main.py

import gradio as gr
import os
import requests
from tqdm import tqdm
import plotly.express as px
import pandas as pd


from svc_inference import main
from whisper.inference import check_and_download_model

# データの作成
data = {
    'id': list(range(1, 57)),  # 1から56までの数字
    'x': [
        28, 25, 5, 12, 8, 2, 0, -20, -15, -12, -20, 8, -30, 25, 0, 0, 2, -25,
        -25, 20, 15, -2, 0, 15, -30, 15, 8, 28, -10, -22, 20, 20, 8, 20, 0,
        0, -8, -10, -32, 0, 0, -8, 2, -25, -32, -20, -18, -5, 15, -22, -25,
        -28, -30, 10, 25, 28
    ],
    'y': [
        0, -5, -15, -20, -18, -3, 8, 8, 12, 10, 10, -20, 6, -3, 12, -15, 12,
        17, 10, -8, -15, -22, 8, 15, 10, -15, -18, -10, 8, 5, -10, -8, -25,
        -5, -12, 12, 15, 6, 17, -12, -8, -8, 15, 17, 25, 4, 4, 0, 0, -20,
        12, 12, 15, -19, 0, 0
    ]
}

df = pd.DataFrame(data)

def create_plot():
    fig = px.scatter(df, x='x', y='y', text='id',
                    title='Voice Timbre Feature Mapping')
    
    # マーカーのスタイルを設定（紫系の色に設定）
    fig.update_traces(
        marker=dict(
            size=10,
            color='#663399',  # 紫色
            line=dict(color='#4B0082', width=1)  # より暗い紫の境界線
        ),
        textposition='top center'
    )
    
    # レイアウトの設定
    fig.update_layout(
        height=600,
        width=800,
        clickmode='event+select',
        plot_bgcolor='#eeeeee',
        paper_bgcolor='white',
        xaxis=dict(
            showgrid=True,
            zeroline=True,
            range=[-35, 35]  # x軸の範囲を設定
        ),
        yaxis=dict(
            showgrid=True,
            zeroline=True,
            range=[-30, 30]  # y軸の範囲を設定
        )
    )
    
    return fig

def run_main(audio_file, shift, speaker_id):
    # 固定の引数を設定
    class Args:
        pass
    
    args = Args()
    args.config = "configs/base.yaml"
    args.model = "./vits_pretrain/sovits5.0.pretrain.pth"
    speaker_str = f"{speaker_id:04d}"
    args.spk = f"./configs/singers/singer{speaker_str}.npy"
    args.wave = audio_file
    print(audio_file)
    args.shift = shift
    
    # オプショナルパラメータのデフォルト値設定
    args.ppg = None
    args.vec = None
    args.pit = None
    args.enable_retrieval = False
    args.retrieval_index_prefix = ""
    args.retrieval_ratio = 0.5
    args.n_retrieval_vectors = 3
    args.hubert_index_path = None
    args.whisper_index_path = None
    args.debug = False

    try:
        main(args)
        return "svc_out.wav"  # 音声ファイルのパスを返す
    except Exception as e:
        return None

# Gradio インターフェースの作成
with gr.Blocks() as demo:
    gr.Markdown("# SVC (Singing Voice Conversion) System")

    with gr.Row():
        with gr.Column(scale=1.15):
            plot = gr.Plot(value=create_plot())

        with gr.Column(scale=1):
            # 入力音声のアップロード
            input_audio = gr.Audio(
                label="Upload the audio you want to convert.",
                type="filepath"  # ファイルパスとして取得
            )
            # Speaker ID の選択
            speaker_id = gr.Number(
                label="Speaker ID (1-56)",
                value=1,
                minimum=1,
                maximum=56,
                step=1
            )
            # Pitch シフトのスライダー
            shift = gr.Slider(
                minimum=-12,
                maximum=12,
                value=0,
                step=1,
                label="Pitch Shift (from -12 to +12) "
            )
            # ボタン
            run_btn = gr.Button(value="Convert Singing Voice", variant="primary", size="lg")

    # 出力表示用
    output_audio = gr.Audio(label="Audio After Conversion")
    
    
    run_btn.click(
        fn=run_main,
        inputs=[input_audio, shift, speaker_id],
        outputs=[output_audio]
    )

# アプリケーションの起動
if __name__ == "__main__":
    demo.launch()