File size: 2,770 Bytes
1d6aec7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import gradio as gr
import logging
numba_logger = logging.getLogger('numba')
numba_logger.setLevel(logging.WARNING)
import torch,pdb
import numpy as np
from models import SynthesizerTrnNoF0256
from fairseq import checkpoint_utils
import torch.nn.functional as F
import librosa
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "checkpoint_best_legacy_500.pt"#checkpoint_best_legacy_500.pt
print("load model(s) from {}".format(model_path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[model_path],
suffix="",
)
model = models[0]
model = model.to(device)
model.eval()
net_g = SynthesizerTrnNoF0256(513,40,192,192,768,2,6,3,0.1,"1", [3,7,11],[[1,3,5], [1,3,5], [1,3,5]],[10,4,2,2,2],512,[16,16,4,4,4],0)
weights=torch.load("trump.pt", map_location=torch.device('cpu'))
net_g.load_state_dict(weights,strict=True)
net_g.eval().to(device)
def vc_fn( input_audio):
if input_audio is None:
return "You need to upload an audio", None
sampling_rate, audio = input_audio
duration = audio.shape[0] / sampling_rate
if duration > 45:
return "请上传小于45s的音频，需要转换长音频请使用colab", None
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
if sampling_rate != 16000:
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
print(audio.shape)
feats = torch.from_numpy(audio).float()
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(device),
"padding_mask": padding_mask.to(device),
"output_layer": 9, # layer 9
}
with torch.no_grad():
logits = model.extract_features(**inputs)
feats = model.final_proj(logits[0])
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
p_len = min(feats.shape[1], 10000) # 太大了爆显存
feats = feats[:, :p_len, :]
p_len = torch.LongTensor([p_len]).to(device)
with torch.no_grad():
audio = net_g.infer(feats, p_len)[0][0, 0].data.cpu().float().numpy()
return "Success", (32000, audio)
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("Basic"):
gr.Markdown(value="""""")
vc_input3 = gr.Audio(label="上传音频（长度小于45秒）")
vc_submit = gr.Button("转换", variant="primary")
vc_output1 = gr.Textbox(label="Output Message")
vc_output2 = gr.Audio(label="Output Audio")
vc_submit.click(vc_fn, [ vc_input3], [vc_output1, vc_output2])
app.launch() |