cstr/qwen3-tts-1.7b-base-GGUF

Qwen3-TTS 12Hz 1.7B Base — GGUF (CrispASR)

GGUF / ggml conversions of Qwen/Qwen3-TTS-12Hz-1.7B-Base for use with the qwen3-tts backend in CrispStrobe/CrispASR.

Qwen3-TTS 12Hz 1.7B Base is the larger sibling of cstr/qwen3-tts-0.6b-base-GGUF, with the same multilingual voice-cloning behaviour:

• 10 supported languages: zh en ja ko de fr ru pt es it
• discrete multi-codebook LM architecture with a separate 12 Hz tokenizer / codec
• runtime voice cloning from (ref_audio, ref_text) or pre-baked voice-pack GGUFs
• Apache-2.0 licence

The 1.7B variant has a wider talker (hidden=2048, vs 1024 for the 0.6B), a matching ECAPA enc_dim=2048, and an additional small_to_mtp_projection bridge from the talker into the 1024-d code predictor. The CrispASR runtime handles all three differences automatically — same CLI, same GGUF schema.

This repo contains the talker / code-predictor / speaker-encoder model. It must be used together with the separate tokenizer / codec GGUF from cstr/qwen3-tts-tokenizer-12hz-GGUF.

Files

File	Size	Notes
qwen3-tts-12hz-1.7b-base.gguf	3.6 GB	F16
qwen3-tts-12hz-1.7b-base-q8_0.gguf	1.9 GB	Q8_0, recommended quantised talker

Quick Start

Build CrispASR:

git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target crispasr

Download the talker + tokenizer:

huggingface-cli download cstr/qwen3-tts-1.7b-base-GGUF \
    qwen3-tts-12hz-1.7b-base-q8_0.gguf --local-dir .

huggingface-cli download cstr/qwen3-tts-tokenizer-12hz-GGUF \
    qwen3-tts-tokenizer-12hz.gguf --local-dir .

Voice clone from a reference WAV:

./build/bin/crispasr \
    --backend qwen3-tts-1.7b-base \
    -m qwen3-tts-12hz-1.7b-base-q8_0.gguf \
    --codec-model qwen3-tts-tokenizer-12hz.gguf \
    --voice clone.wav \
    --ref-text "Exact transcript of clone.wav" \
    --tts "Hello there" \
    --tts-output hello.wav

Or let CrispASR pull both files for you on first run:

./build/bin/crispasr \
    --backend qwen3-tts-1.7b-base -m auto \
    --voice clone.wav \
    --ref-text "Exact transcript of clone.wav" \
    --tts "Hello there" \
    --tts-output hello.wav

When --voice points to a .wav, --ref-text is required. When --voice points to a .gguf, it is treated as a baked voice pack.

Quantisation Notes

• qwen3-tts-12hz-1.7b-base.gguf
  • reference baseline (3.6 GB)
• qwen3-tts-12hz-1.7b-base-q8_0.gguf
  • recommended quantised deployment (1.9 GB)
  • ASR-roundtrips word-exact on English prompts in current CrispASR testing

Lower-bit talker quants (q6_k, q5_k, q4_k) can still load but are not numerically faithful to the F16 reference and should be treated as experimental.

The companion tokenizer / codec should stay at F16.

How this was made

1. The upstream HF safetensors checkpoint was converted to GGUF F16 by models/convert-qwen3-tts-to-gguf.py.
2. Quantised variants are produced with CrispASR's GGUF quantiser.
3. Inference is implemented in src/qwen3_tts.cpp, using ggml graphs for the talker / code-predictor path and the companion tokenizer GGUF for codec encode/decode.

Reference implementation

Architecture and behaviour were checked against the official Qwen release:

• upstream model card: Qwen/Qwen3-TTS-12Hz-1.7B-Base
• upstream repository: QwenLM/Qwen3-TTS

The CrispASR runtime is a clean C++ / ggml re-implementation for this repo's backend stack.

Related

• Smaller sibling: cstr/qwen3-tts-0.6b-base-GGUF
• Companion tokenizer / codec GGUF: cstr/qwen3-tts-tokenizer-12hz-GGUF
• Upstream Base model: Qwen/Qwen3-TTS-12Hz-1.7B-Base
• C++ runtime: CrispStrobe/CrispASR

License

Apache-2.0, inherited from the base model.

Provenance and EU AI Act Art. 53 note

• Upstream model: Qwen/Qwen3-TTS-12Hz-1.7B-Base — published by Qwen.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.