| --- |
| license: apache-2.0 |
| base_model: OpenFormosa/BlueMagpie-TTS |
| tags: |
| - tts |
| - text-to-speech |
| - gguf |
| - llama.cpp |
| - llama.rn |
| - taiwanese-mandarin |
| language: |
| - zh |
| --- |
| |
| # BlueMagpie-TTS — GGUF |
|
|
| GGUF conversions of [OpenFormosa/BlueMagpie-TTS](https://huggingface.co/OpenFormosa/BlueMagpie-TTS), |
| a Taiwanese-Mandarin text-to-speech model, for use with |
| [llama.rn](https://github.com/mybigday/llama.rn) and |
| [codec.cpp](https://github.com/BricksDisplay/codec.cpp). |
|
|
| BlueMagpie is a **continuous-latent autoregressive-diffusion** TTS — |
| VoxCPM2 with its Text-Semantic LM swapped from MiniCPM-4 to |
| [Barbet](https://github.com/OpenFormosa/Barbet) (Mamba2 + attention hybrid, |
| 1B params). The AudioVAE decodes the continuous latent sequence to a |
| 48 kHz waveform. |
|
|
| ## Files |
|
|
| ### Text-Semantic LM (Barbet-1B backbone, runs in llama.cpp / llama.rn) |
|
|
| | File | Quant | Size | |
| |------|-------|------| |
| | `BlueMagpie-Barbet-1B-q4_k_m.gguf` | Q4_K_M | 661 MB | |
| | `BlueMagpie-Barbet-1B-q5_k_m.gguf` | Q5_K_M | 756 MB | |
| | `BlueMagpie-Barbet-1B-q6_k.gguf` | Q6_K | 857 MB | |
| | `BlueMagpie-Barbet-1B-q8_0.gguf` | Q8_0 | 1.08 GB | |
| | `BlueMagpie-Barbet-1B-f16.gguf` | F16 | 2.03 GB | |
| |
| The **BPE (GPT2-family) tokenizer** is baked into every GGUF, so llama.cpp |
| can tokenize text natively — no external tokenizer runtime needed. |
| |
| ### Codec (AudioVAE + LM adaptor stack, runs in codec.cpp) |
| |
| | File | Size | |
| |------|------| |
| | `BlueMagpie-AudioVAE.gguf` | 1.76 GB (F16) | |
| |
| This bundle carries **all continuous-latent codec_lm components** the AR loop |
| needs: `tslm_adapter` + FSQ + RALM (MiniCPM4-8L) + LocEnc + LocDiT (12L CFM |
| diffusion) + `enc_to_lm_proj` + `enc_to_tslm_proj` + `lm_to_dit_proj` + |
| `res_to_dit_proj` + AudioVAE decoder + stop head. codec.cpp probes |
| `codec_common` `codec_lm_get_info().is_continuous == true` at load. |
|
|
| ## Runtime |
|
|
| The llama.rn side loads Barbet as the backbone context and this codec.gguf |
| as the vocoder. `getFormattedAudioCompletion` returns |
| `flow = "continuous_embd"` + `embedding = true`; the standard `completion` |
| loop drives the codec_lm step machine per `llama_decode`, accumulating |
| latent patches into `result.embeddings`, which `decodeAudioEmbeddings` |
| turns into PCM at 48 kHz via the AudioVAE. |
|
|
| Requires **llama.rn ≥ codec branch** (adds `LLM_ARCH_BARBET`, the Mamba2/ |
| attention hybrid graph builder, and the codec_common continuous-latent |
| completion-loop hook). |
| |
| ## License |
| |
| Model weights follow the upstream Apache-2.0 license. |
| |
| ## Provenance |
| |
| - Backbone converted via |
| [`scripts/vendor/convert_barbet_to_gguf.py`](https://github.com/mybigday/llama.rn/blob/codec/scripts/vendor/convert_barbet_to_gguf.py) |
| (llama.rn), which fuses the 5 Mamba2 in-projections + 3 conv1d into the |
| ssm_in / ssm_conv1d tensors llama.cpp expects and bakes the GPT2 BPE |
| tokenizer from the upstream `tokenizer.json`. |
| - Codec converted via |
| [`scripts/convert-to-gguf.py --model-type bluemagpie`](https://github.com/BricksDisplay/codec.cpp) |
| (codec.cpp). |
| |
