def __getitem__(self, index): import soundfile as sf path_or_fp = os.path.join(self.root_dir, str(self.fnames[index])) _path, slice_ptr = parse_path(path_or_fp) if len(slice_ptr) == 2: byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) assert is_sf_audio_data(byte_data) path_or_fp = io.BytesIO(byte_data) if random.random() < self.noise_rir_prob and self.is_training: wav = self.noise_rir_dataset.add_noise_rir(path_or_fp) curr_sample_rate = self.sample_rate else: wav, curr_sample_rate = sf.read(path_or_fp, dtype="float32") feats = torch.from_numpy(wav).float() feats = self.postprocess(feats, curr_sample_rate) if random.random() < self.speed_perturb_prob and self.is_training: feats = self.sp(feats) if random.random() < self.volume_perturb_prob and self.is_training: feats = volume_perturb(feats) if self.is_save: save_path = os.path.join( self.is_save_path, _path.split('/')[-1].split('.')[0]) + '_augtment.wav' self.save_to_wav(feats, save_path) return {"id": index, "source": feats}
def get_feature_value_min_max(feature_paths: List[str]): v_min, v_max = 1e-8, -1e-8 for p in tqdm(feature_paths): _path, slice_ptr = parse_path(p) assert len(slice_ptr) == 2 byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) assert is_npy_data(byte_data) path_or_fp = io.BytesIO(byte_data) features = np.load(path_or_fp).squeeze() v_min = min(v_min, features.min().item()) v_max = max(v_max, features.max().item()) return v_min, v_max
def get_features_or_waveform_from_stored_zip( path, byte_offset, byte_size, need_waveform=False ): assert path.endswith(".zip") data = read_from_stored_zip(path, byte_offset, byte_size) f = io.BytesIO(data) if is_npy_data(data): features_or_waveform = np.load(f) elif is_sf_audio_data(data): features_or_waveform = \ get_waveform(f, always_2d=False)[0] if need_waveform else get_fbank(f) else: raise ValueError(f'Unknown file format for "{path}"') return features_or_waveform
def __getitem__(self, index): import soundfile as sf path_or_fp = os.path.join(self.root_dir, str(self.fnames[index])) _path, slice_ptr = parse_path(path_or_fp) if len(slice_ptr) == 2: byte_data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) assert is_sf_audio_data(byte_data) path_or_fp = io.BytesIO(byte_data) wav, curr_sample_rate = sf.read(path_or_fp, dtype="float32") feats = torch.from_numpy(wav).float() feats = self.postprocess(feats, curr_sample_rate) return {"id": index, "source": feats}
def get_audio(self, index): import soundfile as sf wav_path = os.path.join(self.audio_root, self.audio_names[index]) _path, slice_ptr = parse_path(wav_path) if len(slice_ptr) == 0: wav, cur_sample_rate = sf.read(_path) else: assert _path.endswith(".zip") data = read_from_stored_zip(_path, slice_ptr[0], slice_ptr[1]) f = io.BytesIO(data) wav, cur_sample_rate = sf.read(f) wav = torch.from_numpy(wav).float() wav = self.postprocess(wav, cur_sample_rate) return wav