diff --git a/basic_pitch/callbacks.py b/basic_pitch/callbacks.py new file mode 100644 index 0000000..028d9d3 --- /dev/null +++ b/basic_pitch/callbacks.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2024 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +from typing import Any, Dict + +import tensorflow as tf + +from basic_pitch import visualize + + +class VisualizeCallback(tf.keras.callbacks.Callback): + # TODO RACHEL make this WAY faster + """ + Callback to run during training to create tensorboard visualizations per epoch. + + Attributes: + train_ds: training dataset to use for prediction / visualization / sonification / summarization + valid_ds: validation dataset to use for "" "" + tensorboard_dir: directory to output "" "" + sonify: whether to include sonifications in tensorboard + contours: whether to plot note contours in tensorboard + """ + + def __init__( + self, + train_ds: tf.data.Dataset, + validation_ds: tf.data.Dataset, + tensorboard_dir: str, + sonify: bool, + contours: bool, + ): + super().__init__() + self.train_iter = iter(train_ds) + self.validation_iter = iter(validation_ds) + self.tensorboard_dir = os.path.join(tensorboard_dir, "tensorboard_logs") + self.file_writer = tf.summary.create_file_writer(tensorboard_dir) + self.sonify = sonify + self.contours = contours + + def on_epoch_end(self, epoch: int, logs: Dict[Any, Any]) -> None: + # the first two outputs of generator needs to be the input and the targets + train_inputs, train_targets = next(self.train_iter)[:2] + validation_inputs, validation_targets = next(self.validation_iter)[:2] + for stage, inputs, targets, loss in [ + ("train", train_inputs, train_targets, logs["loss"]), + ("validation", validation_inputs, validation_targets, logs["val_loss"]), + ]: + outputs = self.model.predict(inputs) + visualize.visualize_transcription( + self.file_writer, + stage, + inputs, + targets, + outputs, + loss, + epoch, + sonify=self.sonify, + contours=self.contours, + ) diff --git a/basic_pitch/constants.py b/basic_pitch/constants.py index a78a487..fb96c99 100644 --- a/basic_pitch/constants.py +++ b/basic_pitch/constants.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # encoding: utf-8 # -# Copyright 2022 Spotify AB +# Copyright 2024 Spotify AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -17,6 +17,8 @@ import numpy as np +from enum import Enum + FFT_HOP = 256 N_FFT = 8 * FFT_HOP @@ -59,3 +61,9 @@ def _freq_bins(bins_per_semitone: int, base_frequency: float, n_semitones: int) FREQ_BINS_NOTES = _freq_bins(NOTES_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES) FREQ_BINS_CONTOURS = _freq_bins(CONTOURS_BINS_PER_SEMITONE, ANNOTATIONS_BASE_FREQUENCY, ANNOTATIONS_N_SEMITONES) + + +class Split(Enum): + train = "train" + validation = "validation" + test = "test" diff --git a/basic_pitch/data/datasets/guitarset.py b/basic_pitch/data/datasets/guitarset.py index 978b275..97bb2eb 100644 --- a/basic_pitch/data/datasets/guitarset.py +++ b/basic_pitch/data/datasets/guitarset.py @@ -95,7 +95,6 @@ def process(self, element: List[str], *args: Tuple[Any, Any], **kwargs: Dict[str duration = sox.file_info.duration(local_wav_path) time_scale = np.arange(0, duration + ANNOTATION_HOP, ANNOTATION_HOP) n_time_frames = len(time_scale) - note_indices, note_values = track_local.notes_all.to_sparse_index( time_scale, "s", FREQ_BINS_NOTES, "hz" ) diff --git a/basic_pitch/data/tf_example_deserialization.py b/basic_pitch/data/tf_example_deserialization.py new file mode 100644 index 0000000..ae8b70a --- /dev/null +++ b/basic_pitch/data/tf_example_deserialization.py @@ -0,0 +1,750 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2024 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import uuid +from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple + +import numpy as np +import tensorflow as tf + +# import tensorflow_addons as tfa + +from basic_pitch.constants import ( + ANNOTATIONS_FPS, + ANNOT_N_FRAMES, + AUDIO_N_CHANNELS, + AUDIO_N_SAMPLES, + AUDIO_SAMPLE_RATE, + AUDIO_WINDOW_LENGTH, + N_FREQ_BINS_NOTES, + N_FREQ_BINS_CONTOURS, + Split, +) + +N_SAMPLES_PER_TRACK = 20 + + +def prepare_datasets( + datasets_base_path: str, + training_shuffle_buffer_size: int, + batch_size: int, + validation_steps: int, + datasets_to_use: List[str], + dataset_sampling_frequency: np.ndarray, +) -> Tuple[tf.data.Dataset, tf.data.Dataset]: + """ + Return a training and a testing dataset. + + Args: + datasets_base_path: path to tfrecords for input data + training_shuffle_buffer_size: size of shuffle buffer (only for training set) + batch_size: batch size for training and validation + validation_steps: number of batches to use for validation + datasets_to_use: the underlying datasets to use for creating training and validation sets e.g. guitarset + dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they + are sampled from during training / validation dataset creation. + + Returns: + training and validation datasets derived from the underlying tfrecord data + """ + assert batch_size > 0 + assert validation_steps is not None and validation_steps > 0 + assert training_shuffle_buffer_size is not None + + # init both + ds_train = sample_datasets( + Split.train, + datasets_base_path, + datasets=datasets_to_use, + dataset_sampling_frequency=dataset_sampling_frequency, + ) + ds_validation = sample_datasets( + Split.validation, + datasets_base_path, + datasets=datasets_to_use, + dataset_sampling_frequency=dataset_sampling_frequency, + ) + + # check that the base dataset returned by ds_function is FINITE + for ds in [ds_train, ds_validation]: + assert tf.cast(tf.data.experimental.cardinality(ds), tf.int32) != tf.data.experimental.INFINITE_CARDINALITY + + # training dataset + if training_shuffle_buffer_size > 0: + # Lets try to cache before the shuffle. This is the entire training dataset so we'll cache + # to memory + ds_train = ( + ds_train.shuffle(training_shuffle_buffer_size, reshuffle_each_iteration=True) + .repeat() + .batch(batch_size) + .prefetch(tf.data.AUTOTUNE) + ) + + # validation dataset + ds_validation = ( + ds_validation.repeat() + .batch(batch_size) + .take(validation_steps) + .cache(f"validation_set_cache_{str(uuid.uuid4())}") + .repeat() + .prefetch(tf.data.AUTOTUNE) + ) + + return ds_train, ds_validation + + +def prepare_visualization_datasets( + datasets_base_path: str, + batch_size: int, + validation_steps: int, + datasets_to_use: List[str], + dataset_sampling_frequency: np.ndarray, +) -> Tuple[tf.data.Dataset, tf.data.Dataset]: + """ + Return a training and a testing dataset for visualization + + Args: + datasets_base_path: path to tfrecord datasets for input data + batch_size: batch size for training and validation + validation_steps: number of batches to use for validation + datasets_to_use: the underlying datasets to use for creating training and validation sets e.g. guitarset + dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they + are sampled from during training / validation dataset creation. + + Returns: + training and validation datasets derived from the underlying tfrecord data + """ + + assert batch_size > 0 + assert validation_steps is not None and validation_steps > 0 + + ds_train = sample_datasets( + Split.train, + datasets_base_path, + datasets=datasets_to_use, + dataset_sampling_frequency=dataset_sampling_frequency, + n_samples_per_track=1, + ) + ds_validation = sample_datasets( + Split.validation, + datasets_base_path, + datasets=datasets_to_use, + dataset_sampling_frequency=dataset_sampling_frequency, + n_samples_per_track=1, + ) + + # check that the base dataset returned by ds_function is FINITE + for ds in [ds_train, ds_validation]: + assert tf.cast(tf.data.experimental.cardinality(ds), tf.int32) != tf.data.experimental.INFINITE_CARDINALITY + + # training dataset + ds_train = ds_train.repeat().batch(batch_size).prefetch(tf.data.AUTOTUNE) + + # validation dataset + ds_validation = ( + ds_validation.repeat() + .batch(batch_size) + .take(validation_steps) + .cache(f"validation_set_cache_{str(uuid.uuid4())}") + .repeat() + .prefetch(tf.data.AUTOTUNE) + ) + + return ds_train, ds_validation + + +def sample_datasets( + split: Split, + datasets_base_path: str, + datasets: List[str], + dataset_sampling_frequency: np.ndarray, + n_shuffle: int = 1000, + n_samples_per_track: int = N_SAMPLES_PER_TRACK, + pairs: bool = False, +) -> tf.data.Dataset: + """samples tfrecord data to create a dataset + + Args: + split: whether to use training or validation data + dataset_base_path: directory storing source datasets as tfrecord files + datasets: names of datasets to sample from e.g. guitarset + dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they + are sampled from during training / validation dataset creation. + n_shuffle: size of shuffle buffer (only used for training ds) + n_samples_per_track: the number of samples to take from a track + pairs: generate pairs of samples from the dataset rather than individual samples + + Returns + dataset of samples + """ + if split == Split.validation: + n_shuffle = 0 + pairs = False + if n_samples_per_track != 1: + n_samples_per_track = 5 + + ds_list = [] + + file_generator, random_seed = transcription_file_generator( + split, + datasets, + datasets_base_path, + dataset_sampling_frequency, + ) + + ds_dataset = transcription_dataset(file_generator, n_samples_per_track, random_seed) + + if n_shuffle > 0: + ds_dataset = ds_dataset.shuffle(n_shuffle) + ds_list.append(ds_dataset) + + if pairs: + pairs_generator, random_seed_pairs = transcription_file_generator( + split, + datasets, + datasets_base_path, + dataset_sampling_frequency, + ) + pairs_ds = transcription_dataset( + pairs_generator, + n_samples_per_track, + random_seed_pairs, + ) + + pairs_ds = pairs_ds.shuffle(n_samples_per_track * 10) # shuffle so that different tracks get mixed together + pairs_ds = pairs_ds.batch(2) + pairs_ds = pairs_ds.map(combine_transcription_examples) + ds_list.append(pairs_ds) + + n_datasets = len(ds_list) + choice_dataset = tf.data.Dataset.range( + n_datasets + ).repeat() # this repeat is critical! if not, only n_dataset points will be sampled!! + return tf.data.Dataset.choose_from_datasets(ds_list, choice_dataset) + + +def transcription_file_generator( + split: Split, + dataset_names: List[str], + datasets_base_path: str, + sample_weights: np.ndarray, +) -> Tuple[Callable[[], Iterator[tf.Tensor]], bool]: + """Reads underlying files and returns file generator + + Args: + split: data split to build generator from + dataset_names: list of dataset_names to use + dataset_base_path: directory storing source datasets as tfrecord files + sample_weights: distribution weighting vector corresponding to datasets determining how they + are sampled from during training / validation dataset creation. + """ + file_dict = { + dataset_name: tf.data.Dataset.list_files( + os.path.join(datasets_base_path, dataset_name, "splits", split.name, "*tfrecord") + ) + for dataset_name in dataset_names + } + + if split == Split.train: + return lambda: _train_file_generator(file_dict, sample_weights), False + return lambda: _validation_file_generator(file_dict), True + + +def _train_file_generator(x: Dict[str, tf.data.Dataset], weights: np.ndarray) -> Iterator[tf.Tensor]: + """file generator for training sets""" + x = {k: list(v) for (k, v) in x.items()} + keys = list(x.keys()) + # shuffle each list + for k in keys: + np.random.shuffle(x[k]) + + while all(x.values()): + # choose a random dataset and yield the last file + fpath = x[np.random.choice(keys, p=weights)].pop() + yield fpath + + +def _validation_file_generator(x: Dict[str, tf.data.Dataset]) -> Iterator[tf.Tensor]: + """file generator for validation sets""" + x = {k: list(v) for (k, v) in x.items()} + # loop until there are no more test files + while any(x.values()): + # alternate between datasets (dataset 1 elt 1, dataset 2, elt 1, ...) + # this is so test files in the tensorboard have 4 different datasets + # instead of 4 elements from 1 + for k in x: + # if the list of files for this dataset is empty skip it + if x[k]: + yield x[k].pop() + + +def combine_transcription_examples( + a: tf.Tensor, target: Dict[str, tf.Tensor], w: Dict[str, tf.Tensor] +) -> Tuple[tf.Tensor, Dict[str, tf.Tensor], Dict[str, tf.Tensor]]: + """mix pairs together for paired dataset + + Args: + a: audio data + target: target data (onset, notes, contours) + w: weights + """ + return ( + # mix the audio snippets + tf.math.reduce_mean(a, axis=0), + # annotations are the max per bin - active frames stay active + { + "onset": tf.math.reduce_max(target["onset"], axis=0), + "contour": tf.math.reduce_max(target["contour"], axis=0), + "note": tf.math.reduce_max(target["note"], axis=0), + }, + # weights are the minimum - if an annotation is missing in one, we should set the weights to zero + { + "onset": tf.math.reduce_min(w["onset"], axis=0), + "contour": tf.math.reduce_min(w["contour"], axis=0), + "note": tf.math.reduce_min(w["note"], axis=0), + }, + ) + + +def transcription_dataset( + file_generator: Callable[[], Iterator[str]], n_samples_per_track: int, random_seed: bool +) -> tf.data.Dataset: + """ + `fpaths_in` is a list of .tfrecords files + return a tf.Dataset with the following fields (as tuple): + - audio (shape AUDIO_N_SAMPLES, 1) + - {'contours': contours, 'notes': notes, 'onsets': onsets} + contours has shape (ANNOT_N_FRAMES, N_FREQ_BINS_CONTOURS) + notes and onsets have shape: (ANNOT_N_FRAMES, N_FREQ_BINS_NOTES) + """ + ds = tf.data.Dataset.from_generator(file_generator, output_types=tf.string, output_shapes=()) + ds = tf.data.TFRecordDataset(ds) + ds = ds.map(parse_transcription_tfexample, num_parallel_calls=tf.data.AUTOTUNE) + ds = ds.filter(is_not_bad_shape) + ds = ds.map( + lambda file_id, source, audio_wav, notes_indices, notes_values, onsets_indices, onsets_values, contours_indices, contours_values, notes_onsets_shape, contours_shape: ( # noqa: E501 + file_id, + source, + tf.audio.decode_wav( + audio_wav, + desired_channels=AUDIO_N_CHANNELS, + desired_samples=-1, + name=None, + ), + sparse2dense(notes_values, notes_indices, notes_onsets_shape), + sparse2dense(onsets_values, onsets_indices, notes_onsets_shape), + sparse2dense(contours_values, contours_indices, contours_shape), + ), + num_parallel_calls=tf.data.AUTOTUNE, + ) + ds = ds.map(reduce_transcription_inputs) + ds = ds.map(get_sample_weights, num_parallel_calls=tf.data.AUTOTUNE) + ds = ds.flat_map( + lambda a, o, c, n, ow, cw, nw, m: get_transcription_chunks( + a, o, c, n, ow, cw, nw, n_samples_per_track, random_seed + ) + ) + ds = ds.filter(is_not_all_silent_annotations) # remove examples where all annotations are zero + ds = ds.map(to_transcription_training_input) + ds = ds.apply(tf.data.experimental.ignore_errors(log_warning=True)) # failsafe so training doesn't stop + return ds + + +def parse_transcription_tfexample( + serialized_example: tf.train.Example, +) -> Tuple[ + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, + tf.Tensor, +]: + """ + return a tuple with the following tensors, in order: + - file_id + - source + - audio_wav + - notes_indices + - notes_values + - onsets_indices + - onsets_values + - contours_indices + - contours_values + - notes_onsets_shape + - contours_shape + NB.: notes, onsets and contours are represented as sparse matrices + (to be reconstructed using `tf.SparseTensor(...)`). They share the + time dimension, while contours have a frequency dimension that is + a multiple (`ANNOTATIONS_BINS_PER_SEMITONE`) of that of + notes/onsets. + """ + schema = { + "file_id": tf.io.FixedLenFeature((), tf.string), + "source": tf.io.FixedLenFeature((), tf.string), + "audio_wav": tf.io.FixedLenFeature((), tf.string), + "notes_indices": tf.io.FixedLenFeature((), tf.string), + "notes_values": tf.io.FixedLenFeature((), tf.string), + "onsets_indices": tf.io.FixedLenFeature((), tf.string), + "onsets_values": tf.io.FixedLenFeature((), tf.string), + "contours_indices": tf.io.FixedLenFeature((), tf.string), + "contours_values": tf.io.FixedLenFeature((), tf.string), + "notes_onsets_shape": tf.io.FixedLenFeature((), tf.string), + "contours_shape": tf.io.FixedLenFeature((), tf.string), + } + example = tf.io.parse_single_example(serialized_example, schema) + return ( + example["file_id"], + example["source"], + example["audio_wav"], + tf.io.parse_tensor(example["notes_indices"], out_type=tf.int64), + tf.io.parse_tensor(example["notes_values"], out_type=tf.float32), + tf.io.parse_tensor(example["onsets_indices"], out_type=tf.int64), + tf.io.parse_tensor(example["onsets_values"], out_type=tf.float32), + tf.io.parse_tensor(example["contours_indices"], out_type=tf.int64), + tf.io.parse_tensor(example["contours_values"], out_type=tf.float32), + tf.io.parse_tensor(example["notes_onsets_shape"], out_type=tf.int64), + tf.io.parse_tensor(example["contours_shape"], out_type=tf.int64), + ) + + +def is_not_bad_shape( + _file_id: tf.Tensor, + _source: tf.Tensor, + _audio_wav: tf.Tensor, + _notes_indices: tf.Tensor, + notes_values: tf.Tensor, + _onsets_indices: tf.Tensor, + _onsets_values: tf.Tensor, + _contours_indices: tf.Tensor, + _contours_values: tf.Tensor, + notes_onsets_shape: tf.Tensor, + _contours_shape: tf.Tensor, +) -> tf.Tensor: + """checks for improper datashape for note values and onsets""" + bad_shape = tf.logical_and( + tf.shape(notes_values)[0] == 0, + tf.shape(notes_onsets_shape)[0] == 2, + ) + return tf.math.logical_not(bad_shape) + + +def sparse2dense(values: tf.Tensor, indices: tf.Tensor, dense_shape: tf.Tensor) -> tf.Tensor: + """converts sparse tensor representation to dense vector""" + if tf.rank(indices) != 2 and tf.size(indices) == 0: + indices = tf.zeros([0, 1], dtype=indices.dtype) + tf.assert_rank(indices, 2) + tf.assert_rank(values, 1) + tf.assert_rank(dense_shape, 1) + sp = tf.SparseTensor(indices=indices, values=values, dense_shape=dense_shape) + return tf.sparse.to_dense(sp, validate_indices=False) + + +def reduce_transcription_inputs( + file_id: str, + src: str, + wav: Tuple[tf.Tensor, int], + notes: tf.Tensor, + onsets: tf.Tensor, + contour: tf.Tensor, +) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor, Dict[str, str]]: + """Map tf records data to a tuple + If audio is stereo, it is mixed down to mono. + This will error if the sample rate of the wav file is different from + what we hard code. + Args: + fid : file id (string) + src : name of dataset (string) + wav : tensorflow wav obejct (tuple of audio and sample rate) + The whole audio file length + notes : matrix of note frames (n_frames x N_FREQ_BINS_NOTES) + possibly size 0 + onsets : matrix of note onsets (n_frames x N_FREQ_BINS_NOTES) + possibly size 0 + contours : matrix of contour frames (n_frames x N_FREQ_BINS_CONTOURS) + possibly size 0 + """ + audio, sample_rate = wav + tf.debugging.assert_equal( + sample_rate, + AUDIO_SAMPLE_RATE, + message="audio sample rate {} is inconsistent".format(sample_rate), + ) + return ( + tf.math.reduce_mean(audio, axis=1, keepdims=True), # manually mixdown to mono + onsets, + contour, + notes, + {"fid": file_id, "src": src}, + ) + + +def _infer_time_size(onsets: tf.Tensor, contour: tf.Tensor, notes: tf.Tensor) -> tf.Tensor: + """Some of the targets might be empty, but we need to find out the + number of time frames of one of the non-empty ones. + Returns + number of time frames in the targets + """ + onset_shape = tf.shape(onsets)[0] + contour_shape = tf.shape(contour)[0] + note_shape = tf.shape(notes)[0] + time_size = tf.cast( + tf.math.maximum( + tf.cast(tf.math.maximum(onset_shape, contour_shape), dtype=tf.int32), + note_shape, + ), + dtype=tf.int32, + ) + + return time_size + + +def get_sample_weights( + audio: tf.Tensor, onsets: np.ndarray, contour: np.ndarray, notes: np.ndarray, metadata: Dict[Any, Any] +) -> Tuple[tf.Tensor, np.ndarray, np.ndarray, np.ndarray, tf.cond, tf.cond, tf.cond, Dict[Any, Any]]: + """Add sample weights based on whether or not the target is empty + If it's empty, the weight is 0, otherwise it's 1. Empty targets get filled + with matricies of 0's + Args: + audio : audio signal (full length) + notes : matrix of note frames (n_frames x N_FREQ_BINS_NOTES) + possibly size 0 + onsets : matrix of note onsets (n_frames x N_FREQ_BINS_NOTES) + possibly size 0 + contours : matrix of contour frames (n_frames x N_FREQ_BINS_CONTOURS) + possibly size 0 + metadata : dictionary of metadata + Returns: + audio : audio signal (full length) + notes : matrix of note frames (n_frames x N_FREQ_BINS_NOTES) + onsets : matrix of note onsets (n_frames x N_FREQ_BINS_NOTES) + contours : matrix of contour frames (n_frames x N_FREQ_BINS_CONTOURS) + onset_weight : int (0 or 1) + note_weight : int (0 or 1) + contour_weight : int (0 or 1) + """ + time_size = _infer_time_size(onsets, contour, notes) + + # TODO - if we dont want to worry about batches with no examples for a task + # we can add a tiny constant here, but training will be unstable + onsets_weight = tf.cast(tf.shape(onsets)[0] != 0, tf.float32) + contour_weight = tf.cast(tf.shape(contour)[0] != 0, tf.float32) + note_weight = tf.cast(tf.shape(notes)[0] != 0, tf.float32) + + onsets = tf.cond( + tf.shape(onsets)[0] == 0, + lambda: tf.zeros( + tf.stack([time_size, tf.constant(N_FREQ_BINS_NOTES, dtype=tf.int32)], axis=0), + dtype=tf.float32, + ), + lambda: onsets, + ) + contour = tf.cond( + tf.shape(contour)[0] == 0, + lambda: tf.zeros( + tf.stack([time_size, tf.constant(N_FREQ_BINS_CONTOURS, dtype=tf.int32)], axis=0), + dtype=tf.float32, + ), + lambda: contour, + ) + notes = tf.cond( + tf.shape(notes)[0] == 0, + lambda: tf.zeros( + tf.stack([time_size, tf.constant(N_FREQ_BINS_NOTES, dtype=tf.int32)], axis=0), + dtype=tf.float32, + ), + lambda: notes, + ) + + return ( + audio, + onsets, + contour, + notes, + onsets_weight, + contour_weight, + note_weight, + metadata, + ) + + +def trim_time(data: np.ndarray, start: int, duration: int, sr: int) -> tf.Tensor: + """ + Slice a data file + Args: + data: 2D data as (n_time_samples, n_channels) array + can be audio or a time-frequency matrix + start: trim start time in seconds + duration: trim duration in seconds + sr: data sample rate + Returns: + sliced_data (tf.tensor): (trimmed_time, n_channels) + """ + n_start = tf.cast(tf.math.round(sr * start), dtype=tf.int32) + n_duration = tf.cast(tf.math.ceil(tf.cast(sr * duration, tf.float32)), dtype=tf.int32) + begin = (n_start, 0) + size = (n_duration, -1) + return tf.slice(data, begin=begin, size=size) + + +def extract_window( + audio: tf.Tensor, onsets: np.ndarray, contour: np.ndarray, notes: np.ndarray, t_start: int +) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: + """extracts a window of data from the given audio and its associated metadata + + Args: + audio: audio signal + onsets: note onsets of audio signal + contour: pitch on off of audio signal + notes: note on off of audio signal + + Returns: + tuple of windows of each of the inputs + """ + # needs a hop size extra of samples for good mel spectrogram alignment + audio_trim = trim_time( + audio, + t_start, + tf.cast(AUDIO_N_SAMPLES / AUDIO_SAMPLE_RATE, dtype=tf.dtypes.float32), + AUDIO_SAMPLE_RATE, + ) + onset_trim = trim_time(onsets, t_start, AUDIO_WINDOW_LENGTH, ANNOTATIONS_FPS) + contour_trim = trim_time(contour, t_start, AUDIO_WINDOW_LENGTH, ANNOTATIONS_FPS) + note_trim = trim_time(notes, t_start, AUDIO_WINDOW_LENGTH, ANNOTATIONS_FPS) + return (audio_trim, onset_trim, contour_trim, note_trim) + + +def extract_random_window( + audio: tf.Tensor, onsets: np.ndarray, contour: np.ndarray, notes: np.ndarray, seed: Optional[int] +) -> Tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: + """Trim transcription data to a fixed length of time + starting from a random time index. + Args: + audio : audio signal (full length) + notes : matrix of note frames (n_frames x N_FREQ_BINS_NOTES) + onsets : matrix of note onsets (n_frames x N_FREQ_BINS_NOTES) + contours : matrix of contour frames (n_frames x N_FREQ_BINS_CONTOURS) + Returns: + audio : audio signal (AUDIO_WINDOW_LENGTH * AUDIO_SAMPLE_RATE, 1) + notes : matrix of note frames (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_NOTES) + onsets : matrix of note onsets (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_NOTES) + contours : matrix of contour frames (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_CONTOURS) + """ + n_sec = tf.math.divide( + tf.cast(tf.shape(audio)[0], dtype=tf.float32), + tf.cast(AUDIO_SAMPLE_RATE, dtype=tf.float32), + ) + t_start = tf.random.uniform( + (), + minval=0.0, + maxval=n_sec - (AUDIO_N_SAMPLES / AUDIO_SAMPLE_RATE), + dtype=tf.dtypes.float32, + seed=seed, + ) + return extract_window(audio, onsets, contour, notes, t_start) + + +def get_transcription_chunks( + audio: tf.Tensor, + onsets: np.ndarray, + contour: np.ndarray, + notes: np.ndarray, + onset_weight: int, + contour_weight: int, + note_weight: int, + n_samples_per_track: int, + seed: bool, +) -> tf.data.Dataset: + """Randomly sample fixed-length time chunks for transcription data + Args: + audio : audio signal (full length) + notes : matrix of note frames (n_frames x N_FREQ_BINS_NOTES) + onsets : matrix of note onsets (n_frames x N_FREQ_BINS_NOTES) + contours : matrix of contour frames (n_frames x N_FREQ_BINS_CONTOURS) + onset_weight : int (0 or 1) + note_weight : int (0 or 1) + contour_weight : int (0 or 1) + n_samples_per_track : int - how many samples to yield per track + Returns: + batches of size n_samples_per_track of: + audio : audio signal (AUDIO_WINDOW_LENGTH * AUDIO_SAMPLE_RATE, 1) + notes : matrix of note frames (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_NOTES) + onsets : matrix of note onsets (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_NOTES) + contours : matrix of contour frames (AUDIO_SAMPLE_RATE * ANNOTATIONS_FPS, N_FREQ_BINS_CONTOURS) + onset_weight : int (0 or 1) + note_weight : int (0 or 1) + contour_weight : int (0 or 1) + """ + a = [] + o = [] + c = [] + n = [] + ow = [] + cw = [] + nw = [] + for i in range(n_samples_per_track): + s0, s1, s2, s3 = extract_random_window(audio, onsets, contour, notes, i if seed else None) + a.append(s0) + o.append(s1) + c.append(s2) + n.append(s3) + ow.append(onset_weight) + cw.append(contour_weight) + nw.append(note_weight) + return tf.data.Dataset.from_tensor_slices((a, o, c, n, ow, cw, nw)) + + +def is_not_all_silent_annotations( + audio: tf.Tensor, + onsets: np.ndarray, + contour: np.ndarray, + notes: np.ndarray, + onset_weight: int, + contour_weight: int, + note_weight: int, +) -> tf.Tensor: + """returns a boolean value indicating whether the notes and pitch contour are or are not all zero, or silent.""" + contours_nonsilent = tf.math.reduce_mean(contour) != 0 + notes_nonsilent = tf.math.reduce_mean(notes) != 0 + return tf.math.logical_or(contours_nonsilent, notes_nonsilent) + + +def to_transcription_training_input( + audio: tf.Tensor, + onsets: np.ndarray, + contour: np.ndarray, + notes: np.ndarray, + onset_weight: int, + contour_weight: int, + note_weight: int, +) -> Tuple[tf.Tensor, Dict[str, tf.Tensor], Dict[str, int]]: + """convert transcription data to the format expected by the model""" + return ( + audio, + { + "onset": tf.ensure_shape(onsets, (ANNOT_N_FRAMES, N_FREQ_BINS_NOTES)), + "contour": tf.ensure_shape(contour, (ANNOT_N_FRAMES, N_FREQ_BINS_CONTOURS)), + "note": tf.ensure_shape(notes, (ANNOT_N_FRAMES, N_FREQ_BINS_NOTES)), + }, + {"onset": onset_weight, "contour": contour_weight, "note": note_weight}, + ) diff --git a/basic_pitch/train.py b/basic_pitch/train.py new file mode 100644 index 0000000..4d675fb --- /dev/null +++ b/basic_pitch/train.py @@ -0,0 +1,288 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2024 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import logging +from datetime import datetime, timezone +from typing import List + +import numpy as np +import tensorflow as tf + +from basic_pitch import models +from basic_pitch.callbacks import VisualizeCallback +from basic_pitch.constants import DATASET_SAMPLING_FREQUENCY +from basic_pitch.data import tf_example_deserialization + +logging.basicConfig(level=logging.INFO) + + +def main( + source: str, + output: str, + batch_size: int, + shuffle_size: int, + learning_rate: float, + epochs: int, + steps_per_epoch: int, + validation_steps: int, + size_evaluation_callback_datasets: int, + datasets_to_use: List[str], + dataset_sampling_frequency: np.ndarray, + no_sonify: bool, + no_contours: bool, + weighted_onset_loss: bool, + positive_onset_weight: float, +) -> None: + """Parse config and run training or evaluation. + + Args: + source: source directory for data + output: output directory for trained model / checkpoints / tensorboard + batch_size: batch size for data. + shuffle_size: size of shuffle buffer (only for training set) for the data shuffling mechanism + learning_rate: learning_rate for training + epochs: number of epochs to train for + steps_per_epoch: the number of batches to process per epoch during training + validation_steps: the number of validation batches to evaluate on per epoch + size_evaluation_callback_datasets: the batch size to use for visualization / logging + datasets_to_use: which datasets to train / evaluate on e.g. guitarset, medleydb_pitch, slakh + dataset_sampling_frequency: distribution weighting vector corresponding to datasets determining how they + are sampled from during training / validation dataset creation. + no_sonify: Whether or not to include sonifications in tensorboard. + no_contours: Whether or not to include contours in the output. + weighted_onset_loss: whether or not to use a weighted cross entropy loss. + positive_onset_weight: weighting factor for the positive labels. + """ + # configuration.add_externals() + logging.info(f"source directory: {source}") + logging.info(f"output directory: {output}") + logging.info(f"tensorflow version: {tf.__version__}") + logging.info("parameters to train.main() function:") + logging.info(f"batch_size: {batch_size}") + logging.info(f"shuffle_size: {shuffle_size}") + logging.info(f"learning_rate: {learning_rate}") + logging.info(f"epochs: {epochs}") + logging.info(f"steps_per_epoch: {steps_per_epoch}") + logging.info(f"validation_steps: {validation_steps}") + logging.info(f"size_evaluation_callback_datasets: {size_evaluation_callback_datasets}") + logging.info(f"using datasets: {datasets_to_use} with frequencies {dataset_sampling_frequency}") + logging.info(f"no_contours: {no_contours}") + logging.info(f"weighted_onset_loss: {weighted_onset_loss}") + logging.info(f"positive_onset_weight: {positive_onset_weight}") + + # model + model = models.model(no_contours=no_contours) + input_shape = list(model.input_shape) + if input_shape[0] is None: + input_shape[0] = batch_size + logging.info("input_shape" + str(input_shape)) + + output_shape = model.output_shape + for k, v in output_shape.items(): + output_shape[k] = list(v) + if v[0] is None: + output_shape[k][0] = batch_size + logging.info("output_shape" + str(output_shape)) + # data loaders + train_ds, validation_ds = tf_example_deserialization.prepare_datasets( + source, + shuffle_size, + batch_size, + validation_steps, + datasets_to_use, + dataset_sampling_frequency, + ) + + MAX_EVAL_CBF_BATCH_SIZE = 4 + ( + train_visualization_ds, + validation_visualization_ds, + ) = tf_example_deserialization.prepare_visualization_datasets( + source, + batch_size=min(size_evaluation_callback_datasets, MAX_EVAL_CBF_BATCH_SIZE), + validation_steps=max(1, size_evaluation_callback_datasets // MAX_EVAL_CBF_BATCH_SIZE), + datasets_to_use=datasets_to_use, + dataset_sampling_frequency=dataset_sampling_frequency, + ) + + timestamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M") + tensorboard_log_dir = os.path.join(output, timestamp, "tensorboard") + callbacks = [ + tf.keras.callbacks.TensorBoard(log_dir=tensorboard_log_dir, histogram_freq=1), + tf.keras.callbacks.EarlyStopping(patience=25, verbose=2), + tf.keras.callbacks.ReduceLROnPlateau(verbose=1, patience=10, factor=0.5), + tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(output, timestamp, "model.best"), save_best_only=True), + tf.keras.callbacks.ModelCheckpoint( + filepath=os.path.join(output, timestamp, "checkpoints", "model.{epoch:02d}") + ), + VisualizeCallback( + train_visualization_ds, + validation_visualization_ds, + tensorboard_log_dir, + not no_sonify, + not no_contours, + ), + ] + + # if no_contours: + # loss = models.loss_no_contour(weighted=weighted_onset_loss, positive_weight=positive_onset_weight) + # else: + # loss = models.loss(weighted=weighted_onset_loss, positive_weight=positive_onset_weight) + loss = models.loss(weighted=weighted_onset_loss, positive_weight=positive_onset_weight) + + # train + model.compile( + loss=loss, + optimizer=tf.keras.optimizers.Adam(learning_rate), + sample_weight_mode={"contour": None, "note": None, "onset": None}, + ) + + logging.info("--- Model Training specs ---") + logging.info(f" train_ds: {train_ds}") + logging.info(f" validation_ds: {validation_ds}") + model.summary() + + model.fit( + train_ds, + epochs=epochs, + callbacks=callbacks, + steps_per_epoch=steps_per_epoch, + validation_data=validation_ds, + validation_steps=validation_steps, + ) + + +def console_entry_point() -> None: + """From pip installed script.""" + parser = argparse.ArgumentParser(description="") + parser.add_argument("--source", required=True, help="Path to directory containing train/validation splits.") + parser.add_argument("--output", required=True, help="Directory to save the model in.") + parser.add_argument("-e", "--epochs", type=int, default=500, help="Number of training epochs.") + parser.add_argument( + "-b", + "--batch-size", + type=int, + default=16, + help="batch size of training. Unlike Estimator API, this specifies the batch size per-GPU.", + ) + parser.add_argument( + "-l", + "--learning-rate", + type=float, + default=0.001, + help="ADAM optimizer learning rate", + ) + parser.add_argument( + "-s", + "--steps-per-epoch", + type=int, + default=100, + help="steps_per_epoch (batch) of each training loop", + ) + parser.add_argument( + "-v", + "--validation-steps", + type=int, + default=10, + help="validation steps (number of BATCHES) for each validation run. MUST be a positive integer", + ) + parser.add_argument( + "-z", + "--training-shuffle-size", + type=int, + default=100, + help="training dataset shuffle size", + ) + parser.add_argument( + "--size-evaluation-callback-datasets", + type=int, + default=4, + help="number of elements in the dataset used by the evaluation callback function", + ) + for dataset in DATASET_SAMPLING_FREQUENCY.keys(): + parser.add_argument( + f"--{dataset.lower()}", + action="store_true", + default=False, + help=f"Use {dataset} dataset in training", + ) + parser.add_argument( + "--no-sonify", + action="store_true", + default=False, + help="if given, exclude sonifications from the tensorboard / data visualization", + ) + parser.add_argument( + "--no-contours", + action="store_true", + default=False, + help="if given, trains without supervising the contour layer", + ) + parser.add_argument( + "--weighted-onset-loss", + action="store_true", + default=False, + help="if given, trains onsets with a class-weighted loss", + ) + parser.add_argument( + "--positive-onset-weight", + type=float, + default=0.5, + help="Positive class onset weight. Only applies when weignted onset loss is true.", + ) + + args = parser.parse_args() + datasets_to_use = [ + dataset.lower() + for dataset in DATASET_SAMPLING_FREQUENCY.keys() + if getattr(args, dataset.lower().replace("-", "_")) + ] + dataset_sampling_frequency = np.array( + [ + frequency + for dataset, frequency in DATASET_SAMPLING_FREQUENCY.items() + if getattr(args, dataset.lower().replace("-", "_")) + ] + ) + dataset_sampling_frequency = dataset_sampling_frequency / np.sum(dataset_sampling_frequency) + + assert args.steps_per_epoch is not None + assert args.validation_steps > 0 + + main( + args.source, + args.output, + args.training_shuffle_size, + args.batch_size, + args.learning_rate, + args.epochs, + args.steps_per_epoch, + args.validation_steps, + args.size_evaluation_callback_datasets, + datasets_to_use, + dataset_sampling_frequency, + args.dont_sonify, + args.no_contours, + args.weighted_onset_loss, + args.positive_onset_weight, + ) + + +if __name__ == "__main__": + console_entry_point() diff --git a/basic_pitch/visualize.py b/basic_pitch/visualize.py new file mode 100644 index 0000000..2546d97 --- /dev/null +++ b/basic_pitch/visualize.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2022 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import tensorflow as tf +import mir_eval +import librosa + +from typing import Dict + +from basic_pitch.constants import ( + AUDIO_SAMPLE_RATE, + ANNOTATIONS_FPS, + ANNOTATIONS_N_SEMITONES, + ANNOTATIONS_BASE_FREQUENCY, + ANNOT_N_FRAMES, + NOTES_BINS_PER_SEMITONE, + AUDIO_N_SAMPLES, +) +from basic_pitch import models + +SONIFY_FS = 3000 +MAX_OUTPUTS = 4 + +FREQS = librosa.core.cqt_frequencies( + n_bins=ANNOTATIONS_N_SEMITONES * NOTES_BINS_PER_SEMITONE, + fmin=ANNOTATIONS_BASE_FREQUENCY, + bins_per_octave=12 * NOTES_BINS_PER_SEMITONE, +) +# this function is slow - for speed, only sonify frequencies below +# sonify_fs/2 Hz (e.g. 1000 Hz) +MAX_FREQ_INDEX = np.where(FREQS > SONIFY_FS / 2)[0][0] +TIMES = librosa.core.frames_to_time( + np.arange(ANNOT_N_FRAMES), + sr=AUDIO_SAMPLE_RATE, + hop_length=AUDIO_SAMPLE_RATE // ANNOTATIONS_FPS, +) + + +def get_input_model() -> tf.keras.Model: + """define a model that generates the CQT (Constant-Q Transform) of input audio""" + inputs = tf.keras.Input(shape=(AUDIO_N_SAMPLES, 1)) # (batch, time, ch) + x = models.get_cqt(inputs, 1, False) + model = tf.keras.Model(inputs=inputs, outputs=x) + model.compile() + return model + + +INPUT_MODEL = get_input_model() + + +def visualize_transcription( + file_writer: tf.summary.SummaryWriter, + stage: str, + inputs: tf.Tensor, + targets: Dict[str, tf.Tensor], + outputs: Dict[str, tf.Tensor], + loss: float, + step: int, + sonify: bool = True, + contours: bool = True, +) -> None: + """Create tf.summaries of transcription outputs to be plotted in tensorboard + + Args: + file_writer: tensorboard filewriter object + stage: train or validation + inputs: batch of input data (audio) + targets: batch of target data (dictionary) + outputs: batch of output data (dictionary) + loss: loss value for epoch + step: which epoch this is + sonify: sonify outputs + contours: plot note contours + """ + with file_writer.as_default(): + # create audio player + tf.summary.audio( + f"{stage}/audio/inputs", + inputs, + sample_rate=AUDIO_SAMPLE_RATE, + step=step, + max_outputs=MAX_OUTPUTS, + ) + # plot mel spectrograms + tf.summary.image( + f"{stage}/audio/input", + _audio_input(inputs), + step=step, + max_outputs=MAX_OUTPUTS, + ) + + # plot onsets + tf.summary.image( + f"{stage}/images/onsets/target", + _array_to_image(targets["onset"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + tf.summary.image( + f"{stage}/images/onsets/output", + _array_to_image(outputs["onset"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + + if sonify: + tf.summary.audio( + f"{stage}/audio/onsets-output", + _array_to_sonification(outputs["onset"], MAX_OUTPUTS), + sample_rate=SONIFY_FS, + step=step, + max_outputs=MAX_OUTPUTS, + ) + + if contours: + # plot contours + tf.summary.image( + f"{stage}/images/contours/target", + _array_to_image(targets["contour"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + tf.summary.image( + f"{stage}/images/contours/output", + _array_to_image(outputs["contour"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + + # plot notes + tf.summary.image( + f"{stage}/images/notes/target", + _array_to_image(targets["note"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + tf.summary.image( + f"{stage}/images/notes/output", + _array_to_image(outputs["note"]), + step=step, + max_outputs=MAX_OUTPUTS, + ) + + if sonify: + # sonify notes + tf.summary.audio( + f"{stage}/audio/notes-output", + _array_to_sonification(outputs["note"], MAX_OUTPUTS), + sample_rate=SONIFY_FS, + step=step, + max_outputs=MAX_OUTPUTS, + ) + + # plot loss + tf.summary.scalar(f"{stage}/loss", loss, step=step) + # plot max + if contours: + tf.summary.scalar(f"{stage}/contour-max", np.max(outputs["contour"]), step=step) + + tf.summary.scalar(f"{stage}/note-max", np.max(outputs["note"]), step=step) + tf.summary.scalar(f"{stage}/onset-max", np.max(outputs["onset"]), step=step) + + +def _array_to_sonification(array: tf.Tensor, max_outputs: int, clip: float = 0.3) -> tf.Tensor: + """sonify time frequency representation of audio + + Args: + array: time-frequency representation of audio + max_outputs: the number of grams / batches to process / append to the resulting output + clip: value below which signal is 0'd out. + """ + gram_batch = tf.transpose(array, perm=[0, 2, 1]).numpy() + audio_list = [] + + for i, gram in enumerate(gram_batch): + + gram[gram < clip] = 0.0 + y = mir_eval.sonify.time_frequency( + gram[:MAX_FREQ_INDEX, :], + FREQS[:MAX_FREQ_INDEX], + TIMES, + fs=SONIFY_FS, + ) + audio_list.append(y[:, np.newaxis]) + if i + 1 >= max_outputs: + break + + return tf.convert_to_tensor(np.array(audio_list), dtype=tf.float32) + + +def _audio_input(audio: tf.Tensor) -> tf.Tensor: + """Gets the Constant-Q transform of audio input using the input model defined above. + + Args: + audio: the audio signal to process + + Returns: + constant-q transform of the audio (3 bins per semitone, ~11ms hop size.) + """ + audio_in = INPUT_MODEL(audio) + return tf.transpose(audio_in, perm=[0, 2, 1, 3]) + + +def _array_to_image(array: tf.Tensor) -> tf.Tensor: + """Convert a time-frequency array shape=(batch, time, frequency) to + the shape expected by tf.summary.image (batch, frequency, time, 1) + + Args: + array: a (batch, time, frequency) array + + Returns: + reshaped array + """ + return tf.expand_dims(tf.transpose(array, perm=[0, 2, 1]), 3) diff --git a/tests/data/test_guitarset.py b/tests/data/test_guitarset.py index 708f59d..d93a502 100644 --- a/tests/data/test_guitarset.py +++ b/tests/data/test_guitarset.py @@ -18,6 +18,7 @@ import itertools import os import pathlib +import shutil from apache_beam.testing.test_pipeline import TestPipeline from typing import List @@ -29,24 +30,41 @@ ) from basic_pitch.data.pipeline import WriteBatchToTfRecord +from utils import create_mock_wav + RESOURCES_PATH = pathlib.Path(__file__).parent.parent / "resources" TRACK_ID = "00_BN1-129-Eb_comp" -def test_guitarset_to_tf_example(tmpdir: str) -> None: +def test_guitarset_to_tf_example(tmp_path: pathlib.Path) -> None: + mock_guitarset_home = tmp_path / "guitarset" + mock_guitarset_audio = mock_guitarset_home / "audio_mono-mic" + mock_guitarset_annotations = mock_guitarset_home / "annotation" + output_dir = tmp_path / "output" + + mock_guitarset_audio.mkdir(parents=True) + mock_guitarset_annotations.mkdir(parents=True) + output_dir.mkdir() + + create_mock_wav(mock_guitarset_audio / f"{TRACK_ID}_mic.wav", duration_min=1) + shutil.copy( + RESOURCES_PATH / "data" / "guitarset" / "annotation" / f"{TRACK_ID}.jams", + mock_guitarset_annotations / f"{TRACK_ID}.jams", + ) + input_data: List[str] = [TRACK_ID] with TestPipeline() as p: ( p | "Create PCollection of track IDs" >> beam.Create([input_data]) - | "Create tf.Example" - >> beam.ParDo(GuitarSetToTfExample(str(RESOURCES_PATH / "data" / "guitarset"), download=False)) - | "Write to tfrecord" >> beam.ParDo(WriteBatchToTfRecord(tmpdir)) + | "Create tf.Example" >> beam.ParDo(GuitarSetToTfExample(str(mock_guitarset_home), download=False)) + | "Write to tfrecord" >> beam.ParDo(WriteBatchToTfRecord(str(output_dir))) ) - assert len(os.listdir(tmpdir)) == 1 - assert os.path.splitext(os.listdir(tmpdir)[0])[-1] == ".tfrecord" - with open(os.path.join(tmpdir, os.listdir(tmpdir)[0]), "rb") as fp: + listdir = os.listdir(output_dir) + assert len(listdir) == 1 + assert os.path.splitext(listdir[0])[-1] == ".tfrecord" + with open(output_dir / listdir[0], "rb") as fp: data = fp.read() assert len(data) != 0 @@ -77,7 +95,7 @@ def test_guitarset_create_input_data() -> None: data = create_input_data(train_percent=0.33, validation_percent=0.33) data.sort(key=lambda el: el[1]) # sort by split tolerance = 0.1 - for key, group in itertools.groupby(data, lambda el: el[1]): + for _, group in itertools.groupby(data, lambda el: el[1]): assert (0.33 - tolerance) * len(data) <= len(list(group)) <= (0.33 + tolerance) * len(data) diff --git a/tests/data/test_tf_example_deserialization.py b/tests/data/test_tf_example_deserialization.py new file mode 100644 index 0000000..379f5bc --- /dev/null +++ b/tests/data/test_tf_example_deserialization.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2024 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import apache_beam as beam +import numpy as np +import pathlib +import shutil +import tensorflow as tf + +from apache_beam.testing.test_pipeline import TestPipeline +from typing import List + +from basic_pitch.constants import Split +from basic_pitch.data.datasets.guitarset import GuitarSetToTfExample +from basic_pitch.data.pipeline import WriteBatchToTfRecord +from basic_pitch.data.tf_example_deserialization import ( + prepare_datasets, + prepare_visualization_datasets, + sample_datasets, + transcription_file_generator, +) + +from utils import create_mock_wav + +RESOURCES_PATH = pathlib.Path(__file__).parent.parent / "resources" +TRAIN_TRACK_ID = "00_BN1-129-Eb_comp" +VALID_TRACK_ID = "00_BN1-147-Gb_comp" + + +def create_empty_tfrecord(filepath: pathlib.Path) -> None: + assert filepath.suffix == ".tfrecord" + with tf.io.TFRecordWriter(str(filepath)) as writer: + writer.write("") + + +def create_tfrecord(input_data: List[str], dataset_home: str, output_dir: str) -> None: + with TestPipeline() as p: + ( + p + | "Create PCollection of track IDs" >> beam.Create([input_data]) + | "Create tf.Example" >> beam.ParDo(GuitarSetToTfExample(dataset_home, download=False)) + | "Write to tfrecord" >> beam.ParDo(WriteBatchToTfRecord(output_dir)) + ) + + +def setup_test_resources(tmp_path: pathlib.Path) -> pathlib.Path: + mock_guitarset_home = tmp_path / "guitarset" + mock_guitarset_audio = mock_guitarset_home / "audio_mono-mic" + mock_guitarset_annotations = mock_guitarset_home / "annotation" + + mock_guitarset_audio.mkdir(parents=True) + mock_guitarset_annotations.mkdir(parents=True) + + output_home = tmp_path / "data" / "basic_pitch" + output_splits_dir = output_home / "guitarset" / "splits" + + def mock_and_process(split: str, track_id: str) -> None: + create_mock_wav(mock_guitarset_audio / f"{track_id}_mic.wav", duration_min=1) + shutil.copy( + RESOURCES_PATH / "data" / "guitarset" / "annotation" / f"{track_id}.jams", + mock_guitarset_annotations / f"{track_id}.jams", + ) + + output_dir = output_splits_dir / split + output_dir.mkdir(parents=True) + + create_tfrecord(input_data=[track_id], dataset_home=str(mock_guitarset_home), output_dir=str(output_dir)) + + mock_and_process("train", TRAIN_TRACK_ID) + mock_and_process("validation", VALID_TRACK_ID) + + return output_home + + +def test_prepare_datasets(tmp_path: pathlib.Path) -> None: + datasets_home = setup_test_resources(tmp_path) + + ds_train, ds_valid = prepare_datasets( + datasets_base_path=str(datasets_home), + training_shuffle_buffer_size=1, + batch_size=1, + validation_steps=1, + datasets_to_use=["guitarset"], + dataset_sampling_frequency=np.array([1]), + ) + + assert ds_train is not None and isinstance(ds_train, tf.data.Dataset) + assert ds_valid is not None and isinstance(ds_valid, tf.data.Dataset) + + +def test_prepare_visualization_dataset(tmp_path: pathlib.Path) -> None: + datasets_home = setup_test_resources(tmp_path) + + ds_train, ds_valid = prepare_visualization_datasets( + datasets_base_path=str(datasets_home), + batch_size=1, + validation_steps=1, + datasets_to_use=["guitarset"], + dataset_sampling_frequency=np.array([1]), + ) + + assert ds_train is not None and isinstance(ds_train, tf.data.Dataset) + assert ds_valid is not None and isinstance(ds_train, tf.data.Dataset) + + +def test_sample_datasets(tmp_path: pathlib.Path) -> None: + """touches the following methods: + - transcription_dataset + - parse_transcription_tfexample + - is_not_bad_shape + - sparse2dense + - reduce_transcription_inputs + - get_sample_weights + - _infer_time_size + - get_transcription_chunks + - extract_random_window + - extract_window + - trim_time + - is_not_all_silent_annotations + - to_transcription_training_input + """ + datasets_home = setup_test_resources(tmp_path) + + ds = sample_datasets( + split=Split.train, + datasets_base_path=str(datasets_home), + datasets=["guitarset"], + dataset_sampling_frequency=np.array([1]), + n_shuffle=1, + n_samples_per_track=1, + pairs=True, + ) + + assert ds is not None and isinstance(ds, tf.data.Dataset) + + +def test_transcription_file_generator_train(tmp_path: pathlib.Path) -> None: + dataset_path = tmp_path / "test_ds" / "splits" / Split.train.name + dataset_path.mkdir(parents=True) + create_empty_tfrecord(dataset_path / "test.tfrecord") + + file_gen, random_seed = transcription_file_generator( + Split.train, ["test_ds"], datasets_base_path=str(tmp_path), sample_weights=np.array([1]) + ) + + assert random_seed is False + + generator = file_gen() + assert next(generator).numpy().decode("utf-8") == str(dataset_path / "test.tfrecord") + try: + next(generator) + except Exception as e: + assert isinstance(e, StopIteration) + + +def test_transcription_file_generator_valid(tmp_path: pathlib.Path) -> None: + dataset_path = tmp_path / "test_ds" / "splits" / Split.validation.name + dataset_path.mkdir(parents=True) + create_empty_tfrecord(dataset_path / "test.tfrecord") + + file_gen, random_seed = transcription_file_generator( + Split.validation, ["test_ds"], datasets_base_path=str(tmp_path), sample_weights=np.array([1]) + ) + + assert random_seed is True + + generator = file_gen() + assert next(generator).numpy().decode("utf-8") == str(dataset_path / "test.tfrecord") + try: + next(generator) + except Exception as e: + assert isinstance(e, StopIteration) diff --git a/tests/data/test_tf_example_serialization.py b/tests/data/test_tf_example_serialization.py index 755742d..c26cf6e 100644 --- a/tests/data/test_tf_example_serialization.py +++ b/tests/data/test_tf_example_serialization.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # encoding: utf-8 # -# Copyright 2022 Spotify AB +# Copyright 2024 Spotify AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/tests/data/utils.py b/tests/data/utils.py index 1e916a6..ba5c78c 100644 --- a/tests/data/utils.py +++ b/tests/data/utils.py @@ -25,6 +25,8 @@ def create_mock_wav(output_fpath: pathlib.Path, duration_min: int) -> None: + assert output_fpath.suffix == ".wav" + duration_seconds = duration_min * 60 sample_rate = 44100 n_channels = 2 # Stereo @@ -45,6 +47,8 @@ def create_mock_wav(output_fpath: pathlib.Path, duration_min: int) -> None: def create_mock_flac(output_fpath: pathlib.Path) -> None: + assert output_fpath.suffix == ".flac" + frequency = 440 # A4 duration = 2 # seconds sample_rate = 44100 # standard @@ -60,6 +64,7 @@ def create_mock_flac(output_fpath: pathlib.Path) -> None: def create_mock_midi(output_fpath: pathlib.Path) -> None: + assert output_fpath.suffix in (".mid", ".midi") # Create a new MIDI file with one track mid = MidiFile() track = MidiTrack() diff --git a/tests/resources/data/guitarset/annotation/00_BN1-147-Gb_comp.jams b/tests/resources/data/guitarset/annotation/00_BN1-147-Gb_comp.jams new file mode 100644 index 0000000..6dd9e7b --- /dev/null +++ b/tests/resources/data/guitarset/annotation/00_BN1-147-Gb_comp.jams @@ -0,0 +1,70264 @@ +{ + "annotations": [ + { + "annotation_metadata": { + "curator": { + "name": "", + "email": "" + }, + "annotator": {}, + "version": "", + "corpus": "", + "annotation_tools": "", + "annotation_rules": "", + "validation": "", + "data_source": "0" + }, + "namespace": "pitch_contour", + "data": { + "time": [ + 0.06142947799992271, + 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+} \ No newline at end of file diff --git a/tests/resources/data/guitarset/annotation/annotation.zip b/tests/resources/data/guitarset/annotation/annotation.zip deleted file mode 100644 index 4841626..0000000 Binary files a/tests/resources/data/guitarset/annotation/annotation.zip and /dev/null differ diff --git a/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav b/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav deleted file mode 100644 index b38acec..0000000 Binary files a/tests/resources/data/guitarset/audio_mono-mic/00_BN1-129-Eb_comp_mic.wav and /dev/null differ diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py new file mode 100644 index 0000000..33a3af0 --- /dev/null +++ b/tests/test_callbacks.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# encoding: utf-8 +# +# Copyright 2024 Spotify AB +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import tensorflow as tf + +from typing import Dict + +from basic_pitch.callbacks import VisualizeCallback +from basic_pitch.constants import AUDIO_N_SAMPLES, ANNOTATIONS_N_SEMITONES, ANNOT_N_FRAMES + + +class MockModel(tf.keras.Model): + def __init__(self) -> None: + super(MockModel, self).__init__() + + def call(self, inputs: tf.Tensor) -> Dict[str, tf.Tensor]: + return { + key: tf.random.normal((1, ANNOTATIONS_N_SEMITONES, ANNOT_N_FRAMES)) for key in ["onset", "contour", "note"] + } + + +def create_mock_dataset() -> tf.data.Dataset: + batch_size = 1 + inputs = tf.random.normal((batch_size, AUDIO_N_SAMPLES, 1)) + targets = { + key: tf.random.normal((batch_size, ANNOTATIONS_N_SEMITONES, ANNOT_N_FRAMES)) + for key in ["onset", "contour", "note"] + } + dataset = tf.data.Dataset.from_tensor_slices((inputs, targets)) + dataset = dataset.batch(batch_size) + return dataset + + +def test_visualize_callback_on_epoch_end(tmpdir: str) -> None: + + vc = VisualizeCallback( + train_ds=create_mock_dataset(), + validation_ds=create_mock_dataset(), + tensorboard_dir=str(tmpdir), + sonify=True, + contours=True, + ) + + vc.model = MockModel() + + vc.on_epoch_end(1, {"loss": np.random.random(), "val_loss": np.random.random()})