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from inference.infer_tool import Svc
from vextract.vocal_extract import VEX
import gradio as gr
import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '1,2'
class VitsGradio:
def __init__(self):
self.so = Svc()
self.v = VEX()
self.lspk = []
self.modelPaths = []
for root, dirs, files in os.walk("checkpoints"):
for dir in dirs:
self.modelPaths.append(dir)
with gr.Blocks(title="Sovits歌声合成工具") as self.Vits:
gr.Markdown(
"""
# 歌声合成工具
- 请依次选择语音模型、设备以及运行模式，然后点击"载入模型"
- 输入音频需要是干净的人声
"""
)
with gr.Tab("人声提取"):
with gr.Row():
with gr.Column():
sample_audio = gr.Audio(label="输入音频")
extractAudioBtn = gr.Button("提取人声")
with gr.Row():
with gr.Column():
self.sample_vocal_output = gr.Audio(label="输出音频")
self.sample_accompaniment_output = gr.Audio()
extractAudioBtn.click(self.v.separate, inputs=[sample_audio],
outputs=[self.sample_vocal_output, self.sample_accompaniment_output],
show_progress=True, api_name="extract")
with gr.Tab("歌声合成"):
with gr.Row(visible=False) as self.VoiceConversion:
with gr.Column():
with gr.Row():
with gr.Column():
self.srcaudio = gr.Audio(label="输入音频")
self.btnVC = gr.Button("说话人转换")
with gr.Column():
with gr.Row():
with gr.Column():
self.dsid0 = gr.Dropdown(label="目标角色", choices=self.lspk)
self.tran = gr.Slider(label="升降调", maximum=60, minimum=-60, step=1, value=0)
self.th = gr.Slider(label="切片阈值", maximum=32767, minimum=-32768, step=0.1,
value=-40)
self.ns = gr.Slider(label="噪音级别", maximum=1.0, minimum=0.0, step=0.1,
value=0.4)
with gr.Row():
self.VCOutputs = gr.Audio()
self.btnVC.click(self.so.inference, inputs=[self.srcaudio, self.dsid0, self.tran, self.th, self.ns],
outputs=[self.VCOutputs], show_progress=True, api_name="run")
with gr.Row(visible=False) as self.VoiceBatchConversion:
with gr.Column():
with gr.Row():
with gr.Column():
self.srcaudio = gr.Files(label="上传多个音频文件", file_types=['.wav'],
interactive=True)
self.btnVC = gr.Button("说话人转换")
with gr.Column():
with gr.Row():
with gr.Column():
self.dsid1 = gr.Dropdown(label="目标角色", choices=self.lspk)
self.tran = gr.Slider(label="升降调", maximum=60, minimum=-60, step=1, value=0)
self.th = gr.Slider(label="切片阈值", maximum=32767, minimum=-32768, step=0.1,
value=-40)
self.ns = gr.Slider(label="噪音级别", maximum=1.0, minimum=0.0, step=0.1,
value=0.4)
with gr.Row():
self.VCOutputs = gr.File(label="Output Zip File", interactive=False)
self.btnVC.click(self.batch_inference, inputs=[self.srcaudio, self.dsid1, self.tran, self.th, self.ns],
outputs=[self.VCOutputs], show_progress=True, api_name="batch")
with gr.Row():
with gr.Column():
modelstrs = gr.Dropdown(label="模型", choices=self.modelPaths, value=self.modelPaths[0],
type="value")
devicestrs = gr.Dropdown(label="设备", choices=["cpu", "cuda"], value="cuda", type="value")
isbatchmod = gr.Radio(label="运行模式", choices=["single", "batch"], value="single",
info="single: 单个文件处理. batch:批量处理支持上传多个文件")
btnMod = gr.Button("载入模型")
btnMod.click(self.loadModel, inputs=[modelstrs, devicestrs, isbatchmod],
outputs=[self.dsid0, self.dsid1, self.VoiceConversion, self.VoiceBatchConversion],
show_progress=True, api_name="switch")
def batch_inference(self, files, chara, tran, slice_db, ns, progress=gr.Progress()):
from zipfile import ZipFile
from scipy.io import wavfile
import uuid
temp_directory = "temp"
if not os.path.exists(temp_directory):
os.mkdir(temp_directory)
progress(0.00, desc="初始化文件夹")
tmp_workdir_name = f"{temp_directory}/batch_{uuid.uuid4()}"
if not os.path.exists(tmp_workdir_name):
os.mkdir(tmp_workdir_name)
progress(0.10, desc="初始化文件夹")
output_files = []
for idx, file in enumerate(files):
filename = os.path.basename(file.name)
progress(0.10 + (0.70 / float(len(files))) * (idx + 1.00), desc=f"处理音频{(idx + 1)}/{len(files)}：{filename}")
print(f"{idx}, {file}, {filename}")
sampling_rate, audio = wavfile.read(file.name)
output_sampling_rate, output_audio = self.so.inference((sampling_rate, audio), chara=chara, tran=tran,
slice_db=slice_db, ns=ns)
new_filepath = f"{tmp_workdir_name}/{filename}"
wavfile.write(filename=new_filepath, rate=output_sampling_rate, data=output_audio)
output_files.append(new_filepath)
progress(0.70, desc="音频处理完毕")
zipfilename = f"{tmp_workdir_name}/output.zip"
with ZipFile(zipfilename, "w") as zip_obj:
for idx, filepath in enumerate(output_files):
zip_obj.write(filepath, os.path.basename(filepath))
progress(0.80, desc="压缩完毕")
# todo: remove data
progress(1.00, desc="清理空间")
return zipfilename
def loadModel(self, path, device, process_mode):
self.lspk = []
print(f"path: {path}, device: {device}")
self.so.set_device(device)
print(f"device set.")
self.so.load_checkpoint(path)
print(f"checkpoint loaded")
for spk, sid in self.so.hps_ms.spk.items():
self.lspk.append(spk)
print(f"LSPK: {self.lspk}")
if process_mode == "single":
VChange = gr.update(visible=True)
VBChange = gr.update(visible=False)
else:
VChange = gr.update(visible=False)
VBChange = gr.update(visible=True)
SD0Change = gr.update(choices=self.lspk, value=self.lspk[0])
SD1Change = gr.update(choices=self.lspk, value=self.lspk[0])
print("allset update display")
return [SD0Change, SD1Change, VChange, VBChange]
if __name__ == "__main__":
grVits = VitsGradio()
grVits.Vits\
.queue(concurrency_count=20, status_update_rate=5.0)\
.launch(server_port=7870, share=True, show_api=True)
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