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- README.md +85 -0
- dec_28L_manifest.txt +282 -0
- decoder_28L_512_c.tflite +3 -0
- embd_table.bin +3 -0
- head_q8.tflite +3 -0
- prefill_512_t16_c.tflite +3 -0
- vibe_front_10s_q8.tflite +3 -0
- vocab.json +0 -0
- weights/dec_w000.bin +3 -0
- weights/dec_w001.bin +3 -0
- weights/dec_w002.bin +3 -0
- weights/dec_w003.bin +3 -0
- weights/dec_w004.bin +3 -0
- weights/dec_w005.bin +3 -0
- weights/dec_w006.bin +3 -0
- weights/dec_w007.bin +3 -0
- weights/dec_w008.bin +3 -0
- weights/dec_w009.bin +3 -0
- weights/dec_w010.bin +3 -0
- weights/dec_w011.bin +3 -0
- weights/dec_w012.bin +3 -0
- weights/dec_w013.bin +3 -0
- weights/dec_w014.bin +3 -0
- weights/dec_w015.bin +3 -0
- weights/dec_w016.bin +3 -0
- weights/dec_w017.bin +3 -0
- weights/dec_w018.bin +3 -0
- weights/dec_w019.bin +3 -0
- weights/dec_w020.bin +3 -0
- weights/dec_w021.bin +3 -0
- weights/dec_w022.bin +3 -0
- weights/dec_w023.bin +3 -0
- weights/dec_w024.bin +3 -0
- weights/dec_w025.bin +3 -0
- weights/dec_w026.bin +3 -0
- weights/dec_w027.bin +3 -0
- weights/dec_w028.bin +3 -0
- weights/dec_w029.bin +3 -0
- weights/dec_w030.bin +3 -0
- weights/dec_w031.bin +3 -0
- weights/dec_w032.bin +3 -0
- weights/dec_w033.bin +3 -0
- weights/dec_w034.bin +3 -0
- weights/dec_w035.bin +3 -0
- weights/dec_w036.bin +3 -0
- weights/dec_w037.bin +3 -0
- weights/dec_w038.bin +3 -0
- weights/dec_w039.bin +3 -0
- weights/dec_w040.bin +3 -0
- weights/dec_w041.bin +3 -0
README.md
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| 1 |
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---
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license: mit
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base_model: microsoft/VibeVoice-ASR-BitNet
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tags:
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- litert
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- tflite
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- bitnet
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- ternary
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- automatic-speech-recognition
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- on-device
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- android
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language: [en, zh, fr, it, ko, pt, vi]
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library_name: litert
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pipeline_tag: automatic-speech-recognition
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---
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# VibeVoice-ASR-BitNet — LiteRT export
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LiteRT (TFLite) conversion of [microsoft/VibeVoice-ASR-BitNet](https://huggingface.co/microsoft/VibeVoice-ASR-BitNet),
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so the model runs on Android and Linux with **no ggml / llama.cpp dependency**.
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The decoder is ternary (BitNet I2_S). LiteRT has no ternary kernel, and a normal
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export turns it into an int8 matmul — keeping the *values*, discarding the
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*packing*. Packing is the performance: batch-1 decode is memory-bandwidth-bound, so
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a 1.31 B-parameter decoder moves ~328 MB per token at 2 bits versus ~1310 MB at
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int8. These files keep the 2-bit packing, and the runtime consumes it through a
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**custom op**.
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Engine, kernel and export scripts:
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[vieenrose/LiteRT · samples/asr/vibevoice](https://github.com/vieenrose/LiteRT/tree/vibevoice-ternary/litert/samples/asr/vibevoice)
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## Contents
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| file | what it is |
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|---|---|
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| `vibe_front_10s_q8.tflite` | audio front end — both tokenizer encoders + connectors, summed, per-channel int8, fixed 10 s window @ 24 kHz |
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| `decoder_28L_512_c.tflite` | 28-layer decode step, ctx 512, one token per pass |
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| `prefill_512_t16_c.tflite` | same but 16 tokens per pass, for prompt ingestion |
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| `head_q8.tflite` | output norm + LM head, per-channel int8, weights baked in |
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| `weights/dec_w*.bin` | packed ternary weights + per-row scales, bound at runtime |
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| `dec_28L_manifest.txt` | input order for the decoder graphs |
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| `embd_table.bin` | Q6_K token embedding table, dequantized one row at a time on the host |
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| `vocab.json` | for byte-level BPE detokenization |
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The decoder's weights ship **outside** the graph because LiteRT's dispatcher will
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not hand constant tensors to a custom kernel; the head's are baked in because it
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uses no custom op. Weights are mmap'd at load, so they stay clean file-backed pages.
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## Measured
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Boox Tab Mini C — Snapdragon 662, Cortex-A73, **ARMv8.0 without dotprod**, roughly
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worst case for int8 SIMD. Back to back against the ggml build of the same model:
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| | this export | ggml (`asr_infer`) |
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|---|---|---|
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| decode | **123.5 ms/token** | 123.1 |
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| prefill (T=16) | **68 ms/token** | — |
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| peak RSS | **786 MB** | 1297 MB |
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| peak RssAnon | **241 MB** | 507 MB |
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Parity on speed at **half the unevictable memory**, which is the figure that
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decides whether an Android app survives memory pressure.
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## Fidelity
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| stage | vs reference | note |
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|---|---|---|
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| audio front end | cosine **0.992** | vs f32; the shipped I8_S ggml build scores 0.958 |
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| 28-layer decoder | cosine **0.994938** | vs a dense f32 reference, real embedding input |
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| LM head | cosine **0.999421** | vs f32 |
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| ternary GEMM | **bit-exact** | vs its scalar reference, every shape and thread count |
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The int8 front end is *more* accurate than the ggml build it replaces, because
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per-channel scales beat the single per-tensor scale I8_S uses.
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## Caveats
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* The encoder window is **fixed at 10 s**; longer audio must be windowed by the
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caller. Convolutions are causal, so a window needs left context and no lookahead.
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* The decoder graphs are **context-specific** — a ctx=128 prefill cannot be paired
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with a ctx=512 decode.
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* The **chat template is mandatory**. Feeding audio features without the surrounding
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system/user/assistant turns produces fluent nonsense rather than a transcript.
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* Transcript quality has been spot-checked, not benchmarked. WER against the
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reference implementation is not yet measured.
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dec_28L_manifest.txt
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