mirror of
https://github.com/YuzuZensai/spleeter.git
synced 2026-01-06 04:32:43 +00:00
🚧 typer integration
This commit is contained in:
Faylixe
@@ -1,13 +1,22 @@
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#!/usr/bin/env python
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# coding: utf8
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""" TO DOCUMENT """
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"""
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Python oneliner script usage.
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USAGE: python -m spleeter {train,evaluate,separate} ...
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"""
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import json
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from functools import partial
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from itertools import product
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from glob import glob
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from os.path import join
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from pathlib import Path
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from typing import List
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from typing import Any, Container, Dict, List
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from . import SpleeterError
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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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@@ -16,11 +25,12 @@ 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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from .utils.logging import configure_logger, logger
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# pyright: reportMissingImports=false
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# pylint: disable=import-error
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import numpy as np
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import pandas as pd
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import tensorflow as tf
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from typer import Exit, Typer
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@@ -39,8 +49,7 @@ def train(
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"""
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Train a source separation model
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"""
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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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configure_logger(verbose)
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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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@@ -70,120 +79,19 @@ def train(
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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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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 = 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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# 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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logger.info('Model training done')
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@spleeter.commmand()
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def separate(
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files: List[Path] = AudioInputArgument,
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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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@@ -194,13 +102,7 @@ def separate(
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"""
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Separate audio file(s)
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"""
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# TODO: try / catch or custom decorator for function handling.
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# TODO: enable_logging()
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# TODO: handle MWF
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if verbose:
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# TODO: enable_tensorflow_logging()
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pass
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# PREV: params = load_configuration(arguments.configuration)
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configure_logger(verbose)
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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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@@ -220,6 +122,102 @@ def separate(
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separator.join()
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EVALUATION_SPLIT: str = 'test'
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EVALUATION_METRICS_DIRECTORY: str = 'metrics'
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EVALUATION_INSTRUMENTS: Container[str] = ('vocals', 'drums', 'bass', 'other')
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EVALUATION_METRICS: Container[str] = ('SDR', 'SAR', 'SIR', 'ISR')
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EVALUATION_MIXTURE: str = 'mixture.wav'
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EVALUATION_AUDIO_DIRECTORY: str = 'audio'
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def _compile_metrics(metrics_output_directory) -> Dict:
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"""
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Compiles metrics from given directory and returns results as dict.
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Parameters:
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metrics_output_directory (str):
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Directory to get metrics from.
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Returns:
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Dict:
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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(EVALUATION_INSTRUMENTS, EVALUATION_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 EVALUATION_METRICS}
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for instrument in EVALUATION_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 EVALUATION_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 = 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) -> Dict:
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"""
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Evaluate a model on the musDB test dataset
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"""
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configure_logger(verbose)
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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.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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# Separate musdb sources.
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songs = glob(join(mus_dir, EVALUATION_SPLIT, '*/'))
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mixtures = [join(song, EVALUATION_MIXTURE) for song in songs]
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audio_output_directory = join(output_path, EVALUATION_AUDIO_DIRECTORY)
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separate(
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adapter=adapter,
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params_filename=params_filename,
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files=mixtures,
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output_path=output_path,
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filename_format='{foldername}/{instrument}.{codec}',
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codec=Codec.WAV,
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mwf=mwf,
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verbose=verbose,
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stft_backend=stft_backend)
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# Compute metrics with musdb.
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metrics_output_directory = join(output_path, EVALUATION_METRICS_DIRECTORY)
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logger.info('Starting musdb evaluation (this could be long) ...')
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dataset = musdb.DB(root=mus_dir, is_wav=True, subsets=[EVALUATION_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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logger.info('musdb evaluation done')
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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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logger.info(f'{instrument}:')
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for metric, value in metric.items():
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logger.info(f'{metric}: {np.median(value):.3f}')
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return metrics
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if __name__ == '__main__':
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# TODO: warnings.filterwarnings('ignore')
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spleeter()
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try:
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spleeter()
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except SpleeterError as e:
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logger.error(e)
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@@ -1,166 +0,0 @@
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#!/usr/bin/env python
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# coding: utf8
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"""
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Entrypoint provider for performing model evaluation.
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Evaluation is performed against musDB dataset.
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USAGE: python -m spleeter evaluate \
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-p /path/to/params \
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-o /path/to/output/dir \
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[-m] \
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--mus_dir /path/to/musdb dataset
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"""
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import sys
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import json
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from argparse import Namespace
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from itertools import product
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from glob import glob
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from os.path import join, exists
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# pylint: disable=import-error
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import numpy as np
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import pandas as pd
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# pylint: enable=import-error
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from .separate import entrypoint as separate_entrypoint
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from ..utils.logging import get_logger
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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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sys.exit(1)
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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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_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 _separate_evaluation_dataset(arguments, musdb_root_directory, params):
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""" Performs audio separation on the musdb dataset from
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the given directory and params.
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:param arguments: Entrypoint arguments.
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:param musdb_root_directory: Directory to retrieve dataset from.
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:param params: Spleeter configuration to apply to separation.
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:returns: Separation output directory path.
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"""
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songs = glob(join(musdb_root_directory, _SPLIT, '*/'))
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mixtures = [join(song, _MIXTURE) for song in songs]
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audio_output_directory = join(
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arguments.output_path,
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_AUDIO_DIRECTORY)
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separate_entrypoint(
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Namespace(
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audio_adapter=arguments.audio_adapter,
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configuration=arguments.configuration,
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inputs=mixtures,
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output_path=join(audio_output_directory, _SPLIT),
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filename_format='{foldername}/{instrument}.{codec}',
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codec='wav',
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duration=600.,
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offset=0.,
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bitrate='128k',
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MWF=arguments.MWF,
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verbose=arguments.verbose,
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stft_backend=arguments.stft_backend),
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params)
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return audio_output_directory
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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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|
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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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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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|
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|
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def _compile_metrics(metrics_output_directory):
|
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""" Compiles metrics from given directory and returns
|
||||
results as dict.
|
||||
|
||||
:param metrics_output_directory: Directory to get metrics from.
|
||||
:returns: Compiled metrics as dict.
|
||||
"""
|
||||
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)
|
||||
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]
|
||||
for frame in target['frames']
|
||||
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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|
||||
|
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def entrypoint(arguments, params):
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""" Command entrypoint.
|
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|
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:param arguments: Command line parsed argument as argparse.Namespace.
|
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:param params: Deserialized JSON configuration file provided in CLI args.
|
||||
"""
|
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# Parse and check musdb directory.
|
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musdb_root_directory = arguments.mus_dir
|
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if not exists(musdb_root_directory):
|
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raise IOError(f'musdb directory {musdb_root_directory} not found')
|
||||
# Separate musdb sources.
|
||||
audio_output_directory = _separate_evaluation_dataset(
|
||||
arguments,
|
||||
musdb_root_directory,
|
||||
params)
|
||||
# Compute metrics with musdb.
|
||||
metrics_output_directory = _compute_musdb_metrics(
|
||||
arguments,
|
||||
musdb_root_directory,
|
||||
audio_output_directory)
|
||||
# Compute and pretty print median metrics.
|
||||
metrics = _compile_metrics(metrics_output_directory)
|
||||
for instrument, metric in metrics.items():
|
||||
get_logger().info('%s:', instrument)
|
||||
for metric, value in metric.items():
|
||||
get_logger().info('%s: %s', metric, f'{np.median(value):.3f}')
|
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return metrics
|
||||
@@ -1,51 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf8
|
||||
|
||||
"""
|
||||
Entrypoint provider for performing source separation.
|
||||
|
||||
USAGE: python -m spleeter separate \
|
||||
-p /path/to/params \
|
||||
-i inputfile1 inputfile2 ... inputfilen
|
||||
-o /path/to/output/dir \
|
||||
-i /path/to/audio1.wav /path/to/audio2.mp3
|
||||
"""
|
||||
|
||||
from ..audio.adapter import get_audio_adapter
|
||||
from ..separator import Separator
|
||||
|
||||
__email__ = 'spleeter@deezer.com'
|
||||
__author__ = 'Deezer Research'
|
||||
__license__ = 'MIT License'
|
||||
|
||||
|
||||
from typer import Option
|
||||
|
||||
AudioAdapter = Option()
|
||||
|
||||
|
||||
def entrypoint(arguments, params):
|
||||
""" Command entrypoint.
|
||||
|
||||
:param arguments: Command line parsed argument as argparse.Namespace.
|
||||
:param params: Deserialized JSON configuration file provided in CLI args.
|
||||
"""
|
||||
# TODO: check with output naming.
|
||||
audio_adapter = get_audio_adapter(arguments.audio_adapter)
|
||||
separator = Separator(
|
||||
arguments.configuration,
|
||||
MWF=arguments.MWF,
|
||||
stft_backend=arguments.stft_backend)
|
||||
for filename in arguments.inputs:
|
||||
separator.separate_to_file(
|
||||
filename,
|
||||
arguments.output_path,
|
||||
audio_adapter=audio_adapter,
|
||||
offset=arguments.offset,
|
||||
duration=arguments.duration,
|
||||
codec=arguments.codec,
|
||||
bitrate=arguments.bitrate,
|
||||
filename_format=arguments.filename_format,
|
||||
synchronous=False
|
||||
)
|
||||
separator.join()
|
||||
@@ -1,100 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
# coding: utf8
|
||||
|
||||
"""
|
||||
Entrypoint provider for performing model training.
|
||||
|
||||
USAGE: python -m spleeter train -p /path/to/params
|
||||
"""
|
||||
|
||||
from functools import partial
|
||||
|
||||
# pylint: disable=import-error
|
||||
import tensorflow as tf
|
||||
# pylint: enable=import-error
|
||||
|
||||
from ..audio.adapter import get_audio_adapter
|
||||
from ..dataset import get_training_dataset, get_validation_dataset
|
||||
from ..model import model_fn
|
||||
from ..model.provider import ModelProvider
|
||||
from ..utils.logging import get_logger
|
||||
|
||||
__email__ = 'spleeter@deezer.com'
|
||||
__author__ = 'Deezer Research'
|
||||
__license__ = 'MIT License'
|
||||
|
||||
|
||||
def _create_estimator(params):
|
||||
""" Creates estimator.
|
||||
|
||||
:param params: TF params to build estimator from.
|
||||
:returns: Built estimator.
|
||||
"""
|
||||
session_config = tf.compat.v1.ConfigProto()
|
||||
session_config.gpu_options.per_process_gpu_memory_fraction = 0.45
|
||||
estimator = tf.estimator.Estimator(
|
||||
model_fn=model_fn,
|
||||
model_dir=params['model_dir'],
|
||||
params=params,
|
||||
config=tf.estimator.RunConfig(
|
||||
save_checkpoints_steps=params['save_checkpoints_steps'],
|
||||
tf_random_seed=params['random_seed'],
|
||||
save_summary_steps=params['save_summary_steps'],
|
||||
session_config=session_config,
|
||||
log_step_count_steps=10,
|
||||
keep_checkpoint_max=2))
|
||||
return estimator
|
||||
|
||||
|
||||
def _create_train_spec(params, audio_adapter, audio_path):
|
||||
""" Creates train spec.
|
||||
|
||||
:param params: TF params to build spec from.
|
||||
:returns: Built train spec.
|
||||
"""
|
||||
input_fn = partial(get_training_dataset, params, audio_adapter, audio_path)
|
||||
train_spec = tf.estimator.TrainSpec(
|
||||
input_fn=input_fn,
|
||||
max_steps=params['train_max_steps'])
|
||||
return train_spec
|
||||
|
||||
|
||||
def _create_evaluation_spec(params, audio_adapter, audio_path):
|
||||
""" Setup eval spec evaluating ever n seconds
|
||||
|
||||
:param params: TF params to build spec from.
|
||||
:returns: Built evaluation spec.
|
||||
"""
|
||||
input_fn = partial(
|
||||
get_validation_dataset,
|
||||
params,
|
||||
audio_adapter,
|
||||
audio_path)
|
||||
evaluation_spec = tf.estimator.EvalSpec(
|
||||
input_fn=input_fn,
|
||||
steps=None,
|
||||
throttle_secs=params['throttle_secs'])
|
||||
return evaluation_spec
|
||||
|
||||
|
||||
def entrypoint(arguments, params):
|
||||
""" Command entrypoint.
|
||||
|
||||
:param arguments: Command line parsed argument as argparse.Namespace.
|
||||
:param params: Deserialized JSON configuration file provided in CLI args.
|
||||
"""
|
||||
audio_adapter = get_audio_adapter(arguments.audio_adapter)
|
||||
audio_path = arguments.audio_path
|
||||
estimator = _create_estimator(params)
|
||||
train_spec = _create_train_spec(params, audio_adapter, audio_path)
|
||||
evaluation_spec = _create_evaluation_spec(
|
||||
params,
|
||||
audio_adapter,
|
||||
audio_path)
|
||||
get_logger().info('Start model training')
|
||||
tf.estimator.train_and_evaluate(
|
||||
estimator,
|
||||
train_spec,
|
||||
evaluation_spec)
|
||||
ModelProvider.writeProbe(params['model_dir'])
|
||||
get_logger().info('Model training done')
|
||||
@@ -4,58 +4,41 @@
|
||||
""" Centralized logging facilities for Spleeter. """
|
||||
|
||||
import logging
|
||||
import warnings
|
||||
|
||||
from os import environ
|
||||
|
||||
# pyright: reportMissingImports=false
|
||||
# pylint: disable=import-error
|
||||
from tensorflow.compat.v1 import logging as tflogging
|
||||
# pylint: enable=import-error
|
||||
|
||||
__email__ = 'spleeter@deezer.com'
|
||||
__author__ = 'Deezer Research'
|
||||
__license__ = 'MIT License'
|
||||
|
||||
_FORMAT = '%(levelname)s:%(name)s:%(message)s'
|
||||
|
||||
formatter = logging.Formatter('%(levelname)s:%(name)s:%(message)s')
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(formatter)
|
||||
logger: logging.Logger = logging.getLogger('spleeter')
|
||||
logger.addHandler(handler)
|
||||
logger.setLevel(logging.INFO)
|
||||
|
||||
|
||||
class _LoggerHolder(object):
|
||||
""" Logger singleton instance holder. """
|
||||
|
||||
INSTANCE = None
|
||||
|
||||
|
||||
def get_tensorflow_logger():
|
||||
def configure_logger(verbose: bool) -> None:
|
||||
"""
|
||||
Configure application logger.
|
||||
|
||||
Parameters:
|
||||
verbose (bool):
|
||||
`True` to use verbose logger, `False` otherwise.
|
||||
"""
|
||||
# pylint: disable=import-error
|
||||
from tensorflow.compat.v1 import logging
|
||||
# pylint: enable=import-error
|
||||
return logging
|
||||
|
||||
|
||||
def get_logger():
|
||||
""" Returns library scoped logger.
|
||||
|
||||
:returns: Library logger.
|
||||
"""
|
||||
if _LoggerHolder.INSTANCE is None:
|
||||
formatter = logging.Formatter(_FORMAT)
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(formatter)
|
||||
logger = logging.getLogger('spleeter')
|
||||
logger.addHandler(handler)
|
||||
logger.setLevel(logging.INFO)
|
||||
_LoggerHolder.INSTANCE = logger
|
||||
return _LoggerHolder.INSTANCE
|
||||
|
||||
|
||||
def enable_tensorflow_logging():
|
||||
""" Enable tensorflow logging. """
|
||||
environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
|
||||
tf_logger = get_tensorflow_logger()
|
||||
tf_logger.set_verbosity(tf_logger.INFO)
|
||||
logger = get_logger()
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
def enable_logging():
|
||||
""" Configure default logging. """
|
||||
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
||||
tf_logger = get_tensorflow_logger()
|
||||
tf_logger.set_verbosity(tf_logger.ERROR)
|
||||
if verbose:
|
||||
environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
|
||||
tflogging.set_verbosity(tflogging.INFO)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
else:
|
||||
warnings.filterwarnings('ignore')
|
||||
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
|
||||
tflogging.set_verbosity(tflogging.ERROR)
|
||||
|
||||
Reference in New Issue
Block a user