323 lines
14 KiB
Python
323 lines
14 KiB
Python
import random
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import torch
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import torch.nn.functional as F
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import torchaudio
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from models.audio.tts.unet_diffusion_tts_flat import DiffusionTtsFlat
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from trainer.inject import Injector
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from utils.util import opt_get, load_model_from_config, pad_or_truncate
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TACOTRON_MEL_MAX = 2.3143386840820312
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TACOTRON_MEL_MIN = -11.512925148010254
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def normalize_mel(mel):
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return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1
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def denormalize_mel(norm_mel):
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return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN
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class MelSpectrogramInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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from models.audio.tts.tacotron2 import TacotronSTFT
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# These are the default tacotron values for the MEL spectrogram.
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filter_length = opt_get(opt, ['filter_length'], 1024)
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hop_length = opt_get(opt, ['hop_length'], 256)
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win_length = opt_get(opt, ['win_length'], 1024)
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n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
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mel_fmin = opt_get(opt, ['mel_fmin'], 0)
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mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
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sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
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self.stft = TacotronSTFT(filter_length, hop_length, win_length, n_mel_channels, sampling_rate, mel_fmin, mel_fmax)
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self.do_normalization = opt_get(opt, ['do_normalization'], None) # This is different from the TorchMelSpectrogramInjector. This just normalizes to the range [-1,1]
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def forward(self, state):
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inp = state[self.input]
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if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
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inp = inp.squeeze(1)
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assert len(inp.shape) == 2
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self.stft = self.stft.to(inp.device)
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mel = self.stft.mel_spectrogram(inp)
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if self.do_normalization:
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mel = normalize_mel(mel)
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return {self.output: mel}
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class TorchMelSpectrogramInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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# These are the default tacotron values for the MEL spectrogram.
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self.filter_length = opt_get(opt, ['filter_length'], 1024)
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self.hop_length = opt_get(opt, ['hop_length'], 256)
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self.win_length = opt_get(opt, ['win_length'], 1024)
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self.n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
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self.mel_fmin = opt_get(opt, ['mel_fmin'], 0)
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self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
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self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
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norm = opt_get(opt, ['normalize'], False)
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self.true_norm = opt_get(opt, ['true_normalization'], False)
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self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
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win_length=self.win_length, power=2, normalized=norm,
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sample_rate=self.sampling_rate, f_min=self.mel_fmin,
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f_max=self.mel_fmax, n_mels=self.n_mel_channels,
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norm="slaney")
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self.mel_norm_file = opt_get(opt, ['mel_norm_file'], None)
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if self.mel_norm_file is not None:
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self.mel_norms = torch.load(self.mel_norm_file)
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else:
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self.mel_norms = None
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def forward(self, state):
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inp = state[self.input]
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if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
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inp = inp.squeeze(1)
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assert len(inp.shape) == 2
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self.mel_stft = self.mel_stft.to(inp.device)
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mel = self.mel_stft(inp)
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# Perform dynamic range compression
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mel = torch.log(torch.clamp(mel, min=1e-5))
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if self.mel_norms is not None:
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self.mel_norms = self.mel_norms.to(mel.device)
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mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
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if self.true_norm:
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mel = normalize_mel(mel)
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return {self.output: mel}
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class RandomAudioCropInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.crop_sz = opt['crop_size']
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self.lengths_key = opt['lengths_key']
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def forward(self, state):
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inp = state[self.input]
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lens = state[self.lengths_key]
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len = torch.min(lens)
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margin = len - self.crop_sz
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if margin < 0:
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return {self.output: inp}
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start = random.randint(0, margin)
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return {self.output: inp[:, :, start:start+self.crop_sz]}
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class AudioClipInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.clip_size = opt['clip_size']
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self.ctc_codes = opt['ctc_codes_key']
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self.output_ctc = opt['ctc_out_key']
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def forward(self, state):
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inp = state[self.input]
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ctc = state[self.ctc_codes]
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len = inp.shape[-1]
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if len > self.clip_size:
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proportion_inp_remaining = self.clip_size/len
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inp = inp[:, :, :self.clip_size]
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ctc = ctc[:,:int(proportion_inp_remaining*ctc.shape[-1])]
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return {self.output: inp, self.output_ctc: ctc}
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class AudioResampleInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.input_sr = opt['input_sample_rate']
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self.output_sr = opt['output_sample_rate']
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def forward(self, state):
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inp = state[self.input]
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return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
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class DiscreteTokenInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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cfg = opt_get(opt, ['dvae_config'], "../experiments/train_diffusion_vocoder_22k_level.yml")
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dvae_name = opt_get(opt, ['dvae_name'], 'dvae')
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self.dvae = load_model_from_config(cfg, dvae_name, device=f'cuda:{env["device"]}').eval()
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def forward(self, state):
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inp = state[self.input]
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with torch.no_grad():
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self.dvae = self.dvae.to(inp.device)
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codes = self.dvae.get_codebook_indices(inp)
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return {self.output: codes}
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class GptVoiceLatentInjector(Injector):
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"""
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This injector does all the legwork to generate latents out of a UnifiedVoice model, including encoding all audio
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inputs into a MEL spectrogram and discretizing the inputs.
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"""
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def __init__(self, opt, env):
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super().__init__(opt, env)
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# For discrete tokenization.
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cfg = opt_get(opt, ['dvae_config'], "../experiments/train_diffusion_vocoder_22k_level.yml")
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dvae_name = opt_get(opt, ['dvae_name'], 'dvae')
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self.dvae = load_model_from_config(cfg, dvae_name).cuda().eval()
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# The unified_voice model.
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cfg = opt_get(opt, ['gpt_config'], "../experiments/train_gpt_tts_unified.yml")
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model_name = opt_get(opt, ['gpt_name'], 'gpt')
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pretrained_path = opt['gpt_path']
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self.gpt = load_model_from_config(cfg, model_name=model_name,
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also_load_savepoint=False, load_path=pretrained_path).cuda().eval()
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self.needs_move = True
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# Mel converter
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self.mel_inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'mel_norm_file': '../experiments/clips_mel_norms.pth'},{})
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# Aux input keys.
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self.conditioning_key = opt['conditioning_clip']
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self.text_input_key = opt['text']
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self.text_lengths_key = opt['text_lengths']
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self.input_lengths_key = opt['input_lengths']
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def to_mel(self, t):
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return self.mel_inj({'wav': t})['mel']
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def forward(self, state):
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with torch.no_grad():
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mel_inputs = self.to_mel(state[self.input])
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state_cond = pad_or_truncate(state[self.conditioning_key], 132300)
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mel_conds = []
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for k in range(state_cond.shape[1]):
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mel_conds.append(self.to_mel(state_cond[:, k]))
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mel_conds = torch.stack(mel_conds, dim=1)
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if self.needs_move:
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self.dvae = self.dvae.to(mel_inputs.device)
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self.gpt = self.gpt.to(mel_inputs.device)
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codes = self.dvae.get_codebook_indices(mel_inputs)
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latents = self.gpt(mel_conds, state[self.text_input_key],
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state[self.text_lengths_key], codes, state[self.input_lengths_key],
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text_first=True, raw_mels=None, return_attentions=False, return_latent=True,
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clip_inputs=False)
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assert latents.shape[1] == codes.shape[1]
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return {self.output: latents}
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class ReverseUnivnetInjector(Injector):
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"""
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This injector specifically builds inputs and labels for a univnet detector.g
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"""
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def __init__(self, opt, env):
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super().__init__(opt, env)
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from scripts.audio.gen.speech_synthesis_utils import load_univnet_vocoder
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self.univnet = load_univnet_vocoder().cuda()
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self.mel_input_key = opt['mel']
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self.label_output_key = opt['labels']
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self.do_augmentations = opt_get(opt, ['do_aug'], True)
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def forward(self, state):
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with torch.no_grad():
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original_audio = state[self.input]
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mel = state[self.mel_input_key]
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decoded_mel = self.univnet.inference(mel)[:,:,:original_audio.shape[-1]]
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if self.do_augmentations:
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original_audio = original_audio + torch.rand_like(original_audio) * random.random() * .005
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decoded_mel = decoded_mel + torch.rand_like(decoded_mel) * random.random() * .005
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if(random.random() < .5):
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original_audio = torchaudio.functional.resample(torchaudio.functional.resample(original_audio, 24000, 10000), 10000, 24000)
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if(random.random() < .5):
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decoded_mel = torchaudio.functional.resample(torchaudio.functional.resample(decoded_mel, 24000, 10000), 10000, 24000)
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if(random.random() < .5):
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original_audio = torchaudio.functional.resample(original_audio, 24000, 22000 + random.randint(0,2000))
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if(random.random() < .5):
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decoded_mel = torchaudio.functional.resample(decoded_mel, 24000, 22000 + random.randint(0,2000))
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smallest_dim = min(original_audio.shape[-1], decoded_mel.shape[-1])
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original_audio = original_audio[:,:,:smallest_dim]
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decoded_mel = decoded_mel[:,:,:smallest_dim]
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labels = (torch.rand(mel.shape[0], 1, 1, device=mel.device) > .5)
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output = torch.where(labels, original_audio, decoded_mel)
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return {self.output: output, self.label_output_key: labels[:,0,0].long()}
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class ConditioningLatentDistributionDivergenceInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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if 'gpt_config' in opt.keys():
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# The unified_voice model.
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cfg = opt_get(opt, ['gpt_config'], "../experiments/train_gpt_tts_unified.yml")
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model_name = opt_get(opt, ['gpt_name'], 'gpt')
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pretrained_path = opt['gpt_path']
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self.latent_producer = load_model_from_config(cfg, model_name=model_name,
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also_load_savepoint=False, load_path=pretrained_path).eval()
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self.mel_inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'mel_norm_file': '../experiments/clips_mel_norms.pth'},{})
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else:
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self.latent_producer = DiffusionTtsFlat(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
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in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False,
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num_heads=16, layer_drop=0, unconditioned_percentage=0).eval()
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self.latent_producer.load_state_dict(torch.load(opt['diffusion_path']))
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self.mel_inj = TorchMelSpectrogramInjector({'in': 'wav', 'out': 'mel', 'mel_fmax': 12000, 'sampling_rate': 24000, 'n_mel_channels': 100},{})
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self.needs_move = True
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# Aux input keys.
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self.conditioning_key = opt['conditioning_clip']
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# Output keys
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self.var_loss_key = opt['var_loss']
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def to_mel(self, t):
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return self.mel_inj({'wav': t})['mel']
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def forward(self, state):
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with torch.no_grad():
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state_preds = state[self.input]
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state_cond = pad_or_truncate(state[self.conditioning_key], 132300)
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mel_conds = []
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for k in range(state_cond.shape[1]):
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mel_conds.append(self.to_mel(state_cond[:, k]))
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mel_conds = torch.stack(mel_conds, dim=1)
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if self.needs_move:
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self.latent_producer = self.latent_producer.to(mel_conds.device)
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latents = self.latent_producer.get_conditioning_latent(mel_conds)
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sp_means, sp_vars = state_preds.mean(dim=0), state_preds.var(dim=0)
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tr_means, tr_vars = latents.mean(dim=0), latents.var(dim=0)
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mean_loss = F.mse_loss(sp_means, tr_means)
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var_loss = F.mse_loss(sp_vars, tr_vars)
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return {self.output: mean_loss, self.var_loss_key: var_loss}
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class RandomScaleInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.min_samples = opt['min_samples']
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def forward(self, state):
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inp = state[self.input]
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if self.min_samples < inp.shape[-1]:
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samples = random.randint(self.min_samples, inp.shape[-1])
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start = random.randint(0, inp.shape[-1]-samples)
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inp = inp[:, :, start:start+samples]
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return {self.output: inp}
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def pixel_shuffle_1d(x, upscale_factor):
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batch_size, channels, steps = x.size()
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channels //= upscale_factor
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input_view = x.contiguous().view(batch_size, channels, upscale_factor, steps)
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shuffle_out = input_view.permute(0, 1, 3, 2).contiguous()
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return shuffle_out.view(batch_size, channels, steps * upscale_factor)
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def pixel_unshuffle_1d(x, downscale):
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b, c, s = x.size()
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x = x.view(b, c, s//downscale, downscale)
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x = x.permute(0,1,3,2).contiguous()
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x = x.view(b, c*downscale, s//downscale)
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return x
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class AudioUnshuffleInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.compression = opt['compression']
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def forward(self, state):
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inp = state[self.input]
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return {self.output: pixel_unshuffle_1d(inp, self.compression)}
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