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bark-small-GGUF

Text-to-Speech
GGUF
tts
bark
crispasr
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metadata 
license: mit
tags:
  - text-to-speech
  - tts
  - bark
  - gguf
  - crispasr
language:
  - en
  - de
  - es
  - fr
  - it
  - ja
  - ko
  - pl
  - pt
  - ru
  - tr
  - zh

Bark Small — GGUF

Suno Bark (MIT license) converted to GGUF for native C++ inference with CrispASR.

Model details

  • Architecture: 3-stage hierarchical transformer (semantic → coarse → fine) + EnCodec decoder
  • Parameters: ~300M total across 3 GPT-2 sub-models
  • Output: 24 kHz mono PCM
  • Languages: 13 languages with pre-trained speaker prompts
  • German speakers: v2/de_speaker_0 through v2/de_speaker_9
  • License: MIT

Quantization table

File Quant Size Quality
bark-small-f16.gguf F16 809 MB Reference
bark-small-q8_0.gguf Q8_0 435 MB Near-lossless
bark-small-q4_k.gguf Q4_K 235 MB Good for real-time

All variants pack the 3 sub-models (text/semantic, coarse acoustic, fine acoustic) + EnCodec decoder into a single GGUF file. No companion model needed.

Usage with CrispASR 

# Auto-download and synthesize
crispasr --backend bark -m auto --tts "Hello, how are you today?" --tts-output hello.wav

# With a specific quantization
crispasr --backend bark -m bark-small-q4_k.gguf --tts "The quick brown fox" --tts-output fox.wav

# With a German speaker prompt (when supported)
crispasr --backend bark -m bark-small-q8_0.gguf --tts "Hallo Welt" --voice v2/de_speaker_3 --tts-output hallo.wav

Conversion

Produced with:

python models/convert-bark-to-gguf.py --output bark-small-f16.gguf
crispasr-quantize bark-small-f16.gguf bark-small-q8_0.gguf q8_0
crispasr-quantize bark-small-f16.gguf bark-small-q4_k.gguf q4_k

Architecture details

Stage 1 — Semantic model

  • GPT-2 (12 layers, 768-d) generating semantic tokens from text
  • BERT WordPiece tokenizer (119547 vocab)
  • Output: up to 768 semantic tokens

Stage 2 — Coarse acoustic model

  • GPT-2 (12 layers, 1024-d) converting semantic → coarse EnCodec codes
  • Alternates codebook 0/1 prediction
  • Output: 2 × ~384 coarse tokens

Stage 3 — Fine acoustic model

  • Non-causal GPT-2 (12 layers, 1024-d)
  • Fills codebooks 2-7 from codebooks 0-1
  • Output: 8 codebooks × 384 timesteps

EnCodec decoder

  • 8-codebook RVQ (1024 entries each)
  • SEANet CNN decoder with ELU activation
  • Upsample ratios [8, 5, 4, 2] → 24 kHz

Credits