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README.md

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+---
+license: other
+base_model: MisoLabs/MisoTTS
+pipeline_tag: text-to-speech
+tags:
+- text-to-speech
+- prosody
+---
+
+# MisoTTS bf16 (BigBlueCeiling)
+
+Full-precision (bfloat16) weights for MisoTTS, the **reference** variant in
+BigBlueCeiling's optimization- and deployment-focused fork of
+[MisoLabsAI/MisoTTS](https://github.com/MisoLabsAI/MisoTTS). The model and the
+original inference code are MisoLabs' work; this fork makes it fast and correct in
+practice and easy to run across a range of hardware.
+
+MisoTTS is an expressive, English, ~8B-parameter text-to-speech model: a
+Llama-3.2-style backbone generates Mimi audio codes from text, a smaller
+autoregressive decoder predicts the higher codebooks per frame, and the output is
+watermarked with SilentCipher.
+
+## Variant family
+
+This bf16 repo is the reference and the default. The serving core reads the GPU's
+VRAM and loads the highest-quality weight precision that fits, pulling it at
+runtime:
+
+| Variant | Weights | Fits (gen peak) | Quality vs bf16 |
+|---|---|---|---|
+| **bf16** (this repo) | bfloat16 | ~24 GB (A6000, 3090/4090, A100, ...) | reference |
+| [int8](https://huggingface.co/BigBlueCeiling/MisoTTS-int8) | int8 weight-only | ~16 GB (4060 Ti 16G, 4070 Ti S, A4000) | even (CER/WER/UTMOS ~unchanged) |
+| [int4](https://huggingface.co/BigBlueCeiling/MisoTTS-int4) | int4 weight-only | ~12 GB (3060 12G, 4070) | noticeably lower (experimental) |
+
+int8/int4 are weight-only quantizations of these bf16 weights. They are a **memory**
+lever, not a speed one (the frame-by-frame decode cannot feed the GPU's
+low-precision tensor cores, so they dequantize to bf16 for the matmul). bf16 is
+both the quality reference and the fastest path on a card that fits it.
+
+## Quality and performance
+
+Measured on an A6000 over the 12 canonical eval prompts (3 lengths x 4 emotions),
+scored with perceval: mean ASR **CER 0.10, WER 0.15, UTMOS 3.94**. With
+`torch.compile` (reduce-overhead) generation runs near realtime (**RTF ~1.1** after
+warmup); eager is roughly 14x slower. The compile warmup caches across processes,
+so a persisted Inductor cache brings cold start to a few minutes.
+
+## Use
+
+```python
+import torch, torchaudio
+from generator import load_miso_8b  # from the MisoTTS repo
+
+gen = load_miso_8b("cuda")  # GPU-sense pulls this bf16 repo on a card that fits it
+audio = gen.generate(text="Hello from Miso.", speaker=0, context=[],
+                     max_audio_length_ms=10_000)
+torchaudio.save("miso.wav", audio.unsqueeze(0).cpu(), gen.sample_rate)
+```
+
+Requires torch>=2.7. See the [MisoTTS repo](https://github.com/eoffermann/MisoTTS)
+for the serving container (RunPod and OpenAI-compatible APIs), the GPU-sense variant
+selection, and the quality harness.
+
+## Safety, license, credit
+
+Generated audio is watermarked with SilentCipher; if you deploy the model, use your
+own private watermark key and keep it secret. Do not use the model to impersonate
+people, create deceptive audio, or generate harmful content. The model and the
+original inference code are MisoLabs' work, under the upstream license; see
+[MisoLabsAI/MisoTTS](https://github.com/MisoLabsAI/MisoTTS).