mrq/tortoise-tts
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support latents into the diffusion decoder

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This commit is contained in:
James Betker 2022-04-12 20:53:09 -06:00
parent e2ee843098
commit 3214ca0dfe
5 changed files with 55 additions and 315 deletions
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21
api.py
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@ -117,7 +117,7 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_
cond_mels.append(cond_mel)
cond_mels = torch.stack(cond_mels, dim=1)
output_seq_len = mel_codes.shape[-1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
output_seq_len = mel_codes.shape[1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
output_shape = (mel_codes.shape[0], 100, output_seq_len)
precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False)
@ -151,11 +151,6 @@ class TextToSpeech:
layer_drop=0, unconditioned_percentage=0).cpu().eval()
self.diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
self.diffusion_next = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
layer_drop=0, unconditioned_percentage=0).cpu().eval()
self.diffusion_next.load_state_dict(torch.load('.models/diffusion_next.pth'))
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
self.vocoder.eval(inference=True)
@ -223,12 +218,22 @@ class TextToSpeech:
self.clip = self.clip.cpu()
del samples
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
# results, but will increase memory usage.
self.autoregressive = self.autoregressive.cuda()
best_latents = self.autoregressive(conds, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
return_latent=True, clip_inputs=False)
self.autoregressive = self.autoregressive.cpu()
print("Performing vocoding..")
wav_candidates = []
self.diffusion = self.diffusion.cuda()
self.vocoder = self.vocoder.cuda()
for b in range(best_results.shape[0]):
codes = best_results[b].unsqueeze(0)
latents = best_latents[b].unsqueeze(0)
# Find the first occurrence of the "calm" token and trim the codes to that.
ctokens = 0
@ -238,10 +243,10 @@ class TextToSpeech:
else:
ctokens = 0
if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
codes = codes[:, :k]
latents = latents[:, :k]
break
mel = do_spectrogram_diffusion(self.diffusion, diffuser, codes, voice_samples, temperature=diffusion_temperature)
mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, voice_samples, temperature=diffusion_temperature)
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
self.diffusion = self.diffusion.cpu()

2
eval_multiple.py
View File

@ -7,7 +7,7 @@ from utils.audio import load_audio
if __name__ == '__main__':
fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_auto_256_samp_100_di_4'
outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_new_decoder_1'
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
os.makedirs(outpath, exist_ok=True)

32
models/autoregressive.py
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@ -362,7 +362,7 @@ class UnifiedVoice(nn.Module):
mel_input_tokens[b, actual_end:] = self.stop_mel_token
return mel_input_tokens
def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
if second_inputs is not None:
emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
else:
@ -374,6 +374,10 @@ class UnifiedVoice(nn.Module):
enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
enc = self.final_norm(enc)
if return_latent:
return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
first_logits = enc[:, :first_inputs.shape[1]]
first_logits = first_head(first_logits)
first_logits = first_logits.permute(0,2,1)
@ -385,7 +389,8 @@ class UnifiedVoice(nn.Module):
else:
return first_logits
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False):
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False,
return_latent=False, clip_inputs=True):
"""
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
(actuated by `text_first`).
@ -396,19 +401,23 @@ class UnifiedVoice(nn.Module):
mel_inputs: long tensor, (b,m)
wav_lengths: long tensor, (b,)
raw_mels: MEL float tensor (b,80,s)
"""
assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
If return_attentions is specified, only logits are returned.
If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
"""
if clip_inputs:
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
# chopping the inputs by the maximum actual length.
max_text_len = text_lengths.max()
text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
text_inputs = text_inputs[:, :max_text_len]
max_mel_len = wav_lengths.max() // self.mel_length_compression
mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
mel_codes = mel_codes[:, :max_mel_len]
if raw_mels is not None:
raw_mels = raw_mels[:, :, :max_mel_len*4]
mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
conds = []
@ -427,10 +436,15 @@ class UnifiedVoice(nn.Module):
mel_inp = mel_codes
mel_emb = self.mel_embedding(mel_inp)
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
if text_first:
text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent:
return mel_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
else:
mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent:
return text_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
if return_attentions:
return mel_logits

17
models/diffusion_decoder.py
View File

@ -176,7 +176,13 @@ class DiffusionTts(nn.Module):
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.latent_conditioner = nn.Sequential(
nn.Conv1d(in_latent_channels, model_channels, 3, padding=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),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
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),
@ -190,6 +196,7 @@ class DiffusionTts(nn.Module):
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
)
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)
@ -206,7 +213,7 @@ class DiffusionTts(nn.Module):
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()),
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
@ -227,7 +234,7 @@ class DiffusionTts(nn.Module):
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.autoregressive_latent_converter(aligned_conditioning)
code_emb = self.latent_conditioner(aligned_conditioning)
else:
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
code_emb = self.code_converter(code_emb)
@ -269,7 +276,7 @@ class DiffusionTts(nn.Module):
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()))
unused_params.extend(list(self.latent_conditioner.parameters()))
else:
if precomputed_aligned_embeddings is not None:
code_emb = precomputed_aligned_embeddings
@ -278,7 +285,7 @@ class DiffusionTts(nn.Module):
if is_latent(aligned_conditioning):
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
else:
unused_params.extend(list(self.latent_converter.parameters()))
unused_params.extend(list(self.latent_conditioner.parameters()))
unused_params.append(self.unconditioned_embedding)

286
models/new_autoregressive.py
View File

@ -1,286 +0,0 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import GPT2PreTrainedModel, GPT2Config
from models.xtransformers import TransformerWrapper, Encoder, Decoder
from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
from models.arch_util import AttentionBlock
class InferenceModel(GPT2PreTrainedModel):
"""
Implementation of GPT2PreTrainedModel from transformers, which allows us to use their generation library with
this transformer.
"""
def __init__(self, model):
super().__init__(GPT2Config())
self.transformer = model
self.context = None
def parallelize(self, device_map=None):
# Not implemented.
pass
def deparallelize(self):
# Not implemented.
pass
def get_output_embeddings(self):
assert False, "Unsupported operation."
def set_output_embeddings(self, new_embeddings):
assert False, "Unsupported operation."
def store_context(self, context):
self.context = context
def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
token_type_ids = kwargs.get("token_type_ids", None)
# only last token for inputs_ids if past is defined in kwargs
if past:
input_ids = input_ids[:, -1].unsqueeze(-1)
if token_type_ids is not None:
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
attention_mask = kwargs.get("attention_mask", None)
position_ids = kwargs.get("position_ids", None)
if attention_mask is not None and position_ids is None:
# create position_ids on the fly for batch generation
position_ids = attention_mask.long().cumsum(-1) - 1
position_ids.masked_fill_(attention_mask == 0, 1)
if past:
position_ids = position_ids[:, -1].unsqueeze(-1)
else:
position_ids = None
return {
"input_ids": input_ids,
"past_key_values": past,
"use_cache": kwargs.get("use_cache"),
"position_ids": position_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
assert self.context is not None
assert inputs_embeds is None # Not supported by this inference model.
assert labels is None # Training not supported by this inference model.
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
out = self.transformer.decoder(input_ids, full_context=self.context, return_embeddings=True, past_key_values=past_key_values,
use_cache=use_cache, expected_seq_len=100)
if use_cache:
hidden_states, present_key_values = out
else:
hidden_states = out
present_key_values = None
logits = self.transformer.decoder.to_logits(hidden_states)
if not return_dict:
return (logits, )
return CausalLMOutputWithCrossAttentions(
loss=None,
logits=logits,
past_key_values=present_key_values,
hidden_states=hidden_states,
attentions=None,
cross_attentions=None,
)
@staticmethod
def _reorder_cache(past, beam_idx):
"""
This function is used to re-order the :obj:`past_key_values` cache if
:meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
"""
return tuple(
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past)
for layer_past in past
)
class ResBlock(nn.Module):
"""
Basic residual convolutional block that uses GroupNorm.
"""
def __init__(self, chan):
super().__init__()
self.net = nn.Sequential(
nn.Conv1d(chan, chan, kernel_size=3, padding=1),
nn.GroupNorm(chan//8, chan),
nn.ReLU(),
nn.Conv1d(chan, chan, kernel_size=3, padding=1),
nn.GroupNorm(chan//8, chan)
)
def forward(self, x):
return F.relu(self.net(x) + x)
class ConditioningEncoder(nn.Module):
def __init__(self,
spec_dim,
embedding_dim,
attn_blocks=6,
num_attn_heads=4,
do_checkpointing=False):
super().__init__()
attn = []
self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//4, kernel_size=5, padding=2),
nn.Conv1d(embedding_dim//4, embedding_dim//2, kernel_size=3, padding=1, stride=2),
ResBlock(embedding_dim//2),
nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2))
for a in range(attn_blocks):
attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
self.attn = nn.Sequential(*attn)
self.dim = embedding_dim
def forward(self, x):
h = self.init(x)
h = self.attn(h)
return h.mean(dim=2)
class AutoregressiveCodegen(nn.Module):
def __init__(self, model_dim, depth, num_text_tokens=256, num_mel_tokens=8194, dropout=.1):
super().__init__()
assert depth >= 8 # This is the minimum bound to support the context interleaving that happens later.
self.START_TOKEN=8192
self.STOP_TOKEN=8193
self.START_TEXT_TOKEN = 255
self.STOP_TEXT_TOKEN = 0
self.max_text_token_id = num_text_tokens
self.max_mel_token_id = num_mel_tokens
self.mel_embedding = ConditioningEncoder(80, model_dim, do_checkpointing=False)
self.encoder = TransformerWrapper(
num_tokens=num_text_tokens,
use_pos_emb=False,
max_seq_len=-1,
attn_layers = Encoder(
depth=depth,
heads=model_dim//64,
dim=model_dim,
attn_dropout=dropout,
ff_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
ff_mult=1,
rotary_pos_emb=True,
attn_rel_pos_bias=True,
))
self.encoder.norm = nn.Identity() # This layer and the next are unused.
self.encoder.to_logits = nn.Identity()
self.decoder = TransformerWrapper(
num_tokens=num_mel_tokens,
use_pos_emb=False,
max_seq_len=-1,
attn_layers=Decoder(
depth=depth,
heads=model_dim//64,
dim=model_dim,
attn_dropout=dropout,
ff_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
ff_mult=1,
rotary_pos_emb=True,
cross_attend=True,
attn_rel_pos_bias=True,
))
def get_grad_norm_parameter_groups(self):
return {
'encoder': list(self.encoder.parameters()),
'decoder': list(self.decoder.parameters()),
'minicoder': list(self.mel_embedding.parameters()),
}
def forward(self, text_codes, conditioning_signal, mel_codes, wav_lengths, return_loss=True):
assert text_codes.max() < self.max_text_token_id and text_codes.min() >= 0, f'Invalid text code encountered: {text_codes.max()}, {text_codes.min()}'
assert mel_codes.max() < self.max_mel_token_id and mel_codes.min() >= 0, f'Invalid mel code encountered: {mel_codes.max()}, {mel_codes.min()}'
# Format mel_codes with a stop token on the end.
mel_lengths = wav_lengths // 1024 + 1
for b in range(mel_codes.shape[0]):
mel_codes[b, mel_lengths[b]:] = self.STOP_TOKEN
mel_codes = F.pad(mel_codes, (0, 1), value=self.STOP_TOKEN)
# Build the context
if len(conditioning_signal.shape) != 4:
conditioning_signal = conditioning_signal.unsqueeze(1)
cond_embs = []
for i in range(conditioning_signal.shape[1]):
cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
# Since all positional embeddings are relative, it is (probably) important to "fix" the text with some permanent embeddings.
text_codes = F.pad(text_codes, (1,0), value=self.START_TEXT_TOKEN)
text_codes = F.pad(text_codes, (0,1), value=self.STOP_TEXT_TOKEN)
_, enc_text = self.encoder(text_codes, return_hiddens=True)
# Interleave cond_emb into the first few contexts.
full_context = enc_text
full_context[1] = cond_emb
full_context[3] = cond_emb
full_context[6] = cond_emb
# Execute the decoder
dec_inputs = F.pad(mel_codes, (1,0), value=self.START_TOKEN)[:, :-1]
dec = self.decoder(dec_inputs, full_context=full_context)
if not return_loss:
return dec
loss_mel = F.cross_entropy(dec.permute(0,2,1), mel_codes)
return loss_mel
def generate(self, conditioning_signal, text_codes, max_tokens=256, **hf_generate_kwargs):
inference_model = InferenceModel(self)
# Build the context
if len(conditioning_signal.shape) != 4:
conditioning_signal = conditioning_signal.unsqueeze(1)
cond_embs = []
for i in range(conditioning_signal.shape[1]):
cond_embs.append(self.mel_embedding(conditioning_signal[:, i]))
cond_emb = torch.stack(cond_embs, dim=1).mean(dim=1, keepdim=True)
text_codes = F.pad(text_codes, (1,0), value=self.START_TEXT_TOKEN)
text_codes = F.pad(text_codes, (0,1), value=self.STOP_TEXT_TOKEN)
_, enc_text = self.encoder(text_codes, return_hiddens=True)
# Interleave cond_emb into the first few contexts.
full_context = enc_text
full_context[1] = cond_emb
full_context[3] = cond_emb
full_context[6] = cond_emb
inference_model.store_context(full_context)
gen = inference_model.generate(bos_token_id=self.START_TOKEN, pad_token_id=self.STOP_TOKEN, eos_token_id=self.STOP_TOKEN,
max_length=max_tokens, output_attentions=False, return_dict_in_generate=True, use_cache=False,
**hf_generate_kwargs)
return gen.sequences
if __name__ == '__main__':
codegen = AutoregressiveCodegen(256, 10)
torch.save(codegen.state_dict(), 'sample.pth')
#codegen.generate(torch.randn((1,80,120)), torch.randint(0,256,(1,200)))
codegen(torch.randint(0,256, (2,200)),
torch.randn(2,80,120),
torch.randint(0,8192, (2,350)),
torch.tensor([192,350]))