def open(self, item) -> AudioItem:
p = Path(item)
if self.path is not None and str(self.path) not in str(item): p = self.path/item
if not p.exists():
raise FileNotFoundError(f"Neither '{item}' nor '{p}' could be found")
if not str(p).lower().endswith(AUDIO_EXTENSIONS): raise Exception("Invalid audio file")
cfg = self.config
if cfg.use_spectro:
cache_dir = self.path / cfg.cache_dir
folder = md5(str(asdict(cfg))+str(asdict(cfg.sg_cfg)))
fname = f"{md5(str(p))}-{p.name}.pt"
image_path = cache_dir/(f"{folder}/{fname}")
if cfg.cache and not cfg.force_cache and image_path.exists():
mel = torch.load(image_path).squeeze()
start, end = None, None
if cfg.duration and cfg._processed:
mel, start, end = tfm_crop_time(mel, cfg._sr, cfg.duration, cfg.sg_cfg.hop)
return AudioItem(spectro=mel, path=item, max_to_pad=cfg.max_to_pad, start=start, end=end)
signal, samplerate = torchaudio.load(str(p))
if(cfg._sr is not None and samplerate != cfg._sr):
raise ValueError(f'''Multiple sample rates detected. Sample rate {samplerate} of file {str(p)}
does not match config sample rate {cfg._sr}
this means your dataset has multiple different sample rates,
please choose one and set resample_to to that value''')
if cfg.max_to_pad or cfg.segment_size:
pad_len = cfg.max_to_pad if cfg.max_to_pad is not None else cfg.segment_size
signal = PadTrim(max_len=int(pad_len/1000*samplerate))(signal)
mel = None
if cfg.use_spectro:
if cfg.mfcc: mel = MFCC(sr=samplerate, n_mfcc=cfg.sg_cfg.n_mfcc, melkwargs=asdict(cfg.sg_cfg))(signal.reshape(1,-1))
else:
mel = MelSpectrogram(**(cfg.sg_cfg.mel_args()))(signal.reshape(1, -1))
if cfg.sg_cfg.to_db_scale: mel = SpectrogramToDB(top_db=cfg.sg_cfg.top_db)(mel)
mel = mel.squeeze().permute(1, 0)
if cfg.standardize: mel = standardize(mel)
if cfg.delta: mel = torch.stack([mel, torchdelta(mel), torchdelta(mel, order=2)])
else: mel = mel.expand(3,-1,-1)
if cfg.cache:
os.makedirs(image_path.parent, exist_ok=True)
torch.save(mel, image_path)
start, end = None, None
if cfg.duration and cfg._processed:
mel, start, end = tfm_crop_time(mel, cfg._sr, cfg.duration, cfg.sg_cfg.hop)
return AudioItem(sig=signal.squeeze(), sr=samplerate, spectro=mel, path=item, start=start, end=end)
def open(self, item) -> AudioItem:
p = Path(item)
if self.path is not None and str(self.path) not in str(item):
p = self.path / item
if not p.exists():
raise FileNotFoundError(
f"Neither '{item}' nor '{p}' could be found")
if not str(p).lower().endswith(AUDIO_EXTENSIONS):
raise Exception("Invalid audio file")
cfg = self.config
if cfg.use_spectro:
folder = md5(str(asdict(cfg)) + str(asdict(cfg.sg_cfg)))
fname = f"{md5(str(p))}-{p.name}.pt"
image_path = cfg.cache_dir / (f"{folder}/{fname}")
if cfg.cache and not cfg.force_cache and image_path.exists():
mel = torch.load(image_path).squeeze()
start, end = None, None
if cfg.duration and cfg._processed:
mel, start, end = tfm_crop_time(mel, cfg._sr, cfg.duration,
cfg.sg_cfg.hop,
cfg.pad_mode)
return AudioItem(spectro=mel,
path=item,
max_to_pad=cfg.max_to_pad,
start=start,
end=end)
sig, sr = torchaudio.load(str(p))
if (cfg._sr is not None and sr != cfg._sr):
raise ValueError(
f'''Multiple sample rates detected. Sample rate {sr} of file {str(p)}
does not match config sample rate {cfg._sr}
this means your dataset has multiple different sample rates,
please choose one and set resample_to to that value'''
)
if (sig.shape[0] > 1):
if not cfg.downmix:
warnings.warn(
f'''Audio file {p} has {sig.shape[0]} channels, automatically downmixing to mono,
set AudioConfig.downmix=True to remove warnings'''
)
sig = DownmixMono(channels_first=True)(sig)
if cfg.max_to_pad or cfg.segment_size:
pad_len = cfg.max_to_pad if cfg.max_to_pad is not None else cfg.segment_size
sig = tfm_padtrim_signal(sig,
int(pad_len / 1000 * sr),
pad_mode="zeros")
mel = None
if cfg.use_spectro:
if cfg.mfcc:
mel = MFCC(sr=sr,
n_mfcc=cfg.sg_cfg.n_mfcc,
melkwargs=cfg.sg_cfg.mel_args())(sig)
else:
mel = MelSpectrogram(**(cfg.sg_cfg.mel_args()))(sig)
if cfg.sg_cfg.to_db_scale:
mel = SpectrogramToDB(top_db=cfg.sg_cfg.top_db)(mel)
mel = mel.squeeze().permute(1, 0).flip(0)
if cfg.standardize: mel = standardize(mel)
if cfg.delta:
mel = torch.stack(
[mel, torchdelta(mel),
torchdelta(mel, order=2)])
else:
mel = mel.expand(3, -1, -1)
if cfg.cache:
os.makedirs(image_path.parent, exist_ok=True)
torch.save(mel, image_path)
_record_cache_contents(cfg, [image_path])
start, end = None, None
if cfg.duration and cfg._processed:
mel, start, end = tfm_crop_time(mel, cfg._sr, cfg.duration,
cfg.sg_cfg.hop, cfg.pad_mode)
return AudioItem(sig=sig.squeeze(),
sr=sr,
spectro=mel,
path=item,
start=start,
end=end)