import tarfile import warnings from unittest.mock import patch import torch from torchaudio._internal import module_utils as _mod_utils from torchaudio.backend import soundfile_backend from torchaudio_unittest.backend.common import get_bits_per_sample, get_encoding from torchaudio_unittest.common_utils import ( get_wav_data, nested_params, PytorchTestCase, save_wav, skipIfNoModule, TempDirMixin, ) from .common import parameterize, skipIfFormatNotSupported if _mod_utils.is_module_available("soundfile"): import soundfile @skipIfNoModule("soundfile") class TestInfo(TempDirMixin, PytorchTestCase): @parameterize( ["float32", "int32", "int16", "uint8"], [8000, 16000], [1, 2], ) def test_wav(self, dtype, sample_rate, num_channels): """`soundfile_backend.info` can check wav file correctly""" duration = 1 path = self.get_temp_path("data.wav") data = get_wav_data(dtype, num_channels, normalize=False, num_frames=duration * sample_rate) save_wav(path, data, sample_rate) info = soundfile_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels assert info.bits_per_sample == get_bits_per_sample("wav", dtype) assert info.encoding == get_encoding("wav", dtype) @parameterize([8000, 16000], [1, 2]) @skipIfFormatNotSupported("FLAC") def test_flac(self, sample_rate, num_channels): """`soundfile_backend.info` can check flac file correctly""" duration = 1 num_frames = sample_rate * duration data = torch.randn(num_frames, num_channels).numpy() path = self.get_temp_path("data.flac") soundfile.write(path, data, sample_rate) info = soundfile_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == num_frames assert info.num_channels == num_channels assert info.bits_per_sample == 16 assert info.encoding == "FLAC" @parameterize([8000, 16000], [1, 2]) @skipIfFormatNotSupported("OGG") def test_ogg(self, sample_rate, num_channels): """`soundfile_backend.info` can check ogg file correctly""" duration = 1 num_frames = sample_rate * duration data = torch.randn(num_frames, num_channels).numpy() path = self.get_temp_path("data.ogg") soundfile.write(path, data, sample_rate) info = soundfile_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels assert info.bits_per_sample == 0 assert info.encoding == "VORBIS" @nested_params( [8000, 16000], [1, 2], [("PCM_24", 24), ("PCM_32", 32)], ) @skipIfFormatNotSupported("NIST") def test_sphere(self, sample_rate, num_channels, subtype_and_bit_depth): """`soundfile_backend.info` can check sph file correctly""" duration = 1 num_frames = sample_rate * duration data = torch.randn(num_frames, num_channels).numpy() path = self.get_temp_path("data.nist") subtype, bits_per_sample = subtype_and_bit_depth soundfile.write(path, data, sample_rate, subtype=subtype) info = soundfile_backend.info(path) assert info.sample_rate == sample_rate assert info.num_frames == sample_rate * duration assert info.num_channels == num_channels assert info.bits_per_sample == bits_per_sample assert info.encoding == "PCM_S" def test_unknown_subtype_warning(self): """soundfile_backend.info issues a warning when the subtype is unknown This will happen if a new subtype is supported in SoundFile: the _SUBTYPE_TO_BITS_PER_SAMPLE dict should be updated. """ def _mock_info_func(_): class MockSoundFileInfo: samplerate = 8000 frames = 356 channels = 2 subtype = "UNSEEN_SUBTYPE" format = "UNKNOWN" return MockSoundFileInfo() with patch("soundfile.info", _mock_info_func): with warnings.catch_warnings(record=True) as w: info = soundfile_backend.info("foo") assert len(w) == 1 assert "UNSEEN_SUBTYPE subtype is unknown to TorchAudio" in str(w[-1].message) assert info.bits_per_sample == 0 @skipIfNoModule("soundfile") class TestFileObject(TempDirMixin, PytorchTestCase): def _test_fileobj(self, ext, subtype, bits_per_sample): """Query audio via file-like object works""" duration = 2 sample_rate = 16000 num_channels = 2 num_frames = sample_rate * duration path = self.get_temp_path(f"test.{ext}") data = torch.randn(num_frames, num_channels).numpy() soundfile.write(path, data, sample_rate, subtype=subtype) with open(path, "rb") as fileobj: info = soundfile_backend.info(fileobj) assert info.sample_rate == sample_rate assert info.num_frames == num_frames assert info.num_channels == num_channels assert info.bits_per_sample == bits_per_sample assert info.encoding == "FLAC" if ext == "flac" else "PCM_S" def test_fileobj_wav(self): """Loading audio via file-like object works""" self._test_fileobj("wav", "PCM_16", 16) @skipIfFormatNotSupported("FLAC") def test_fileobj_flac(self): """Loading audio via file-like object works""" self._test_fileobj("flac", "PCM_16", 16) def _test_tarobj(self, ext, subtype, bits_per_sample): """Query compressed audio via file-like object works""" duration = 2 sample_rate = 16000 num_channels = 2 num_frames = sample_rate * duration audio_file = f"test.{ext}" audio_path = self.get_temp_path(audio_file) archive_path = self.get_temp_path("archive.tar.gz") data = torch.randn(num_frames, num_channels).numpy() soundfile.write(audio_path, data, sample_rate, subtype=subtype) with tarfile.TarFile(archive_path, "w") as tarobj: tarobj.add(audio_path, arcname=audio_file) with tarfile.TarFile(archive_path, "r") as tarobj: fileobj = tarobj.extractfile(audio_file) info = soundfile_backend.info(fileobj) assert info.sample_rate == sample_rate assert info.num_frames == num_frames assert info.num_channels == num_channels assert info.bits_per_sample == bits_per_sample assert info.encoding == "FLAC" if ext == "flac" else "PCM_S" def test_tarobj_wav(self): """Query compressed audio via file-like object works""" self._test_tarobj("wav", "PCM_16", 16) @skipIfFormatNotSupported("FLAC") def test_tarobj_flac(self): """Query compressed audio via file-like object works""" self._test_tarobj("flac", "PCM_16", 16)