Sample from the converted model (plain fp16 weights + int8 AR, streaming vocoder) cloning a CC0 volunteer voice from kyutai/tts-voices (`voice-donations/0a67`): assets/hero_sample.wav — “The morning light fell across the garden while a small bird sang beside the open window.” Source and license of the reference voice: assets/ATTRIBUTION.md. These classic `.tflite` graphs convert Samuel Vitorino's Sopro v2 turbo, a reference-conditioned text-to-speech model for English, European Portuguese, French and German, to the LiteRT CompiledModel API. The package provides fp32 reference graphs, plain weight-only fp16 graphs and a native-int8 merged AR alternative. It produces 24 kHz audio and includes a streaming vocoder, while resampling, spectral DSP, token sampling and the acoustic solver loop stay on the host. The upstream model and source are Apache-2.0; the included LICENSE is retained verbatim. Conversion used `sopro==2.2.0` and upstream model revision `f747f9edfb7b0233a3b7105af3a75603a7213d26`. The file sizes below are exact bytes. There are fourteen fp32 graph files, fourteen plain wfp16 graph files and one native-int8 merged AR file. The Android variants add eight fp32, seven wfp16 and one int8 graph file (rows marked "Android variant"), plus the app source under `android/`. Separate AR prefill/step files and merged alternatives are included so callers can choose one form without loading both. This table covers the executable artifacts, host assets and conversion sources.

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语音合成

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Spark-X2.5-1.7B

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Spark-X2.5-4B

litert-community

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Speaker-Diarization-LiteRT

litert-community

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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语音识别

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Ternary-Bonsai-1.7B

litert-community

Ternary (INT2) `.litertlm` bundles of `prism-ml/Ternary-Bonsai-1.7B-unpacked`, packaged for the LiteRT-LM runtime and Google AI Edge Gallery. > **Recommended: `bonsai-1.7b-int2pc-4k-gpu.litertlm`.** The plain, unmodified build with > no optimizations applied. Everything else in this repo is **experimental** — longer > context or lighter activations, but whether a given one loads depends on your app's > LiteRT / LiteRT-LM version and dependencies. Start with the recommended build; reach for > an experimental one only if you specifically need what it offers. `32768` is the model's full context — `max_position_embeddings` for Ternary-Bonsai-1.7B is 32768, so the long-context builds here sit at the ceiling rather than truncating it.

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