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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/G_98000.pth" | |
config_name = "configs/config.json" | |
svc_model = Svc(model_name, config_name) | |
sid_map = { | |
"Ztech": "Ztech" | |
} | |
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=""" | |
这是sovits 3.0 32khz版本ai草莓猫taffy的在线demo | |
在使用此模型前请阅读[AI粘连科技模型使用协议](https://huggingface.co/spaces/reha/Stick_Tech/blob/main/terms.md) | |
粘连科技Official@bilibili:[点击关注](https://space.bilibili.com/248582596) | |
如果要在本地使用该demo,请使用git lfs clone 该仓库,安装requirements.txt后运行app.py即可 | |
项目改写基于 https://huggingface.co/spaces/innnky/nyaru-svc-3.0 | |
本地合成可以删除26、27两行代码以解除合成45s长度限制""") | |
sid = gr.Dropdown(label="音色", choices=["taffy"], value="taffy") | |
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() | |