];
|
| 178 |
+
tensor<int32, [609]> offset = add(x = var_298, y = var_301)[name = tensor<string, []>("offset")];
|
| 179 |
+
tensor<int32, [609]> var_305 = add(x = out_pos, y = offset)[name = tensor<string, []>("op_305")];
|
| 180 |
+
tensor<int32, []> var_309 = const()[name = tensor<string, []>("op_309"), val = tensor<int32, []>(608)];
|
| 181 |
+
tensor<int32, []> var_310 = const()[name = tensor<string, []>("op_310"), val = tensor<int32, []>(0)];
|
| 182 |
+
tensor<int32, [609]> minimum_0 = minimum(x = var_305, y = var_309)[name = tensor<string, []>("minimum_0")];
|
| 183 |
+
tensor<int32, [609]> maximum_0 = maximum(x = minimum_0, y = var_310)[name = tensor<string, []>("maximum_0")];
|
| 184 |
+
tensor<int32, [1]> var_313_axes_0 = const()[name = tensor<string, []>("op_313_axes_0"), val = tensor<int32, [1]>([0])];
|
| 185 |
+
tensor<int32, [1, 609]> var_313 = expand_dims(axes = var_313_axes_0, x = maximum_0)[name = tensor<string, []>("op_313")];
|
| 186 |
+
tensor<int32, [1]> var_315_axes_0 = const()[name = tensor<string, []>("op_315_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 187 |
+
tensor<int32, [1, 609, 1]> var_315 = expand_dims(axes = var_315_axes_0, x = var_313)[name = tensor<string, []>("op_315")];
|
| 188 |
+
tensor<int32, [3]> gather_idx_reps_0 = const()[name = tensor<string, []>("gather_idx_reps_0"), val = tensor<int32, [3]>([1, 1, 512])];
|
| 189 |
+
tensor<int32, [1, 609, 512]> gather_idx = tile(reps = gather_idx_reps_0, x = var_315)[name = tensor<string, []>("gather_idx")];
|
| 190 |
+
tensor<int32, []> var_320 = const()[name = tensor<string, []>("op_320"), val = tensor<int32, []>(1)];
|
| 191 |
+
tensor<fp32, [1, 609, 512]> output = gather_along_axis(axis = var_320, indices = gather_idx, x = full_concat)[name = tensor<string, []>("output")];
|
| 192 |
+
tensor<bool, [609]> var_323 = less(x = out_pos, y = pre_encoder_lengths)[name = tensor<string, []>("op_323")];
|
| 193 |
+
tensor<string, []> var_328_dtype_0 = const()[name = tensor<string, []>("op_328_dtype_0"), val = tensor<string, []>("fp32")];
|
| 194 |
+
tensor<int32, [1]> var_330_axes_0 = const()[name = tensor<string, []>("op_330_axes_0"), val = tensor<int32, [1]>([0])];
|
| 195 |
+
tensor<fp32, [609]> var_328 = cast(dtype = var_328_dtype_0, x = var_323)[name = tensor<string, []>("cast_1")];
|
| 196 |
+
tensor<fp32, [1, 609]> var_330 = expand_dims(axes = var_330_axes_0, x = var_328)[name = tensor<string, []>("op_330")];
|
| 197 |
+
tensor<int32, [1]> var_332_axes_0 = const()[name = tensor<string, []>("op_332_axes_0"), val = tensor<int32, [1]>([-1])];
|
| 198 |
+
tensor<fp32, [1, 609, 1]> var_332 = expand_dims(axes = var_332_axes_0, x = var_330)[name = tensor<string, []>("op_332")];
|
| 199 |
+
tensor<fp32, [1, 609, 512]> pre_encoder_embs = mul(x = output, y = var_332)[name = tensor<string, []>("op_333")];
|
| 200 |
+
} -> (pre_encoder_embs, pre_encoder_lengths, chunk_pre_encoder_embs, chunk_pre_encoder_lengths);
|
| 201 |
+
}
|
SortformerNvidiaHighV2.mlmodelc/model0/weights/0-weight.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:88a98803e35186b1dfb41d7f748f7cee5093bb6efeb117f56953c17549792fa4
|
| 3 |
+
size 8948544
|
SortformerNvidiaHighV2.mlmodelc/model1/analytics/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:5a8281049b2a65a3be541cfd9f949e84b8fe1c5251ce90e46da1626fed54e58a
|
| 3 |
+
size 108
|
SortformerNvidiaHighV2.mlmodelc/model1/coremldata.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
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|
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|
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