Instructions to use cstr/kyutai-stt-2.6b-en-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Moshi
How to use cstr/kyutai-stt-2.6b-en-GGUF with Moshi:
# pip install moshi # Run the interactive web server python -m moshi.server --hf-repo "cstr/kyutai-stt-2.6b-en-GGUF" # Then open https://localhost:8998 in your browser
# pip install moshi import torch from moshi.models import loaders # Load checkpoint info from HuggingFace checkpoint = loaders.CheckpointInfo.from_hf_repo("cstr/kyutai-stt-2.6b-en-GGUF") # Load the Mimi audio codec mimi = checkpoint.get_mimi(device="cuda") mimi.set_num_codebooks(8) # Encode audio (24kHz, mono) wav = torch.randn(1, 1, 24000 * 10) # [batch, channels, samples] with torch.no_grad(): codes = mimi.encode(wav.cuda()) decoded = mimi.decode(codes) - Notebooks
- Google Colab
- Kaggle
Kyutai STT 2.6B English โ GGUF
GGUF conversion of kyutai/stt-2.6b-en for the CrispASR kyutai-stt backend.
High-quality English ASR from the Kyutai/Moshi team. Causal transformer LM conditioned on Mimi audio codec features โ streaming-capable architecture.
Architecture
- Mimi encoder: SEANet CNN + transformer + RVQ (audio -> discrete tokens)
- Causal LM: 16-layer transformer (2048-dim, RoPE, SwiGLU, RMSNorm)
- Input: 16 kHz mono audio
- Output: English text transcription
Files
| File | Size | Description |
|---|---|---|
kyutai-stt-2.6b-q4_k.gguf |
1.5 GB | Q4_K quantized (recommended) |
kyutai-stt-2.6b-q8_0.gguf |
2.8 GB | Q8_0 quantized |
kyutai-stt-2.6b-f16.gguf |
5.1 GB | F16 full precision |
kyutai-stt-2.6b-ref.gguf |
4.0 MB | Diff harness reference |
Usage
# Auto-download:
crispasr --backend kyutai-stt -m auto --auto-download -f audio.wav
# Local model:
crispasr --backend kyutai-stt -m kyutai-stt-2.6b-q4_k.gguf -f audio.wav -osrt
License
Apache 2.0 (same as the original model).
Credits
- Downloads last month
- 672
Hardware compatibility
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Model tree for cstr/kyutai-stt-2.6b-en-GGUF
Base model
kyutai/stt-2.6b-en