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tortoise-tts-v2 / models /autoregressive.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
	from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
	from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
	from models.arch_util import AttentionBlock
	from 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 <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:
	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 <zero>).
	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 forward(self, speech_conditioning_input, 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,80,s)
	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)

	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)
	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_input, 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)

	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)

	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([250256,195256]))
	gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))