Bases: ExternalModelsBase, ExternalTorchBase
Source code in Broken/Externals/Spleeter.py
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60 | class BrokenSpleeter(ExternalModelsBase, ExternalTorchBase):
cache: Annotated[Path, Option("--cache", "-c",
help="[bold green](🟢 Basic)[/] Output directory for the stems, works as cache")] = \
BROKEN.DIRECTORIES.SYSTEM_TEMP/"Spleeter"
format: Annotated[str, Option("--format", "-f",
help="[bold green](🟢 Basic)[/] Output format for the stems")] = \
Field("ogg")
def _load_model(self) -> None:
self.load_torch()
gpu = ("[gpu]" if torch.cuda.is_available() else "")
install(packages="audio_separator", pypi=f"audio_separator{gpu}")
from audio_separator.separator import Separator
self._model = Separator(
output_format=self.format,
output_dir=self.cache,
)
self._model.load_model()
def separate(self, audio: Path, *, cache: bool=True) -> tuple[Path, Path]:
if not (audio := Path(audio).expanduser().resolve()).exists():
raise FileNotFoundError(f"File not found: {audio}")
# Build the expected output paths for this audio
identifier = f"{audio.stem}-{str(hashlib.md5(audio.read_bytes()).hexdigest())[0:8]}"
instrumental = (self.cache/identifier).with_suffix(f".instrumental.{self.format}")
vocals = (self.cache/identifier).with_suffix(f".vocals.{self.format}")
# Early return if already previously inferenced
if (cache and instrumental.exists() and vocals.exists()):
return instrumental, vocals
self.load_model()
# Inference and place on the proper cache
_instrumental, _vocals = self._model.separate(audio)
(self.cache/_instrumental).rename(instrumental)
(self.cache/_vocals).rename(vocals)
return instrumental, vocals
|
cache: Annotated[
Path,
Option(
--cache,
-c,
help="[bold green](🟢 Basic)[/] Output directory for the stems, works as cache",
),
] = (
BROKEN.DIRECTORIES.SYSTEM_TEMP / "Spleeter"
)
format: Annotated[
str,
Option(
--format,
-f,
help="[bold green](🟢 Basic)[/] Output format for the stems",
),
] = Field("ogg")
separate(
audio: Path, *, cache: bool = True
) -> tuple[Path, Path]
Source code in Broken/Externals/Spleeter.py
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60 | def separate(self, audio: Path, *, cache: bool=True) -> tuple[Path, Path]:
if not (audio := Path(audio).expanduser().resolve()).exists():
raise FileNotFoundError(f"File not found: {audio}")
# Build the expected output paths for this audio
identifier = f"{audio.stem}-{str(hashlib.md5(audio.read_bytes()).hexdigest())[0:8]}"
instrumental = (self.cache/identifier).with_suffix(f".instrumental.{self.format}")
vocals = (self.cache/identifier).with_suffix(f".vocals.{self.format}")
# Early return if already previously inferenced
if (cache and instrumental.exists() and vocals.exists()):
return instrumental, vocals
self.load_model()
# Inference and place on the proper cache
_instrumental, _vocals = self._model.separate(audio)
(self.cache/_instrumental).rename(instrumental)
(self.cache/_vocals).rename(vocals)
return instrumental, vocals
|