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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import os
import shutil
import torch
import time
from pathlib import Path
import torch
from tqdm import tqdm
import torch.nn as nn
from .base_trainer import BaseTrainer
def make_pad_mask(
lengths: torch.Tensor, max_len: int = 0, left_pad=False
) -> torch.Tensor:
"""
Args:
lengths:
A 1-D tensor containing sentence lengths.
max_len:
The length of masks.
left_pad:
A boolean indicating whether to left pad the mask.
Returns:
Return a 2-D bool tensor, where masked positions
are filled with `True` and non-masked positions are
filled with `False`.
>>> lengths = torch.tensor([1, 3, 2, 5])
>>> make_pad_mask(lengths)
tensor([[False, True, True, True, True],
[False, False, False, True, True],
[False, False, True, True, True],
[False, False, False, False, False]])
"""
assert lengths.ndim == 1, lengths.ndim
max_len = max(max_len, lengths.max())
n = lengths.size(0)
seq_range = torch.arange(0, max_len, device=lengths.device)
expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
mask = expaned_lengths >= lengths.unsqueeze(-1)
if left_pad:
mask = mask.flip(dims=[1])
return mask
class ValleARTrainer(BaseTrainer):
def __init__(self, args=None, cfg=None):
super().__init__(args, cfg)
if self.cfg.use_speechtokenizer:
from models.codec.speechtokenizer.model import SpeechTokenizer
config_path = "./ckpts/speechtokenizer_hubert_avg/config.json"
ckpt_path = "./ckpts/speechtokenizer_hubert_avg/SpeechTokenizer.pt"
assert os.path.isfile(
config_path
), f"codec model {config_path} not found! Download with huggingface-cli download fnlp/SpeechTokenizer speechtokenizer_hubert_avg/SpeechTokenizer.pt speechtokenizer_hubert_avg/config.json --local-dir ckpts"
assert os.path.isfile(
ckpt_path
), f"codec model {ckpt_path} not found! Download with huggingface-cli download fnlp/SpeechTokenizer speechtokenizer_hubert_avg/SpeechTokenizer.pt speechtokenizer_hubert_avg/config.json --local-dir ckpts"
self.codec_encoder = SpeechTokenizer.load_from_checkpoint(
config_path, ckpt_path
)
self.codec_encoder.eval()
self.codec_encoder.to(self.accelerator.device)
print(f"Loaded SpeechTokenizer from {config_path} and {ckpt_path}")
else:
from encodec import EncodecModel
with self.accelerator.main_process_first():
self.codec_encoder = EncodecModel.encodec_model_24khz()
self.codec_encoder.set_target_bandwidth(6.0)
self.codec_encoder.to(self.accelerator.device)
self.codec_decoder = None
print("Loaded EncodecModel")
self.top1_accuracies = []
self.top5_accuracies = []
self.top10_accuracies = []
if hasattr(self.cfg, "flatten_first_2_layers"):
self.flatten_first_2_layers = self.cfg.flatten_first_2_layers
print("flattened:", self.flatten_first_2_layers)
else:
self.flatten_first_2_layers = False
if hasattr(self.cfg, "num_prediction_heads"):
self.num_prediction_heads = self.cfg.num_prediction_heads
print("num_prediction_heads:", self.num_prediction_heads)
def _accelerator_prepare(self):
# if self.accelerator.is_main_process:
# breakpoint()
# self.accelerator.wait_for_everyone()
(
self.model,
self.optimizer,
) = self.accelerator.prepare(
self.model,
self.optimizer,
)
def _build_criterion(self):
pass # loss is directly returned from model
def _build_scheduler(self):
from transformers import (
get_cosine_schedule_with_warmup,
get_constant_schedule_with_warmup,
)
return get_cosine_schedule_with_warmup(
self.optimizer,
num_warmup_steps=self.cfg.train.scheduler.warmup_steps,
num_training_steps=self.cfg.train.scheduler.total_steps,
)
def _build_model(self):
if hasattr(self.cfg.model, "num_prediction_heads"):
from .valle_ar_multihead import ValleAR
else:
from .valle_ar import ValleAR
return ValleAR(**self.cfg.model)
def _train_step(self, batch):
# inference codec
"""Returns: dict('speech', 'speech_len', 'phone_ids', 'phone_lens')
speech: [B, T]
speech_len: [B]
phone_ids: [B, T]
phone_lens: [B]
"""
device = self.accelerator.device
for k, v in batch.items():
if isinstance(v, torch.Tensor):
batch[k] = v.to(device)
with torch.no_grad():
if self.cfg.use_speechtokenizer:
# Extract discrete codes from SpeechTokenizer
vq_id = self.codec_encoder.encode(
batch["speech"].unsqueeze(1)
) # [B,1,T] -> (n_q, B, T)
else:
vq_id = self.codec_encoder.encode(batch["speech"].unsqueeze(1))
vq_id = torch.cat([encoded[0] for encoded in vq_id], dim=-1).transpose(
0, 1
)
# recovered_audio = self.codec_decoder(vq_emb, vq=False)
# torchaudio.save('a.wav', recovered_audio[0], 16000)
# vq_id: [8, B, T//320]
if self.flatten_first_2_layers:
first_layer = vq_id[0]
second_layer = vq_id[1]
# flatten the first two layers
batch["speech"] = torch.stack(
[first_layer, second_layer], dim=-1
).flatten(-2, -1)
batch["speech_len"] = batch["speech_len"] // 160
elif hasattr(self.cfg.model, "num_prediction_heads"):
batch["speech"] = vq_id[:2] # first two layers
batch["speech_len"] = (
batch["speech_len"] // 320
) # our codec downsamples 320x
else:
batch["speech"] = vq_id[0] # use first layer
batch["speech_len"] = (
batch["speech_len"] // 320
) # our codec downsamples 320x
assert batch["speech_len"].max() <= batch["speech"].shape[-1]
phone_mask = 1 - make_pad_mask(
batch["phone_lens"], max_len=batch["phone_ids"].size(1), left_pad=False
).to(torch.long)
speech_mask = 1 - make_pad_mask(
batch["speech_len"], max_len=batch["speech"].size(1)
).to(torch.long)
out = self.model(
phone_ids=batch["phone_ids"],
phone_mask=phone_mask,
target_ids=batch["speech"],
target_mask=speech_mask,
)
loss = out.loss
# if self.accelerator.is_main_process:
# print(loss)
# if hasattr(out, 'top1_acc'):
# self.top1_accuracies.append(out.top1_acc)
# self.top5_accuracies.append(out.top5_acc)
# self.top10_accuracies.append(out.top10_acc)
# print(f'avgs: top1: {sum(self.top1_accuracies)/len(self.top1_accuracies)}, top5: {sum(self.top5_accuracies)/len(self.top5_accuracies)}, top10: {sum(self.top10_accuracies)/len(self.top10_accuracies)}')
# breakpoint()
return loss
##########add your own dataloader to the trainer#############
def _build_dataloader(self):
from torch.utils.data import ConcatDataset, DataLoader
if self.cfg.train.dataset.name == "emilia":
from .emilia_dataset import EmiliaDataset as VALLEDataset
train_dataset = VALLEDataset()
elif self.cfg.train.dataset.name == "mls":
from .mls_dataset import VALLEDataset as VALLEDataset
train_dataset = VALLEDataset(self.cfg.dataset, resample_to_24k=False)
elif self.cfg.train.dataset.name == "libritts":
from .libritts_dataset import VALLEDataset as VALLEDataset
train_dataset = VALLEDataset(self.cfg.dataset)
from .valle_collator import VALLECollator
import numpy as np
print("length of train_dataset:", len(train_dataset))
collator = VALLECollator()
if self.cfg.train.dataset.use_dynamic_batchsize:
if self.accelerator.is_main_process:
self.logger.info("Use Dynamic Batchsize......")
from .mls_dataset import batch_by_size
batch_sampler = batch_by_size(
train_dataset.num_frame_indices,
train_dataset.get_num_frames,
max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes,
max_sentences=self.cfg.train.max_sentences
* self.accelerator.num_processes,
required_batch_size_multiple=self.accelerator.num_processes,
)
np.random.shuffle(batch_sampler)
print(batch_sampler[0])
batches = [
x[
self.accelerator.local_process_index :: self.accelerator.num_processes
]
for x in batch_sampler
if len(x) % self.accelerator.num_processes == 0
]
from models.base.base_sampler import VariableSampler
train_loader = DataLoader(
train_dataset,
collate_fn=collator,
num_workers=self.cfg.train.dataloader.num_worker,
batch_sampler=VariableSampler(
batches, drop_last=True, use_random_sampler=True
),
pin_memory=self.cfg.train.dataloader.pin_memory,
persistent_workers=self.cfg.train.dataloader.persistent_workers,
prefetch_factor=4,
)
print(
f"process {self.accelerator.local_process_index} has {len(batches)} batches"
)
self.accelerator.wait_for_everyone()
else:
sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=self.accelerator.num_processes,
rank=self.accelerator.local_process_index,
shuffle=True,
)
train_loader = DataLoader(
train_dataset,
batch_size=self.cfg.train.batch_size,
num_workers=self.cfg.train.dataloader.num_worker,
pin_memory=self.cfg.train.dataloader.pin_memory,
collate_fn=collator,
sampler=sampler,
)
print(
f"process {self.accelerator.local_process_index} has {len(train_loader)} batches"
)
return train_loader, None
def _test_step(self, batch):
# inference codec
"""Returns: dict('speech', 'speech_len', 'phone_ids', 'phone_lens')
speech: [B, T]
speech_len: [B]
phone_ids: [B, T]
phone_lens: [B]
"""
import torchaudio
device = self.accelerator.device
for k, v in batch.items():
if isinstance(v, torch.Tensor):
batch[k] = v.to(device)
with torch.no_grad():
if self.cfg.use_speechtokenizer:
# Extract discrete codes from SpeechTokenizer
vq_id = self.codec_encoder.encode(
batch["speech"].unsqueeze(1)
) # [B,1,T] -> (n_q, B, T)
else:
vq_id = self.codec_encoder.encode(batch["speech"].unsqueeze(1))
vq_id = torch.cat([encoded[0] for encoded in vq_id], dim=-1).transpose(
0, 1
)
# recovered_audio = self.codec_decoder(vq_emb, vq=False)
# torchaudio.save('a.wav', recovered_audio[0], 16000)
# vq_id: [8, B, T//200]
# vq_emb = self.codec_decoder.quantizer.vq2emb(vq=vq_id[:1], n_quantizers=1)
# recovered_audio = self.codec_decoder(vq_emb, vq=False)
# recovered_audio.shape: torch.Size([1, 1, 50200])
if self.flatten_first_2_layers:
first_layer = vq_id[0]
second_layer = vq_id[1]
# flatten the first two layers
batch["speech"] = torch.stack(
[first_layer, second_layer], dim=-1
).flatten(-2, -1)
batch["speech_len"] = batch["speech_len"] // 160
elif hasattr(self.cfg.model, "num_prediction_heads"):
batch["speech"] = vq_id[:2] # first two layers
batch["speech_len"] = (
batch["speech_len"] // 320
) # our codec downsamples 320x
else:
batch["speech"] = vq_id[0] # use first layer
batch["speech_len"] = (
batch["speech_len"] // 320
) # our codec downsamples 320x
# save gt
breakpoint()
recovered_audio = self.codec_encoder.decode(vq_id[:1, :1])
# recovered_audio = self.codec_encoder.decode([(vq_id[:1].transpose(0,1), None)])
torchaudio.save("gt.wav", recovered_audio[0].cpu(), 16000)
out_vq_ids = self.model.sample_hf(
batch["phone_ids"][:1, ...], batch["speech"][:1, :225], temperature=0.9
)
# out_vq_ids = torch.cat([batch['speech'][:1, :225], out_vq_ids[:1, ...]], dim=1)
# reconstruct form tokens
recovered_audio = self.codec_encoder.decode(out_vq_ids.unsqueeze(0))
# recovered_audio = self.codec_encoder.decode([(out_vq_ids, None)])
torchaudio.save("a.wav", recovered_audio[0].cpu(), 16000)
breakpoint()
print()
@torch.inference_mode()
def _valid_epoch(self):
r"""Testing epoch. Should return average loss of a batch (sample) over
one epoch. See ``train_loop`` for usage.
"""
epoch_sum_loss = 0.0
return epoch_sum_loss
def _inference(self):
pass
def test_loop(self):
self.model.eval()
for batch in self.train_dataloader:
self._test_step(batch)
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