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EchoX
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import subprocess
import sys

subprocess.check_call([sys.executable, "-m", "pip", "install", "pip==24.0"])
subprocess.check_call([sys.executable, "-m", "pip", "install", "omegaconf==2.0.6"])
subprocess.check_call([sys.executable, "-m", "pip", "install", "git+https://github.com/facebookresearch/fairseq.git@v0.12.2"])
import gradio as gr
import os
import torch
import librosa
import soundfile as sf
import tempfile
import spaces  # ZeroGPU requirement

# 导入你的模块
import Echox_copy_stream as Echox

os.environ["TOKENIZERS_PARALLELISM"] = "false"

# 全局变量
_MODEL_ON_CUDA = False
inference_model = None

def init_model():
    """在CPU上初始化模型"""
    global inference_model
    if inference_model is None:
        inference_model = Echox.EchoxAssistant()
    return inference_model

def process_audio_input(audio):
    """处理音频输入"""
    if audio is None:
        return None
    
    try:
        # 如果是文件路径，直接返回
        if isinstance(audio, str):
            return audio
        
        # 如果是numpy数组格式 (sr, data)
        if isinstance(audio, tuple):
            sr, y = audio
            if y.ndim > 1:
                y = y[:, 0]  # 只保留第一个声道
        else:
            # 如果直接是数组
            y = audio
            sr = 16000  # 默认采样率
        
        # 保存为临时文件
        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as tmp_file:
            sf.write(tmp_file.name, y, sr)
            return tmp_file.name
            
    except Exception as e:
        print(f"Error processing audio: {e}")
        return None

@spaces.GPU(duration=180)  # 使用ZeroGPU，3分钟超时
def process_audio_text(audio):
    """主要处理函数"""
    global _MODEL_ON_CUDA, inference_model
    
    # 初始化模型（如果还没初始化）
    if inference_model is None:
        init_model()
    
    # 首次使用GPU时移动模型
    if not _MODEL_ON_CUDA:
        try:
            # 将模型移动到GPU
            if hasattr(inference_model, 'model'):
                inference_model.model = inference_model.model.to("cuda")
            if hasattr(inference_model, 'unit_translator'):
                inference_model.unit_translator = inference_model.unit_translator.to("cuda")
            
            inference_model.device = "cuda"
            _MODEL_ON_CUDA = True
            print("Model moved to GPU")
        except Exception as e:
            print(f"Error moving model to GPU: {e}")
    
    # 处理音频输入
    audio_path = process_audio_input(audio)
    
    text = ""
    
    tmp = [{
        "conversations": [
            {
                "from": "user",
                "value": text,
                "audio": audio_path
            }
        ]
    }]
    
    accumulated_text = ""
    
    try:
        for text_response, audio_data in inference_model._inference(tmp):
            if text_response:
                accumulated_text = text_response
            
            if audio_data is not None:
                sr, audio_array = audio_data
                yield accumulated_text, (sr, audio_array)
            else:
                yield accumulated_text, None
    except Exception as e:
        yield f"Error: {str(e)}", None
    finally:
        # 清理临时文件
        if audio_path and audio_path != audio and os.path.exists(audio_path):
            try:
                os.unlink(audio_path)
            except:
                pass

# 初始化模型（在CPU上）
init_model()

if __name__ == "__main__":
    examples = [
        ["./show_case/1.wav"],
        ["./show_case/2.wav"],
    ]  

    iface = gr.Interface(
        fn=process_audio_text,
        inputs=[
            gr.Audio(type="filepath", label="Upload Audio")
        ],
        outputs=[
            gr.Audio(label="Streamed Audio", streaming=True, autoplay=True),
            gr.Textbox(label="Model output")
        ],
        examples=examples,
        live=False,
        allow_flagging="never"
    )

    iface.launch(server_name="0.0.0.0", server_port=7860, share=True)