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import io
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from inference.infer_tool import Svc
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
model_name = "logs/32k/tiehu.pth"
config_name = "configs/tiehu.json"
svc_model = Svc(model_name, config_name)
sid_map = {
"南云铁虎": "tiehu"
}
def vc_fn(sid, input_audio, vc_transform):
if input_audio is None:
return "You need to upload an audio", None
sampling_rate, audio = input_audio
# print(audio.shape,sampling_rate)
duration = audio.shape[0] / sampling_rate
if duration > 45:
return "请上传小于45s的音频，需要转换长音频请本地进行转换", 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)
out_wav_path = io.BytesIO()
soundfile.write(out_wav_path, audio, 16000, format="wav")
out_wav_path.seek(0)
sid = sid_map[sid]
out_audio, out_sr = svc_model.infer(sid, vc_transform, out_wav_path)
_audio = out_audio.cpu().numpy()
return "Success", (32000, _audio)
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("Basic"):
gr.Markdown(value="""
南云铁虎Sovits3.0模型\n
使用前请仔细阅读terms.md了解相关条款，使用模型将默认您同意条款\n
如需本地使用，下载files里configs文件夹中.json格式文件,\n
logs/32k文件夹中.pth格式文件\n
https://huggingface.co/datasets/chilge/3.0tuili/tree/main下载3.0.zip文件并解压，查看说明.txt文件\n
项目改写基于 https://huggingface.co/spaces/innnky/nyaru-svc-3.0\n
""")
sid = gr.Dropdown(label="音色", choices=["南云铁虎"], value="tiehu")
vc_input3 = gr.Audio(label="上传音频（长度小于45秒）")
vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
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, [sid, vc_input3, vc_transform], [vc_output1, vc_output2])
app.launch()
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