113 lines
4.6 KiB
Python
113 lines
4.6 KiB
Python
import random
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import torchaudio
|
|
|
|
from trainer.inject import Injector
|
|
from utils.util import opt_get
|
|
|
|
|
|
class MelSpectrogramInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
from models.tacotron2.layers import TacotronSTFT
|
|
# These are the default tacotron values for the MEL spectrogram.
|
|
filter_length = opt_get(opt, ['filter_length'], 1024)
|
|
hop_length = opt_get(opt, ['hop_length'], 256)
|
|
win_length = opt_get(opt, ['win_length'], 1024)
|
|
n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
|
|
mel_fmin = opt_get(opt, ['mel_fmin'], 0)
|
|
mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
|
|
sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
|
|
self.stft = TacotronSTFT(filter_length, hop_length, win_length, n_mel_channels, sampling_rate, mel_fmin, mel_fmax)
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
|
|
inp = inp.squeeze(1)
|
|
assert len(inp.shape) == 2
|
|
self.stft = self.stft.to(inp.device)
|
|
return {self.output: self.stft.mel_spectrogram(inp)}
|
|
|
|
|
|
class TorchMelSpectrogramInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
# These are the default tacotron values for the MEL spectrogram.
|
|
self.filter_length = opt_get(opt, ['filter_length'], 1024)
|
|
self.hop_length = opt_get(opt, ['hop_length'], 256)
|
|
self.win_length = opt_get(opt, ['win_length'], 1024)
|
|
self.n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
|
|
self.mel_fmin = opt_get(opt, ['mel_fmin'], 0)
|
|
self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
|
|
self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
|
|
norm = opt_get(opt, ['normalize'], False)
|
|
self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
|
|
win_length=self.win_length, power=2, normalized=norm,
|
|
sample_rate=self.sampling_rate, f_min=self.mel_fmin,
|
|
f_max=self.mel_fmax, n_mels=self.n_mel_channels,
|
|
norm="slaney")
|
|
self.mel_norm_file = opt_get(opt, ['mel_norm_file'], None)
|
|
if self.mel_norm_file is not None:
|
|
self.mel_norms = torch.load(self.mel_norm_file)
|
|
else:
|
|
self.mel_norms = None
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
|
|
inp = inp.squeeze(1)
|
|
assert len(inp.shape) == 2
|
|
self.mel_stft = self.mel_stft.to(inp.device)
|
|
mel = self.mel_stft(inp)
|
|
# Perform dynamic range compression
|
|
mel = torch.log(torch.clamp(mel, min=1e-5))
|
|
if self.mel_norms is not None:
|
|
self.mel_norms = self.mel_norms.to(mel.device)
|
|
mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
|
|
return {self.output: mel}
|
|
|
|
|
|
class RandomAudioCropInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.crop_sz = opt['crop_size']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
len = inp.shape[-1]
|
|
margin = len - self.crop_sz
|
|
start = random.randint(0, margin)
|
|
return {self.output: inp[:, :, start:start+self.crop_sz]}
|
|
|
|
|
|
class AudioClipInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.clip_size = opt['clip_size']
|
|
self.ctc_codes = opt['ctc_codes_key']
|
|
self.output_ctc = opt['ctc_out_key']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
ctc = state[self.ctc_codes]
|
|
len = inp.shape[-1]
|
|
if len > self.clip_size:
|
|
proportion_inp_remaining = self.clip_size/len
|
|
inp = inp[:, :, :self.clip_size]
|
|
ctc = ctc[:,:int(proportion_inp_remaining*ctc.shape[-1])]
|
|
return {self.output: inp, self.output_ctc: ctc}
|
|
|
|
|
|
class AudioResampleInjector(Injector):
|
|
def __init__(self, opt, env):
|
|
super().__init__(opt, env)
|
|
self.input_sr = opt['input_sample_rate']
|
|
self.output_sr = opt['output_sample_rate']
|
|
|
|
def forward(self, state):
|
|
inp = state[self.input]
|
|
return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
|