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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import os | |
| import numpy as np | |
| import torch | |
| import torchaudio | |
| def save_feature(process_dir, feature_dir, item, feature, overrides=True): | |
| """Save features to path | |
| Args: | |
| process_dir (str): directory to store features | |
| feature_dir (_type_): directory to store one type of features (mel, energy, ...) | |
| item (str): uid | |
| feature (tensor): feature tensor | |
| overrides (bool, optional): whether to override existing files. Defaults to True. | |
| """ | |
| process_dir = os.path.join(process_dir, feature_dir) | |
| os.makedirs(process_dir, exist_ok=True) | |
| out_path = os.path.join(process_dir, item + ".npy") | |
| if os.path.exists(out_path): | |
| if overrides: | |
| np.save(out_path, feature) | |
| else: | |
| np.save(out_path, feature) | |
| def save_txt(process_dir, feature_dir, item, feature, overrides=True): | |
| process_dir = os.path.join(process_dir, feature_dir) | |
| os.makedirs(process_dir, exist_ok=True) | |
| out_path = os.path.join(process_dir, item + ".txt") | |
| if os.path.exists(out_path): | |
| if overrides: | |
| f = open(out_path, "w") | |
| f.writelines(feature) | |
| f.close() | |
| else: | |
| f = open(out_path, "w") | |
| f.writelines(feature) | |
| f.close() | |
| def save_audio(path, waveform, fs, add_silence=False, turn_up=False, volume_peak=0.9): | |
| if turn_up: | |
| # continue to turn up to volume_peak | |
| ratio = volume_peak / max(waveform.max(), abs(waveform.min())) | |
| waveform = waveform * ratio | |
| if add_silence: | |
| silence_len = fs // 20 | |
| silence = np.zeros((silence_len,), dtype=waveform.dtype) | |
| result = np.concatenate([silence, waveform, silence]) | |
| waveform = result | |
| waveform = torch.as_tensor(waveform, dtype=torch.float32, device="cpu") | |
| if len(waveform.size()) == 1: | |
| waveform = waveform[None, :] | |
| elif waveform.size(0) != 1: | |
| # Stereo to mono | |
| waveform = torch.mean(waveform, dim=0, keepdim=True) | |
| torchaudio.save(path, waveform, fs, encoding="PCM_S", bits_per_sample=16) | |
| async def async_load_audio(path, sample_rate: int = 24000): | |
| r""" | |
| Args: | |
| path: The source loading path. | |
| sample_rate: The target sample rate, will automatically resample if necessary. | |
| Returns: | |
| waveform: The waveform object. Should be [1 x sequence_len]. | |
| """ | |
| async def use_torchaudio_load(path): | |
| return torchaudio.load(path) | |
| waveform, sr = await use_torchaudio_load(path) | |
| waveform = torch.mean(waveform, dim=0, keepdim=True) | |
| if sr != sample_rate: | |
| waveform = torchaudio.functional.resample(waveform, sr, sample_rate) | |
| if torch.any(torch.isnan(waveform) or torch.isinf(waveform)): | |
| raise ValueError("NaN or Inf found in waveform.") | |
| return waveform | |
| async def async_save_audio( | |
| path, | |
| waveform, | |
| sample_rate: int = 24000, | |
| add_silence: bool = False, | |
| volume_peak: float = 0.9, | |
| ): | |
| r""" | |
| Args: | |
| path: The target saving path. | |
| waveform: The waveform object. Should be [n_channel x sequence_len]. | |
| sample_rate: Sample rate. | |
| add_silence: If ``true``, concat 0.05s silence to beginning and end. | |
| volume_peak: Turn up volume for larger number, vice versa. | |
| """ | |
| async def use_torchaudio_save(path, waveform, sample_rate): | |
| torchaudio.save( | |
| path, waveform, sample_rate, encoding="PCM_S", bits_per_sample=16 | |
| ) | |
| waveform = torch.as_tensor(waveform, device="cpu", dtype=torch.float32) | |
| shape = waveform.size()[:-1] | |
| ratio = abs(volume_peak) / max(waveform.max(), abs(waveform.min())) | |
| waveform = waveform * ratio | |
| if add_silence: | |
| silence_len = sample_rate // 20 | |
| silence = torch.zeros((*shape, silence_len), dtype=waveform.type()) | |
| waveform = torch.concatenate((silence, waveform, silence), dim=-1) | |
| if waveform.dim() == 1: | |
| waveform = waveform[None] | |
| await use_torchaudio_save(path, waveform, sample_rate) | |
| def load_mel_extrema(cfg, dataset_name, split): | |
| dataset_dir = os.path.join( | |
| cfg.OUTPUT_PATH, | |
| "preprocess/{}_version".format(cfg.data.process_version), | |
| dataset_name, | |
| ) | |
| min_file = os.path.join( | |
| dataset_dir, | |
| "mel_min_max", | |
| split.split("_")[-1], | |
| "mel_min.npy", | |
| ) | |
| max_file = os.path.join( | |
| dataset_dir, | |
| "mel_min_max", | |
| split.split("_")[-1], | |
| "mel_max.npy", | |
| ) | |
| mel_min = np.load(min_file) | |
| mel_max = np.load(max_file) | |
| return mel_min, mel_max | |