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# Copyright (c) 2025 Resemble AI
# MIT License
import logging
from typing import Union, Optional, List
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from transformers import LlamaModel, LlamaConfig
from transformers.generation.logits_process import MinPLogitsWarper, RepetitionPenaltyLogitsProcessor, TopPLogitsWarper
from .modules.learned_pos_emb import LearnedPositionEmbeddings
from .modules.cond_enc import T3CondEnc, T3Cond
from .modules.t3_config import T3Config
from .llama_configs import LLAMA_CONFIGS
from .inference.t3_hf_backend import T3HuggingfaceBackend
from ..utils import AttrDict
logger = logging.getLogger(__name__)
def _ensure_BOT_EOT(text_tokens: Tensor, hp):
B = text_tokens.size(0)
assert (text_tokens == hp.start_text_token).int().sum() >= B, "missing start_text_token"
assert (text_tokens == hp.stop_text_token).int().sum() >= B, "missing stop_text_token"
class T3(nn.Module):
"""
Token-To-Token (T3) TTS model using huggingface transformer models as backbones,
* tokenization, including start / stop tokens are always added externally to this class
* conditioning data like CLAP, emotion, etc are all in a separate file for more modularity
* careful! this class assumes relative positional encoding -- with absolute PE, we would at
least want to reset the position to 0 when speech tokens begin, and optionally use a
different PE embedding space for speech.
"""
def __init__(self, hp=T3Config()):
super().__init__()
self.hp = hp
self.cfg = LlamaConfig(**LLAMA_CONFIGS[hp.llama_config_name])
self.tfmr = LlamaModel(self.cfg)
self.dim = self.cfg.hidden_size
self.deepspeed_patch_applied = False
# conditioning / embedding
self.cond_enc = T3CondEnc(hp)
self.text_emb = nn.Embedding(hp.text_tokens_dict_size, self.dim)
self.speech_emb = nn.Embedding(hp.speech_tokens_dict_size, self.dim)
# custom position embedding
if hp.input_pos_emb == "learned":
max_text_seq_len = hp.max_text_tokens + 2
self.text_pos_emb = LearnedPositionEmbeddings(max_text_seq_len, self.dim)
max_mel_seq_len = hp.max_speech_tokens + 2 + 2
self.speech_pos_emb = LearnedPositionEmbeddings(max_mel_seq_len, self.dim)
# logit projection
self.text_head = nn.Linear(self.cfg.hidden_size, hp.text_tokens_dict_size, bias=False)
self.speech_head = nn.Linear(self.cfg.hidden_size, hp.speech_tokens_dict_size, bias=False)
self.compiled = False
@property
def device(self):
return self.speech_head.weight.device
def prepare_conditioning(self, t3_cond: T3Cond):
"""
Token cond data needs to be embedded, so that needs to be here instead of in `T3CondEnc`.
"""
if t3_cond.cond_prompt_speech_tokens is not None and t3_cond.cond_prompt_speech_emb is None:
t3_cond.cond_prompt_speech_emb = self.speech_emb(t3_cond.cond_prompt_speech_tokens) + \
self.speech_pos_emb(t3_cond.cond_prompt_speech_tokens)
return self.cond_enc(t3_cond) # (B, len_cond, dim)
def prepare_input_embeds(
self,
*,
t3_cond: T3Cond,
text_tokens: torch.LongTensor,
speech_tokens: torch.LongTensor,
cfg_weight: float = 0.0,
):
# prepare input embeddings (skip backbone tranformer embeddings)
cond_emb = self.prepare_conditioning(t3_cond) # (B, len_cond, dim)
text_emb = self.text_emb(text_tokens) # (B, len_text, dim)
if cfg_weight > 0.0:
text_emb[1].zero_() # CFG uncond
speech_emb = self.speech_emb(speech_tokens) # (B, len_speech, dim)
if self.hp.input_pos_emb == "learned":
text_emb = text_emb + self.text_pos_emb(text_tokens)
speech_emb = speech_emb + self.speech_pos_emb(speech_tokens)
len_cond = cond_emb.size(1)
if cond_emb.size(0) != text_emb.size(0):
cond_emb = cond_emb.expand(text_emb.size(0), -1, -1)
# concat
embeds = torch.stack([
torch.cat((ce, te, se))
for ce, te, se in zip(cond_emb, text_emb, speech_emb)
]) # (B, length, dim)
return embeds, len_cond
def forward(
self,
*,
t3_cond: T3Cond,
text_tokens: torch.LongTensor,
text_token_lens: torch.LongTensor,
speech_tokens: torch.LongTensor,
speech_token_lens: torch.LongTensor,
training=False,
):
_ensure_BOT_EOT(text_tokens, self.hp)
# prepare custom input embeds
embeds, len_cond = self.prepare_input_embeds(
t3_cond=t3_cond,
text_tokens=text_tokens,
speech_tokens=speech_tokens,
)
# backbone tranformer forward
tfmr_out = self.tfmr.forward(
input_ids=None,
# position_ids=position_ids, # TODO? ROPE should be fine?
inputs_embeds=embeds,
output_hidden_states=True,
return_dict=True,
use_cache=(not training),
)
hidden_states = tfmr_out.hidden_states[-1] # final tfmr layer output, (B, seq, dim)
# post-processing: splice out text and speech parts of hidden states
len_text = text_tokens.size(1)
len_speech = speech_tokens.size(1)
B, _, dim = hidden_states.shape
device, dtype = hidden_states.device, hidden_states.dtype
text_latents = torch.zeros(B, len_text, dim, dtype=dtype, device=device)
speech_latents = torch.zeros(B, len_speech, dim, dtype=dtype, device=device)
ttl, stl = text_token_lens, speech_token_lens
for i in range(B):
text_end = len_cond + ttl[i].item()
speech_start = len_cond + text_tokens.size(1)
speech_end = speech_start + stl[i].item()
text_latents[i, :ttl[i]] = hidden_states[i, len_cond:text_end]
speech_latents[i, :stl[i]] = hidden_states[i, speech_start:speech_end]
# logit projection
text_logits = self.text_head(text_latents)
speech_logits = self.speech_head(speech_latents)
return AttrDict(
text_logits=text_logits,
text_latents=text_latents,
speech_logits=speech_logits,
speech_latents=speech_latents,
hidden_states=hidden_states,
)
def loss(
self,
*,
t3_cond: T3Cond,
text_tokens: torch.LongTensor,
text_token_lens: torch.LongTensor,
speech_tokens: torch.LongTensor,
speech_token_lens: torch.LongTensor,
):
"training method"
len_text = text_tokens.size(1)
len_speech = speech_tokens.size(1)
assert len_text == text_token_lens.max()
assert len_speech == speech_token_lens.max()
out = self.forward(
t3_cond=t3_cond,
text_tokens=text_tokens,
text_token_lens=text_token_lens,
speech_tokens=speech_tokens,
speech_token_lens=speech_token_lens,
training=True,
) # (B, seq, vocab_size)
# Calc CCE losses
IGNORE_ID = -100
device = out.text_logits.device
mask_text = torch.arange(len_text, device=device)[None] >= text_token_lens[:, None] # (B, len_text)
mask_speech = torch.arange(len_speech, device=device)[None] >= speech_token_lens[:, None] # (B, len_speech)
masked_text = text_tokens.masked_fill(mask_text, IGNORE_ID)
masked_speech = speech_tokens.masked_fill(mask_speech, IGNORE_ID)
loss_text = F.cross_entropy(out.text_logits, masked_text, ignore_index=IGNORE_ID)
loss_speech = F.cross_entropy(out.speech_logits, masked_speech, ignore_index=IGNORE_ID)
return loss_text, loss_speech
@torch.inference_mode()
def inference(
self,
*,
t3_cond: T3Cond,
text_tokens: Tensor,
initial_speech_tokens: Optional[Tensor]=None,
# misc conditioning
prepend_prompt_speech_tokens: Optional[Tensor]=None,
# HF generate args
num_return_sequences=1,
max_new_tokens=None,
stop_on_eos=True,
do_sample=True,
temperature=0.8,
min_p=0.05,
top_p=1.00,
length_penalty=1.0,
repetition_penalty=1.2,
cfg_weight=0,
):
"""
Args:
text_tokens: a 1D (unbatched) or 2D (batched) tensor.
"""
# Validate / sanitize inputs
assert prepend_prompt_speech_tokens is None, "not implemented"
_ensure_BOT_EOT(text_tokens, self.hp)
text_tokens = torch.atleast_2d(text_tokens).to(dtype=torch.long, device=self.device)
# Default initial speech to a single start-of-speech token
if initial_speech_tokens is None:
initial_speech_tokens = self.hp.start_speech_token * torch.ones_like(text_tokens[:, :1])
# Prepare custom input embeds
embeds, len_cond = self.prepare_input_embeds(
t3_cond=t3_cond,
text_tokens=text_tokens,
speech_tokens=initial_speech_tokens,
cfg_weight=cfg_weight,
)
# In order to use the standard HF generate method, we need to extend some methods to inject our custom logic
# Note the llama-specific logic. Other tfmr types can be added later.
self.compiled = False
# TODO? synchronize the expensive compile function
# with self.compile_lock:
if not self.compiled:
patched_model = T3HuggingfaceBackend(
config=self.cfg,
llama=self.tfmr,
speech_enc=self.speech_emb,
speech_head=self.speech_head,
alignment_stream_analyzer=None,
)
self.patched_model = patched_model
self.compiled = True
# # Run normal generate method, which calls our custom extended methods
# return self.patched_model.generate(
# inputs=initial_speech_tokens,
# decoder_cond=embeds,
# bos_token_id=self.hp.start_speech_token,
# eos_token_id=(self.hp.stop_speech_token if stop_on_eos else -1),
# pad_token_id=self.hp.stop_speech_token,
# max_new_tokens=max_new_tokens or self.hp.max_speech_tokens,
# num_return_sequences=num_return_sequences,
# temperature=temperature,
# min_p=min_p,
# length_penalty=length_penalty,
# repetition_penalty=repetition_penalty,
# do_sample=do_sample,
# # cache_implementation=None if not self.compiled else "static",
# )
device = embeds.device
bos_token = torch.tensor([[self.hp.start_speech_token]], dtype=torch.long, device=device)
bos_embed = self.speech_emb(bos_token) # shape: (B, 1, embed_dim)
bos_embed = bos_embed + self.speech_pos_emb.get_fixed_embedding(0)
# batch_size=2 for CFG
bos_embed = torch.cat([bos_embed, bos_embed])
# Combine condition and BOS token for the initial input if cfg_weight > 0
if cfg_weight > 0:
inputs_embeds = torch.cat([embeds, bos_embed], dim=1)
else:
inputs_embeds = embeds
# Track generated token ids; start with the BOS token.
generated_ids = bos_token.clone()
predicted = [] # To store the predicted tokens
# Instantiate the logits processors.
min_p_warper = MinPLogitsWarper(min_p=min_p)
top_p_warper = TopPLogitsWarper(top_p=top_p)
repetition_penalty_processor = RepetitionPenaltyLogitsProcessor(penalty=float(repetition_penalty))
# ---- Initial Forward Pass (no kv_cache yet) ----
output = self.patched_model(
inputs_embeds=inputs_embeds,
past_key_values=None,
use_cache=True,
output_attentions=True,
output_hidden_states=True,
return_dict=True,
)
# Initialize kv_cache with the full context.
past = output.past_key_values
# ---- Generation Loop using kv_cache ----
for i in tqdm(range(max_new_tokens), desc="Sampling", dynamic_ncols=True):
logits = output.logits[:, -1, :]
# CFG
if cfg_weight > 0.0:
logits_cond = logits[0:1]
logits_uncond = logits[1:2]
logits = logits_cond + cfg_weight * (logits_cond - logits_uncond)
logits = logits.squeeze(1)
# Apply temperature scaling.
if temperature != 1.0:
logits = logits / temperature
# Apply repetition penalty and top‑p filtering.
logits = repetition_penalty_processor(generated_ids, logits)
logits = min_p_warper(None, logits)
logits = top_p_warper(None, logits)
# Convert logits to probabilities and sample the next token.
probs = torch.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1) # shape: (B, 1)
predicted.append(next_token)
generated_ids = torch.cat([generated_ids, next_token], dim=1)
# Check for EOS token.
if next_token.view(-1) == self.hp.stop_speech_token:
break
# Get embedding for the new token.
next_token_embed = self.speech_emb(next_token)
next_token_embed = next_token_embed + self.speech_pos_emb.get_fixed_embedding(i + 1)
# For CFG
if cfg_weight > 0.0:
next_token_embed = torch.cat([next_token_embed, next_token_embed])
# Forward pass with only the new token and the cached past.
output = self.patched_model(
inputs_embeds=next_token_embed,
past_key_values=past,
output_attentions=True,
output_hidden_states=True,
return_dict=True,
)
# Update the kv_cache.
past = output.past_key_values
# Concatenate all predicted tokens along the sequence dimension.
predicted_tokens = torch.cat(predicted, dim=1) # shape: (B, num_tokens)
return predicted_tokens
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