Chatterbox-Flash-CoreML

Compiled Core ML export of [ResembleAI/chatterbox-flash](https://huggingface.co/ResembleAI/chatterbox-flash) for Apple runtimes. This bundle contains the Chatterbox Flash T3 token generator and the S3Gen audio back-half as static-shape `.mlmodelc` graphs. It is intended for an application runtime that owns text tokenization, T3 denoising/sampling, reference conditioning, and graph orchestration. - [speech-swift](https://github.com/soniqo/speech-swift) — Apple SDK - [Speech Studio](https://soniqo.audio/speech-studio) — local speech generation and voice cloning app - [Docs](https://soniqo.audio/getting-started) — install and CLI docs - [soniqo.audio](https://soniqo.audio) — website - [blog](https://soniqo.audio/blog) — blog The exported graphs cover: - T3 conditioning, text prefill, and block decoding. - S3Gen speaker projection, flow encoder, meanflow estimator step, and HiFT vocoder. The host runtime must still provide: - text normalization and tokenization - T3 denoising loop, PMI/CFG scoring, sampling, and EOS trimming - reference waveform encoders: - VoiceEncoder: `ref.wav -> speaker_emb` - prompt speech tokenizer: `ref.wav -> prompt_speech_tokens` - S3Gen reference encoder: `ref.wav -> prompt_token`, `prompt_feat`, `embedding` - padding/cropping and the two-step meanflow loop over `(t,r) = (0,0.5), (0.5,1.0)` This means the bundle supports voice-cloning TTS when the runtime supplies reference conditioning tensors, but it is not yet a fully Core ML `ref.wav -> cloned wav` pipeline. Core ML warnings from local export: - `CPU_ONLY` prediction crashed for the T3 package in local coremltools 8.3 testing. Use `ALL`, `CPU_AND_NE`, or a compiled-device runtime. - The uploaded artifact ships compiled `.mlmodelc` folders. The numerical parity tests were run against the source `.mlpackage` exports before compilation. The runtime loads graphs from `t3/` and `audio/`, then: 1. Prepare T3 reference conditioning tensors and S3Gen `ref_dict` tensors from a prompt wav. 2. Tokenize text with `t3/tokenizer.json`. 3. Run T3 prefill and block denoising/sampling to produce S3 speech tokens. 4. Concatenate S3Gen `prompt_token` and generated speech tokens. 5. Run `audio/FlowEncoder.mlmodelc`. 6. Build `cond` by copying `prompt_feat.T` into the mel prefix. 7. Run `audio/FlowEstimator.mlmodelc` twice for `(0,0.5)` and `(0.5,1.0)`. 8. Crop generated mel frames after the prompt prefix. 9. Run `audio/HiFTVocoder.mlmodelc` and crop padded samples. Converted from [ResembleAI/chatterbox-flash](https://huggingface.co/ResembleAI/chatterbox-flash), revision `4385507288b8197e6dab8b4e6b1603328d549d9d`.