diff --git a/codes/scripts/audio/preparation/spleeter_filter_noisy_clips.py b/codes/scripts/audio/preparation/spleeter_filter_noisy_clips.py index cb1418b0..41b00c3b 100644 --- a/codes/scripts/audio/preparation/spleeter_filter_noisy_clips.py +++ b/codes/scripts/audio/preparation/spleeter_filter_noisy_clips.py @@ -1,14 +1,11 @@ -import multiprocessing - import argparse + +import numpy as np +from spleeter.separator import Separator from torch.utils.data import DataLoader from tqdm import tqdm -from scripts.audio.preparation.spleeter_utils.filter_noisy_clips_collector import invert_spectrogram_and_save from scripts.audio.preparation.spleeter_utils.spleeter_dataset import SpleeterDataset -from scripts.audio.preparation.spleeter_utils.spleeter_separator_mod import Separator - - def main(): @@ -21,24 +18,34 @@ def main(): args = parser.parse_args() src_dir = args.path + out_file = args.out output_sample_rate=22050 resume_file = args.resume - worker_queue = multiprocessing.Queue() - worker = multiprocessing.Process(target=invert_spectrogram_and_save, args=(args, worker_queue)) - worker.start() - loader = DataLoader(SpleeterDataset(src_dir, batch_sz=16, sample_rate=output_sample_rate, max_duration=10, partition=args.partition, partition_size=args.partition_size, resume=resume_file), batch_size=1, num_workers=1) - separator = Separator('spleeter:2stems', multiprocess=False) + separator = Separator('spleeter:2stems') + unacceptable_files = open(out_file, 'a') for batch in tqdm(loader): - audio, files, ends, stft = batch['audio'], batch['files'], batch['ends'], batch['stft'] - sep = separator.separate_spectrogram(stft.squeeze(0).numpy()) - worker_queue.put((sep['vocals'], sep['accompaniment'], audio.shape[1], files, ends)) - worker_queue.put(None) - worker.join() + audio, files, ends = batch['audio'], batch['files'], batch['ends'] + sep = separator.separate(audio.squeeze(0).numpy()) + vocals = sep['vocals'] + bg = sep['accompaniment'] + start = 0 + for path, end in zip(files, ends): + vmax = np.abs(vocals[start:end]).mean() + bmax = np.abs(bg[start:end]).mean() + start = end + + # Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough. + ratio = vmax / (bmax+.0000001) + if ratio < 18: # These values were derived empirically + unacceptable_files.write(f'{path[0]}\n') + unacceptable_files.flush() + + unacceptable_files.close() if __name__ == '__main__': diff --git a/codes/scripts/audio/preparation/spleeter_utils/filter_noisy_clips_collector.py b/codes/scripts/audio/preparation/spleeter_utils/filter_noisy_clips_collector.py deleted file mode 100644 index 1f59367c..00000000 --- a/codes/scripts/audio/preparation/spleeter_utils/filter_noisy_clips_collector.py +++ /dev/null @@ -1,28 +0,0 @@ -from scripts.audio.preparation.spleeter_utils.spleeter_separator_mod import Separator -import numpy as np - -def invert_spectrogram_and_save(args, queue): - separator = Separator('spleeter:2stems', multiprocess=False, load_tf=False) - out_file = args.out - unacceptable_files = open(out_file, 'a') - - while True: - combo = queue.get() - if combo is None: - break - vocals, bg, wavlen, files, ends = combo - vocals = separator.stft(vocals, inverse=True, length=wavlen) - bg = separator.stft(bg, inverse=True, length=wavlen) - start = 0 - for path, end in zip(files, ends): - vmax = np.abs(vocals[start:end]).mean() - bmax = np.abs(bg[start:end]).mean() - start = end - - # Only output to the "good" sample dir if the ratio of background noise to vocal noise is high enough. - ratio = vmax / (bmax+.0000001) - if ratio < 18: # These values were derived empirically - unacceptable_files.write(f'{path[0]}\n') - unacceptable_files.flush() - - unacceptable_files.close() \ No newline at end of file diff --git a/codes/scripts/audio/preparation/spleeter_utils/spleeter_dataset.py b/codes/scripts/audio/preparation/spleeter_utils/spleeter_dataset.py index e96befa4..22df89c6 100644 --- a/codes/scripts/audio/preparation/spleeter_utils/spleeter_dataset.py +++ b/codes/scripts/audio/preparation/spleeter_utils/spleeter_dataset.py @@ -6,7 +6,6 @@ from spleeter.audio.adapter import AudioAdapter from torch.utils.data import Dataset from data.util import find_audio_files -from scripts.audio.preparation.spleeter_utils.spleeter_separator_mod import Separator class SpleeterDataset(Dataset): @@ -15,7 +14,6 @@ class SpleeterDataset(Dataset): self.max_duration = max_duration self.files = find_audio_files(src_dir, include_nonwav=True) self.sample_rate = sample_rate - self.separator = Separator('spleeter:2stems', multiprocess=False, load_tf=False) # Partition files if needed. if partition_size is not None: @@ -45,25 +43,23 @@ class SpleeterDataset(Dataset): if ind >= len(self.files): break - try: - wav, sr = self.loader.load(self.files[ind], sample_rate=self.sample_rate) - assert sr == 22050 - # Get rid of all channels except one. - if wav.shape[1] > 1: - wav = wav[:, 0] + #try: + wav, sr = self.loader.load(self.files[ind], sample_rate=self.sample_rate) + assert sr == 22050 + # Get rid of all channels except one. + if wav.shape[1] > 1: + wav = wav[:, 0] - if wavs is None: - wavs = wav - else: - wavs = np.concatenate([wavs, wav]) - ends.append(wavs.shape[0]) - files.append(self.files[ind]) - except: - print(f'Error loading {self.files[ind]}') - stft = self.separator.stft(wavs) + if wavs is None: + wavs = wav + else: + wavs = np.concatenate([wavs, wav]) + ends.append(wavs.shape[0]) + files.append(self.files[ind]) + #except: + # print(f'Error loading {self.files[ind]}') return { 'audio': wavs, 'files': files, - 'ends': ends, - 'stft': stft - } + 'ends': ends + } \ No newline at end of file diff --git a/codes/scripts/audio/preparation/spleeter_utils/spleeter_separator_mod.py b/codes/scripts/audio/preparation/spleeter_utils/spleeter_separator_mod.py deleted file mode 100644 index c472f1e3..00000000 --- a/codes/scripts/audio/preparation/spleeter_utils/spleeter_separator_mod.py +++ /dev/null @@ -1,501 +0,0 @@ -#!/usr/bin/env python -# coding: utf8 - -""" - Module that provides a class wrapper for source separation. - - Modified to support directly feeding in spectrograms. - - Examples: - - ```python - >>> from spleeter.separator import Separator - >>> separator = Separator('spleeter:2stems') - >>> separator.separate(waveform, lambda instrument, data: ...) - >>> separator.separate_to_file(...) - ``` -""" - -import atexit -import os -from multiprocessing import Pool -from os.path import basename, dirname, join, splitext -from typing import Dict, Generator, Optional - -# pyright: reportMissingImports=false -# pylint: disable=import-error -import numpy as np -import tensorflow as tf -from librosa.core import istft, stft -from scipy.signal.windows import hann - -from spleeter.model.provider import ModelProvider - -from spleeter import SpleeterError -from spleeter.audio import Codec, STFTBackend -from spleeter.audio.adapter import AudioAdapter -from spleeter.audio.convertor import to_stereo -from spleeter.model import EstimatorSpecBuilder, InputProviderFactory, model_fn -from spleeter.model.provider import ModelProvider -from spleeter.types import AudioDescriptor -from spleeter.utils.configuration import load_configuration - -# pylint: enable=import-error - -__email__ = "spleeter@deezer.com" -__author__ = "Deezer Research" -__license__ = "MIT License" - - -class DataGenerator(object): - """ - Generator object that store a sample and generate it once while called. - Used to feed a tensorflow estimator without knowing the whole data at - build time. - """ - - def __init__(self) -> None: - """ Default constructor. """ - self._current_data = None - - def update_data(self, data) -> None: - """ Replace internal data. """ - self._current_data = data - - def __call__(self) -> Generator: - """ Generation process. """ - buffer = self._current_data - while buffer: - yield buffer - buffer = self._current_data - - -def create_estimator(params, MWF): - """ - Initialize tensorflow estimator that will perform separation - - Params: - - params: a dictionary of parameters for building the model - - Returns: - a tensorflow estimator - """ - # Load model. - provider: ModelProvider = ModelProvider.default() - params["model_dir"] = provider.get(params["model_dir"]) - params["MWF"] = MWF - # Setup config - session_config = tf.compat.v1.ConfigProto() - session_config.gpu_options.per_process_gpu_memory_fraction = 0.7 - config = tf.estimator.RunConfig(session_config=session_config) - # Setup estimator - estimator = tf.estimator.Estimator( - model_fn=model_fn, model_dir=params["model_dir"], params=params, config=config - ) - return estimator - - -class Separator(object): - """ A wrapper class for performing separation. """ - - def __init__( - self, - params_descriptor: str, - MWF: bool = False, - stft_backend: STFTBackend = STFTBackend.AUTO, - multiprocess: bool = True, - load_tf: bool = True - ) -> None: - """ - Default constructor. - - Parameters: - params_descriptor (str): - Descriptor for TF params to be used. - MWF (bool): - (Optional) `True` if MWF should be used, `False` otherwise. - """ - self._params = load_configuration(params_descriptor) - self._sample_rate = self._params["sample_rate"] - self._MWF = MWF - if load_tf: - self._tf_graph = tf.Graph() - else: - self._tf_graph = None - self._prediction_generator = None - self._input_provider = None - self._builder = None - self._features = None - self._session = None - if multiprocess: - self._pool = Pool() - atexit.register(self._pool.close) - else: - self._pool = None - self._tasks = [] - self._params["stft_backend"] = STFTBackend.resolve(stft_backend) - self._data_generator = DataGenerator() - - def _get_prediction_generator(self) -> Generator: - """ - Lazy loading access method for internal prediction generator - returned by the predict method of a tensorflow estimator. - - Returns: - Generator: - Generator of prediction. - """ - if self._prediction_generator is None: - estimator = create_estimator(self._params, self._MWF) - - def get_dataset(): - return tf.data.Dataset.from_generator( - self._data_generator, - output_types={"waveform": tf.float32, "audio_id": tf.string}, - output_shapes={"waveform": (None, 2), "audio_id": ()}, - ) - - self._prediction_generator = estimator.predict( - get_dataset, yield_single_examples=False - ) - return self._prediction_generator - - def join(self, timeout: int = 200) -> None: - """ - Wait for all pending tasks to be finished. - - Parameters: - timeout (int): - (Optional) task waiting timeout. - """ - while len(self._tasks) > 0: - task = self._tasks.pop() - task.get() - task.wait(timeout=timeout) - - def stft( - self, data: np.ndarray, inverse: bool = False, length: Optional[int] = None - ) -> np.ndarray: - """ - Single entrypoint for both stft and istft. This computes stft and - istft with librosa on stereo data. The two channels are processed - separately and are concatenated together in the result. The - expected input formats are: (n_samples, 2) for stft and (T, F, 2) - for istft. - - Parameters: - data (numpy.array): - Array with either the waveform or the complex spectrogram - depending on the parameter inverse - inverse (bool): - (Optional) Should a stft or an istft be computed. - length (Optional[int]): - - Returns: - numpy.ndarray: - Stereo data as numpy array for the transform. The channels - are stored in the last dimension. - """ - assert not (inverse and length is None) - data = np.asfortranarray(data) - N = self._params["frame_length"] - H = self._params["frame_step"] - win = hann(N, sym=False) - fstft = istft if inverse else stft - win_len_arg = {"win_length": None, "length": None} if inverse else {"n_fft": N} - n_channels = data.shape[-1] - out = [] - for c in range(n_channels): - d = ( - np.concatenate((np.zeros((N,)), data[:, c], np.zeros((N,)))) - if not inverse - else data[:, :, c].T - ) - s = fstft(d, hop_length=H, window=win, center=False, **win_len_arg) - if inverse: - s = s[N : N + length] - s = np.expand_dims(s.T, 2 - inverse) - out.append(s) - if len(out) == 1: - return out[0] - return np.concatenate(out, axis=2 - inverse) - - def _get_input_provider(self): - if self._input_provider is None: - self._input_provider = InputProviderFactory.get(self._params) - return self._input_provider - - def _get_features(self): - if self._features is None: - provider = self._get_input_provider() - self._features = provider.get_input_dict_placeholders() - return self._features - - def _get_builder(self): - if self._builder is None: - self._builder = EstimatorSpecBuilder(self._get_features(), self._params) - return self._builder - - def _get_session(self): - if self._session is None: - saver = tf.compat.v1.train.Saver() - provider = ModelProvider.default() - model_directory: str = provider.get(self._params["model_dir"]) - latest_checkpoint = tf.train.latest_checkpoint(model_directory) - self._session = tf.compat.v1.Session() - saver.restore(self._session, latest_checkpoint) - return self._session - - def _separate_raw_spec(self, stft: np.ndarray, audio_descriptor: AudioDescriptor) -> Dict: - with self._tf_graph.as_default(): - out = {} - features = self._get_features() - outputs = self._get_builder().outputs - if stft.shape[-1] == 1: - stft = np.concatenate([stft, stft], axis=-1) - elif stft.shape[-1] > 2: - stft = stft[:, :2] - sess = self._get_session() - outputs = sess.run( - outputs, - feed_dict=self._get_input_provider().get_feed_dict( - features, stft, audio_descriptor - ), - ) - for inst in self._get_builder().instruments: - out[inst] = outputs[inst] - return out - - def _separate_librosa( - self, waveform: np.ndarray, audio_descriptor: AudioDescriptor - ) -> Dict: - """ - Performs separation with librosa backend for STFT. - - Parameters: - waveform (numpy.ndarray): - Waveform to be separated (as a numpy array) - audio_descriptor (AudioDescriptor): - """ - with self._tf_graph.as_default(): - out = {} - features = self._get_features() - # TODO: fix the logic, build sometimes return, - # sometimes set attribute. - outputs = self._get_builder().outputs - stft = self.stft(waveform) - if stft.shape[-1] == 1: - stft = np.concatenate([stft, stft], axis=-1) - elif stft.shape[-1] > 2: - stft = stft[:, :2] - sess = self._get_session() - outputs = sess.run( - outputs, - feed_dict=self._get_input_provider().get_feed_dict( - features, stft, audio_descriptor - ), - ) - for inst in self._get_builder().instruments: - out[inst] = self.stft( - outputs[inst], inverse=True, length=waveform.shape[0] - ) - return out - - def _separate_tensorflow( - self, waveform: np.ndarray, audio_descriptor: AudioDescriptor - ) -> Dict: - """ - Performs source separation over the given waveform with tensorflow - backend. - - Parameters: - waveform (numpy.ndarray): - Waveform to be separated (as a numpy array) - audio_descriptor (AudioDescriptor): - - Returns: - Separated waveforms. - """ - if not waveform.shape[-1] == 2: - waveform = to_stereo(waveform) - prediction_generator = self._get_prediction_generator() - # NOTE: update data in generator before performing separation. - self._data_generator.update_data( - {"waveform": waveform, "audio_id": np.array(audio_descriptor)} - ) - # NOTE: perform separation. - prediction = next(prediction_generator) - prediction.pop("audio_id") - return prediction - - def separate( - self, waveform: np.ndarray, audio_descriptor: Optional[str] = "" - ) -> None: - """ - Performs separation on a waveform. - - Parameters: - waveform (numpy.ndarray): - Waveform to be separated (as a numpy array) - audio_descriptor (str): - (Optional) string describing the waveform (e.g. filename). - """ - backend: str = self._params["stft_backend"] - if backend == STFTBackend.TENSORFLOW: - return self._separate_tensorflow(waveform, audio_descriptor) - elif backend == STFTBackend.LIBROSA: - return self._separate_librosa(waveform, audio_descriptor) - raise ValueError(f"Unsupported STFT backend {backend}") - - def separate_spectrogram( - self, stft: np.ndarray, audio_descriptor: Optional[str] = "" - ) -> None: - """ - Performs separation on a spectrogram. - - Parameters: - stft (numpy.ndarray): - Spectrogram to be separated (as a numpy array) - audio_descriptor (str): - (Optional) string describing the waveform (e.g. filename). - """ - return self._separate_raw_spec(stft, audio_descriptor) - - def separate_to_file( - self, - audio_descriptor: AudioDescriptor, - destination: str, - audio_adapter: Optional[AudioAdapter] = None, - offset: int = 0, - duration: float = 600.0, - codec: Codec = Codec.WAV, - bitrate: str = "128k", - filename_format: str = "{filename}/{instrument}.{codec}", - synchronous: bool = True, - ) -> None: - """ - Performs source separation and export result to file using - given audio adapter. - - Filename format should be a Python formattable string that could - use following parameters : - - - {instrument} - - {filename} - - {foldername} - - {codec}. - - Parameters: - audio_descriptor (AudioDescriptor): - Describe song to separate, used by audio adapter to - retrieve and load audio data, in case of file based - audio adapter, such descriptor would be a file path. - destination (str): - Target directory to write output to. - audio_adapter (Optional[AudioAdapter]): - (Optional) Audio adapter to use for I/O. - offset (int): - (Optional) Offset of loaded song. - duration (float): - (Optional) Duration of loaded song (default: 600s). - codec (Codec): - (Optional) Export codec. - bitrate (str): - (Optional) Export bitrate. - filename_format (str): - (Optional) Filename format. - synchronous (bool): - (Optional) True is should by synchronous. - """ - if audio_adapter is None: - audio_adapter = AudioAdapter.default() - waveform, _ = audio_adapter.load( - audio_descriptor, - offset=offset, - duration=duration, - sample_rate=self._sample_rate, - ) - sources = self.separate(waveform, audio_descriptor) - self.save_to_file( - sources, - audio_descriptor, - destination, - filename_format, - codec, - audio_adapter, - bitrate, - synchronous, - ) - - def save_to_file( - self, - sources: Dict, - audio_descriptor: AudioDescriptor, - destination: str, - filename_format: str = "{filename}/{instrument}.{codec}", - codec: Codec = Codec.WAV, - audio_adapter: Optional[AudioAdapter] = None, - bitrate: str = "128k", - synchronous: bool = True, - ) -> None: - """ - Export dictionary of sources to files. - - Parameters: - sources (Dict): - Dictionary of sources to be exported. The keys are the name - of the instruments, and the values are `N x 2` numpy arrays - containing the corresponding intrument waveform, as - returned by the separate method - audio_descriptor (AudioDescriptor): - Describe song to separate, used by audio adapter to - retrieve and load audio data, in case of file based audio - adapter, such descriptor would be a file path. - destination (str): - Target directory to write output to. - filename_format (str): - (Optional) Filename format. - codec (Codec): - (Optional) Export codec. - audio_adapter (Optional[AudioAdapter]): - (Optional) Audio adapter to use for I/O. - bitrate (str): - (Optional) Export bitrate. - synchronous (bool): - (Optional) True is should by synchronous. - """ - if audio_adapter is None: - audio_adapter = AudioAdapter.default() - foldername = basename(dirname(audio_descriptor)) - filename = splitext(basename(audio_descriptor))[0] - generated = [] - for instrument, data in sources.items(): - path = join( - destination, - filename_format.format( - filename=filename, - instrument=instrument, - foldername=foldername, - codec=codec, - ), - ) - directory = os.path.dirname(path) - if not os.path.exists(directory): - os.makedirs(directory) - if path in generated: - raise SpleeterError( - ( - f"Separated source path conflict : {path}," - "please check your filename format" - ) - ) - generated.append(path) - if self._pool: - task = self._pool.apply_async( - audio_adapter.save, (path, data, self._sample_rate, codec, bitrate) - ) - self._tasks.append(task) - else: - audio_adapter.save(path, data, self._sample_rate, codec, bitrate) - if synchronous and self._pool: - self.join() \ No newline at end of file