Instructions to use theoracleguy/pocket-tts-coreml-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Pocket-TTS
How to use theoracleguy/pocket-tts-coreml-v3 with Pocket-TTS:
from pocket_tts import TTSModel import scipy.io.wavfile tts_model = TTSModel.load_model("theoracleguy/pocket-tts-coreml-v3") voice_state = tts_model.get_state_for_audio_prompt( "hf://kyutai/tts-voices/alba-mackenna/casual.wav" ) audio = tts_model.generate_audio(voice_state, "Hello world, this is a test.") # Audio is a 1D torch tensor containing PCM data. scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy()) - Notebooks
- Google Colab
- Kaggle
Pocket TTS Core ML v3 runtime
Runtime-only, non-24L Pocket TTS packages for iOS/macOS.
Source: FluidInference/pocket-tts-coreml at commit 91748676fe3c8b2eb3007b3125253bcd898202c3.
Included for every language:
cond_prefill_ane.mlmodelcflowlm_step_ane.mlmodelcflow_decoder_fused.mlmodelcmimi_decoder.mlmodelc- complete
constants_bintokenizer, embeddings, protocol constants, and voices - a per-language download manifest with file sizes and SHA-256 checksums
German, Italian, Portuguese, and Spanish include their language-specific
mimi_encoderv3.mlmodelc. English uses the upstream shared
mimi_encoderv2.mlmodelc, because no English mimi_encoderv3 is published.
The English pocket_state.mlmodelc is included for compatibility/testing;
the rank-4 ANE path remains the stable runtime set.
The shared Mimi initialization tensors and encoder recovery matrix are also included for compatibility with existing FluidAudio hosts. The English v2 encoder is additionally retained at its established repository-root path.
The original Pocket TTS and converted weights are distributed under CC BY 4.0. See the upstream model card for attribution and base-model details.