import logging
from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Union
import torch
import torch.nn as nn
import torchaudio
from coqpit import Coqpit
from torch.utils.data import DataLoader
from trainer.io import load_fsspec
from trainer.torch import DistributedSampler
from trainer.trainer_utils import get_optimizer, get_scheduler
from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.datasets.dataset import TTSDataset
from TTS.tts.layers.tortoise.arch_utils import TorchMelSpectrogram
from TTS.tts.layers.xtts.dvae import DiscreteVAE
from TTS.tts.layers.xtts.tokenizer import VoiceBpeTokenizer
from TTS.tts.layers.xtts.trainer.dataset import XTTSDataset
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.models.xtts import Xtts, XttsArgs, XttsAudioConfig
logger = logging.getLogger(__name__)
@dataclass
class GPTTrainerConfig(XttsConfig):
lr: float = 5e-06
training_seed: int = 1
optimizer_wd_only_on_weights: bool = False
weighted_loss_attrs: dict = field(default_factory=lambda: {})
weighted_loss_multipliers: dict = field(default_factory=lambda: {})
test_sentences: List[dict] = field(default_factory=lambda: [])
@dataclass
class XttsAudioConfig(XttsAudioConfig):
dvae_sample_rate: int = 22050
@dataclass
class GPTArgs(XttsArgs):
min_conditioning_length: int = 66150
max_conditioning_length: int = 132300
gpt_loss_text_ce_weight: float = 0.01
gpt_loss_mel_ce_weight: float = 1.0
gpt_num_audio_tokens: int = 8194
debug_loading_failures: bool = False
max_wav_length: int = 255995 # ~11.6 seconds
max_text_length: int = 200
tokenizer_file: str = ""
mel_norm_file: str = "https://coqui.gateway.scarf.sh/v0.14.0_models/mel_norms.pth"
dvae_checkpoint: str = ""
xtts_checkpoint: str = ""
gpt_checkpoint: str = "" # if defined it will replace the gpt weights on xtts model
vocoder: str = "" # overide vocoder key on the config to avoid json write issues
def callback_clearml_load_save(operation_type, model_info):
# return None means skip the file upload/log, returning model_info will continue with the log/upload
# you can also change the upload destination file name model_info.upload_filename or check the local file size with Path(model_info.local_model_path).stat().st_size
assert operation_type in ("load", "save")
logger.debug("%s %s", operation_type, model_info.__dict__)
if "similarities.pth" in model_info.__dict__["local_model_path"]:
return None
return model_info
class GPTTrainer(BaseTTS):
def __init__(self, config: Coqpit):
"""
Tortoise GPT training class
"""
super().__init__(config, ap=None, tokenizer=None)
self.config = config
# init XTTS model
self.xtts = Xtts(self.config)
# create the tokenizer with the target vocabulary
self.xtts.tokenizer = VoiceBpeTokenizer(self.args.tokenizer_file)
# init gpt encoder and hifigan decoder
self.xtts.init_models()
if self.args.xtts_checkpoint:
self.load_checkpoint(self.config, self.args.xtts_checkpoint, eval=False, strict=False)
# set mel stats
if self.args.mel_norm_file:
self.xtts.mel_stats = load_fsspec(self.args.mel_norm_file)
# load GPT if available
if self.args.gpt_checkpoint:
gpt_checkpoint = torch.load(self.args.gpt_checkpoint, map_location=torch.device("cpu"), weights_only=True)
# deal with coqui Trainer exported model
if "model" in gpt_checkpoint.keys() and "config" in gpt_checkpoint.keys():
logger.info("Coqui Trainer checkpoint detected! Converting it!")
gpt_checkpoint = gpt_checkpoint["model"]
states_keys = list(gpt_checkpoint.keys())
for key in states_keys:
if "gpt." in key:
new_key = key.replace("gpt.", "")
gpt_checkpoint[new_key] = gpt_checkpoint[key]
del gpt_checkpoint[key]
else:
del gpt_checkpoint[key]
# edit checkpoint if the number of tokens is changed to ensures the better transfer learning possible
if (
"text_embedding.weight" in gpt_checkpoint
and gpt_checkpoint["text_embedding.weight"].shape != self.xtts.gpt.text_embedding.weight.shape
):
num_new_tokens = (
self.xtts.gpt.text_embedding.weight.shape[0] - gpt_checkpoint["text_embedding.weight"].shape[0]
)
logger.info("Loading checkpoint with %d additional tokens.", num_new_tokens)
# add new tokens to a linear layer (text_head)
emb_g = gpt_checkpoint["text_embedding.weight"]
new_row = torch.randn(num_new_tokens, emb_g.shape[1])
start_token_row = emb_g[-1, :]
emb_g = torch.cat([emb_g, new_row], axis=0)
emb_g[-1, :] = start_token_row
gpt_checkpoint["text_embedding.weight"] = emb_g
# add new weights to the linear layer (text_head)
text_head_weight = gpt_checkpoint["text_head.weight"]
start_token_row = text_head_weight[-1, :]
new_entry = torch.randn(num_new_tokens, self.xtts.gpt.text_head.weight.shape[1])
text_head_weight = torch.cat([text_head_weight, new_entry], axis=0)
text_head_weight[-1, :] = start_token_row
gpt_checkpoint["text_head.weight"] = text_head_weight
# add new biases to the linear layer (text_head)
text_head_bias = gpt_checkpoint["text_head.bias"]
start_token_row = text_head_bias[-1]
new_bias_entry = torch.zeros(num_new_tokens)
text_head_bias = torch.cat([text_head_bias, new_bias_entry], axis=0)
text_head_bias[-1] = start_token_row
gpt_checkpoint["text_head.bias"] = text_head_bias
self.xtts.gpt.load_state_dict(gpt_checkpoint, strict=True)
logger.info("GPT weights restored from: %s", self.args.gpt_checkpoint)
# Mel spectrogram extractor for conditioning
if self.args.gpt_use_perceiver_resampler:
self.torch_mel_spectrogram_style_encoder = TorchMelSpectrogram(
filter_length=2048,
hop_length=256,
win_length=1024,
normalize=False,
sampling_rate=config.audio.sample_rate,
mel_fmin=0,
mel_fmax=8000,
n_mel_channels=80,
mel_norm_file=self.args.mel_norm_file,
)
else:
self.torch_mel_spectrogram_style_encoder = TorchMelSpectrogram(
filter_length=4096,
hop_length=1024,
win_length=4096,
normalize=False,
sampling_rate=config.audio.sample_rate,
mel_fmin=0,
mel_fmax=8000,
n_mel_channels=80,
mel_norm_file=self.args.mel_norm_file,
)
# Load DVAE
self.dvae = DiscreteVAE(
channels=80,
normalization=None,
positional_dims=1,
num_tokens=self.args.gpt_num_audio_tokens - 2,
codebook_dim=512,
hidden_dim=512,
num_resnet_blocks=3,
kernel_size=3,
num_layers=2,
use_transposed_convs=False,
)
self.dvae.eval()
if self.args.dvae_checkpoint:
dvae_checkpoint = torch.load(self.args.dvae_checkpoint, map_location=torch.device("cpu"), weights_only=True)
self.dvae.load_state_dict(dvae_checkpoint, strict=False)
logger.info("DVAE weights restored from: %s", self.args.dvae_checkpoint)
else:
raise RuntimeError(
"You need to specify config.model_args.dvae_checkpoint path to be able to train the GPT decoder!!"
)
# Mel spectrogram extractor for DVAE
self.torch_mel_spectrogram_dvae = TorchMelSpectrogram(
mel_norm_file=self.args.mel_norm_file, sampling_rate=config.audio.dvae_sample_rate
)
@property
def device(self):
return next(self.parameters()).device
def forward(self, text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens):
"""
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
(actuated by `text_first`).
text_inputs: long tensor, (b,t)
text_lengths: long tensor, (b,)
mel_inputs: long tensor, (b,m)
wav_lengths: long tensor, (b,)
cond_mels: MEL float tensor, (b, num_samples, 80,t_m)
cond_idxs: cond start and end indexs, (b, 2)
cond_lens: long tensor, (b,)
"""
losses = self.xtts.gpt(
text_inputs,
text_lengths,
audio_codes,
wav_lengths,
cond_mels=cond_mels,
cond_idxs=cond_idxs,
cond_lens=cond_lens,
)
return losses
@torch.no_grad()
def test_run(self, assets) -> Tuple[Dict, Dict]: # pylint: disable=W0613
test_audios = {}
if self.config.test_sentences:
# init gpt for inference mode
self.xtts.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache, use_deepspeed=False)
self.xtts.gpt.eval()
logger.info("Synthesizing test sentences.")
for idx, s_info in enumerate(self.config.test_sentences):
wav = self.xtts.synthesize(
s_info["text"],
self.config,
s_info["speaker_wav"],
s_info["language"],
gpt_cond_len=3,
)["wav"]
test_audios["{}-audio".format(idx)] = wav
# delete inference layers
del self.xtts.gpt.gpt_inference
del self.xtts.gpt.gpt.wte
return {"audios": test_audios}
def test_log(
self, outputs: dict, logger: "Logger", assets: dict, steps: int # pylint: disable=unused-argument
) -> None:
logger.test_audios(steps, outputs["audios"], self.args.output_sample_rate)
def format_batch(self, batch: Dict) -> Dict:
return batch
@torch.no_grad() # torch no grad to avoid gradients from the pre-processing and DVAE codes extraction
def format_batch_on_device(self, batch):
"""Compute spectrograms on the device."""
batch["text_lengths"] = batch["text_lengths"]
batch["wav_lengths"] = batch["wav_lengths"]
batch["text_inputs"] = batch["padded_text"]
batch["cond_idxs"] = batch["cond_idxs"]
# compute conditioning mel specs
# transform waves from torch.Size([B, num_cond_samples, 1, T] to torch.Size([B * num_cond_samples, 1, T] because if is faster than iterate the tensor
B, num_cond_samples, C, T = batch["conditioning"].size()
conditioning_reshaped = batch["conditioning"].view(B * num_cond_samples, C, T)
paired_conditioning_mel = self.torch_mel_spectrogram_style_encoder(conditioning_reshaped)
# transform torch.Size([B * num_cond_samples, n_mel, T_mel]) in torch.Size([B, num_cond_samples, n_mel, T_mel])
n_mel = self.torch_mel_spectrogram_style_encoder.n_mel_channels # paired_conditioning_mel.size(1)
T_mel = paired_conditioning_mel.size(2)
paired_conditioning_mel = paired_conditioning_mel.view(B, num_cond_samples, n_mel, T_mel)
# get the conditioning embeddings
batch["cond_mels"] = paired_conditioning_mel
# compute codes using DVAE
if self.config.audio.sample_rate != self.config.audio.dvae_sample_rate:
dvae_wav = torchaudio.functional.resample(
batch["wav"],
orig_freq=self.config.audio.sample_rate,
new_freq=self.config.audio.dvae_sample_rate,
lowpass_filter_width=64,
rolloff=0.9475937167399596,
resampling_method="kaiser_window",
beta=14.769656459379492,
)
else:
dvae_wav = batch["wav"]
dvae_mel_spec = self.torch_mel_spectrogram_dvae(dvae_wav)
codes = self.dvae.get_codebook_indices(dvae_mel_spec)
batch["audio_codes"] = codes
# delete useless batch tensors
del batch["padded_text"]
del batch["wav"]
del batch["conditioning"]
return batch
def train_step(self, batch, criterion):
loss_dict = {}
cond_mels = batch["cond_mels"]
text_inputs = batch["text_inputs"]
text_lengths = batch["text_lengths"]
audio_codes = batch["audio_codes"]
wav_lengths = batch["wav_lengths"]
cond_idxs = batch["cond_idxs"]
cond_lens = batch["cond_lens"]
loss_text, loss_mel, _ = self.forward(
text_inputs, text_lengths, audio_codes, wav_lengths, cond_mels, cond_idxs, cond_lens
)
loss_dict["loss_text_ce"] = loss_text * self.args.gpt_loss_text_ce_weight
loss_dict["loss_mel_ce"] = loss_mel * self.args.gpt_loss_mel_ce_weight
loss_dict["loss"] = loss_dict["loss_text_ce"] + loss_dict["loss_mel_ce"]
return {"model_outputs": None}, loss_dict
def eval_step(self, batch, criterion):
# ignore masking for more consistent evaluation
batch["cond_idxs"] = None
return self.train_step(batch, criterion)
def on_train_epoch_start(self, trainer):
trainer.model.eval() # the whole model to eval
# put gpt model in training mode
if hasattr(trainer.model, "module") and hasattr(trainer.model.module, "xtts"):
trainer.model.module.xtts.gpt.train()
else:
trainer.model.xtts.gpt.train()
def on_init_end(self, trainer): # pylint: disable=W0613
# ignore similarities.pth on clearml save/upload
if self.config.dashboard_logger.lower() == "clearml":
from clearml.binding.frameworks import WeightsFileHandler
WeightsFileHandler.add_pre_callback(callback_clearml_load_save)
@torch.no_grad()
def inference(
self,
x,
aux_input=None,
): # pylint: disable=dangerous-default-value
return None
@staticmethod
def get_criterion():
return None
def get_sampler(self, dataset: TTSDataset, num_gpus=1):
# sampler for DDP
batch_sampler = DistributedSampler(dataset) if num_gpus > 1 else None
return batch_sampler
def get_data_loader(
self,
config: Coqpit,
assets: Dict,
is_eval: bool,
samples: Union[List[Dict], List[List]],
verbose: bool,
num_gpus: int,
rank: int = None,
) -> "DataLoader": # pylint: disable=W0613
if is_eval and not config.run_eval:
loader = None
else:
# init dataloader
dataset = XTTSDataset(self.config, samples, self.xtts.tokenizer, config.audio.sample_rate, is_eval)
# wait all the DDP process to be ready
if num_gpus > 1:
torch.distributed.barrier()
# sort input sequences from short to long
# dataset.preprocess_samples()
# get samplers
sampler = self.get_sampler(dataset, num_gpus)
# ignore sampler when is eval because if we changed the sampler parameter we will not be able to compare previous runs
if sampler is None or is_eval:
loader = DataLoader(
dataset,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
shuffle=False,
drop_last=False,
collate_fn=dataset.collate_fn,
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
else:
loader = DataLoader(
dataset,
sampler=sampler,
batch_size=config.eval_batch_size if is_eval else config.batch_size,
collate_fn=dataset.collate_fn,
num_workers=config.num_eval_loader_workers if is_eval else config.num_loader_workers,
pin_memory=False,
)
return loader
def get_optimizer(self) -> List:
"""Initiate and return the optimizer based on the config parameters."""
# ToDo: deal with multi GPU training
if self.config.optimizer_wd_only_on_weights:
# parameters to only GPT model
net = self.xtts.gpt
# normalizations
norm_modules = (
nn.BatchNorm2d,
nn.InstanceNorm2d,
nn.BatchNorm1d,
nn.InstanceNorm1d,
nn.BatchNorm3d,
nn.InstanceNorm3d,
nn.GroupNorm,
nn.LayerNorm,
)
# nn.Embedding
emb_modules = (nn.Embedding, nn.EmbeddingBag)
param_names_notweights = set()
all_param_names = set()
param_map = {}
for mn, m in net.named_modules():
for k, v in m.named_parameters():
v.is_bias = k.endswith(".bias")
v.is_weight = k.endswith(".weight")
v.is_norm = isinstance(m, norm_modules)
v.is_emb = isinstance(m, emb_modules)
fpn = "%s.%s" % (mn, k) if mn else k # full param name
all_param_names.add(fpn)
param_map[fpn] = v
if v.is_bias or v.is_norm or v.is_emb:
param_names_notweights.add(fpn)
params_names_notweights = sorted(list(param_names_notweights))
params_notweights = [param_map[k] for k in params_names_notweights]
params_names_weights = sorted(list(all_param_names ^ param_names_notweights))
params_weights = [param_map[k] for k in params_names_weights]
groups = [
{"params": params_weights, "weight_decay": self.config.optimizer_params["weight_decay"]},
{"params": params_notweights, "weight_decay": 0},
]
# torch.optim.AdamW
opt = get_optimizer(
self.config.optimizer,
self.config.optimizer_params,
self.config.lr,
parameters=groups,
)
opt._group_names = [params_names_weights, params_names_notweights]
return opt
return get_optimizer(
self.config.optimizer,
self.config.optimizer_params,
self.config.lr,
# optimize only for the GPT model
parameters=self.xtts.gpt.parameters(),
)
def get_scheduler(self, optimizer) -> List:
"""Set the scheduler for the optimizer.
Args:
optimizer: `torch.optim.Optimizer`.
"""
return get_scheduler(self.config.lr_scheduler, self.config.lr_scheduler_params, optimizer)
def load_checkpoint(
self,
config,
checkpoint_path,
eval=False,
strict=True,
cache_storage="/tmp/tts_cache",
target_protocol="s3",
target_options={"anon": True},
): # pylint: disable=unused-argument, disable=W0201, disable=W0102, redefined-builtin
"""Load the model checkpoint and setup for training or inference"""
state = self.xtts.get_compatible_checkpoint_state_dict(checkpoint_path)
# load the model weights
self.xtts.load_state_dict(state, strict=strict)
if eval:
self.xtts.gpt.init_gpt_for_inference(kv_cache=self.args.kv_cache, use_deepspeed=False)
self.eval()
assert not self.training
@staticmethod
def init_from_config(config: "GPTTrainerConfig", samples: Union[List[List], List[Dict]] = None):
"""Initiate model from config
Args:
config (GPTTrainerConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
"""
return GPTTrainer(config)