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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 time
import numpy as np
from tqdm import tqdm
import torch
from models.svc.base import SVCInference
from models.svc.vits.vits import SynthesizerTrn
from models.svc.base.svc_dataset import SVCTestDataset, SVCTestCollator
from utils.io import save_audio
from utils.audio_slicer import is_silence
class VitsInference(SVCInference):
def __init__(self, args=None, cfg=None, infer_type="from_dataset"):
SVCInference.__init__(self, args, cfg)
def _build_model(self):
net_g = SynthesizerTrn(
self.cfg.preprocess.n_fft // 2 + 1,
self.cfg.preprocess.segment_size // self.cfg.preprocess.hop_size,
self.cfg,
)
self.model = net_g
return net_g
def build_save_dir(self, dataset, speaker):
save_dir = os.path.join(
self.args.output_dir,
"svc_am_step-{}_{}".format(self.am_restore_step, self.args.mode),
)
if dataset is not None:
save_dir = os.path.join(save_dir, "data_{}".format(dataset))
if speaker != -1:
save_dir = os.path.join(
save_dir,
"spk_{}".format(speaker),
)
os.makedirs(save_dir, exist_ok=True)
print("Saving to ", save_dir)
return save_dir
@torch.inference_mode()
def inference(self):
res = []
for i, batch in enumerate(self.test_dataloader):
pred_audio_list = self._inference_each_batch(batch)
for it, wav in zip(self.test_dataset.metadata, pred_audio_list):
uid = it["Uid"]
file = os.path.join(self.args.output_dir, f"{uid}.wav")
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),
)
res.append(file)
return res
def _inference_each_batch(self, batch_data, noise_scale=0.667):
device = self.accelerator.device
pred_res = []
self.model.eval()
with torch.no_grad():
# Put the data to device
# device = self.accelerator.device
for k, v in batch_data.items():
batch_data[k] = v.to(device)
audios, f0 = self.model.infer(batch_data, noise_scale=noise_scale)
pred_res.extend(audios)
return pred_res
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