Instructions to use byzp/muscriptor-large-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use byzp/muscriptor-large-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # 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
Install 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-gguf
Use 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-gguf
Build 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-gguf
Use Docker
docker model run hf.co/byzp/muscriptor-large-gguf
- LM Studio
- Jan
- Ollama
How to use byzp/muscriptor-large-gguf with Ollama:
ollama run hf.co/byzp/muscriptor-large-gguf
- Unsloth Desktop
- Docker Model Runner
How to use byzp/muscriptor-large-gguf with Docker Model Runner:
docker model run hf.co/byzp/muscriptor-large-gguf
- Lemonade
How to use byzp/muscriptor-large-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull byzp/muscriptor-large-gguf
Run and chat with the model
lemonade run user.muscriptor-large-gguf-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 11,429 Bytes
53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f d4a85dd 53b111f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 | """Persistent FastAPI queue for MuScriptor transcription."""
import argparse
import asyncio
import hashlib
import json
import os
import re
import subprocess
import threading
from contextlib import asynccontextmanager
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from uuid import uuid4
import uvicorn
from fastapi import FastAPI, File, HTTPException, Query, UploadFile
from fastapi.responses import FileResponse
from muscriptor_cli import MuScriptorLlama
def _convert_to_wav(input_path: Path, output_path: Path) -> None:
cmd = [
"ffmpeg", "-y", "-i", str(input_path),
"-ac", "1", "-ar", "16000",
"-c:a", "pcm_s16le",
str(output_path),
]
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
raise RuntimeError(f"ffmpeg failed: {result.stderr}")
def _now() -> str:
return datetime.now(timezone.utc).isoformat()
def _md5(path: Path) -> str:
digest = hashlib.md5()
with path.open("rb") as file:
for chunk in iter(lambda: file.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def _safe_name(name: str | None) -> str:
name = Path(name or "audio.bin").name
name = re.sub(r"[^A-Za-z0-9._-]", "_", name).strip("._")
return name[:160] or "audio.bin"
def _task_response(task: dict[str, Any]) -> dict[str, Any]:
response = {
"sequence": task["sequence"],
"status": task["status"],
"completed": task["status"] == "completed",
"filename": task.get("display_name", ""),
}
if task["status"] == "completed":
response["md5"] = task["midi_md5"]
elif task["status"] == "error":
response["error"] = task["error"]
return response
class TaskStore:
def __init__(self, data_dir: Path):
self.data_dir = data_dir.resolve()
self.audio_dir = self.data_dir / "audio"
self.midi_dir = self.data_dir / "midi"
self.audio_dir.mkdir(parents=True, exist_ok=True)
self.midi_dir.mkdir(parents=True, exist_ok=True)
self.path = self.data_dir / "muscriptor_tasks.json"
self._lock = threading.Lock()
self._data: dict[str, Any] = {"next_sequence": 1, "tasks": {}}
self._load()
def _load(self) -> None:
if not self.path.exists():
return
with self.path.open("r", encoding="utf-8") as file:
data = json.load(file)
if not isinstance(data.get("tasks"), dict):
raise ValueError(f"invalid task database: {self.path}")
self._data = data
def _save_locked(self) -> None:
temp = self.path.with_name(f".{self.path.name}.{uuid4().hex}.tmp")
with temp.open("w", encoding="utf-8") as file:
json.dump(self._data, file, ensure_ascii=False, indent=2)
file.flush()
os.fsync(file.fileno())
temp.replace(self.path)
def add(self, original_name: str) -> dict[str, Any]:
with self._lock:
sequence = int(self._data["next_sequence"])
self._data["next_sequence"] = sequence + 1
suffix = Path(original_name or "audio.bin").suffix or ".bin"
safe_stem = _safe_name(original_name).rsplit(".", 1)[0] if "." in _safe_name(original_name) else _safe_name(original_name)
audio_name = f"audio_{sequence:08d}_{safe_stem}{suffix}"
midi_name = f"midi_{sequence:08d}.mid"
display_name = Path(original_name or "audio").stem
task = {
"sequence": sequence,
"status": "uploading",
"audio_path": audio_name,
"midi_path": midi_name,
"display_name": display_name,
"midi_md5": None,
"error": None,
"created_at": _now(),
"updated_at": _now(),
}
self._data["tasks"][str(sequence)] = task
self._save_locked()
return dict(task)
def get(self, sequence: int) -> dict[str, Any] | None:
with self._lock:
task = self._data["tasks"].get(str(sequence))
return dict(task) if task else None
def page(self, page: int, page_size: int = 30) -> tuple[list[dict[str, Any]], int]:
with self._lock:
tasks = sorted(
self._data["tasks"].values(),
key=lambda task: int(task["sequence"]),
reverse=True,
)
start = (page - 1) * page_size
return [dict(task) for task in tasks[start : start + page_size]], len(tasks)
def update(self, sequence: int, **values: Any) -> dict[str, Any]:
with self._lock:
task = self._data["tasks"][str(sequence)]
task.update(values, updated_at=_now())
self._save_locked()
return dict(task)
def recoverable(self) -> list[int]:
with self._lock:
result = []
changed = False
for task in self._data["tasks"].values():
if task["status"] == "uploading":
task.update(
status="error",
error="upload was interrupted before completion",
updated_at=_now(),
)
changed = True
continue
if task["status"] in {"queued", "processing"}:
task.update(status="queued", error=None, updated_at=_now())
result.append(int(task["sequence"]))
changed = True
if changed:
self._save_locked()
return sorted(result)
def find_by_md5(self, midi_md5: str) -> dict[str, Any] | None:
with self._lock:
for task in self._data["tasks"].values():
if task["status"] == "completed" and task["midi_md5"] == midi_md5:
return dict(task)
return None
class TranscriptionQueue:
def __init__(self, store: TaskStore, weights: str, server_url: str, parallel: int):
self.store = store
self.weights = weights
self.server_url = server_url
self.parallel = parallel
self.queue: asyncio.Queue[int] = asyncio.Queue()
self.model: MuScriptorLlama | None = None
async def start(self) -> None:
for sequence in self.store.recoverable():
self.queue.put_nowait(sequence)
def _transcribe(self, sequence: int) -> None:
task = self.store.get(sequence)
if task is None:
return
if self.model is None:
self.model = MuScriptorLlama(
self.weights,
server_url=self.server_url,
parallel=self.parallel,
)
audio_path = self.store.audio_dir / task["audio_path"]
wav_path = audio_path.with_suffix(".wav")
_convert_to_wav(audio_path, wav_path)
try:
midi_path = self.store.midi_dir / task["midi_path"]
midi = self.model.transcribe_to_midi(str(wav_path))
finally:
wav_path.unlink(missing_ok=True)
midi_path.write_bytes(midi)
self.store.update(sequence, status="completed", midi_md5=_md5(midi_path))
async def worker(self) -> None:
while True:
sequence = await self.queue.get()
try:
self.store.update(sequence, status="processing", error=None)
await asyncio.to_thread(self._transcribe, sequence)
except asyncio.CancelledError:
raise
except Exception as exc:
self.store.update(sequence, status="error", error=str(exc))
finally:
self.queue.task_done()
def create_app(
data_dir: Path = Path.cwd(),
weights: str = "model.safetensors",
server_url: str = "http://127.0.0.1:8081",
parallel: int = 8,
) -> FastAPI:
store = TaskStore(data_dir)
manager = TranscriptionQueue(store, weights, server_url, parallel)
@asynccontextmanager
async def lifespan(_: FastAPI):
await manager.start()
worker = asyncio.create_task(manager.worker())
try:
yield
finally:
worker.cancel()
await asyncio.gather(worker, return_exceptions=True)
app = FastAPI(title="MuScriptor API", lifespan=lifespan)
app.state.task_store = store
app.state.transcription_queue = manager
@app.post("/tasks", status_code=202)
async def submit_audio(file: UploadFile = File(...)):
task = store.add(file.filename or "audio.bin")
audio_path = store.audio_dir / task["audio_path"]
try:
with audio_path.open("wb") as output:
while chunk := await file.read(1024 * 1024):
output.write(chunk)
if audio_path.stat().st_size == 0:
raise ValueError("uploaded audio file is empty")
except Exception as exc:
audio_path.unlink(missing_ok=True)
store.update(task["sequence"], status="error", error=str(exc))
return {"success": False, "sequence": task["sequence"], "error": str(exc)}
finally:
await file.close()
store.update(task["sequence"], status="queued")
manager.queue.put_nowait(task["sequence"])
return {"success": True, "sequence": task["sequence"]}
@app.get("/tasks")
async def list_tasks(page: int = Query(default=1, ge=1)):
page_size = 30
tasks, total = store.page(page, page_size)
return {
"page": page,
"page_size": page_size,
"total": total,
"total_pages": (total + page_size - 1) // page_size,
"tasks": [_task_response(task) for task in tasks],
}
@app.get("/tasks/{sequence}")
async def task_status(sequence: int):
task = store.get(sequence)
if task is None:
raise HTTPException(status_code=404, detail="task not found")
return _task_response(task)
@app.get("/midi/{midi_md5}")
async def download_midi(midi_md5: str):
if not re.fullmatch(r"[0-9a-f]{32}", midi_md5):
raise HTTPException(status_code=404, detail="MIDI not found")
task = store.find_by_md5(midi_md5)
if task is None:
raise HTTPException(status_code=404, detail="MIDI not found")
path = store.midi_dir / task["midi_path"]
if not path.is_file() or _md5(path) != midi_md5:
raise HTTPException(status_code=404, detail="MIDI not found")
return FileResponse(path, media_type="audio/midi", filename=path.name)
return app
app = create_app()
def main() -> None:
parser = argparse.ArgumentParser(description="MuScriptor FastAPI service")
parser.add_argument("--host", default="0.0.0.0")
parser.add_argument("--port", type=int, default=1240)
parser.add_argument("--weights", default="conditioning_weights.safetensors")
parser.add_argument("--server", default="http://127.0.0.1:8081")
parser.add_argument("--parallel", type=int, default=8)
args = parser.parse_args()
uvicorn.run(
create_app(weights=args.weights, server_url=args.server, parallel=args.parallel),
host=args.host,
port=args.port,
)
if __name__ == "__main__":
main()
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