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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. | |
| """ | |
| This module aims to be an entrance that integrates all the functions for extracting features from raw audio. | |
| The common audio features include: | |
| 1. Acoustic features such as Mel Spectrogram, F0, Energy, etc. | |
| 2. Content features such as phonetic posteriorgrams (PPG) and bottleneck features (BNF) from pretrained models | |
| Note: | |
| All the features extraction are designed to utilize GPU to the maximum extent, which can ease the on-the-fly extraction for large-scale dataset. | |
| """ | |
| import torch | |
| from torch.nn.utils.rnn import pad_sequence | |
| from utils.mel import extract_mel_features | |
| from utils.f0 import get_f0 as extract_f0_features | |
| from processors.content_extractor import ( | |
| WhisperExtractor, | |
| ContentvecExtractor, | |
| WenetExtractor, | |
| ) | |
| class AudioFeaturesExtractor: | |
| def __init__(self, cfg): | |
| """ | |
| Args: | |
| cfg: Amphion config that would be used to specify the processing parameters | |
| """ | |
| self.cfg = cfg | |
| def get_mel_spectrogram(self, wavs): | |
| """Get Mel Spectrogram Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| Returns: | |
| Tensor whose shape is (B, n_mels, n_frames) | |
| """ | |
| return extract_mel_features(y=wavs, cfg=self.cfg.preprocess) | |
| def get_f0(self, wavs, wav_lens=None, use_interpolate=False, return_uv=False): | |
| """Get F0 Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| Returns: | |
| Tensor whose shape is (B, n_frames) | |
| """ | |
| device = wavs.device | |
| f0s = [] | |
| uvs = [] | |
| for i, w in enumerate(wavs): | |
| if wav_lens is not None: | |
| w = w[: wav_lens[i]] | |
| f0, uv = extract_f0_features( | |
| # Use numpy to extract | |
| w.cpu().numpy(), | |
| self.cfg.preprocess, | |
| use_interpolate=use_interpolate, | |
| return_uv=True, | |
| ) | |
| f0s.append(torch.as_tensor(f0, device=device)) | |
| uvs.append(torch.as_tensor(uv, device=device, dtype=torch.long)) | |
| # (B, n_frames) | |
| f0s = pad_sequence(f0s, batch_first=True, padding_value=0) | |
| uvs = pad_sequence(uvs, batch_first=True, padding_value=0) | |
| if return_uv: | |
| return f0s, uvs | |
| return f0s | |
| def get_energy(self, wavs, mel_spec=None): | |
| """Get Energy Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| mel_spec: Tensor whose shape is (B, n_mels, n_frames) | |
| Returns: | |
| Tensor whose shape is (B, n_frames) | |
| """ | |
| if mel_spec is None: | |
| mel_spec = self.get_mel_spectrogram(wavs) | |
| energies = (mel_spec.exp() ** 2).sum(dim=1).sqrt() | |
| return energies | |
| def get_whisper_features(self, wavs, target_frame_len): | |
| """Get Whisper Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| target_frame_len: int | |
| Returns: | |
| Tensor whose shape is (B, target_frame_len, D) | |
| """ | |
| if not hasattr(self, "whisper_extractor"): | |
| self.whisper_extractor = WhisperExtractor(self.cfg) | |
| self.whisper_extractor.load_model() | |
| whisper_feats = self.whisper_extractor.extract_content_features(wavs) | |
| whisper_feats = self.whisper_extractor.ReTrans(whisper_feats, target_frame_len) | |
| return whisper_feats | |
| def get_contentvec_features(self, wavs, target_frame_len): | |
| """Get ContentVec Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| target_frame_len: int | |
| Returns: | |
| Tensor whose shape is (B, target_frame_len, D) | |
| """ | |
| if not hasattr(self, "contentvec_extractor"): | |
| self.contentvec_extractor = ContentvecExtractor(self.cfg) | |
| self.contentvec_extractor.load_model() | |
| contentvec_feats = self.contentvec_extractor.extract_content_features(wavs) | |
| contentvec_feats = self.contentvec_extractor.ReTrans( | |
| contentvec_feats, target_frame_len | |
| ) | |
| return contentvec_feats | |
| def get_wenet_features(self, wavs, target_frame_len, wav_lens=None): | |
| """Get WeNet Features | |
| Args: | |
| wavs: Tensor whose shape is (B, T) | |
| target_frame_len: int | |
| wav_lens: Tensor whose shape is (B) | |
| Returns: | |
| Tensor whose shape is (B, target_frame_len, D) | |
| """ | |
| if not hasattr(self, "wenet_extractor"): | |
| self.wenet_extractor = WenetExtractor(self.cfg) | |
| self.wenet_extractor.load_model() | |
| wenet_feats = self.wenet_extractor.extract_content_features(wavs, lens=wav_lens) | |
| wenet_feats = self.wenet_extractor.ReTrans(wenet_feats, target_frame_len) | |
| return wenet_feats | |