cstr/granite-speech-4.1-2b-GGUF

granite-speech-4.1-2b — GGUF

GGUF conversions of ibm-granite/granite-speech-4.1-2b for use with CrispASR.

Files

File	Quantisation	Size	Notes
granite-speech-4.1-2b-f16.gguf	F16	~5.2 GB	Encoder + projector in F32, LLM weights in F16 — full parity reference
granite-speech-4.1-2b-q4_k.gguf	Q4_K	~2.94 GB	Recommended. LLM layers Q4_K; encoder + projector kept F32 (precision-sensitive). Bit-identical-quality to F16 on encoder + projector
granite-speech-4.1-2b-q4_k-f16enc.gguf	Q4_K + F16 encoder	~2.07 GB	LLM Q4_K, encoder + projector F16 (norms / biases / BN stats stay F32). Sweet spot: ~1 GB smaller than the recommended Q4_K with virtually no parity loss
granite-speech-4.1-2b-q4_k-mini.gguf	Q4_K (aggressive)	~1.7 GB	Encoder, projector and LLM all Q4_K. Smaller / faster to download but lower cosine parity (~0.93). Still produces correct transcriptions on JFK and similar clips, but expect quality regressions on harder material

Cosine parity (vs PyTorch BF16 reference, JFK 11 s clip)

Stage	F16 cos_min	Q4_K cos_min	Q4_K-f16enc cos_min	Q4_K-mini cos_min
mel_spectrogram	0.999997	0.999997	0.999997	0.999997
encoder_out	0.999908	0.999908	0.999855	0.929
projector_out	0.999995	0.999995	0.999993	0.922

The encoder is a 16-layer Conformer where Q4_K rounding error compounds across layers; the recommended Q4_K file pins those weights at F32 to preserve numerical fidelity. The -f16enc file relaxes that to F16 and ships ~1 GB smaller while keeping cosine essentially indistinguishable from F16 (every Whisper / Llama / parakeet GGUF in the wild already runs F16 weights). The -mini file applies Q4_K to every quantisable 2D weight including the encoder — useful when disk or download size matters more than transcript quality.

Tested with crispasr-diff granite-4.1 <model.gguf> <ref.gguf> samples/jfk.wav

Architecture

Granite Speech 4.1 2B is a speech-LLM with three components:

• Encoder: 16-layer Macaron Conformer (hidden 1024, 8 heads, 15-tap depthwise conv, dual CTC heads for characters + BPE). Input: 80-bin log-mel × 2-frame stacked = 160-dim, 10 ms hop.
• Projector: 2-layer BLIP-2 Q-Former with 3 learned queries per 15-frame window (5× temporal downsampling). Combined with encoder's 2× → 10 Hz acoustic token rate for the LLM.
• LLM: Granite 4.0-1B (40 layers, 2048 hidden, GQA 16/4, SwiGLU, RoPE θ=10000, μP multipliers).

Total ~2.2 B parameters. Named "2B" to reflect the full system size rather than the base LLM alone.

Usage with CrispASR

# auto-download and transcribe
crispasr --backend granite-4.1 -m auto samples/audio.wav

# or with explicit path
crispasr --backend granite-4.1 \
  -m granite-speech-4.1-2b-q4_k.gguf \
  samples/audio.wav

Supported tasks via prompt (-p flag):

Task	Prompt
ASR (raw)	can you transcribe the speech into a written format?
ASR (with punctuation)	transcribe the speech with proper punctuation and capitalization.
AST to English	translate the speech to English.
AST with punctuation	translate the speech to English with proper punctuation and capitalization.

Supported languages: English, French, German, Spanish, Portuguese, Japanese.

Conversion

# Convert HF safetensors → GGUF F16
python models/convert-granite-speech-to-gguf.py \
  --input /path/to/granite-speech-4.1-2b \
  --output granite-speech-4.1-2b-f16.gguf

# Quantise F16 → Q4_K
crispasr-quantize granite-speech-4.1-2b-f16.gguf \
                  granite-speech-4.1-2b-q4_k.gguf q4_k

The converter handles all three Granite Speech 4.x releases (4.0-1b, 4.1-2b) from the same script; parameters are read from config.json at conversion time.

Licence

Apache 2.0 — same as the original ibm-granite/granite-speech-4.1-2b.

Provenance and EU AI Act Art. 53 note

• Upstream model: ibm-granite/granite-speech-4.1-2b — published by ibm-granite.
• 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). 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.
• 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.