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import gradio as gr
import os, sys
from colorama import Fore
from rvcsynch import download_from_url
now_dir = os.getcwd()
sys.path.append(now_dir)
def show_available(filepath,format=None):
if format:
print(f"Format: {format}")
files = []
for file in os.listdir(filepath):
if file.endswith(format):
print(f"Matches format: {file}")
files.append(file)
else:
print(f"Does not match format: {file}")
print(f"Matches: {files}")
if len(files) < 1:
return ['']
return files
if len(os.listdir(filepath)) < 1:
return ['']
return os.listdir(filepath)
# Function to detect the .pth and .index files
def detect_files(model_name):
model_dir = f"{now_dir}/assets/weights/{model_name}"
index_dir = f"{now_dir}/logs/{model_name}"
# Detect .pth file
model_pth_file = None
for file in os.listdir(model_dir):
if file.endswith(".pth"):
model_pth_file = os.path.join(model_dir, file)
break
# Detect .index file
index_file = None
for file in os.listdir(index_dir):
if file.endswith(".index"):
index_file = os.path.join(index_dir, file)
break
if model_pth_file and index_file:
return f"Model .pth file: {model_pth_file}\nIndex file: {index_file}"
else:
return "Model .pth or index file not found."
# Function to process the audio using the detected files
def process_audio(model_name, pitch, input_path, f0_method, save_as, index_rate, volume_normalization, consonant_protection):
model_dir = f"{now_dir}/assets/weights/{model_name}"
index_dir = f"{now_dir}/logs/{model_name}"
# Detect files
model_pth_file = None
index_file = None
for file in os.listdir(model_dir):
if file.endswith(".pth"):
model_pth_file = os.path.join(model_dir, file)
break
for file in os.listdir(index_dir):
if file.endswith(".index"):
index_file = os.path.join(index_dir, file)
break
if not model_pth_file or not index_file:
return "Model .pth or index file not found.", None
if not os.path.exists(input_path):
return f"{input_path} was not found in your RVC folder.", None
# Set environment variables for paths
os.environ['index_root'] = os.path.dirname(index_file)
index_path = os.path.basename(index_file)
os.environ['weight_root'] = os.path.dirname(model_pth_file)
# Remove any previous output
if os.path.exists(save_as):
os.remove(save_as)
# Execute the CLI command
os.system(f"python {now_dir}/tools/infer_cli.py --f0up_key {pitch} --input_path {input_path} --index_path {index_path} --f0method {f0_method} --opt_path {save_as} --model_name {model_name} --index_rate {index_rate} --device 'cuda:0' --is_half True --filter_radius 3 --resample_sr 0 --rms_mix_rate {volume_normalization} --protect {consonant_protection}")
if os.path.exists(save_as):
return "Processing complete. Here is your output audio:", save_as
else:
return "Error in processing audio.", None
# Gradio interface
with gr.Blocks(theme="gradio/soft") as demo:
gr.Markdown("# 🔊 ** RVC GUI**")
with gr.Tabs():
model_name = gr.Textbox(label="Model Name", value="Ren")
pitch = gr.Slider(minimum=-12, maximum=12, step=1, label="Pitch", value=0)
with gr.Tab("Infernece"):
input_path = gr.Dropdown(label="",choices=show_available('audios'),value='',interactive=True)
f0_method = gr.Radio(choices=["rmvpe", "pm", "crepe"], label="F0 Method", value="rmvpe")
save_as = gr.Textbox(label="Save As", value="/content/RVC/audios/cli_output.wav")
index_rate = gr.Slider(minimum=0, maximum=1, step=0.01, label="Index Rate", value=0.5)
volume_normalization = gr.Slider(minimum=0, maximum=1, step=0.01, label="Volume Normalization", value=0)
consonant_protection = gr.Slider(minimum=0, maximum=1, step=0.01, label="Consonant Protection", value=0.5)
output_text = gr.Textbox(label="Output")
output_audio = gr.Audio(label="Processed Audio")
# Button to detect files
detect_btn = gr.Button("Detect Files")
detect_btn.click(fn=detect_files, inputs=[model_name], outputs=output_text)
# Button to process the audio and return audio output
submit_btn = gr.Button("Submit")
submit_btn.click(fn=process_audio,
inputs=[model_name, pitch, input_path, f0_method, save_as, index_rate, volume_normalization, consonant_protection],
outputs=[output_text, output_audio])
with gr.Tab("Download a model"):
url = gr.Textbox(label="url models...")
url_name = gr.Textbox(label="models names...")
download_fm = gr.Button("download model")
download_fm.click(fn=download_from_url, inputs=[url, url_name], outputs=url_name)
# Launch the app
demo.launch()
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