How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf byzp/muscriptor-large-gguf# Run inference directly in the terminal:
llama cli -hf byzp/muscriptor-large-ggufUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf byzp/muscriptor-large-gguf# Run inference directly in the terminal:
./llama-cli -hf byzp/muscriptor-large-ggufBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf byzp/muscriptor-large-gguf# Run inference directly in the terminal:
./build/bin/llama-cli -hf byzp/muscriptor-large-ggufUse Docker
docker model run hf.co/byzp/muscriptor-large-ggufQuick Links
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Check out the documentation for more information.
MuScriptor
Audio-to-MIDI transcription using llama.cpp as the transformer backend.
8GB VRAM, 8x fast
Prerequisites
- Python with torch, torchaudio
- https://github.com/byzp/llama.cpp built with
llama-serveror download from this repo(linux amd64 cu128) - Model file:
model_fp32_8k.ggufconditioning_weights.safetensors
Extract Conditioning Weights
Only needed once, if starting from the full safetensors:
python extract_conditioning_weights.py
This creates conditioning_weights.safetensors (~11 MB) from the full model weights.
This model can also be downloaded from this repo
CLI Transcription
Terminal 1 — start llama-server:
llama-server -m model_fp32_8k.gguf --port 8081 -ngl 99 --parallel 8
Terminal 2 — run transcription:
python muscriptor_cli.py audio.mp3 -o out.mid
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Hardware compatibility
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf byzp/muscriptor-large-gguf# Run inference directly in the terminal: llama cli -hf byzp/muscriptor-large-gguf