import functools import torch import torch.nn as nn import torch.nn.functional as F import transformers from transformers import GPT2Config, LogitsProcessorList from indextts.gpt.transformers_gpt2 import GPT2PreTrainedModel, GPT2Model # from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList 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): 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): """ 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, 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, condition_num_latent=32, condition_type="perceiver", condition_module=None, emo_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 and . 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) self.emo_cond_mask_pad = nn.ConstantPad1d((1, 0), True) if condition_type == "perceiver": self.conditioning_encoder = ConditioningEncoder(1024, 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=1024, 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(1024, model_dim, num_attn_heads=heads, mean=True) self.emo_conditioning_encoder = ConformerEncoder(input_size=1024, output_size=emo_condition_module['output_size'], linear_units=emo_condition_module['linear_units'], attention_heads=emo_condition_module['attention_heads'], num_blocks=emo_condition_module['num_blocks'], input_layer=emo_condition_module['input_layer']) self.emo_perceiver_encoder = PerceiverResampler(1024, dim_context=emo_condition_module['output_size'], ff_mult=emo_condition_module['perceiver_mult'], heads=emo_condition_module['attention_heads'], num_latents=1) self.text_embedding = nn.Embedding(self.number_text_tokens * types + 1, model_dim) self.emo_layer = nn.Linear(model_dim, model_dim) self.emovec_layer = nn.Linear(1024, 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) 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) self.speed_emb = nn.Embedding(2, model_dim) self.speed_emb.weight.data.normal_(mean=0.0, std=0.0) # 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=True, 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=True, 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 get_emo_conditioning(self, speech_conditioning_input, cond_mel_lengths=None): speech_conditioning_input, mask = self.emo_conditioning_encoder(speech_conditioning_input.transpose(1, 2), cond_mel_lengths) # (b, s, d), (b, 1, s) conds_mask = self.emo_cond_mask_pad(mask.squeeze(1)) conds = self.emo_perceiver_encoder(speech_conditioning_input, conds_mask) # (b, 1, d) return conds.squeeze(1) def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, mel_codes_lengths, emo_speech_conditioning_latent, cond_mel_lengths=None, emo_cond_mel_lengths=None, emo_vec=None, use_speed=None, do_spk_cond=False): """ Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode 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,) 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 do_spk_cond: speech_conditioning_latent = self.get_conditioning(speech_conditioning_latent.transpose(1,2), cond_mel_lengths) else: speech_conditioning_latent = speech_conditioning_latent if emo_vec is None: emo_vec_syn_ori = self.get_emo_conditioning(emo_speech_conditioning_latent.transpose(1,2), emo_cond_mel_lengths) emo_vec_syn = self.emovec_layer(emo_vec_syn_ori) emo_vec = self.emo_layer(emo_vec_syn) 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 = self.set_mel_padding(mel_codes, mel_codes_lengths) mel_codes = F.pad(mel_codes, (0, 1), value=self.stop_mel_token) duration_emb = self.speed_emb(torch.zeros_like(use_speed)) duration_emb_half = self.speed_emb(torch.ones_like(use_speed)) conds = torch.cat((speech_conditioning_latent + emo_vec.unsqueeze(1), duration_emb_half.unsqueeze(1), duration_emb.unsqueeze(1)), 1) 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) mel_emb = self.mel_embedding(mel_codes) mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=False, return_latent=True) return mel_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass. 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_condition, text_inputs, emo_speech_condition=None, cond_lengths=None, emo_cond_lengths=None, emo_vec=None, use_speed=False, input_tokens=None, num_return_sequences=1, max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs): """ Args: speech_condition: (b, d, frames) or (d, 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_condition.ndim == 2: speech_condition = speech_condition.unsqueeze(0) if emo_speech_condition is None: emo_speech_condition = speech_condition if cond_lengths is None: cond_lengths = torch.tensor([speech_condition.shape[-1]], device=speech_condition.device) if emo_cond_lengths is None: emo_cond_lengths = torch.tensor([emo_speech_condition.shape[-1]], device=speech_condition.device) speech_conditioning_latent = self.get_conditioning(speech_condition.transpose(1,2), cond_lengths) if emo_vec is None: print('compute emo vec') emo_vec = self.get_emo_conditioning(emo_speech_condition.transpose(1,2), emo_cond_lengths) emo_vec = self.emovec_layer(emo_vec) emo_vec = self.emo_layer(emo_vec) else: print('Use the specified emotion vector') tmp = torch.zeros(text_inputs.size(0)).to(text_inputs.device) duration_emb = self.speed_emb(torch.zeros_like(tmp).long()) duration_emb_half = self.speed_emb(torch.ones_like(tmp).long()) conds_latent = torch.cat((speech_conditioning_latent + emo_vec.unsqueeze(1), duration_emb_half.unsqueeze(1), duration_emb.unsqueeze(1)), 1) 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:], speech_conditioning_latent # GenerateOutput output.sequences = output.sequences[:, trunc_index:] return output, speech_conditioning_latent def get_emovec(self, emo_speech_conditioning_latent, emo_cond_lengths): emo_vec_syn_ori = self.get_emo_conditioning(emo_speech_conditioning_latent.transpose(1,2), emo_cond_lengths) emo_vec_syn = self.emovec_layer(emo_vec_syn_ori) emo_vec = self.emo_layer(emo_vec_syn) return emo_vec def merge_emovec(self, speech_conditioning_latent, emo_speech_conditioning_latent, cond_lengths, emo_cond_lengths, alpha = 1.0): emo_vec = self.get_emovec(emo_speech_conditioning_latent, emo_cond_lengths) base_vec = self.get_emovec(speech_conditioning_latent, cond_lengths) out = base_vec + alpha * (emo_vec - base_vec) return out