cstr/chatterbox-turbo-GGUF

Chatterbox-Turbo TTS — GGUF (ggml)

GGUF / ggml conversion of ResembleAI/chatterbox-turbo for use with CrispStrobe/CrispASR.

Chatterbox-Turbo is a distilled 350M-parameter TTS pipeline: GPT-2 tokenizer + AR text-to-speech model + meanflow S3Gen (2-step CFM, vs 10 for base Chatterbox) + HiFTGenerator vocoder. Distributed under MIT license.

Two GGUF files are needed: the T3 model (text to speech tokens) and the S3Gen model (speech tokens to audio).

Files

File	Size	Notes
chatterbox-turbo-t3-f16.gguf	964 MB	T3 GPT-2 AR model (24L, 1024D)
chatterbox-turbo-t3-q8_0.gguf	628 MB	Quantized T3, recommended deployment default
chatterbox-turbo-t3-q4_k.gguf	457 MB	Smaller T3 quant for memory-constrained use
chatterbox-turbo-s3gen-f16.gguf	628 MB	S3Gen encoder + meanflow CFM + HiFT vocoder
chatterbox-turbo-s3gen-q8_0.gguf	350 MB	Quantized S3Gen, recommended deployment default
chatterbox-turbo-s3gen-q4_k.gguf	244 MB	Smaller S3Gen quant for memory-constrained use

Encoder attention/FFN weights are stored at F32 precision for quality. Vocoder weights (conv_pre, resblocks, conv_post, source fusion, F0 predictor) are F32.

Quick start

# 1. Build CrispASR
git clone https://github.com/CrispStrobe/CrispASR
cd CrispASR
cmake -B build -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=OFF
cmake --build build -j --target chatterbox

# 2. Pull both model files (Q8_0 recommended)
huggingface-cli download cstr/chatterbox-turbo-GGUF chatterbox-turbo-t3-q8_0.gguf --local-dir .
huggingface-cli download cstr/chatterbox-turbo-GGUF chatterbox-turbo-s3gen-q8_0.gguf --local-dir .

# 3. Synthesise (C API — CLI adapter in progress)
# See test programs in SESSION_HANDOVER.md for usage examples

Architecture

Text -> GPT-2 BPE tokenizer (50257 tokens)
     -> T3 GPT-2 AR (24 layers, 1024D, 16 heads, learned pos emb, SwiGLU)
     -> 25 Hz speech tokens (6561 codebook)
     -> UpsampleConformerEncoder (6 pre + 4 post upsample, 512D, 8 heads, rel-pos attn)
        -> Upsample1D: nearest-neighbor 2x + Conv1d(512,512,k=5) + Linear + LayerNorm + xscale
     -> 80-channel mel spectrogram (50 Hz)
     -> Meanflow CFM denoiser (2 Euler steps, linear schedule, no CFG)
        UNet1D: 1 down + 12 mid + 1 up blocks, 256 ch, 4 transformer blocks each
     -> HiFTGenerator vocoder (F0 predictor + SineGen + 3x ConvTranspose1d + iSTFT)
     -> 24 kHz mono WAV

Key differences from base Chatterbox

Feature	Base Chatterbox	Chatterbox-Turbo
T3 architecture	Llama (30L, 520M)	GPT-2 Medium (24L, 350M)
T3 tokenizer	Character (704 tokens)	BPE (50257 tokens)
CFM steps	10 (cosine schedule)	2 (linear, meanflow distilled)
CFG	Yes (rate=0.7)	No (distilled)
Total params	~520M	~350M

Quality verification

ASR roundtrip using same speech tokens as Python reference:

Metric	Value
ASR output (moonshine-base)	"Hello world" (correct)
Language detection confidence	0.939
encoder_out RMS	0.4602 (exact match to Python)
matrix_bd (rel-pos scores) h0[0,0]	24.70 (matches Python to 2dp)

Conversion

# From HuggingFace model (requires chatterbox-tts pip package):
python models/convert-chatterbox-to-gguf.py \
  --input ResembleAI/chatterbox-turbo \
  --output-dir /path/to/output \
  --variant turbo

Related models

• cstr/chatterbox-GGUF — base Chatterbox (Llama T3, 10-step CFM)
• cstr/lahgtna-chatterbox-v1-GGUF — Arabic T3 variant