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import gradio as gr
import os
from lib.infer import infer_audio
from pydub import AudioSegment
main_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Function for inference
def inference(model_name, audio, f0_change, f0_method, min_pitch, max_pitch, crepe_hop_length,
index_rate, filter_radius, rms_mix_rate, protect, split_infer, min_silence,
silence_threshold, seek_step, keep_silence, quefrency, timbre,
f0_autotune, output_format):
# Perform inference
inferred_audio = infer_audio(
model_name,
audio_path,
f0_change,
f0_method,
min_pitch,
max_pitch,
crepe_hop_length,
index_rate,
filter_radius,
rms_mix_rate,
protect,
split_infer,
min_silence,
silence_threshold,
seek_step,
keep_silence,
quefrency,
timbre,
f0_autotune,
output_format
)
# Convert the output audio
os.chdir(main_dir)
output_audio = AudioSegment.from_file(inferred_audio)
# Save the output audio and return
output_path = f"output.{output_format}"
output_audio.export(output_path, format=output_format)
return output_path
# Gradio UI
with gr.Blocks(theme="Ryouko65777/ryo", js="() => {document.body.classList.toggle('dark');}") as demo:
gr.Markdown("# Ryo RVC ")
with gr.Tabs():
audio_input = gr.Audio(label="Input Audio", type="filepath")
model_name = gr.Textbox(label="Model Name")
f0_change = gr.Number(label="Pitch Change (F0 Change)", value=0)
f0_method = gr.Dropdown(
label="F0 Method",
choices=
[
"crepe",
"harvest",
"mangio-crepe",
"rmvpe",
"rmvpe+",
"fcpe",
"fcpe_legacy",
"hybrid[mangio-crepe+rmvpe]",
"hybrid[mangio-crepe+fcpe]",
"hybrid[rmvpe+fcpe]",
"hybrid[mangio-crepe+rmvpe+fcpe]",
],
value="fcpe",
)
min_pitch = gr.Textbox(label="Min Pitch", value="50")
max_pitch = gr.Textbox(label="Max Pitch", value="1100")
crepe_hop_length = gr.Number(label="CREPE Hop Length", value=120)
index_rate = gr.Slider(label="Index Rate", minimum=0, maximum=1, value=0.75)
filter_radius = gr.Number(label="Filter Radius", value=3)
rms_mix_rate = gr.Slider(label="RMS Mix Rate", minimum=0, maximum=1, value=0.25)
protect = gr.Slider(label="Protect", minimum=0, maximum=1, value=0.33)
split_infer = gr.Checkbox(label="Enable Split Inference", value=False)
min_silence = gr.Number(label="Min Silence (ms)", value=500)
silence_threshold = gr.Number(label="Silence Threshold (dB)", value=-50)
seek_step = gr.Slider(label="Seek Step (ms)", minimum=1, maximum=10, value=1)
keep_silence = gr.Number(label="Keep Silence (ms)", value=200)
quefrency = gr.Number(label="Quefrency", value=0)
timbre = gr.Number(label="Timbre", value=1)
f0_autotune = gr.Checkbox(label="Enable F0 Autotune", value=False)
output_format = gr.Dropdown(label="Output Format", choices=["wav", "flac", "mp3"], value="wav")
output_audio = gr.Audio(label="Output Audio")
submit_btn = gr.Button("Run Inference")
# Define the interaction between input and function
submit_btn.click(fn=inference,
inputs=[model_name, audio_input, f0_change, f0_method, min_pitch, max_pitch,
crepe_hop_length, index_rate, filter_radius, rms_mix_rate, protect,
split_infer, min_silence, silence_threshold, seek_step, keep_silence,
quefrency, timbre, f0_autotune, output_format],
outputs=output_audio)
# Launch the demo
demo.launch()
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