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import os
import time
import torch
import torch.multiprocessing
from torch.nn.utils.rnn import pad_sequence
from torch.optim import RAdam
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from Architectures.Aligner.Aligner import Aligner
from Architectures.Aligner.Reconstructor import Reconstructor
from Preprocessing.AudioPreprocessor import AudioPreprocessor
from Preprocessing.EnCodecAudioPreprocessor import CodecAudioPreprocessor
def collate_and_pad(batch):
# text, text_len, speech, speech_len, embed
return (pad_sequence([datapoint[0] for datapoint in batch], batch_first=True),
torch.stack([datapoint[1] for datapoint in batch]).squeeze(1),
[datapoint[2] for datapoint in batch],
None,
torch.stack([datapoint[4] for datapoint in batch]).squeeze())
def train_loop(train_dataset,
device,
save_directory,
batch_size,
steps,
path_to_checkpoint=None,
fine_tune=False,
resume=False,
debug_img_path=None,
use_reconstruction=True,
gpu_count=1,
rank=0,
steps_per_checkpoint=None):
"""
Args:
resume: whether to resume from the most recent checkpoint
steps: How many steps to train
path_to_checkpoint: reloads a checkpoint to continue training from there
fine_tune: whether to load everything from a checkpoint, or only the model parameters
train_dataset: Pytorch Dataset Object for train data
device: Device to put the loaded tensors on
save_directory: Where to save the checkpoints
batch_size: How many elements should be loaded at once
debug_img_path: where to put images of the training progress if desired
use_reconstruction: whether to use the auxiliary reconstruction procedure/loss, which can make the alignment sharper
"""
os.makedirs(save_directory, exist_ok=True)
torch.multiprocessing.set_sharing_strategy('file_system')
torch.multiprocessing.set_start_method('spawn', force=True)
if steps_per_checkpoint is None:
steps_per_checkpoint = len(train_dataset) // batch_size
ap = CodecAudioPreprocessor(input_sr=-1, device=device) # only used to transform features into continuous matrices
spectrogram_extractor = AudioPreprocessor(input_sr=16000, output_sr=16000, device=device)
asr_model = Aligner().to(device)
optim_asr = RAdam(asr_model.parameters(), lr=0.0001)
tiny_tts = Reconstructor().to(device)
optim_tts = RAdam(tiny_tts.parameters(), lr=0.0001)
if gpu_count > 1:
asr_model.to(rank)
tiny_tts.to(rank)
asr_model = torch.nn.parallel.DistributedDataParallel(
asr_model,
device_ids=[rank],
output_device=rank,
find_unused_parameters=True,
).module
tiny_tts = torch.nn.parallel.DistributedDataParallel(
tiny_tts,
device_ids=[rank],
output_device=rank,
find_unused_parameters=True,
).module
torch.distributed.barrier()
train_sampler = torch.utils.data.RandomSampler(train_dataset)
batch_sampler_train = torch.utils.data.BatchSampler(train_sampler, batch_size, drop_last=True)
train_loader = DataLoader(dataset=train_dataset,
num_workers=0, # unfortunately necessary for big data due to mmap errors
batch_sampler=batch_sampler_train,
prefetch_factor=None,
collate_fn=collate_and_pad)
step_counter = 0
loss_sum = list()
if resume:
previous_checkpoint = os.path.join(save_directory, "aligner.pt")
path_to_checkpoint = previous_checkpoint
fine_tune = False
if path_to_checkpoint is not None:
check_dict = torch.load(os.path.join(path_to_checkpoint), map_location=device)
asr_model.load_state_dict(check_dict["asr_model"])
tiny_tts.load_state_dict(check_dict["tts_model"])
if not fine_tune:
optim_asr.load_state_dict(check_dict["optimizer"])
optim_tts.load_state_dict(check_dict["tts_optimizer"])
step_counter = check_dict["step_counter"]
if step_counter > steps:
print("Desired steps already reached in loaded checkpoint.")
return
start_time = time.time()
while True:
asr_model.train()
tiny_tts.train()
for batch in tqdm(train_loader):
tokens = batch[0].to(device)
tokens_len = batch[1].to(device)
speaker_embeddings = batch[4].to(device)
mels = list()
mel_lengths = list()
for datapoint in batch[2]:
with torch.inference_mode():
# extremely unfortunate that we have to do this over here, but multiprocessing and this don't go together well
speech = ap.indexes_to_audio(datapoint.int().to(device))
mel = spectrogram_extractor.audio_to_mel_spec_tensor(speech, explicit_sampling_rate=16000).transpose(0, 1).cpu()
speech_len = torch.LongTensor([len(mel)])
mels.append(mel.clone())
mel_lengths.append(speech_len)
mel = pad_sequence(mels, batch_first=True).to(device)
mel_len = torch.stack(mel_lengths).squeeze(1).to(device)
pred = asr_model(mel, mel_len)
ctc_loss = asr_model.ctc_loss(pred.transpose(0, 1).log_softmax(2),
tokens,
mel_len,
tokens_len)
if use_reconstruction:
speaker_embeddings_expanded = torch.nn.functional.normalize(speaker_embeddings).unsqueeze(1).expand(-1, pred.size(1), -1)
tts_lambda = min([0.1, step_counter / 10000]) # super simple schedule
reconstruction_loss = tiny_tts(x=torch.cat([pred, speaker_embeddings_expanded], dim=-1),
# combine ASR prediction with speaker embeddings to allow for reconstruction loss on multiple speakers
lens=mel_len,
ys=mel) * tts_lambda # reconstruction loss to make the states more distinct
loss = ctc_loss + reconstruction_loss
else:
loss = ctc_loss
optim_asr.zero_grad()
if use_reconstruction:
optim_tts.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(asr_model.parameters(), 1.0)
if use_reconstruction:
torch.nn.utils.clip_grad_norm_(tiny_tts.parameters(), 1.0)
optim_asr.step()
if use_reconstruction:
optim_tts.step()
loss_sum.append(loss.item())
step_counter += 1
if step_counter % steps_per_checkpoint == 0 and rank == 0:
asr_model.eval()
torch.save({
"asr_model" : asr_model.state_dict(),
"optimizer" : optim_asr.state_dict(),
"tts_model" : tiny_tts.state_dict(),
"tts_optimizer": optim_tts.state_dict(),
"step_counter" : step_counter,
},
os.path.join(save_directory, "aligner.pt"))
print("Total Loss: {}".format(round(sum(loss_sum) / len(loss_sum), 3)))
print("Time elapsed: {} Minutes".format(round((time.time() - start_time) / 60)))
print("Steps: {}".format(step_counter))
if debug_img_path is not None:
asr_model.inference(features=mel[0][:mel_len[0]],
tokens=tokens[0][:tokens_len[0]],
save_img_for_debug=debug_img_path + f"/{step_counter}.png",
train=True) # for testing
asr_model.train()
loss_sum = list()
if step_counter > steps and step_counter % steps_per_checkpoint == 0:
return
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