Instructions to use AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16 # Run inference directly in the terminal: llama cli -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16 # Run inference directly in the terminal: llama cli -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16 # Run inference directly in the terminal: ./build/bin/llama cli -hf AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
Use Docker
docker model run hf.co/AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
- Llama new
- LM Studio
- Jan
- Ollama
How to use AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp with Ollama:
ollama run hf.co/AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
- Unsloth Desktop
- Docker Model Runner
How to use AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp with Docker Model Runner:
docker model run hf.co/AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
- Lemonade
How to use AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AXERA-TECH/Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp:BF16
Run and chat with the model
lemonade run user.Qwen3-TTS-1.7B-VoiceDesign-AX8860-llama.cpp-BF16
List all available models
lemonade list
- Atomic Chat
Qwen3-TTS-1.7B-VoiceDesign on AX8860 (llama.cpp + ggml-axera, 2026-11-02)
在爱芯 AX8860 的 NPU(4 核)上用 llama.cpp 运行 Qwen3-TTS-1.7B-VoiceDesign:按文字描述设计音色的语音合成。 Qwen3-TTS-1.7B-VoiceDesign on the 4-core NPU of the Axera AX8860, with llama.cpp: speech synthesis with a voice designed from a text description.
文件 / Files
| 文件 / File | 说明 / Description |
|---|---|
qwen3-tts-1.7b-voicedesign-bf16.gguf |
talker 权重(GGUF BF16,3312 MB,md5 c0c13fc8bb1c946f33739d4538b5056e)/ talker weights |
mmproj-qwen3-tts-1.7b-voicedesign-bf16.gguf |
code predictor、声码器权重(GGUF BF16,646 MB,md5 6200b0762d600203d46a52df6a3d9a91)/ code predictor and codec decoder weights |
bin/ |
llama.cpp 板端程序 llama-tts(静态链接,只依赖系统 /opt/lib 与 libc)/ the board program (static; needs only the system /opt/lib and libc) |
kernels/ |
NPU kernel 4 个(与 Qwen3-TTS-1.7B Base 相同):talker prefill、talker decode(含 codec 输出头)、code predictor(一次一帧,运行时 top-k)、声码器(一次 72 帧)+ manifest.json / four kernels (the same as Qwen3-TTS-1.7B Base) + manifest.json |
run.sh |
运行脚本 / run script |
权重只来自 GGUF:kernel 不含权重,加载时从 GGUF 转换(talker 与 code predictor MXFP8,声码器 BF16)。 The weights come only from the GGUFs: the kernels hold none; they are converted at load (talker and code predictor MXFP8, codec decoder BF16).
运行 / Run
环境 / Requirements:AX8860,SDK V0.5.0 或更新(cat /proc/ax_proc/version)/ SDK V0.5.0 or newer.
./run.sh "今天天气很好,我们一起去公园散步吧。" "一位声音温柔的年轻女性,语速适中,语气平静" out.wav zh
./run.sh "The quick brown fox jumps over the lazy dog." "A deep, warm male narrator voice, calm and measured." out.wav en
参数 / Arguments:文本、音色描述、输出文件(默认 out.wav)、语言(auto zh en de it pt es ja ko fr ru,默认 auto)。
text, voice description, output file (default out.wav), language (default auto).
采样 temperature 0.9、top-k 50(官方默认);-n 2000 帧(约 160 s)为上限。
Sampling temperature 0.9, top-k 50 (the official defaults); at most 2000 frames (~160 s).
直接调用程序时设置 / When calling the program directly: LD_LIBRARY_PATH=/opt/lib GGML_AXERA_KERNEL_DIR=<repo>/kernels,
-ngl 99 -c 4096,top-k 不超过 50 / top-k up to 50。
性能 / Performance
AX8860,SDK V0.5.1,NPU 4 核;一段中文(一种音色描述)生成 37.4 s 语音 / 4 cores; one Chinese paragraph (one voice description), 37.4 s of speech:
| 项目 / Item | 结果 / Result |
|---|---|
| 合成耗时 / synthesis time | 18.06 s(RTF 0.48) |
| 不计首次声码器权重转换 / without the first codec weight conversion | RTF 0.42,每帧(80 ms 音频)33 ms / 33 ms per 80 ms frame |
| NPU:code predictor / talker decode 每帧 / per frame | 7.8 ms / 8.1 ms |
每个进程一次性的开销 / once per process:加载与权重转换约 25 s;第一次合成另加约 2.5 s 声码器权重转换(已计入上表的合成耗时)。 load and weight conversion ~25 s; the first synthesis adds ~2.5 s of codec weight conversion (in the synthesis time above).
本版 decode kernel 直接读 prefill 的权重 bank,NPU 内存里只放一份层权重:加载后 NPU 内存(CMM)6376 → 5452 MB,输出不变,decode 略快。 This release's decode kernel reads the prefill weight banks (one copy of the layer weights in NPU memory): CMM after load 6376 -> 5452 MB, same outputs, slightly faster decoding.
正确性 / Correctness
中、英文测试句(两种音色描述)生成的语音经 Qwen3-ASR 转写与原文逐字一致。 Speech of the Chinese and English test sentences (two voice descriptions), transcribed back with Qwen3-ASR, matches the text exactly.
已知限制 / Known limits
- 运行时独占 NPU 4 核 / takes all 4 NPU cores; do not run other NPU programs alongside.
- 上下文 4096;top-k 最大 50 / context 4096; top-k at most 50.
- 贪心解码(
--temp 0)的语音偏长、停顿多,建议用默认采样 / greedy decoding gives long, pause-heavy speech; keep the default sampling.
源码 / Sources
- llama.cpp:ZHEQIUSHUI/llama.cpp
axera-dev@57a30bd(ggml/src/ggml-axera/models/qwen3、qwen3_tts;CustomVoice / VoiceDesign 的 prompt:tools/mtmd/mtmd-helper-gen.cpp;转换:conversion/qwen3tts.py) - kernel:ZHEQIUSHUI/ax-dsl-kernels
main@804a260(models/qwen3_tts)
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