mirror of
https://github.com/YuzuZensai/spleeter.git
synced 2026-01-06 04:32:43 +00:00
🚧 typer finalization
This commit is contained in:
Faylixe
@@ -3,143 +3,194 @@
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""" TO DOCUMENT """
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from functools import partial
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from pathlib import Path
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from os.path import join
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from spleeter.separator import STFTBackend
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from tempfile import gettempdir
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from typing import List
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from .audio import Codec
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from .audio.adapter import AudioAdapter
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from .options import *
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from .dataset import get_training_dataset, get_validation_dataset
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from .model import model_fn
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from .model.provider import ModelProvider
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from .separator import Separator
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from .utils.configuration import load_configuration
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from .utils.logging import get_logger
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# pyright: reportMissingImports=false
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# pylint: disable=import-error
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from typer import Argument, Option, Typer
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from typer.models import OptionInfo
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import tensorflow as tf
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from typer import Exit, Typer
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# pylint: enable=import-error
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spleeter: Typer = Typer()
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""" """
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AudioOutput: OptionInfo = Option(
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join(gettempdir(), 'separated_audio'),
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help='Path of the output directory to write audio files in')
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AudioSTFTBackend: OptionInfo = Option(
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STFTBackend.AUTO,
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'--stft-backend',
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'-B',
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case_sensitive=False,
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help=(
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'Who should be in charge of computing the stfts. Librosa is faster '
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'than tensorflow on CPU and uses less memory. "auto" will use '
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'tensorflow when GPU acceleration is available and librosa when not'))
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AudioAdapterDescriptor: OptionInfo = Option(
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'spleeter.audio.ffmpeg.FFMPEGProcessAudioAdapter',
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help='Name of the audio adapter to use for audio I/O')
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MWF: OptionInfo = Option(
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False,
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'--mwf',
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help='Whether to use multichannel Wiener filtering for separation')
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ModelParameters: OptionInfo = Option(
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'spleeter:2stems',
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help='JSON filename that contains params')
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Verbose: OptionInfo = Option(
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False,
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'--verbose',
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help='Enable verbose logs')
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@spleeter.command()
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def train(
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adapter=None,
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verbose: bool = Verbose,
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params_filename: str = ModelParameters,
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data: Path = Option(
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...,
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exists=True,
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dir_okay=True,
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file_okay=False,
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readable=True,
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resolve_path=True,
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help='Path of the folder containing audio data for training')
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) -> None:
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adapter: str = AudioAdapterOption,
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data: Path = TrainingDataDirectoryOption,
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params_filename: str = ModelParametersOption,
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verbose: bool = VerboseOption) -> None:
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"""
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Train a source separation model
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"""
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pass
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# TODO: try / catch or custom decorator for function handling.
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# TODO: handle verbose flag ?
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audio_adapter = AudioAdapter.get(adapter)
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audio_path = str(data)
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params = load_configuration(params_filename)
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session_config = tf.compat.v1.ConfigProto()
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session_config.gpu_options.per_process_gpu_memory_fraction = 0.45
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estimator = tf.estimator.Estimator(
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model_fn=model_fn,
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model_dir=params['model_dir'],
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params=params,
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config=tf.estimator.RunConfig(
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save_checkpoints_steps=params['save_checkpoints_steps'],
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tf_random_seed=params['random_seed'],
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save_summary_steps=params['save_summary_steps'],
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session_config=session_config,
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log_step_count_steps=10,
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keep_checkpoint_max=2))
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input_fn = partial(get_training_dataset, params, audio_adapter, audio_path)
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train_spec = tf.estimator.TrainSpec(
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input_fn=input_fn,
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max_steps=params['train_max_steps'])
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input_fn = partial(
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get_validation_dataset,
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params,
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audio_adapter,
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audio_path)
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evaluation_spec = tf.estimator.EvalSpec(
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input_fn=input_fn,
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steps=None,
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throttle_secs=params['throttle_secs'])
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get_logger().info('Start model training')
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tf.estimator.train_and_evaluate(estimator, train_spec, evaluation_spec)
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ModelProvider.writeProbe(params['model_dir'])
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get_logger().info('Model training done')
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_SPLIT = 'test'
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_MIXTURE = 'mixture.wav'
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_AUDIO_DIRECTORY = 'audio'
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_METRICS_DIRECTORY = 'metrics'
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_INSTRUMENTS = ('vocals', 'drums', 'bass', 'other')
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_METRICS = ('SDR', 'SAR', 'SIR', 'ISR')
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def _compute_musdb_metrics(
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arguments,
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musdb_root_directory,
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audio_output_directory):
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""" Generates musdb metrics fro previsouly computed audio estimation.
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:param arguments: Entrypoint arguments.
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:param audio_output_directory: Directory to get audio estimation from.
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:returns: Path of generated metrics directory.
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"""
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metrics_output_directory = join(
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arguments.output_path,
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_METRICS_DIRECTORY)
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get_logger().info('Starting musdb evaluation (this could be long) ...')
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try:
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import musdb
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import museval
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except ImportError:
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logger = get_logger()
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logger.error('Extra dependencies musdb and museval not found')
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logger.error('Please install musdb and museval first, abort')
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raise Exit(10)
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dataset = musdb.DB(
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root=musdb_root_directory,
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is_wav=True,
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subsets=[_SPLIT])
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museval.eval_mus_dir(
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dataset=dataset,
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estimates_dir=audio_output_directory,
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output_dir=metrics_output_directory)
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get_logger().info('musdb evaluation done')
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return metrics_output_directory
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def _compile_metrics(metrics_output_directory):
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""" Compiles metrics from given directory and returns
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results as dict.
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:param metrics_output_directory: Directory to get metrics from.
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:returns: Compiled metrics as dict.
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"""
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songs = glob(join(metrics_output_directory, 'test/*.json'))
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index = pd.MultiIndex.from_tuples(
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product(_INSTRUMENTS, _METRICS),
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names=['instrument', 'metric'])
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pd.DataFrame([], index=['config1', 'config2'], columns=index)
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metrics = {
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instrument: {k: [] for k in _METRICS}
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for instrument in _INSTRUMENTS}
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for song in songs:
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with open(song, 'r') as stream:
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data = json.load(stream)
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for target in data['targets']:
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instrument = target['name']
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for metric in _METRICS:
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sdr_med = np.median([
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frame['metrics'][metric]
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for frame in target['frames']
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if not np.isnan(frame['metrics'][metric])])
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metrics[instrument][metric].append(sdr_med)
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return metrics
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@spleeter.command()
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def evaluate(
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adapter: str = AudioAdapterDescriptor,
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output_path: Path = AudioOutput,
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stft_backend: STFTBackend = AudioSTFTBackend,
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params_filename: str = ModelParameters,
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mwf: bool = MWF,
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verbose: bool = Verbose,
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mus_dir: Path = Option(
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...,
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'--mus_dir',
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exists=True,
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dir_okay=True,
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file_okay=False,
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readable=True,
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resolve_path=True,
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help='Path to musDB dataset directory')
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) -> None:
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adapter: str = AudioAdapterOption,
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output_path: Path = AudioAdapterOption,
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stft_backend: STFTBackend = AudioSTFTBackendOption,
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params_filename: str = ModelParametersOption,
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mus_dir: Path = MUSDBDirectoryOption,
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mwf: bool = MWFOption,
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verbose: bool = VerboseOption) -> None:
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"""
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Evaluate a model on the musDB test dataset
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"""
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pass
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# Separate musdb sources.
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audio_output_directory = _separate_evaluation_dataset(
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arguments,
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mus_dir,
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params)
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# Compute metrics with musdb.
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metrics_output_directory = _compute_musdb_metrics(
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arguments,
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mus_dir,
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audio_output_directory)
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# Compute and pretty print median metrics.
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metrics = _compile_metrics(metrics_output_directory)
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for instrument, metric in metrics.items():
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get_logger().info('%s:', instrument)
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for metric, value in metric.items():
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get_logger().info('%s: %s', metric, f'{np.median(value):.3f}')
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return metrics
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@spleeter.commmand()
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def separate(
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adapter: str = AudioAdapterDescriptor,
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output_path: Path = AudioOutput,
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stft_backend: STFTBackend = AudioSTFTBackend,
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params_filename: str = ModelParameters,
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mwf: bool = MWF,
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verbose: bool = Verbose,
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files: List[Path] = Argument(
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...,
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help='List of input audio file path',
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exists=True,
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file_okay=True,
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dir_okay=False,
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readable=True,
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resolve_path=True),
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filename_format: str = Option(
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'{filename}/{instrument}.{codec}',
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help=(
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'Template string that will be formatted to generated'
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'output filename. Such template should be Python formattable'
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'string, and could use {filename}, {instrument}, and {codec}'
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'variables')),
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duration: float = Option(
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600.,
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help=(
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'Set a maximum duration for processing audio '
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'(only separate offset + duration first seconds of '
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'the input file)')),
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offset: float = Option(
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0.,
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'--offset',
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'-s',
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help='Set the starting offset to separate audio from'),
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codec: Codec = Option(
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Codec.WAV,
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help='Audio codec to be used for the separated output'),
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bitrate: str = Option(
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'128k',
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help='Audio bitrate to be used for the separated output')
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) -> None:
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adapter: str = AudioAdapterOption,
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bitrate: str = AudioBitrateOption,
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codec: Codec = AudioCodecOption,
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duration: float = AudioDurationOption,
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files: List[Path] = AudioInputArgument,
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offset: float = AudioOffsetOption,
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output_path: Path = AudioAdapterOption,
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stft_backend: STFTBackend = AudioSTFTBackendOption,
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filename_format: str = FilenameFormatOption,
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params_filename: str = ModelParametersOption,
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mwf: bool = MWFOption,
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verbose: bool = VerboseOption) -> None:
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"""
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Separate audio file(s)
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"""
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@@ -153,7 +204,7 @@ def separate(
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audio_adapter: AudioAdapter = AudioAdapter.get(adapter)
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separator: Separator = Separator(
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params_filename,
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MWF=MWF,
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MWF=mwf,
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stft_backend=stft_backend)
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for filename in files:
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separator.separate_to_file(
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@@ -1,197 +0,0 @@
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#!/usr/bin/env python
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# coding: utf8
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""" This modules provides spleeter command as well as CLI parsing methods. """
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from tempfile import gettempdir
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from os.path import join
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from ..separator import STFTBackend
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from ..audio import Codec
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from typer import Argument, Option
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from typer.models import ArgumentInfo, OptionInfo
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__email__ = 'spleeter@deezer.com'
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__author__ = 'Deezer Research'
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__license__ = 'MIT License'
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AudioInput: ArgumentInfo = Argument(
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...,
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help='List of input audio file path',
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exists=True,
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file_okay=True,
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dir_okay=False,
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readable=True,
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resolve_path=True)
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AudioOutput: OptionInfo = Option(
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join(gettempdir(), 'separated_audio'),
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'--output_path',
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'-o',
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help='Path of the output directory to write audio files in')
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AudioOffset: OptionInfo = Option(
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0.,
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'--offset',
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'-s',
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help='Set the starting offset to separate audio from')
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AudioDuration: OptionInfo = Option(
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600.,
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'--duration',
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'-d',
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help=(
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'Set a maximum duration for processing audio '
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'(only separate offset + duration first seconds of '
|
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'the input file)'))
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FilenameFormat: OptionInfo = Option(
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'{filename}/{instrument}.{codec}',
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'--filename_format',
|
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'-f',
|
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help=(
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'Template string that will be formatted to generated'
|
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'output filename. Such template should be Python formattable'
|
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'string, and could use {filename}, {instrument}, and {codec}'
|
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'variables'))
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ModelParameters: OptionInfo = Option(
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'spleeter:2stems',
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'--params_filename',
|
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'-p',
|
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help='JSON filename that contains params')
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AudioSTFTBackend: OptionInfo = Option(
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STFTBackend.AUTO,
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'--stft-backend',
|
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'-B',
|
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case_sensitive=False,
|
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help=(
|
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'Who should be in charge of computing the stfts. Librosa is faster '
|
||||
'than tensorflow on CPU and uses less memory. "auto" will use '
|
||||
'tensorflow when GPU acceleration is available and librosa when not'))
|
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|
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AudioCodec: OptionInfo = Option(
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Codec.WAV,
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'--codec',
|
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'-c',
|
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help='Audio codec to be used for the separated output')
|
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|
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AudioBitrate: OptionInfo = Option(
|
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'128k',
|
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'--bitrate',
|
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'-b',
|
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help='Audio bitrate to be used for the separated output')
|
||||
|
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MWF: OptionInfo = Option(
|
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False,
|
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'--mwf',
|
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help='Whether to use multichannel Wiener filtering for separation')
|
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|
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MUSDBDirectory: OptionInfo = Option(
|
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...,
|
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'--mus_dir',
|
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exists=True,
|
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dir_okay=True,
|
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file_okay=False,
|
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readable=True,
|
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resolve_path=True,
|
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help='Path to musDB dataset directory')
|
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|
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TrainingDataDirectory: OptionInfo = Option(
|
||||
...,
|
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'--data',
|
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'-d',
|
||||
exists=True,
|
||||
dir_okay=True,
|
||||
file_okay=False,
|
||||
readable=True,
|
||||
resolve_path=True,
|
||||
help='Path of the folder containing audio data for training')
|
||||
|
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AudioAdapter: OptionInfo = Option(
|
||||
'spleeter.audio.ffmpeg.FFMPEGProcessAudioAdapter',
|
||||
'--adapter',
|
||||
'-a',
|
||||
help='Name of the audio adapter to use for audio I/O')
|
||||
|
||||
Verbose: OptionInfo = Option(
|
||||
False,
|
||||
'--verbose',
|
||||
help='Enable verbose logs')
|
||||
|
||||
|
||||
def _add_common_options(parser):
|
||||
""" Add common option to the given parser.
|
||||
|
||||
:param parser: Parser to add common opt to.
|
||||
"""
|
||||
parser.add_argument('-a', '--adapter', **OPT_ADAPTER)
|
||||
parser.add_argument('-p', '--params_filename', **OPT_PARAMS)
|
||||
parser.add_argument('--verbose', **OPT_VERBOSE)
|
||||
|
||||
|
||||
def _create_train_parser(parser_factory):
|
||||
""" Creates an argparser for training command
|
||||
|
||||
:param parser_factory: Factory to use to create parser instance.
|
||||
:returns: Created and configured parser.
|
||||
"""
|
||||
parser = parser_factory('train', help='Train a source separation model')
|
||||
_add_common_options(parser)
|
||||
parser.add_argument('-d', '--data', **OPT_DATA)
|
||||
return parser
|
||||
|
||||
|
||||
def _create_evaluate_parser(parser_factory):
|
||||
""" Creates an argparser for evaluation command
|
||||
|
||||
:param parser_factory: Factory to use to create parser instance.
|
||||
:returns: Created and configured parser.
|
||||
"""
|
||||
parser = parser_factory(
|
||||
'evaluate',
|
||||
help='Evaluate a model on the musDB test dataset')
|
||||
_add_common_options(parser)
|
||||
parser.add_argument('-o', '--output_path', **OPT_OUTPUT)
|
||||
parser.add_argument('--mus_dir', **OPT_MUSDB)
|
||||
parser.add_argument('-m', '--mwf', **OPT_MWF)
|
||||
parser.add_argument('-B', '--stft-backend', **OPT_STFT_BACKEND)
|
||||
return parser
|
||||
|
||||
|
||||
def _create_separate_parser(parser_factory):
|
||||
""" Creates an argparser for separation command
|
||||
|
||||
:param parser_factory: Factory to use to create parser instance.
|
||||
:returns: Created and configured parser.
|
||||
"""
|
||||
parser = parser_factory('separate', help='Separate audio files')
|
||||
_add_common_options(parser)
|
||||
parser.add_argument('-i', '--inputs', **OPT_INPUT)
|
||||
parser.add_argument('-o', '--output_path', **OPT_OUTPUT)
|
||||
parser.add_argument('-f', '--filename_format', **OPT_FORMAT)
|
||||
parser.add_argument('-d', '--duration', **OPT_DURATION)
|
||||
parser.add_argument('-s', '--offset', **OPT_OFFSET)
|
||||
parser.add_argument('-c', '--codec', **OPT_CODEC)
|
||||
parser.add_argument('-b', '--birate', **OPT_BITRATE)
|
||||
parser.add_argument('-m', '--mwf', **OPT_MWF)
|
||||
parser.add_argument('-B', '--stft-backend', **OPT_STFT_BACKEND)
|
||||
return parser
|
||||
|
||||
|
||||
def create_argument_parser():
|
||||
""" Creates overall command line parser for Spleeter.
|
||||
|
||||
:returns: Created argument parser.
|
||||
"""
|
||||
parser = ArgumentParser(prog='spleeter')
|
||||
subparsers = parser.add_subparsers()
|
||||
subparsers.dest = 'command'
|
||||
subparsers.required = True
|
||||
_create_separate_parser(subparsers.add_parser)
|
||||
_create_train_parser(subparsers.add_parser)
|
||||
_create_evaluate_parser(subparsers.add_parser)
|
||||
return parser
|
||||
@@ -163,5 +163,4 @@ def entrypoint(arguments, params):
|
||||
get_logger().info('%s:', instrument)
|
||||
for metric, value in metric.items():
|
||||
get_logger().info('%s: %s', metric, f'{np.median(value):.3f}')
|
||||
|
||||
return metrics
|
||||
|
||||
123
spleeter/options.py
Normal file
123
spleeter/options.py
Normal file
@@ -0,0 +1,123 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf8
|
||||
|
||||
""" This modules provides spleeter command as well as CLI parsing methods. """
|
||||
|
||||
from tempfile import gettempdir
|
||||
from os.path import join
|
||||
|
||||
from .separator import STFTBackend
|
||||
from .audio import Codec
|
||||
|
||||
from typer import Argument, Option
|
||||
from typer.models import ArgumentInfo, OptionInfo
|
||||
|
||||
__email__ = 'spleeter@deezer.com'
|
||||
__author__ = 'Deezer Research'
|
||||
__license__ = 'MIT License'
|
||||
|
||||
AudioInputArgument: ArgumentInfo = Argument(
|
||||
...,
|
||||
help='List of input audio file path',
|
||||
exists=True,
|
||||
file_okay=True,
|
||||
dir_okay=False,
|
||||
readable=True,
|
||||
resolve_path=True)
|
||||
|
||||
AudioAdapterOption: OptionInfo = Option(
|
||||
'spleeter.audio.ffmpeg.FFMPEGProcessAudioAdapter',
|
||||
'--adapter',
|
||||
'-a',
|
||||
help='Name of the audio adapter to use for audio I/O')
|
||||
|
||||
AudioOutputOption: OptionInfo = Option(
|
||||
join(gettempdir(), 'separated_audio'),
|
||||
'--output_path',
|
||||
'-o',
|
||||
help='Path of the output directory to write audio files in')
|
||||
|
||||
AudioOffsetOption: OptionInfo = Option(
|
||||
0.,
|
||||
'--offset',
|
||||
'-s',
|
||||
help='Set the starting offset to separate audio from')
|
||||
|
||||
AudioDurationOption: OptionInfo = Option(
|
||||
600.,
|
||||
'--duration',
|
||||
'-d',
|
||||
help=(
|
||||
'Set a maximum duration for processing audio '
|
||||
'(only separate offset + duration first seconds of '
|
||||
'the input file)'))
|
||||
|
||||
AudioSTFTBackendOption: OptionInfo = Option(
|
||||
STFTBackend.AUTO,
|
||||
'--stft-backend',
|
||||
'-B',
|
||||
case_sensitive=False,
|
||||
help=(
|
||||
'Who should be in charge of computing the stfts. Librosa is faster '
|
||||
'than tensorflow on CPU and uses less memory. "auto" will use '
|
||||
'tensorflow when GPU acceleration is available and librosa when not'))
|
||||
|
||||
AudioCodecOption: OptionInfo = Option(
|
||||
Codec.WAV,
|
||||
'--codec',
|
||||
'-c',
|
||||
help='Audio codec to be used for the separated output')
|
||||
|
||||
AudioBitrateOption: OptionInfo = Option(
|
||||
'128k',
|
||||
'--bitrate',
|
||||
'-b',
|
||||
help='Audio bitrate to be used for the separated output')
|
||||
|
||||
FilenameFormatOption: OptionInfo = Option(
|
||||
'{filename}/{instrument}.{codec}',
|
||||
'--filename_format',
|
||||
'-f',
|
||||
help=(
|
||||
'Template string that will be formatted to generated'
|
||||
'output filename. Such template should be Python formattable'
|
||||
'string, and could use {filename}, {instrument}, and {codec}'
|
||||
'variables'))
|
||||
|
||||
ModelParametersOption: OptionInfo = Option(
|
||||
'spleeter:2stems',
|
||||
'--params_filename',
|
||||
'-p',
|
||||
help='JSON filename that contains params')
|
||||
|
||||
|
||||
MWFOption: OptionInfo = Option(
|
||||
False,
|
||||
'--mwf',
|
||||
help='Whether to use multichannel Wiener filtering for separation')
|
||||
|
||||
MUSDBDirectoryOption: OptionInfo = Option(
|
||||
...,
|
||||
'--mus_dir',
|
||||
exists=True,
|
||||
dir_okay=True,
|
||||
file_okay=False,
|
||||
readable=True,
|
||||
resolve_path=True,
|
||||
help='Path to musDB dataset directory')
|
||||
|
||||
TrainingDataDirectoryOption: OptionInfo = Option(
|
||||
...,
|
||||
'--data',
|
||||
'-d',
|
||||
exists=True,
|
||||
dir_okay=True,
|
||||
file_okay=False,
|
||||
readable=True,
|
||||
resolve_path=True,
|
||||
help='Path of the folder containing audio data for training')
|
||||
|
||||
VerboseOption: OptionInfo = Option(
|
||||
False,
|
||||
'--verbose',
|
||||
help='Enable verbose logs')
|
||||
Reference in New Issue
Block a user