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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 os
import torch
import time
import accelerate
import random
import numpy as np
from tqdm import tqdm
from accelerate.logging import get_logger
from torch.utils.data import DataLoader
from abc import abstractmethod
from pathlib import Path
from utils.io import save_audio
from utils.util import load_config
from models.vocoders.vocoder_inference import synthesis
class TTSInference(object):
def __init__(self, args=None, cfg=None):
super().__init__()
start = time.monotonic_ns()
self.args = args
self.cfg = cfg
self.infer_type = args.mode
# get exp_dir
if self.args.acoustics_dir is not None:
self.exp_dir = self.args.acoustics_dir
elif self.args.checkpoint_path is not None:
self.exp_dir = os.path.dirname(os.path.dirname(self.args.checkpoint_path))
# Init accelerator
self.accelerator = accelerate.Accelerator()
self.accelerator.wait_for_everyone()
self.device = self.accelerator.device
# Get logger
with self.accelerator.main_process_first():
self.logger = get_logger("inference", log_level=args.log_level)
# Log some info
self.logger.info("=" * 56)
self.logger.info("||\t\t" + "New inference process started." + "\t\t||")
self.logger.info("=" * 56)
self.logger.info("\n")
self.acoustic_model_dir = args.acoustics_dir
self.logger.debug(f"Acoustic model dir: {args.acoustics_dir}")
if args.vocoder_dir is not None:
self.vocoder_dir = args.vocoder_dir
self.logger.debug(f"Vocoder dir: {args.vocoder_dir}")
os.makedirs(args.output_dir, exist_ok=True)
# Set random seed
with self.accelerator.main_process_first():
start = time.monotonic_ns()
self._set_random_seed(self.cfg.train.random_seed)
end = time.monotonic_ns()
self.logger.debug(
f"Setting random seed done in {(end - start) / 1e6:.2f}ms"
)
self.logger.debug(f"Random seed: {self.cfg.train.random_seed}")
# Setup data loader
if self.infer_type == "batch":
with self.accelerator.main_process_first():
self.logger.info("Building dataset...")
start = time.monotonic_ns()
self.test_dataloader = self._build_test_dataloader()
end = time.monotonic_ns()
self.logger.info(
f"Building dataset done in {(end - start) / 1e6:.2f}ms"
)
# Build model
with self.accelerator.main_process_first():
self.logger.info("Building model...")
start = time.monotonic_ns()
self.model = self._build_model()
end = time.monotonic_ns()
self.logger.info(f"Building model done in {(end - start) / 1e6:.3f}ms")
# Init with accelerate
self.logger.info("Initializing accelerate...")
start = time.monotonic_ns()
self.accelerator = accelerate.Accelerator()
self.model = self.accelerator.prepare(self.model)
if self.infer_type == "batch":
self.test_dataloader = self.accelerator.prepare(self.test_dataloader)
end = time.monotonic_ns()
self.accelerator.wait_for_everyone()
self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.3f}ms")
with self.accelerator.main_process_first():
self.logger.info("Loading checkpoint...")
start = time.monotonic_ns()
if args.acoustics_dir is not None:
self._load_model(
checkpoint_dir=os.path.join(args.acoustics_dir, "checkpoint")
)
elif args.checkpoint_path is not None:
self._load_model(checkpoint_path=args.checkpoint_path)
else:
print("Either checkpoint dir or checkpoint path should be provided.")
end = time.monotonic_ns()
self.logger.info(f"Loading checkpoint done in {(end - start) / 1e6:.3f}ms")
self.model.eval()
self.accelerator.wait_for_everyone()
def _build_test_dataset(self):
pass
def _build_model(self):
pass
# TODO: LEGACY CODE
def _build_test_dataloader(self):
datasets, collate = self._build_test_dataset()
self.test_dataset = datasets(self.args, self.cfg)
self.test_collate = collate(self.cfg)
self.test_batch_size = min(
self.cfg.train.batch_size, len(self.test_dataset.metadata)
)
test_dataloader = DataLoader(
self.test_dataset,
collate_fn=self.test_collate,
num_workers=1,
batch_size=self.test_batch_size,
shuffle=False,
)
return test_dataloader
def _load_model(
self,
checkpoint_dir: str = None,
checkpoint_path: str = None,
old_mode: bool = False,
):
r"""Load model from checkpoint. If checkpoint_path is None, it will
load the latest checkpoint in checkpoint_dir. If checkpoint_path is not
None, it will load the checkpoint specified by checkpoint_path. **Only use this
method after** ``accelerator.prepare()``.
"""
if checkpoint_path is None:
assert checkpoint_dir is not None
# Load the latest accelerator state dicts
ls = [
str(i) for i in Path(checkpoint_dir).glob("*") if not "audio" in str(i)
]
ls.sort(key=lambda x: int(x.split("_")[-3].split("-")[-1]), reverse=True)
checkpoint_path = ls[0]
self.accelerator.load_state(str(checkpoint_path))
return str(checkpoint_path)
def inference(self):
if self.infer_type == "single":
out_dir = os.path.join(self.args.output_dir, "single")
os.makedirs(out_dir, exist_ok=True)
pred_audio = self.inference_for_single_utterance()
save_path = os.path.join(out_dir, "test_pred.wav")
save_audio(save_path, pred_audio, self.cfg.preprocess.sample_rate)
elif self.infer_type == "batch":
out_dir = os.path.join(self.args.output_dir, "batch")
os.makedirs(out_dir, exist_ok=True)
pred_audio_list = self.inference_for_batches()
for it, wav in zip(self.test_dataset.metadata, pred_audio_list):
uid = it["Uid"]
save_audio(
os.path.join(out_dir, f"{uid}.wav"),
wav.numpy(),
self.cfg.preprocess.sample_rate,
add_silence=True,
turn_up=True,
)
tmp_file = os.path.join(out_dir, f"{uid}.pt")
if os.path.exists(tmp_file):
os.remove(tmp_file)
print("Saved to: ", out_dir)
@torch.inference_mode()
def inference_for_batches(self):
y_pred = []
for i, batch in tqdm(enumerate(self.test_dataloader)):
y_pred, mel_lens, _ = self._inference_each_batch(batch)
y_ls = y_pred.chunk(self.test_batch_size)
tgt_ls = mel_lens.chunk(self.test_batch_size)
j = 0
for it, l in zip(y_ls, tgt_ls):
l = l.item()
it = it.squeeze(0)[:l].detach().cpu()
uid = self.test_dataset.metadata[i * self.test_batch_size + j]["Uid"]
torch.save(it, os.path.join(self.args.output_dir, f"{uid}.pt"))
j += 1
vocoder_cfg, vocoder_ckpt = self._parse_vocoder(self.args.vocoder_dir)
res = synthesis(
cfg=vocoder_cfg,
vocoder_weight_file=vocoder_ckpt,
n_samples=None,
pred=[
torch.load(
os.path.join(self.args.output_dir, "{}.pt".format(item["Uid"]))
).numpy()
for item in self.test_dataset.metadata
],
)
for it, wav in zip(self.test_dataset.metadata, res):
uid = it["Uid"]
save_audio(
os.path.join(self.args.output_dir, f"{uid}.wav"),
wav.numpy(),
22050,
add_silence=True,
turn_up=True,
)
@abstractmethod
@torch.inference_mode()
def _inference_each_batch(self, batch_data):
pass
def inference_for_single_utterance(self, text):
pass
def synthesis_by_vocoder(self, pred):
audios_pred = synthesis(
self.vocoder_cfg,
self.checkpoint_dir_vocoder,
len(pred),
pred,
)
return audios_pred
@staticmethod
def _parse_vocoder(vocoder_dir):
r"""Parse vocoder config"""
vocoder_dir = os.path.abspath(vocoder_dir)
ckpt_list = [ckpt for ckpt in Path(vocoder_dir).glob("*.pt")]
ckpt_list.sort(key=lambda x: int(x.stem), reverse=True)
ckpt_path = str(ckpt_list[0])
vocoder_cfg = load_config(
os.path.join(vocoder_dir, "args.json"), lowercase=True
)
return vocoder_cfg, ckpt_path
def _set_random_seed(self, seed):
"""Set random seed for all possible random modules."""
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed)
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