model card

README.md

@@ -1,3 +1,124 @@
----
-license: apache-2.0
----
+---
+base_model: BreezeBlue/Breeze-TTS-2
+language:
+  - en
+  - zh
+library_name: gguf
+license: other
+license_name: breezeblue-research-and-non-commercial
+license_link: https://huggingface.co/BreezeBlue/Breeze-TTS-2
+pipeline_tag: text-to-speech
+tags:
+  - text-to-speech
+  - tts
+  - gguf
+  - ggml
+  - vulkan
+  - voice-cloning
+  - voice-conversion
+---
+
+# Breeze-TTS-2 GGUF
+
+GGUF conversions of [BreezeBlue/Breeze-TTS-2](https://huggingface.co/BreezeBlue/Breeze-TTS-2) for
+[**Breeze-TTS-2.cpp**](https://github.com/HoppouAI/Breeze-TTS-2.cpp), a C++ reimplementation running on
+ggml with a Vulkan backend, so it works on NVIDIA, AMD and Intel GPUs and falls back to CPU.
+
+Bilingual English and Mandarin, 24 kHz, around 1.2x realtime at Q8_0 on an RTX 3060.
+
+These files will not load in llama.cpp. They need the Breeze-TTS-2.cpp runtime, which implements all
+four stages of the model: the T5Gemma2 text encoder, the Qwen3 backbone, the 15 step depth decoder and
+the vocoder.
+
+## Files
+
+| File | Size | Notes |
+| --- | --- | --- |
+| `breeze-tts-2-f16.gguf` | 5.9 GB | Reference quality, unquantized |
+| `breeze-tts-2-q8_0.gguf` | 3.3 GB | **Recommended.** No audible loss against F16 |
+| `breeze-tts-2-q6_k.gguf` | 2.9 GB | |
+| `breeze-tts-2-q4_k.gguf` | 2.4 GB | Smallest safe choice, holds up well |
+| `breeze-tts-2-q8_0-dd4.gguf` | 3.2 GB | Experimental, Q8_0 base with a Q4_K depth decoder |
+| `breeze-tts-2-q8_0-dd2.gguf` | 3.1 GB | Experimental, Q8_0 base with a Q2_K depth decoder |
+| `breeze-tts-2-q4_k-dd2.gguf` | 2.3 GB | Experimental, Q4_K base with a Q2_K depth decoder |
+
+Approximate VRAM is about 1 GB above the file size.
+
+### About the `-dd` variants
+
+Everything except the `-dd` files keeps the depth decoder at higher precision than the rest of the
+model. The `-dd` variants quantize it too, which is why they are smaller.
+
+The depth decoder runs **15 sequential steps for every single frame of audio**, so on hardware where
+that is the bottleneck rather than memory bandwidth, shrinking it can speed generation up noticeably.
+That is the reason these exist and it is worth benchmarking on your own card.
+
+The tradeoff is that depth codes feed back into the backbone every frame, so quantization error
+compounds as generation continues. Output holds up early and then drifts progressively muffled and
+thin past **roughly 45 seconds of continuous generation**. Short lines and dialogue are fine. Long
+narration is not, and the failure is gradual rather than obvious, so it is easy to miss on quick tests.
+
+Treat them as experimental. If in doubt, use `q8_0` or `q4_k`.
+
+## Usage
+
+```bash
+git clone --recursive https://github.com/HoppouAI/Breeze-TTS-2.cpp
+cd Breeze-TTS-2.cpp
+cmake -B build -DCMAKE_BUILD_TYPE=Release
+cmake --build build -j
+```
+
+```bash
+# invent a voice from a description
+build/breeze-cli breeze-tts-2-q8_0.gguf \
+  --text "Welcome aboard. Your journey begins now." \
+  --instruction "A warm, thoughtful young woman with a clear, calm delivery." \
+  --output design.wav
+
+# clone a voice from a clip
+build/breeze-cli breeze-tts-2-q8_0.gguf \
+  --text "It is good to hear your voice again." \
+  --ref-audio ref_voice.wav --ref-text "The harbour lights came on one by one as the evening tide began to turn." \
+  --output clone.wav
+```
+
+Or run the server, which has a web UI built in plus HTTP and WebSocket streaming:
+
+```bash
+build/breeze-server breeze-tts-2-q8_0.gguf --host 127.0.0.1 --port 8080 --webui
+```
+
+`ref_voice.wav` in this repo is a sample reference clip. Its transcript is
+"The harbour lights came on one by one as the evening tide began to turn."
+
+## Vocal events
+
+Inline tags in round brackets produce non speech sounds: `(laugh)`, `(sigh)`, `(cough)`,
+`(clears throat)`, and `[笑]` or `[叹气]` in Chinese. The vocabulary is free form rather than a fixed
+token list, so descriptive tags like `(nervous chuckle)` often work.
+
+They usually need `--cfg-scale 2` to `3` to actually fire. At the default of 1.0 the model treats a tag
+as a suggestion and tends to read straight past anything outside the common set.
+
+## Voice conversion
+
+The runtime can also respeak an existing recording in a different voice, keeping the original timing,
+phrasing and emphasis while changing only the speaker. This is not part of the upstream model, it falls
+out of how the codec separates semantic content from acoustic detail. It is experimental, and pitch does
+not carry over, so singing comes out spoken unless you retain some source acoustic codebooks.
+
+## Conversion
+
+Produced with `scripts/convert_hf_to_gguf.py` and `breeze-quantize` from the repo. The source download
+must include the `audio_tokenizer/` directory, which holds the vocoder that the model actually uses at
+inference time.
+
+## License
+
+Weights are governed by the **BreezeBlue Research and Non-Commercial License** from the
+[original model](https://huggingface.co/BreezeBlue/Breeze-TTS-2). Converting to GGUF does not change
+that. The Breeze-TTS-2.cpp source code is Apache 2.0.
+
+You are responsible for complying with the weight license and for obtaining consent for any reference
+audio or voices you use.