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import gradio as gr
import subprocess
import os
def audio_model_inference(files, output_folder, model_path, denoise, margin, chunks, n_fft, dim_t, dim_f):
# 构建命令行调用字符串
cmd = f"separate.py {' '.join(files)}"
if output_folder:
cmd += f" -o {output_folder}"
if model_path:
cmd += f" -m {model_path}"
if denoise:
cmd += " -d"
if margin:
cmd += f" -M {margin}"
if chunks:
cmd += f" -c {chunks}"
if n_fft:
cmd += f" -F {n_fft}"
if dim_t:
cmd += f" -t {dim_t}"
if dim_f:
cmd += f" -f {dim_f}"
# 执行命令行调用
result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
# 检查命令是否成功执行
if result.returncode != 0:
return f"错误：{result.stderr}"
# 读取输出文件
vocals_file = f"{os.path.splitext(os.path.basename(files[0]))[0]}_vocals.wav"
no_vocals_file = f"{os.path.splitext(os.path.basename(files[0]))[0]}_no_vocals.wav"
vocals_path = os.path.join(output_folder, vocals_file)
no_vocals_path = os.path.join(output_folder, no_vocals_file)
# 确保文件存在
if not os.path.exists(vocals_path) or not os.path.exists(no_vocals_path):
return "错误：输出文件未找到。"
# 读取音频文件
vocals_audio = open(vocals_path, 'rb').read()
no_vocals_audio = open(no_vocals_path, 'rb').read()
return (vocals_audio, no_vocals_audio)
# Gradio 界面组件
inputs = [
gr.File(label="源音频文件", type='binary', file_count='multiple'),
gr.Textbox(label="输出文件夹", default="./"),
gr.Textbox(label="模型路径", default="./models/MDX_Net_Models/UVR-MDX-NET-Inst_HQ_3.onnx"),
gr.Checkbox(label="启用降噪", default=False),
gr.Number(label="边距", default=0.1),
gr.Number(label="块大小", default=1024),
gr.Number(label="FFT大小", default=2048),
gr.Number(label="时间维度", default=512),
gr.Number(label="频率维度", default=64)
]
outputs = [gr.Audio(label="人声"), gr.Audio(label="无人声")]
# 创建界面
iface = gr.Interface(
fn=audio_model_inference,
inputs=inputs,
outputs=outputs,
title="音频分离模型",
description="上传音频文件并配置参数，使用音频分离模型处理它们。"
)
iface.launch()
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