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indextts/gpt/model.py

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+import functools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList, GenerationMixin
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from transformers.utils.model_parallel_utils import (assert_device_map,
+                                                     get_device_map)
+
+from indextts.gpt.conformer_encoder import ConformerEncoder
+from indextts.gpt.perceiver import PerceiverResampler
+from indextts.utils.arch_util import AttentionBlock
+from indextts.utils.typical_sampling import TypicalLogitsWarper
+
+
+def null_position_embeddings(range, dim):
+    return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
+
+
+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 GPT2InferenceModel(GPT2PreTrainedModel, GenerationMixin):
+    def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache=False):
+        super().__init__(config)
+        # Note: the argument named `text_pos_emb` here actually represents the mel position embedding
+        self.transformer = gpt
+        self.text_pos_embedding = text_pos_emb
+        self.embeddings = embeddings
+        self.final_norm = norm
+        self.lm_head = nn.Sequential(norm, linear)
+        self.kv_cache = kv_cache
+
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.cached_mel_emb = None
+
+    def parallelize(self, device_map=None):
+        self.device_map = (
+            get_device_map(len(self.transformer.h), range(max(1, torch.cuda.device_count())))
+            if device_map is None
+            else device_map
+        )
+        assert_device_map(self.device_map, len(self.transformer.h))
+        self.transformer.parallelize(self.device_map)
+        self.lm_head = self.lm_head.to(self.transformer.first_device)
+        self.model_parallel = True
+
+    def deparallelize(self):
+        self.transformer.deparallelize()
+        self.transformer = self.transformer.to("cpu")
+        self.lm_head = self.lm_head.to("cpu")
+        self.model_parallel = False
+        torch.cuda.empty_cache()
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def store_mel_emb(self, mel_emb):
+        self.cached_mel_emb = mel_emb
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)  # usually None
+        if not self.kv_cache:
+            past_key_values = None
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            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, 0)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "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.cached_mel_emb 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
+        )
+        # Create embedding
+        mel_len = self.cached_mel_emb.shape[1]
+        if input_ids.shape[1] != 1:
+            text_inputs = input_ids[:, mel_len:]
+            text_emb = self.embeddings(text_inputs)
+            text_emb = text_emb + self.text_pos_embedding(text_emb)
+            if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
+                mel_emb = self.cached_mel_emb.repeat_interleave(
+                    text_emb.shape[0] // self.cached_mel_emb.shape[0], 0
+                )
+            else:  # this outcome only occurs once per loop in most cases
+                mel_emb = self.cached_mel_emb
+            emb = torch.cat([mel_emb, text_emb], dim=1)
+        else:
+            emb = self.embeddings(input_ids)
+            emb = emb + self.text_pos_embedding.get_fixed_embedding(
+                attention_mask.shape[1] - mel_len, attention_mask.device
+            )
+        transformer_outputs = self.transformer(
+            inputs_embeds=emb,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            if torch.backends.mps.is_available():
+                self.to(self.transformer.first_device)
+            else:
+                torch.cuda.set_device(self.transformer.first_device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+        lm_logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            return (lm_logits,) + transformer_outputs[1:]
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=None,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+    @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 ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=4,
+                 do_checkpointing=False,
+                 mean=False):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+        self.do_checkpointing = do_checkpointing
+        self.mean = mean
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h)
+        if self.mean:
+            return h.mean(dim=2)
+        else:
+            return h
+            # return h[:, :, 0]
+
+
+class LearnedPositionEmbeddings(nn.Module):
+    def __init__(self, seq_len, model_dim, init=.02):
+        super().__init__()
+        self.emb = nn.Embedding(seq_len, model_dim)
+        # Initializing this way is standard for GPT-2
+        self.emb.weight.data.normal_(mean=0.0, std=init)
+
+    def forward(self, x):
+        sl = x.shape[1]
+        return self.emb(torch.arange(0, sl, device=x.device))
+
+    def get_fixed_embedding(self, ind, dev):
+        return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
+
+
+def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing, activation_function):
+    """
+    GPT-2 implemented by the HuggingFace library.
+    """
+    from transformers import GPT2Config, GPT2Model
+    gpt_config = GPT2Config(vocab_size=256,  # Unused.
+                            n_positions=max_mel_seq_len + max_text_seq_len,
+                            n_ctx=max_mel_seq_len + max_text_seq_len,
+                            n_embd=model_dim,
+                            n_layer=layers,
+                            n_head=heads,
+                            activation_function=activation_function or "gelu_new",
+                            gradient_checkpointing=checkpointing,
+                            use_cache=not checkpointing)
+    gpt = GPT2Model(gpt_config)
+    # Override the built in positional embeddings
+    del gpt.wpe
+    gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
+    # Built-in token embeddings are unused.
+    del gpt.wte
+    return gpt, LearnedPositionEmbeddings(max_mel_seq_len, model_dim), LearnedPositionEmbeddings(max_text_seq_len, model_dim), \
+        None, None
+
+
+class MelEncoder(nn.Module):
+    def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
+        super().__init__()
+        self.channels = channels
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels // 4, kernel_size=3, padding=1),
+                                     nn.Sequential(*[ResBlock(channels // 4) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels // 4, channels // 2, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels // 16, channels // 2),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels // 2) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels // 2, channels, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels // 8, channels),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
+                                     )
+        self.reduction = 4
+
+    def forward(self, x):
+        for e in self.encoder:
+            x = e(x)
+        return x.permute(0, 2, 1)
+
+
+class UnifiedVoice(nn.Module):
+    def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
+                 mel_length_compression=1024, number_text_tokens=256,
+                 start_text_token=0, stop_text_token=1, number_mel_codes=8194, start_mel_token=8192, stop_mel_token=8193,
+                 train_solo_embeddings=False, use_mel_codes_as_input=True,
+                 checkpointing=True, types=1, activation_function=None,
+                 condition_num_latent=32, condition_type="perceiver", condition_module=None):
+        """
+        Args:
+            layers: Number of layers in transformer stack.
+            model_dim: Operating dimensions of the transformer
+            heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
+            max_text_tokens: Maximum number of text tokens that will be encountered by model.
+            max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
+            max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
+            mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
+            number_text_tokens:
+            start_text_token:
+            stop_text_token:
+            number_mel_codes:
+            start_mel_token:
+            stop_mel_token:
+            train_solo_embeddings:
+            use_mel_codes_as_input:
+            checkpointing:
+            condition_type: perceiver, gst or default encoder
+        """
+        super().__init__()
+        self.number_text_tokens = number_text_tokens
+        self.start_text_token = start_text_token
+        self.stop_text_token = stop_text_token
+        self.number_mel_codes = number_mel_codes
+        self.start_mel_token = start_mel_token
+        self.stop_mel_token = stop_mel_token
+        self.layers = layers
+        self.heads = heads
+        self.max_mel_tokens = max_mel_tokens
+        self.max_text_tokens = max_text_tokens
+        self.model_dim = model_dim
+        self.max_conditioning_inputs = max_conditioning_inputs
+        self.mel_length_compression = mel_length_compression
+        self.condition_type = condition_type
+        self.cond_num = condition_num_latent
+        self.cond_mask_pad = nn.ConstantPad1d((self.cond_num, 0), True)
+        if condition_type == "perceiver":
+            self.conditioning_encoder = ConditioningEncoder(100, model_dim, num_attn_heads=heads)
+            self.perceiver_encoder = PerceiverResampler(model_dim, dim_context=model_dim, num_latents=self.cond_num)
+        elif condition_type == "conformer_perceiver" or condition_type == "conformer_encoder":
+            self.conditioning_encoder = ConformerEncoder(input_size=100,
+                                                         output_size=condition_module['output_size'],
+                                                         linear_units=condition_module['linear_units'],
+                                                         attention_heads=condition_module['attention_heads'],
+                                                         num_blocks=condition_module['num_blocks'],
+                                                         input_layer=condition_module['input_layer'])
+            if condition_type == "conformer_perceiver":
+                self.perceiver_encoder = PerceiverResampler(model_dim, dim_context=condition_module['output_size'],
+                                                            ff_mult=condition_module['perceiver_mult'],
+                                                            heads=condition_module['attention_heads'],
+                                                            num_latents=self.cond_num)
+        else:
+            self.conditioning_encoder = ConditioningEncoder(100, model_dim, num_attn_heads=heads, mean=True)
+
+        self.text_embedding = nn.Embedding(self.number_text_tokens * types + 1, model_dim)
+        if use_mel_codes_as_input:
+            self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+        else:
+            self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
+        self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
+            build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens + 2 + self.max_conditioning_inputs,
+                                     self.max_text_tokens + 2, checkpointing, activation_function)
+        if train_solo_embeddings:
+            self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+            self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+        else:
+            self.mel_solo_embedding = 0
+            self.text_solo_embedding = 0
+
+        self.final_norm = nn.LayerNorm(model_dim)
+        self.text_head = nn.Linear(model_dim, self.number_text_tokens * types + 1)
+        self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
+
+        # Initialize the embeddings per the GPT-2 scheme
+        embeddings = [self.text_embedding]
+        if use_mel_codes_as_input:
+            embeddings.append(self.mel_embedding)
+        for module in embeddings:
+            module.weight.data.normal_(mean=0.0, std=.02)
+
+    def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False, half=False):
+        seq_length = self.max_mel_tokens + self.max_text_tokens + 2
+        gpt_config = GPT2Config(
+            vocab_size=self.number_mel_codes,
+            n_positions=seq_length,
+            n_ctx=seq_length,
+            n_embd=self.model_dim,
+            n_layer=self.layers,
+            n_head=self.heads,
+            gradient_checkpointing=False,
+            use_cache=True,
+        )
+        self.inference_model = GPT2InferenceModel(
+            gpt_config,
+            self.gpt,
+            self.mel_pos_embedding,
+            self.mel_embedding,
+            self.final_norm,
+            self.mel_head,
+            kv_cache=kv_cache,
+        )
+        if use_deepspeed and half and torch.cuda.is_available():
+            import deepspeed
+            self.ds_engine = deepspeed.init_inference(model=self.inference_model,
+                                                      mp_size=1,
+                                                      replace_with_kernel_inject=False,
+                                                      dtype=torch.float16)
+            self.inference_model = self.ds_engine.module.eval()
+        elif use_deepspeed and torch.cuda.is_available():
+            import deepspeed
+            self.ds_engine = deepspeed.init_inference(model=self.inference_model,
+                                                      mp_size=1,
+                                                      replace_with_kernel_inject=False,
+                                                      dtype=torch.float32)
+            self.inference_model = self.ds_engine.module.eval()
+        else:
+            self.inference_model = self.inference_model.eval()
+
+        # self.inference_model = PrunedGPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
+        self.gpt.wte = self.mel_embedding
+
+    def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
+        inp = F.pad(input, (1, 0), value=start_token)
+        tar = F.pad(input, (0, 1), value=stop_token)
+        return inp, tar
+
+    def set_mel_padding(self, mel_input_tokens, mel_lengths):
+        """
+        Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
+        that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
+        preformatting to create a working TTS model.
+        """
+        for b in range(len(mel_lengths)):
+            # Due to the convolutional nature of how these tokens are generated,
+            # it would be best if the model predicts a token past the actual last token.
+            actual_end = mel_lengths[b]
+            if actual_end < mel_input_tokens.shape[-1]:
+                mel_input_tokens[b, actual_end:] = self.stop_mel_token
+        return mel_input_tokens
+
+    def set_text_padding(self, text_input_tokens, text_lengths):
+        """
+        Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
+        that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
+        preformatting to create a working TTS model.
+        """
+        for b in range(len(text_lengths)):
+            # Due to the convolutional nature of how these tokens are generated,
+            # it would be best if the model predicts a token past the actual last token.
+            actual_end = text_lengths[b]
+            if actual_end < text_input_tokens.shape[-1]:
+                text_input_tokens[b, actual_end:] = self.stop_text_token
+        return text_input_tokens
+
+    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:
+            emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1)
+
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        if get_attns:
+            return gpt_out.attentions
+
+        offset = speech_conditioning_inputs.shape[1]
+        enc = gpt_out.last_hidden_state[:, offset:]
+        enc = self.final_norm(enc)
+
+        if return_latent:
+            return enc[:, :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)
+        if second_inputs is not None:
+            second_logits = enc[:, -second_inputs.shape[1]:]
+            second_logits = second_head(second_logits)
+            second_logits = second_logits.permute(0, 2, 1)
+            return first_logits, second_logits
+        else:
+            return first_logits
+
+    def get_conditioning(self, speech_conditioning_input, cond_mel_lengths=None):
+        if self.condition_type == "perceiver":
+            if speech_conditioning_input.ndim == 4:
+                speech_conditioning_input = speech_conditioning_input.squeeze(1)
+            speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input)  # (b, d, s)
+            conds = self.perceiver_encoder(speech_conditioning_input.transpose(1, 2))  # (b, 32, d)
+        elif self.condition_type == "conformer_perceiver":
+            speech_conditioning_input, mask = self.conditioning_encoder(speech_conditioning_input.transpose(1, 2),
+                                                                        cond_mel_lengths)  # (b, s, d), (b, 1, s)
+            if self.condition_type == "conformer_perceiver":
+                # conds_mask = torch.cat([torch.ones((mask.shape[0], self.cond_num), dtype=torch.bool), mask.squeeze(1)], dim=1)
+                conds_mask = self.cond_mask_pad(mask.squeeze(1))
+                conds = self.perceiver_encoder(speech_conditioning_input, conds_mask)  # (b, 32, d)
+        elif self.condition_type == "gst":
+            if speech_conditioning_input.ndim == 4:
+                speech_conditioning_input = speech_conditioning_input.squeeze(1)
+            conds = self.gst_encoder(speech_conditioning_input.transpose(1, 2))  # (b, 1, d)
+        else:
+            speech_conditioning_input = (
+                speech_conditioning_input.unsqueeze(1)
+                if len(speech_conditioning_input.shape) == 3
+                else speech_conditioning_input
+            )
+            conds = []
+            for j in range(speech_conditioning_input.shape[1]):
+                conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
+            conds = torch.stack(conds, dim=1)
+            conds = conds.mean(dim=1)
+            conds = conds.unsqueeze(1)
+        return conds
+
+    def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths,
+                cond_mel_lengths=None, types=None, text_first=True, raw_mels=None, return_attentions=False,
+                return_latent=False, clip_inputs=False):
+        """
+        Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
+        (actuated by `text_first`).
+
+        speech_conditioning_input: MEL float tensor, (b,1024)
+        text_inputs: long tensor, (b,t)
+        text_lengths: long tensor, (b,)
+        mel_inputs:  long tensor, (b,m)
+        wav_lengths: long tensor, (b,)
+        raw_mels: MEL float tensor (b,80,s)
+
+        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.
+        """
+
+        speech_conditioning_latent = self.get_conditioning(speech_conditioning_latent, cond_mel_lengths)
+        # Types are expressed by expanding the text embedding space.
+        if types is not None:
+            text_inputs = text_inputs * (1 + types).unsqueeze(-1)
+
+        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 = text_inputs[:, :max_text_len]
+            max_mel_len = wav_lengths.max() // self.mel_length_compression
+            mel_codes = mel_codes[:, :max_mel_len]
+            if raw_mels is not None:
+                raw_mels = raw_mels[:, :, :max_mel_len * 4]
+
+        # Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
+        # mel_codes_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc')
+        mel_codes_lengths = torch.ceil(wav_lengths / self.mel_length_compression).long() + 1
+        mel_codes = self.set_mel_padding(mel_codes, mel_codes_lengths)
+        text_inputs = self.set_text_padding(text_inputs, text_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)
+
+        conds = speech_conditioning_latent
+        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+        mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
+        if raw_mels is not None:
+            mel_inp = F.pad(raw_mels, (0, 8))
+        else:
+            mel_inp = mel_codes
+        mel_emb = self.mel_embedding(mel_inp)
+        mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
+
+        if text_first:
+            # print(f"conds: {conds.shape}, text_emb: {text_emb.shape}, mel_emb: {mel_emb.shape}")
+            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, 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
+
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        return loss_text.mean(), loss_mel.mean(), mel_logits
+
+    def prepare_gpt_inputs(
+        self,
+        conditional_latents: torch.Tensor,
+        text_inputs: torch.Tensor,
+    ):
+        
+        """
+        Prepare the inputs for the GPT2InferenceModel to generate.
+        Args:
+            conds_latent: (b, 32, dim) audio conditioning embedding by `get_conditioning()`
+            text_inputs: (b, L)
+        Returns:
+            input_ids: (b, s+1) the input ids for the GPT2InferenceModel.generate()
+            inputs_embeds: (b, s+1, dim) the input embeddings for the GPT2InferenceModel.forward()
+            attention_mask: (b, s+1) the attention mask for the GPT2InferenceModel.generate()
+        """
+        b, L = text_inputs.shape[:2]
+        device = text_inputs.device
+        single_cond = conditional_latents.ndim == 3 and conditional_latents.shape[0] == 1
+        if not single_cond:
+            assert conditional_latents.shape[0] == b, f"batch size mismatch: {conditional_latents.shape[0]} vs {b}"
+        batched_mel_emb = []
+        attention_masks = []
+        target_len = conditional_latents.shape[1] + L + 2
+        for i in range(b):
+            valid_mask = (text_inputs[i] != self.stop_text_token) & (text_inputs[i] != self.start_text_token)
+            text_input = text_inputs[i][valid_mask]
+            text_input = F.pad(text_input, (1, 0), value=self.start_text_token)
+            text_input = F.pad(text_input, (0, 1), value=self.stop_text_token)
+            text_input_pos = torch.arange(0, text_input.size(-1), device=device)
+            text_emb = self.text_embedding(text_input) + self.text_pos_embedding.emb(text_input_pos)
+            # concatenate [conditional latents][text embeddings]
+            conds_text_emb = [
+                conditional_latents.squeeze(0) if single_cond else conditional_latents[i],
+                text_emb,
+            ]
+            # +1 for the start_mel_token
+            attention_mask = torch.ones(target_len+1, dtype=torch.long, device=device)
+            # check this text input is padded
+            padding: int = L + 2 - text_input.size(-1)
+            # pad left of [cond][text] -> [pad][cond][text]
+            if padding > 0:
+                pad = torch.zeros((padding, conditional_latents.size(-1)), dtype=text_emb.dtype, device=device) # [p, dim]
+                conds_text_emb.insert(0, pad)
+                attention_mask[:padding] = 0
+            mel_emb = torch.cat(conds_text_emb) #[s, dim]
+            assert mel_emb.shape[0] == target_len, f"mel_emb.shape: {mel_emb.shape}, target_len: {target_len}"
+            batched_mel_emb.append(mel_emb)
+            attention_masks.append(attention_mask)
+        # [b, s, dim]
+        batched_mel_emb = torch.stack(batched_mel_emb, dim=0)
+        # [b, s+1]
+        attention_mask = torch.stack(attention_masks, dim=0)
+        # [b, s+1]
+        fake_inputs = torch.ones(
+            (
+                batched_mel_emb.shape[0],
+                batched_mel_emb.shape[1] + 1,  # +1 for the start_mel_token
+            ),
+            dtype=torch.long,
+            device=device,
+        )
+        fake_inputs[:, -1] = self.start_mel_token
+        return fake_inputs, batched_mel_emb, attention_mask
+    def inference_speech(self, speech_conditioning_mel, text_inputs, cond_mel_lengths=None, input_tokens=None, num_return_sequences=1,
+                         max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):
+        """
+        Args:
+            speech_conditioning_mel: (b, n_mels, frames) or (n_mels, frames)
+            text_inputs: (b, L)
+            cond_mel_lengths: lengths of the conditioning mel spectrograms in shape (b,) or (1,)
+            input_tokens: additional tokens for generation in shape (b, s) or (s,)
+            max_generate_length: limit the number of generated tokens
+            hf_generate_kwargs: kwargs for `GPT2InferenceModel.generate(**hf_generate_kwargs)`
+        """
+        if speech_conditioning_mel.ndim == 2:
+            speech_conditioning_mel = speech_conditioning_mel.unsqueeze(0)
+        if cond_mel_lengths is None:
+            cond_mel_lengths = torch.tensor([speech_conditioning_mel.shape[-1]], device=speech_conditioning_mel.device)
+        conds_latent = self.get_conditioning(speech_conditioning_mel, cond_mel_lengths)
+        input_ids, inputs_embeds, attention_mask = self.prepare_gpt_inputs(conds_latent, text_inputs)
+        self.inference_model.store_mel_emb(inputs_embeds)
+        if input_tokens is None:
+            inputs = input_ids
+        else:
+            if input_tokens.ndim == 1:
+                input_tokens = input_tokens.unsqueeze(0)
+            assert num_return_sequences % input_tokens.shape[0] == 0, \
+                    "The num_return_sequences must be divisible by the batch number of input_tokens"
+            assert num_return_sequences % text_inputs.shape[0] == 0, \
+                    "The num_return_sequences must be divisible by the batch number of text_inputs"
+            b = num_return_sequences // input_ids.shape[0]
+            if b > 1:
+                input_ids = input_ids.repeat(b, 1)
+                attention_mask = attention_mask.repeat(b, 1)
+            input_tokens = input_tokens.repeat(num_return_sequences // input_tokens.shape[0], 1)
+            inputs = torch.cat([input_ids, input_tokens], dim=1)
+            attention_mask = F.pad(attention_mask, (0, input_tokens.shape[1]), value=1)
+        trunc_index = inputs.shape[1]
+        logits_processor = LogitsProcessorList()
+        if typical_sampling:
+            # employ custom typical sampling
+            if not (typical_mass > 0.0 and typical_mass < 1.0):
+                raise ValueError(f"`typical_mass` has to be a float > 0 and < 1, but is {typical_mass}")
+            min_tokens_to_keep = 2 if hf_generate_kwargs.get("num_beams", 1) > 1 else 1
+            logits_processor.append(TypicalLogitsWarper(mass=typical_mass, min_tokens_to_keep=min_tokens_to_keep))
+        max_length = (trunc_index + self.max_mel_tokens - 1) if max_generate_length is None else trunc_index + max_generate_length
+        output = self.inference_model.generate(inputs, 
+                                            bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token,
+                                            eos_token_id=self.stop_mel_token, attention_mask=attention_mask,
+                                            max_length=max_length, logits_processor=logits_processor,
+                                            num_return_sequences=num_return_sequences,
+                                            **hf_generate_kwargs)
+        if isinstance(output, torch.Tensor):
+            return output[:, trunc_index:]
+        # GenerateOutput
+        output.sequences = output.sequences[:, trunc_index:]
+        return output