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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
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