# 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