support latents into the diffusion decoder

api.py

@@ -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()

eval_multiple.py

@@ -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)

models/autoregressive.py

@@ -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

models/diffusion_decoder.py

@@ -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)
 

models/new_autoregressive.py

@@ -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]))