import functools import torch import torch.nn as nn import torch.nn.functional as F from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions from transformers.utils.model_parallel_utils import get_device_map, assert_device_map from tortoise.models.arch_util import AttentionBlock from tortoise.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): super().__init__(config) self.transformer = gpt self.text_pos_embedding = text_pos_emb self.embeddings = embeddings self.lm_head = nn.Sequential(norm, linear) # 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(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() 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=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.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: 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: 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[:, :, 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=None, number_mel_codes=8194, start_mel_token=8192, stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True, checkpointing=True, average_conditioning_embeddings=False, types=1): """ 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: average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model. """ super().__init__() self.number_text_tokens = number_text_tokens self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token self.stop_text_token = 0 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.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) self.average_conditioning_embeddings = average_conditioning_embeddings 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) 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 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, wav_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. """ # Set padding areas within MEL (currently it is coded with the MEL code for ). mel_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc') for b in range(len(mel_lengths)): actual_end = mel_lengths[b] + 1 # 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. if actual_end < mel_input_tokens.shape[-1]: 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, 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 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) 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): 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) if self.average_conditioning_embeddings: conds = conds.mean(dim=1).unsqueeze(1) return conds def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, 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`). 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. """ # 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] 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) conds = speech_conditioning_latent.unsqueeze(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) 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: 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 text_forward(self, speech_conditioning_input, text_inputs, text_lengths): """ Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided). """ assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}' # 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) 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) if self.average_conditioning_embeddings: conds = conds.mean(dim=1).unsqueeze(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) + self.text_solo_embedding text_logits = self.get_logits(conds, text_emb, self.text_head) loss_text = F.cross_entropy(text_logits, text_targets.long()) return loss_text.mean() def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None): """ Performs autoregressive modeling on only speech data. """ assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}' # 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_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 = self.set_mel_padding(mel_codes, wav_lengths) if raw_mels is not None: raw_mels = raw_mels[:, :, :max_mel_len*4] 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) if self.average_conditioning_embeddings: conds = conds.mean(dim=1).unsqueeze(1) 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, 4)) else: mel_inp = mel_codes mel_emb = self.mel_embedding(mel_inp) mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + self.mel_solo_embedding mel_logits = self.get_logits(conds, mel_emb, self.mel_head) loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) return loss_mel.mean() def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1, max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs): seq_length = self.max_mel_tokens + self.max_text_tokens + 2 if not hasattr(self, 'inference_model'): # TODO: Decouple gpt_config from this inference model. gpt_config = GPT2Config(vocab_size=self.max_mel_tokens, 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) self.gpt.wte = self.mel_embedding text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token) 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) conds = speech_conditioning_latent.unsqueeze(1) emb = torch.cat([conds, text_emb], dim=1) self.inference_model.store_mel_emb(emb) fake_inputs = torch.full((emb.shape[0], conds.shape[1] + emb.shape[1],), fill_value=1, dtype=torch.long, device=text_inputs.device) fake_inputs[:, -1] = self.start_mel_token trunc_index = fake_inputs.shape[1] if input_tokens is None: inputs = fake_inputs else: assert num_return_sequences % input_tokens.shape[0] == 0, "The number of return sequences must be divisible by the number of input sequences" fake_inputs = fake_inputs.repeat(num_return_sequences, 1) input_tokens = input_tokens.repeat(num_return_sequences // input_tokens.shape[0], 1) inputs = torch.cat([fake_inputs, input_tokens], dim=1) logits_processor = LogitsProcessorList([TypicalLogitsWarper(mass=typical_mass)]) if typical_sampling else LogitsProcessorList() max_length = trunc_index + self.max_mel_tokens - 1 if max_generate_length is None else trunc_index + max_generate_length gen = 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, max_length=max_length, logits_processor=logits_processor, num_return_sequences=num_return_sequences, **hf_generate_kwargs) return gen[:, trunc_index:] if __name__ == '__main__': gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4) l = gpt(torch.randn(2, 3, 80, 800), torch.randint(high=120, size=(2,120)), torch.tensor([32, 120]), torch.randint(high=8192, size=(2,250)), torch.tensor([250*256,195*256])) gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))