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MusicGen / tests /data /test_audio_dataset.py

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	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	from functools import partial
	from itertools import product
	import json
	import math
	import os
	import random
	import typing as tp

	import pytest
	import torch
	from torch.utils.data import DataLoader

	from audiocraft.data.audio_dataset import (
	AudioDataset,
	AudioMeta,
	_get_audio_meta,
	load_audio_meta,
	save_audio_meta
	)
	from audiocraft.data.zip import PathInZip

	from ..common_utils import TempDirMixin, get_white_noise, save_wav


	class TestAudioMeta(TempDirMixin):

	def test_get_audio_meta(self):
	sample_rates = [8000, 16_000]
	channels = [1, 2]
	duration = 1.
	for sample_rate, ch in product(sample_rates, channels):
	n_frames = int(duration * sample_rate)
	wav = get_white_noise(ch, n_frames)
	path = self.get_temp_path('sample.wav')
	save_wav(path, wav, sample_rate)
	m = _get_audio_meta(path, minimal=True)
	assert m.path == path, 'path does not match'
	assert m.sample_rate == sample_rate, 'sample rate does not match'
	assert m.duration == duration, 'duration does not match'
	assert m.amplitude is None
	assert m.info_path is None

	def test_save_audio_meta(self):
	audio_meta = [
	AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
	AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
	]
	empty_audio_meta = []
	for idx, meta in enumerate([audio_meta, empty_audio_meta]):
	path = self.get_temp_path(f'data_{idx}_save.jsonl')
	save_audio_meta(path, meta)
	with open(path, 'r') as f:
	lines = f.readlines()
	read_meta = [AudioMeta.from_dict(json.loads(line)) for line in lines]
	assert len(read_meta) == len(meta)
	for m, read_m in zip(meta, read_meta):
	assert m == read_m

	def test_load_audio_meta(self):
	try:
	import dora
	except ImportError:
	dora = None # type: ignore

	audio_meta = [
	AudioMeta("mypath1", 1., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file1.json')),
	AudioMeta("mypath2", 2., 16_000, None, None, PathInZip('/foo/bar.zip:/relative/file2.json'))
	]
	empty_meta = []
	for idx, meta in enumerate([audio_meta, empty_meta]):
	path = self.get_temp_path(f'data_{idx}_load.jsonl')
	with open(path, 'w') as f:
	for m in meta:
	json_str = json.dumps(m.to_dict()) + '\n'
	f.write(json_str)
	read_meta = load_audio_meta(path)
	assert len(read_meta) == len(meta)
	for m, read_m in zip(meta, read_meta):
	if dora:
	m.path = dora.git_save.to_absolute_path(m.path)
	assert m == read_m, f'original={m}, read={read_m}'


	class TestAudioDataset(TempDirMixin):

	def _create_audio_files(self,
	root_name: str,
	num_examples: int,
	durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
	sample_rate: int = 16_000,
	channels: int = 1):
	root_dir = self.get_temp_dir(root_name)
	for i in range(num_examples):
	if isinstance(durations, float):
	duration = durations
	elif isinstance(durations, tuple) and len(durations) == 1:
	duration = durations[0]
	elif isinstance(durations, tuple) and len(durations) == 2:
	duration = random.uniform(durations[0], durations[1])
	else:
	assert False
	n_frames = int(duration * sample_rate)
	wav = get_white_noise(channels, n_frames)
	path = os.path.join(root_dir, f'example_{i}.wav')
	save_wav(path, wav, sample_rate)
	return root_dir

	def _create_audio_dataset(self,
	root_name: str,
	total_num_examples: int,
	durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.),
	sample_rate: int = 16_000,
	channels: int = 1,
	segment_duration: tp.Optional[float] = None,
	num_examples: int = 10,
	shuffle: bool = True,
	return_info: bool = False):
	root_dir = self._create_audio_files(root_name, total_num_examples, durations, sample_rate, channels)
	dataset = AudioDataset.from_path(root_dir,
	minimal_meta=True,
	segment_duration=segment_duration,
	num_samples=num_examples,
	sample_rate=sample_rate,
	channels=channels,
	shuffle=shuffle,
	return_info=return_info)
	return dataset

	def test_dataset_full(self):
	total_examples = 10
	min_duration, max_duration = 1., 4.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration),
	sample_rate=sample_rate, channels=channels, segment_duration=None)
	assert len(dataset) == total_examples
	assert dataset.sample_rate == sample_rate
	assert dataset.channels == channels
	for idx in range(len(dataset)):
	sample = dataset[idx]
	assert sample.shape[0] == channels
	assert sample.shape[1] <= int(max_duration * sample_rate)
	assert sample.shape[1] >= int(min_duration * sample_rate)

	def test_dataset_segment(self):
	total_examples = 10
	num_samples = 20
	min_duration, max_duration = 1., 4.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples)
	assert len(dataset) == num_samples
	assert dataset.sample_rate == sample_rate
	assert dataset.channels == channels
	for idx in range(len(dataset)):
	sample = dataset[idx]
	assert sample.shape[0] == channels
	assert sample.shape[1] == int(segment_duration * sample_rate)

	def test_dataset_equal_audio_and_segment_durations(self):
	total_examples = 1
	num_samples = 2
	audio_duration = 1.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples)
	assert len(dataset) == num_samples
	assert dataset.sample_rate == sample_rate
	assert dataset.channels == channels
	for idx in range(len(dataset)):
	sample = dataset[idx]
	assert sample.shape[0] == channels
	assert sample.shape[1] == int(segment_duration * sample_rate)
	# the random seek_time adds variability on audio read
	sample_1 = dataset[0]
	sample_2 = dataset[1]
	assert not torch.allclose(sample_1, sample_2)

	def test_dataset_samples(self):
	total_examples = 1
	num_samples = 2
	audio_duration = 1.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1

	create_dataset = partial(
	self._create_audio_dataset,
	'dset', total_examples, durations=audio_duration, sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples,
	)

	dataset = create_dataset(shuffle=True)
	# when shuffle = True, we have different inputs for the same index across epoch
	sample_1 = dataset[0]
	sample_2 = dataset[0]
	assert not torch.allclose(sample_1, sample_2)

	dataset_noshuffle = create_dataset(shuffle=False)
	# when shuffle = False, we have same inputs for the same index across epoch
	sample_1 = dataset_noshuffle[0]
	sample_2 = dataset_noshuffle[0]
	assert torch.allclose(sample_1, sample_2)

	def test_dataset_return_info(self):
	total_examples = 10
	num_samples = 20
	min_duration, max_duration = 1., 4.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
	assert len(dataset) == num_samples
	assert dataset.sample_rate == sample_rate
	assert dataset.channels == channels
	for idx in range(len(dataset)):
	sample, segment_info = dataset[idx]
	assert sample.shape[0] == channels
	assert sample.shape[1] == int(segment_duration * sample_rate)
	assert segment_info.sample_rate == sample_rate
	assert segment_info.total_frames == int(segment_duration * sample_rate)
	assert segment_info.n_frames <= int(segment_duration * sample_rate)
	assert segment_info.seek_time >= 0

	def test_dataset_return_info_no_segment_duration(self):
	total_examples = 10
	num_samples = 20
	min_duration, max_duration = 1., 4.
	segment_duration = None
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
	assert len(dataset) == total_examples
	assert dataset.sample_rate == sample_rate
	assert dataset.channels == channels
	for idx in range(len(dataset)):
	sample, segment_info = dataset[idx]
	assert sample.shape[0] == channels
	assert sample.shape[1] == segment_info.total_frames
	assert segment_info.sample_rate == sample_rate
	assert segment_info.n_frames <= segment_info.total_frames

	def test_dataset_collate_fn(self):
	total_examples = 10
	num_samples = 20
	min_duration, max_duration = 1., 4.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=False)
	batch_size = 4
	dataloader = DataLoader(
	dataset,
	batch_size=batch_size,
	num_workers=0
	)
	for idx, batch in enumerate(dataloader):
	assert batch.shape[0] == batch_size

	@pytest.mark.parametrize("segment_duration", [1.0, None])
	def test_dataset_with_meta_collate_fn(self, segment_duration):
	total_examples = 10
	num_samples = 20
	min_duration, max_duration = 1., 4.
	segment_duration = 1.
	sample_rate = 16_000
	channels = 1
	dataset = self._create_audio_dataset(
	'dset', total_examples, durations=(min_duration, max_duration), sample_rate=sample_rate,
	channels=channels, segment_duration=segment_duration, num_examples=num_samples, return_info=True)
	batch_size = 4
	dataloader = DataLoader(
	dataset,
	batch_size=batch_size,
	collate_fn=dataset.collater,
	num_workers=0
	)
	for idx, batch in enumerate(dataloader):
	wav, infos = batch
	assert wav.shape[0] == batch_size
	assert len(infos) == batch_size

	@pytest.mark.parametrize("segment_duration,sample_on_weight,sample_on_duration,a_hist,b_hist,c_hist", [
	[1, True, True, 0.5, 0.5, 0.0],
	[1, False, True, 0.25, 0.5, 0.25],
	[1, True, False, 0.666, 0.333, 0.0],
	[1, False, False, 0.333, 0.333, 0.333],
	[None, False, False, 0.333, 0.333, 0.333]])
	def test_sample_with_weight(self, segment_duration, sample_on_weight, sample_on_duration, a_hist, b_hist, c_hist):
	random.seed(1234)
	rng = torch.Generator()
	rng.manual_seed(1234)

	def _get_histogram(dataset, repetitions=20_000):
	counts = {file_meta.path: 0. for file_meta in meta}
	for _ in range(repetitions):
	file_meta = dataset.sample_file(rng)
	counts[file_meta.path] += 1
	return {name: count / repetitions for name, count in counts.items()}

	meta = [
	AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
	AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
	AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
	]
	dataset = AudioDataset(
	meta, segment_duration=segment_duration, sample_on_weight=sample_on_weight,
	sample_on_duration=sample_on_duration)
	hist = _get_histogram(dataset)
	assert math.isclose(hist['a'], a_hist, abs_tol=0.01)
	assert math.isclose(hist['b'], b_hist, abs_tol=0.01)
	assert math.isclose(hist['c'], c_hist, abs_tol=0.01)

	def test_meta_duration_filter_all(self):
	meta = [
	AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
	AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
	AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
	]
	try:
	AudioDataset(meta, segment_duration=11, min_segment_ratio=1)
	assert False
	except AssertionError:
	assert True

	def test_meta_duration_filter_long(self):
	meta = [
	AudioMeta(path='a', duration=5, sample_rate=1, weight=2),
	AudioMeta(path='b', duration=10, sample_rate=1, weight=None),
	AudioMeta(path='c', duration=5, sample_rate=1, weight=0),
	]
	dataset = AudioDataset(meta, segment_duration=None, min_segment_ratio=1, max_audio_duration=7)
	assert len(dataset) == 2