forked from mrq/tortoise-tts
Integrate new diffusion network
This commit is contained in:
James Betker
parent
287debd1d3
commit
9043dde3f9
49
api.py
49
api.py
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@ -49,6 +49,15 @@ def download_models():
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print('Done.')
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def pad_or_truncate(t, length):
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if t.shape[-1] == length:
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return t
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elif t.shape[-1] < length:
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return F.pad(t, (0, length-t.shape[-1]))
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else:
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return t[..., :length]
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def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
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"""
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Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
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@ -96,26 +105,25 @@ def fix_autoregressive_output(codes, stop_token):
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return codes
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def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_input, temperature=1):
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def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_samples, temperature=1):
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"""
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Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
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Uses the specified diffusion model to convert discrete codes into a spectrogram.
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"""
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with torch.no_grad():
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cond_mel = wav_to_univnet_mel(conditioning_input.squeeze(1), do_normalization=False)
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# Pad MEL to multiples of 32
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msl = mel_codes.shape[-1]
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dsl = 32
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gap = dsl - (msl % dsl)
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if gap > 0:
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mel = torch.nn.functional.pad(mel_codes, (0, gap))
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cond_mels = []
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for sample in conditioning_samples:
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sample = pad_or_truncate(sample, 102400)
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cond_mel = wav_to_univnet_mel(sample.to(mel_codes.device), do_normalization=False)
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cond_mels.append(cond_mel)
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cond_mels = torch.stack(cond_mels, dim=1)
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output_shape = (mel.shape[0], 100, mel.shape[-1]*4)
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precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mel)
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output_shape = (mel_codes.shape[0], 100, mel_codes.shape[-1]*4)
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precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, False)
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noise = torch.randn(output_shape, device=mel_codes.device) * temperature
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mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
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model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings})
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return denormalize_tacotron_mel(mel)[:,:,:msl*4]
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return denormalize_tacotron_mel(mel)[:,:,:mel_codes.shape[-1]*4]
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class TextToSpeech:
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@ -137,12 +145,9 @@ class TextToSpeech:
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use_xformers=True).cpu().eval()
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self.clip.load_state_dict(torch.load('.models/clip.pth'))
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self.diffusion = DiffusionTts(model_channels=512, in_channels=100, out_channels=200, in_latent_channels=1024,
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channel_mult=[1, 2, 3, 4], num_res_blocks=[3, 3, 3, 3],
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token_conditioning_resolutions=[1, 4, 8],
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dropout=0, attention_resolutions=[4, 8], num_heads=8, kernel_size=3, scale_factor=2,
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time_embed_dim_multiplier=4, unconditioned_percentage=0, conditioning_dim_factor=2,
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conditioning_expansion=1).cpu().eval()
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self.diffusion = DiffusionTts(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, num_heads=16,
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layer_drop=0, unconditioned_percentage=0).cpu().eval()
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self.diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
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self.vocoder = UnivNetGenerator().cpu()
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@ -164,12 +169,6 @@ class TextToSpeech:
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for vs in voice_samples:
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conds.append(load_conditioning(vs))
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conds = torch.stack(conds, dim=1)
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cond_diffusion = voice_samples[0].cuda()
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# The diffusion model expects = 88200 conditioning samples.
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if cond_diffusion.shape[-1] < 88200:
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cond_diffusion = F.pad(cond_diffusion, (0, 88200-cond_diffusion.shape[-1]))
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else:
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cond_diffusion = cond_diffusion[:, :88200]
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diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
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@ -211,7 +210,7 @@ class TextToSpeech:
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self.vocoder = self.vocoder.cuda()
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for b in range(best_results.shape[0]):
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code = best_results[b].unsqueeze(0)
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, cond_diffusion, temperature=diffusion_temperature)
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mel = do_spectrogram_diffusion(self.diffusion, diffuser, code, voice_samples, temperature=diffusion_temperature)
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wav = self.vocoder.inference(mel)
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wav_candidates.append(wav.cpu())
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self.diffusion = self.diffusion.cpu()
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@ -6,6 +6,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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from x_transformers import ContinuousTransformerWrapper
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from x_transformers.x_transformers import RelativePositionBias
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def zero_module(module):
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@ -49,7 +50,7 @@ class QKVAttentionLegacy(nn.Module):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv, mask=None):
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def forward(self, qkv, mask=None, rel_pos=None):
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"""
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Apply QKV attention.
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@ -64,6 +65,8 @@ class QKVAttentionLegacy(nn.Module):
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weight = torch.einsum(
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"bct,bcs->bts", q * scale, k * scale
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) # More stable with f16 than dividing afterwards
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if rel_pos is not None:
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weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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if mask is not None:
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# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
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@ -87,9 +90,12 @@ class AttentionBlock(nn.Module):
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channels,
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num_heads=1,
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num_head_channels=-1,
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do_checkpoint=True,
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relative_pos_embeddings=False,
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):
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super().__init__()
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self.channels = channels
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self.do_checkpoint = do_checkpoint
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if num_head_channels == -1:
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self.num_heads = num_heads
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else:
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@ -99,21 +105,20 @@ class AttentionBlock(nn.Module):
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self.num_heads = channels // num_head_channels
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self.norm = normalization(channels)
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self.qkv = nn.Conv1d(channels, channels * 3, 1)
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# split heads before split qkv
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
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if relative_pos_embeddings:
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self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
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else:
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self.relative_pos_embeddings = None
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def forward(self, x, mask=None):
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if mask is not None:
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return self._forward(x, mask)
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else:
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return self._forward(x)
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def _forward(self, x, mask=None):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv, mask)
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h = self.attention(qkv, mask, self.relative_pos_embeddings)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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@ -1,22 +1,13 @@
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"""
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This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal
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and an audio conditioning input. It has also been simplified somewhat.
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Credit: https://github.com/openai/improved-diffusion
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"""
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import functools
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import math
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import random
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from abc import abstractmethod
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import autocast
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from torch.nn import Linear
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from torch.utils.checkpoint import checkpoint
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from x_transformers import ContinuousTransformerWrapper, Encoder
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from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock, \
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CheckpointedXTransformerEncoder
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from models.arch_util import normalization, AttentionBlock
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def is_latent(t):
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@ -27,13 +18,6 @@ def is_sequence(t):
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return t.dtype == torch.long
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def ceil_multiple(base, multiple):
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res = base % multiple
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if res == 0:
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return base
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return base + (multiple - res)
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def timestep_embedding(timesteps, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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@ -56,10 +40,6 @@ def timestep_embedding(timesteps, dim, max_period=10000):
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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@ -68,11 +48,6 @@ class TimestepBlock(nn.Module):
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
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"""
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A sequential module that passes timestep embeddings to the children that
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support it as an extra input.
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"""
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def forward(self, x, emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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@ -89,6 +64,7 @@ class ResBlock(TimestepBlock):
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emb_channels,
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dropout,
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out_channels=None,
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dims=2,
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kernel_size=3,
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efficient_config=True,
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use_scale_shift_norm=False,
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@ -111,7 +87,7 @@ class ResBlock(TimestepBlock):
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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Linear(
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nn.Linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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@ -120,9 +96,7 @@ class ResBlock(TimestepBlock):
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
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),
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nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
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)
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if self.out_channels == channels:
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@ -131,18 +105,6 @@ class ResBlock(TimestepBlock):
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self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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@ -158,372 +120,205 @@ class ResBlock(TimestepBlock):
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return self.skip_connection(x) + h
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class DiffusionLayer(TimestepBlock):
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def __init__(self, model_channels, dropout, num_heads):
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super().__init__()
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self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
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self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
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def forward(self, x, time_emb):
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y = self.resblk(x, time_emb)
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return self.attn(y)
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class DiffusionTts(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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Customized to be conditioned on an aligned prior derived from a autoregressive
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GPT-style model.
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:param in_channels: channels in the input Tensor.
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:param in_latent_channels: channels from the input latent.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(
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self,
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model_channels,
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in_channels=1,
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in_latent_channels=1024,
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model_channels=512,
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num_layers=8,
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in_channels=100,
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in_latent_channels=512,
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in_tokens=8193,
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conditioning_dim_factor=8,
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conditioning_expansion=4,
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out_channels=2, # mean and variance
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out_channels=200, # mean and variance
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dropout=0,
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# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
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channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
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num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
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# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
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# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
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token_conditioning_resolutions=(1,16,),
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attention_resolutions=(512,1024,2048),
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conv_resample=True,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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freeze_main_net=False,
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efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
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use_scale_shift_norm=True,
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num_heads=16,
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# Parameters for regularization.
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layer_drop=.1,
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Parameters for super-sampling.
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super_sampling=False,
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super_sampling_max_noising_factor=.1,
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):
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super().__init__()
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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if super_sampling:
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in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.super_sampling_enabled = super_sampling
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self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.alignment_size = 2 ** (len(channel_mult)+1)
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self.freeze_main_net = freeze_main_net
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padding = 1 if kernel_size == 3 else 2
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down_kernel = 1 if efficient_convs else 3
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self.layer_drop = layer_drop
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time_embed_dim = model_channels * time_embed_dim_multiplier
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self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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Linear(model_channels, time_embed_dim),
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nn.Linear(model_channels, model_channels),
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nn.SiLU(),
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Linear(time_embed_dim, time_embed_dim),
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nn.Linear(model_channels, model_channels),
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)
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conditioning_dim = model_channels * conditioning_dim_factor
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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self.code_embedding = nn.Embedding(in_tokens, model_channels)
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self.code_converter = nn.Sequential(
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nn.Embedding(in_tokens, conditioning_dim),
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CheckpointedXTransformerEncoder(
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needs_permute=False,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=conditioning_dim,
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depth=3,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_emb_dim=True,
|
||||
)
|
||||
))
|
||||
self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1)
|
||||
self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1))
|
||||
if in_channels > 60: # It's a spectrogram.
|
||||
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,conditioning_dim,3,padding=1,stride=2),
|
||||
CheckpointedXTransformerEncoder(
|
||||
needs_permute=True,
|
||||
max_seq_len=-1,
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=conditioning_dim,
|
||||
depth=4,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_emb_dim=True,
|
||||
)
|
||||
))
|
||||
else:
|
||||
self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1)
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
)
|
||||
self.code_norm = normalization(model_channels)
|
||||
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
|
||||
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
|
||||
nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
|
||||
self.conditioning_timestep_integrator = TimestepEmbedSequential(
|
||||
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
|
||||
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
|
||||
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
|
||||
AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels),
|
||||
ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
)
|
||||
self.conditioning_expansion = conditioning_expansion
|
||||
self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
|
||||
self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1)
|
||||
token_conditioning_block.weight.data *= .02
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
for _ in range(num_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(mult * model_channels),
|
||||
kernel_size=kernel_size,
|
||||
efficient_config=efficient_convs,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, conv_resample, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
kernel_size=kernel_size,
|
||||
efficient_config=efficient_convs,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
kernel_size=kernel_size,
|
||||
efficient_config=efficient_convs,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
|
||||
for i in range(num_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(model_channels * mult),
|
||||
kernel_size=kernel_size,
|
||||
efficient_config=efficient_convs,
|
||||
use_scale_shift_norm=use_scale_shift_norm,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
if level and i == num_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
|
||||
[ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
normalization(model_channels),
|
||||
nn.SiLU(),
|
||||
zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)),
|
||||
nn.Conv1d(model_channels, out_channels, 3, padding=1),
|
||||
)
|
||||
|
||||
def fix_alignment(self, x, aligned_conditioning):
|
||||
"""
|
||||
The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
|
||||
padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
|
||||
"""
|
||||
cm = ceil_multiple(x.shape[-1], self.alignment_size)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
# Also fix aligned_latent, which is aligned to x.
|
||||
if is_latent(aligned_conditioning):
|
||||
aligned_conditioning = torch.cat([aligned_conditioning,
|
||||
self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
|
||||
else:
|
||||
aligned_conditioning = F.pad(aligned_conditioning, (0, int(pc*aligned_conditioning.shape[-1])))
|
||||
return x, aligned_conditioning
|
||||
def get_grad_norm_parameter_groups(self):
|
||||
groups = {
|
||||
'minicoder': list(self.contextual_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()),
|
||||
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()),
|
||||
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def timestep_independent(self, aligned_conditioning, conditioning_input):
|
||||
def timestep_independent(self, aligned_conditioning, conditioning_input, return_code_pred):
|
||||
# Shuffle aligned_latent to BxCxS format
|
||||
if is_latent(aligned_conditioning):
|
||||
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
|
||||
|
||||
with autocast(aligned_conditioning.device.type, enabled=self.enable_fp16):
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
if len(cond_emb.shape) == 3: # Just take the first element.
|
||||
cond_emb = cond_emb[:, :, 0]
|
||||
if is_latent(aligned_conditioning):
|
||||
code_emb = self.latent_converter(aligned_conditioning)
|
||||
else:
|
||||
code_emb = self.code_converter(aligned_conditioning)
|
||||
cond_emb = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
|
||||
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1))
|
||||
return code_emb
|
||||
# Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
|
||||
speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
|
||||
conditioning_input.shape) == 3 else conditioning_input
|
||||
conds = []
|
||||
for j in range(speech_conditioning_input.shape[1]):
|
||||
conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
|
||||
conds = torch.cat(conds, dim=-1)
|
||||
cond_emb = conds.mean(dim=-1)
|
||||
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
|
||||
if is_latent(aligned_conditioning):
|
||||
code_emb = self.latent_converter(aligned_conditioning)
|
||||
else:
|
||||
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
|
||||
|
||||
def forward(self, x, timesteps, precomputed_aligned_embeddings, conditioning_free=False):
|
||||
assert x.shape[-1] % self.alignment_size == 0
|
||||
unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
|
||||
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
|
||||
if self.training and self.unconditioned_percentage > 0:
|
||||
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
|
||||
device=code_emb.device) < self.unconditioned_percentage
|
||||
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
|
||||
code_emb)
|
||||
expanded_code_emb = F.interpolate(code_emb, size=aligned_conditioning.shape[-1]*4, mode='nearest')
|
||||
|
||||
with autocast(x.device.type, enabled=self.enable_fp16):
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
|
||||
else:
|
||||
if not return_code_pred:
|
||||
return expanded_code_emb
|
||||
else:
|
||||
mel_pred = self.mel_head(expanded_code_emb)
|
||||
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
|
||||
mel_pred = mel_pred * unconditioned_batches.logical_not()
|
||||
return expanded_code_emb, mel_pred
|
||||
|
||||
|
||||
def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
|
||||
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
|
||||
:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
|
||||
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None)
|
||||
assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.
|
||||
|
||||
unused_params = []
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
unused_params.extend(list(self.latent_converter.parameters()))
|
||||
else:
|
||||
if precomputed_aligned_embeddings is not None:
|
||||
code_emb = precomputed_aligned_embeddings
|
||||
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
|
||||
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
|
||||
|
||||
first = True
|
||||
time_emb = time_emb.float()
|
||||
h = x
|
||||
hs = []
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
if isinstance(module, nn.Conv1d):
|
||||
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, True)
|
||||
if is_latent(aligned_conditioning):
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
else:
|
||||
with autocast(x.device.type, enabled=self.enable_fp16 and not first):
|
||||
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
|
||||
h = module(h, time_emb)
|
||||
hs.append(h)
|
||||
first = False
|
||||
h = self.middle_block(h, time_emb)
|
||||
for module in self.output_blocks:
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, time_emb)
|
||||
unused_params.extend(list(self.latent_converter.parameters()))
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
# Last block also has autocast disabled for high-precision outputs.
|
||||
h = h.float()
|
||||
out = self.out(h)
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
|
||||
x = self.inp_block(x)
|
||||
x = torch.cat([x, code_emb], dim=1)
|
||||
x = self.integrating_conv(x)
|
||||
for i, lyr in enumerate(self.layers):
|
||||
# Do layer drop where applicable. Do not drop first and last layers.
|
||||
if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
|
||||
unused_params.extend(list(lyr.parameters()))
|
||||
else:
|
||||
# First and last blocks will have autocast disabled for improved precision.
|
||||
with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
|
||||
x = lyr(x, time_emb)
|
||||
|
||||
x = x.float()
|
||||
out = self.out(x)
|
||||
|
||||
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
|
||||
extraneous_addition = 0
|
||||
for p in unused_params:
|
||||
extraneous_addition = extraneous_addition + p.mean()
|
||||
out = out + extraneous_addition * 0
|
||||
|
||||
if return_code_pred:
|
||||
return out, mel_pred
|
||||
return out
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 1, 32868)
|
||||
aligned_latent = torch.randn(2,388,1024)
|
||||
aligned_sequence = torch.randint(0,8192,(2,388))
|
||||
cond = torch.randn(2, 1, 44000)
|
||||
clip = torch.randn(2, 100, 400)
|
||||
aligned_latent = torch.randn(2,388,512)
|
||||
aligned_sequence = torch.randint(0,8192,(2,100))
|
||||
cond = torch.randn(2, 100, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = DiffusionTts(128,
|
||||
channel_mult=[1,1.5,2, 3, 4, 6, 8],
|
||||
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
|
||||
token_conditioning_resolutions=[1,4,16,64],
|
||||
attention_resolutions=[],
|
||||
num_heads=8,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
time_embed_dim_multiplier=4,
|
||||
super_sampling=False,
|
||||
efficient_convs=False)
|
||||
model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5)
|
||||
# Test with latent aligned conditioning
|
||||
o = model(clip, ts, aligned_latent, cond)
|
||||
#o = model(clip, ts, aligned_latent, cond)
|
||||
# Test with sequence aligned conditioning
|
||||
o = model(clip, ts, aligned_sequence, cond)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user