# 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 random
import re
import time
from abc import abstractmethod
from pathlib import Path
import accelerate
import json5
import numpy as np
import torch
from accelerate.logging import get_logger
from torch.utils.data import DataLoader
from models.vocoders.vocoder_inference import synthesis
from utils.io import save_audio
from utils.util import load_config
from utils.audio_slicer import is_silence
EPS = 1.0e-12
class BaseInference(object):
def __init__(self, args=None, cfg=None, infer_type="from_dataset"):
super().__init__()
start = time.monotonic_ns()
self.args = args
self.cfg = cfg
assert infer_type in ["from_dataset", "from_file"]
self.infer_type = infer_type
# init with accelerate
self.accelerator = accelerate.Accelerator()
self.accelerator.wait_for_everyone()
# Use accelerate logger for distributed inference
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.logger.debug(f"Using {args.log_level.upper()} logging level.")
self.acoustics_dir = args.acoustics_dir
self.logger.debug(f"Acoustic dir: {args.acoustics_dir}")
self.vocoder_dir = args.vocoder_dir
self.logger.debug(f"Vocoder dir: {args.vocoder_dir}")
# should be in svc inferencer
# self.target_singer = args.target_singer
# self.logger.info(f"Target singers: {args.target_singer}")
# self.trans_key = args.trans_key
# self.logger.info(f"Trans key: {args.trans_key}")
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
with self.accelerator.main_process_first():
self.logger.info("Building dataset...")
start = time.monotonic_ns()
self.test_dataloader = self._build_dataloader()
end = time.monotonic_ns()
self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms")
# setup 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.debug(self.model)
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)
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()
# TODO: Also, suppose only use latest one yet
self.__load_model(os.path.join(args.acoustics_dir, "checkpoint"))
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()
### Abstract methods ###
@abstractmethod
def _build_test_dataset(self):
pass
@abstractmethod
def _build_model(self):
pass
@abstractmethod
@torch.inference_mode()
def _inference_each_batch(self, batch_data):
pass
### Abstract methods end ###
@torch.inference_mode()
def inference(self):
for i, batch in enumerate(self.test_dataloader):
y_pred = self._inference_each_batch(batch).cpu()
mel_min, mel_max = self.test_dataset.target_mel_extrema
y_pred = (y_pred + 1.0) / 2.0 * (mel_max - mel_min + EPS) + mel_min
y_ls = y_pred.chunk(self.test_batch_size)
tgt_ls = batch["target_len"].cpu().chunk(self.test_batch_size)
j = 0
for it, l in zip(y_ls, tgt_ls):
l = l.item()
it = it.squeeze(0)[:l]
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(i["Uid"]))
).numpy(force=True)
for i in self.test_dataset.metadata
],
)
output_audio_files = []
for it, wav in zip(self.test_dataset.metadata, res):
uid = it["Uid"]
file = os.path.join(self.args.output_dir, f"{uid}.wav")
output_audio_files.append(file)
wav = wav.numpy(force=True)
save_audio(
file,
wav,
self.cfg.preprocess.sample_rate,
add_silence=False,
turn_up=not is_silence(wav, self.cfg.preprocess.sample_rate),
)
os.remove(os.path.join(self.args.output_dir, f"{uid}.pt"))
return sorted(output_audio_files)
# TODO: LEGACY CODE
def _build_dataloader(self):
datasets, collate = self._build_test_dataset()
self.test_dataset = datasets(self.args, self.cfg, self.infer_type)
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):
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:
ls = []
for i in Path(checkpoint_dir).iterdir():
if re.match(r"epoch-\d+_step-\d+_loss-[\d.]+", str(i.stem)):
ls.append(i)
ls.sort(
key=lambda x: int(x.stem.split("_")[-3].split("-")[-1]), reverse=True
)
checkpoint_path = ls[0]
else:
checkpoint_path = Path(checkpoint_path)
self.accelerator.load_state(str(checkpoint_path))
# set epoch and step
self.epoch = int(checkpoint_path.stem.split("_")[-3].split("-")[-1])
self.step = int(checkpoint_path.stem.split("_")[-2].split("-")[-1])
return str(checkpoint_path)
@staticmethod
def _set_random_seed(seed):
r"""Set random seed for all possible random modules."""
random.seed(seed)
np.random.seed(seed)
torch.random.manual_seed(seed)
@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
@staticmethod
def __count_parameters(model):
return sum(p.numel() for p in model.parameters())
def __dump_cfg(self, path):
os.makedirs(os.path.dirname(path), exist_ok=True)
json5.dump(
self.cfg,
open(path, "w"),
indent=4,
sort_keys=True,
ensure_ascii=False,
quote_keys=True,
)