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import io
import json
import gradio as gr
import requests
import soundfile as sf
import torch.multiprocessing as multiprocessing
from scipy.io.wavfile import write
from modules.ui import Tab
from server import app
proc = None
def server_options_ui(show_out_dir=True):
with gr.Row().style(equal_height=False):
with gr.Row():
host = gr.Textbox(value="127.0.0.1", label="host")
port = gr.Textbox(value="5001", label="port")
with gr.Row().style(equal_height=False):
with gr.Row():
rvc_model_file = gr.Textbox(value="", label="RVC model file path")
faiss_index_file = gr.Textbox(value="", label="Faiss index file path")
with gr.Row().style(equal_height=False):
with gr.Row():
input_voice_file = gr.Textbox(value="", label="input voice file path")
speaker_id = gr.Number(
value=0,
label="speaker_id",
)
transpose = gr.Slider(
minimum=-20, maximum=20, value=0, step=1, label="transpose"
)
pitch_extraction_algo = gr.Radio(
choices=["dio", "harvest", "mangio-crepe", "crepe"],
value="crepe",
label="pitch_extraction_algo",
)
retrieval_feature_ratio = gr.Slider(
minimum=0,
maximum=1,
value=1,
step=0.01,
label="retrieval_feature_ratio",
)
return (
host,
port,
rvc_model_file,
faiss_index_file,
input_voice_file,
speaker_id,
transpose,
pitch_extraction_algo,
retrieval_feature_ratio,
)
def run(**kwargs):
app.run(**kwargs)
class Server(Tab):
def title(self):
return "Server(experimental)"
def sort(self):
return 6
def ui(self, outlet):
def start(host, port):
if multiprocessing.get_start_method() == 'fork':
multiprocessing.set_start_method('spawn', force=True)
proc = multiprocessing.Process(target = run, kwargs = {'host': host, 'port': port})
proc.start()
yield "start server"
def upload(host, port, rvc_model_file, faiss_index_file):
file_names = {"rvc_model_file": rvc_model_file, "faiss_index_file": faiss_index_file}
res = requests.post(f"http://{host}:{port}/upload_model", json=file_names)
yield res.text
def convert(host, port, input_voice_file, speaker_id, transpose, pitch_extraction_algo, retrieval_feature_ratio):
params = {
"speaker_id": speaker_id,
"transpose": transpose,
"pitch_extraction_algo": pitch_extraction_algo,
"retrieval_feature_ratio": retrieval_feature_ratio
}
audio, sr = sf.read(input_voice_file)
audio_buffer = io.BytesIO()
write(audio_buffer, rate=sr, data=audio)
json_buffer = io.BytesIO(json.dumps(params).encode('utf-8'))
files = {
"input_wav": audio_buffer,
"params": json_buffer
}
res = requests.post(f"http://{host}:{port}/convert_sound", files=files)
audio, sr = sf.read(io.BytesIO(res.content))
yield "convert succeed", (sr, audio)
with gr.Group():
with gr.Box():
with gr.Column():
(
host,
port,
rvc_model_file,
faiss_index_file,
input_voice_file,
speaker_id,
transpose,
pitch_extraction_algo,
retrieval_feature_ratio,
) = server_options_ui()
with gr.Row().style(equal_height=False):
with gr.Column():
status = gr.Textbox(value="", label="Status")
output = gr.Audio(label="Output", interactive=False)
with gr.Row():
start_button = gr.Button("Start server", variant="primary")
upload_button = gr.Button("Upload Model")
convert_button = gr.Button("Convert Voice")
start_button.click(
start,
inputs=[
host,
port
],
outputs=[status],
queue=True,
)
upload_button.click(
upload,
inputs=[
host,
port,
rvc_model_file,
faiss_index_file
],
outputs=[status],
queue=True,
)
convert_button.click(
convert,
inputs=[
host,
port,
input_voice_file,
speaker_id,
transpose,
pitch_extraction_algo,
retrieval_feature_ratio
],
outputs=[status, output],
queue=True,
)
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