app.py

import gradio as gr
import os
import argparse
import torch
import logging
import threading
from datetime import datetime
import torchaudio
import librosa
import soundfile as sf

# ZeroGPU support
try:
    import spaces
    ZEROGPU_AVAILABLE = True
except ImportError:
    ZEROGPU_AVAILABLE = False
    # Create a dummy decorator for non-ZeroGPU environments
    class spaces:
        @staticmethod
        def GPU(duration=10):
            def decorator(func):
                return func
            return decorator

# Project imports
from tokenizer import StepAudioTokenizer
from tts import StepAudioTTS
from model_loader import ModelSource
from config.edit_config import get_supported_edit_types

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

# Global variables for ZeroGPU-optimized loading
encoder = None
common_tts_engine = None
args_global = None
_model_lock = threading.Lock()  # Thread lock for model initialization

def initialize_models():
    """Initialize models on first GPU call (ZeroGPU optimization: load inside GPU context)"""
    global encoder, common_tts_engine, args_global

    # Fast path: check if already initialized (without lock)
    if common_tts_engine is not None:
        return  # Already initialized

    # Slow path: acquire lock and double-check
    with _model_lock:
        # Double-check pattern: another thread might have initialized while waiting for lock
        if common_tts_engine is not None:
            return  # Already initialized by another thread

        if args_global is None:
            raise RuntimeError("Global args not set. Cannot initialize models.")

        try:
            logger.info("🚀 Initializing models inside GPU context (first call)...")

            # Determine model source
            source_mapping = {
                "auto": ModelSource.AUTO,
                "local": ModelSource.LOCAL,
                "modelscope": ModelSource.MODELSCOPE,
                "huggingface": ModelSource.HUGGINGFACE
            }
            model_source = source_mapping[args_global.model_source]

            # Load StepAudioTokenizer (avoid CUDA initialization in main process)
            encoder = StepAudioTokenizer(
                os.path.join(args_global.model_path, "Step-Audio-Tokenizer"),
                model_source=model_source,
                funasr_model_id=args_global.tokenizer_model_id
            )
            logger.info("✓ StepAudioTokenizer loaded")

            # Initialize common TTS engine (avoid CUDA initialization in main process)
            common_tts_engine = StepAudioTTS(
                os.path.join(args_global.model_path, "Step-Audio-EditX"),
                encoder,
                model_source=model_source,
                tts_model_id=args_global.tts_model_id
            )
            logger.info("✓ StepCommonAudioTTS loaded")
            print("Models initialized inside GPU context.")

            if ZEROGPU_AVAILABLE:
                logger.info("💡 Models loaded inside GPU context - ready for inference")
            else:
                logger.info("💡 Models loaded - ready for inference")

        except Exception as e:
            logger.error(f"❌ Error loading models: {e}")
            raise

def get_model_config():
    """Get model configuration without initializing GPU models"""
    if args_global is None:
        raise RuntimeError("Global args not set. Cannot get model config.")

    return {
        "encoder_path": os.path.join(args_global.model_path, "Step-Audio-Tokenizer"),
        "tts_path": os.path.join(args_global.model_path, "Step-Audio-EditX"),
        "model_source": args_global.model_source,
        "tokenizer_model_id": args_global.tokenizer_model_id,
        "tts_model_id": args_global.tts_model_id
    }

def get_gpu_duration(audio_input, text_input, target_text, task_type, task_info):
    """Dynamic GPU duration based on whether models need initialization"""
    global common_tts_engine

    if common_tts_engine is None:
        # First call - need time for model loading (up to 5 minutes)
        return 300  # Maximum allowed duration for model initialization
    else:
        # Subsequent calls - only inference time needed
        return 120  # Standard inference duration

@spaces.GPU(duration=get_gpu_duration)  # Dynamic duration based on model state
def process_audio_with_gpu(audio_input, text_input, target_text, task_type, task_info):
    """Process audio using GPU (models are loaded inside GPU context to avoid main process errors)"""
    global common_tts_engine

    # Initialize models if not already loaded (inside GPU context to avoid main process errors)
    if common_tts_engine is None:
        print("Initializing common_tts_engine inside GPU context...")
        logger.info("🎯 GPU allocated for 300s (first call with model loading)...")
        initialize_models()
        logger.info("✅ Models loaded successfully inside GPU context")
    else:
        print("common_tts_engine already initialized.")
        logger.info("🎯 GPU allocated for 120s (inference with loaded models)...")

    try:
        # Use loaded models (first call may include loading time, subsequent calls are fast)
        if task_type == "clone":
            output_audio, sr = common_tts_engine.clone(audio_input, text_input, target_text)
        else:
            output_audio, sr = common_tts_engine.edit(audio_input, text_input, task_type, task_info, target_text)

        logger.info("✅ Audio processing completed")
        return output_audio, sr

    except Exception as e:
        logger.error(f"❌ Audio processing failed: {e}")
        raise
    # GPU automatically deallocated when function exits

# Save audio to temporary directory
def save_audio(audio_type, audio_data, sr, tmp_dir):
    """Save audio data to a temporary file with timestamp"""
    current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
    save_path = os.path.join(tmp_dir, audio_type, f"{current_time}.wav")
    os.makedirs(os.path.dirname(save_path), exist_ok=True)

    try:
        if isinstance(audio_data, torch.Tensor):
            torchaudio.save(save_path, audio_data, sr)
        else:
            sf.write(save_path, audio_data, sr)
        logger.debug(f"Audio saved to: {save_path}")
        return save_path
    except Exception as e:
        logger.error(f"Failed to save audio: {e}")
        raise


class EditxTab:
    """Audio editing and voice cloning interface tab"""

    def __init__(self, args):
        self.args = args
        self.edit_type_list = list(get_supported_edit_types().keys())
        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")

    def history_messages_to_show(self, messages):
        """Convert message history to gradio chatbot format"""
        show_msgs = []
        for message in messages:
            edit_type = message['edit_type']
            edit_info = message['edit_info']
            source_text = message['source_text']
            target_text = message['target_text']
            raw_audio_part = message['raw_wave']
            edit_audio_part = message['edit_wave']
            type_str = f"{edit_type}-{edit_info}" if edit_info is not None else f"{edit_type}"
            show_msgs.extend([
                {"role": "user", "content": f"任务类型：{type_str}\n文本：{source_text}"},
                {"role": "user", "content": gr.Audio(value=raw_audio_part, interactive=False)},
                {"role": "assistant", "content": f"输出音频：\n文本：{target_text}"},
                {"role": "assistant", "content": gr.Audio(value=edit_audio_part, interactive=False)}
            ])
        return show_msgs

    def generate_clone(self, prompt_text_input, prompt_audio_input, generated_text, edit_type, edit_info, state):
        """Generate cloned audio (models are loaded on first GPU call)"""
        self.logger.info("Starting voice cloning process")
        state['history_audio'] = []
        state['history_messages'] = []

        # Input validation
        if not prompt_text_input or prompt_text_input.strip() == "":
            error_msg = "[Error] Uploaded text cannot be empty."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state
        if not prompt_audio_input:
            error_msg = "[Error] Uploaded audio cannot be empty."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state
        if not generated_text or generated_text.strip() == "":
            error_msg = "[Error] Clone content cannot be empty."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state
        if edit_type != "clone":
            error_msg = "[Error] CLONE button must use clone task."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state

        try:
            # Use GPU inference with models loaded inside GPU context
            output_audio, output_sr = process_audio_with_gpu(
                prompt_audio_input, prompt_text_input, generated_text, "clone", edit_info
            )

            if output_audio is not None and output_sr is not None:
                # Convert tensor to numpy if needed
                if isinstance(output_audio, torch.Tensor):
                    audio_numpy = output_audio.cpu().numpy().squeeze()
                else:
                    audio_numpy = output_audio

                # Load original audio for comparison
                input_audio_data_numpy, input_sample_rate = librosa.load(prompt_audio_input)

                # Create message for history
                cur_assistant_msg = {
                    "edit_type": edit_type,
                    "edit_info": edit_info,
                    "source_text": prompt_text_input,
                    "target_text": generated_text,
                    "raw_wave": (input_sample_rate, input_audio_data_numpy),
                    "edit_wave": (output_sr, audio_numpy),
                }
                state["history_audio"].append((output_sr, audio_numpy, generated_text))
                state["history_messages"].append(cur_assistant_msg)

                show_msgs = self.history_messages_to_show(state["history_messages"])
                self.logger.info("Voice cloning completed successfully")
                return show_msgs, state
            else:
                error_msg = "[Error] Clone failed"
                self.logger.error(error_msg)
                return [{"role": "user", "content": error_msg}], state

        except Exception as e:
            error_msg = f"[Error] Clone failed: {str(e)}"
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state
        
    def generate_edit(self, prompt_text_input, prompt_audio_input, generated_text, edit_type, edit_info, state):
        """Generate edited audio (models are loaded on first GPU call)"""
        self.logger.info("Starting audio editing process")

        # Input validation
        if not prompt_text_input or prompt_text_input.strip() == "":
            error_msg = "[Error] Uploaded text cannot be empty."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state
        if not prompt_audio_input:
            error_msg = "[Error] Uploaded audio cannot be empty."
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state

        try:
            # Determine which audio to use
            if len(state["history_audio"]) == 0:
                # First edit - use uploaded audio
                audio_to_edit = prompt_audio_input
                text_to_use = prompt_text_input
                self.logger.debug("Using prompt audio, no history found")
            else:
                # Use previous edited audio - save it to temp file first
                sample_rate, audio_numpy, previous_text = state["history_audio"][-1]
                temp_path = save_audio("temp", audio_numpy, sample_rate, self.args.tmp_dir)
                audio_to_edit = temp_path
                text_to_use = previous_text
                self.logger.debug(f"Using previous audio from history, count: {len(state['history_audio'])}")

            # For para-linguistic, use generated_text; otherwise use source text
            if edit_type not in {"para-linguistic"}:
                generated_text = text_to_use

            # Use GPU inference with models loaded inside GPU context
            output_audio, output_sr = process_audio_with_gpu(
                audio_to_edit, text_to_use, generated_text, edit_type, edit_info
            )

            if output_audio is not None and output_sr is not None:
                # Convert tensor to numpy if needed
                if isinstance(output_audio, torch.Tensor):
                    audio_numpy = output_audio.cpu().numpy().squeeze()
                else:
                    audio_numpy = output_audio

                # Load original audio for comparison
                if len(state["history_audio"]) == 0:
                    input_audio_data_numpy, input_sample_rate = librosa.load(prompt_audio_input)
                else:
                    input_sample_rate, input_audio_data_numpy, _ = state["history_audio"][-1]

                # Create message for history
                cur_assistant_msg = {
                    "edit_type": edit_type,
                    "edit_info": edit_info,
                    "source_text": text_to_use,
                    "target_text": generated_text,
                    "raw_wave": (input_sample_rate, input_audio_data_numpy),
                    "edit_wave": (output_sr, audio_numpy),
                }
                state["history_audio"].append((output_sr, audio_numpy, generated_text))
                state["history_messages"].append(cur_assistant_msg)

                show_msgs = self.history_messages_to_show(state["history_messages"])
                self.logger.info("Audio editing completed successfully")
                return show_msgs, state
            else:
                error_msg = "[Error] Edit failed"
                self.logger.error(error_msg)
                return [{"role": "user", "content": error_msg}], state

        except Exception as e:
            error_msg = f"[Error] Edit failed: {str(e)}"
            self.logger.error(error_msg)
            return [{"role": "user", "content": error_msg}], state

    def clear_history(self, state):
        """Clear conversation history"""
        state["history_messages"] = []
        state["history_audio"] = []
        return [], state

    def init_state(self):
        """Initialize conversation state"""
        return {
            "history_messages": [],
            "history_audio": []
        }

    def register_components(self):
        """Register gradio components - maintaining exact layout from original"""
        with gr.Tab("Editx"):
            with gr.Row():
                with gr.Column():
                    self.model_input = gr.Textbox(label="Model Name", value="Step-Audio-EditX", scale=1)
                    self.prompt_text_input = gr.Textbox(label="Audio Text Content", value="", scale=1)
                    self.prompt_audio_input = gr.Audio(
                        sources=["upload", "microphone"],
                        format="wav",
                        type="filepath",
                        label="Input Audio",
                    )
                    self.generated_text = gr.Textbox(label="Clone Text", lines=1, max_lines=200)
                    with gr.Row():
                        self.button_tts = gr.Button("CLONE")
                        self.button_edit = gr.Button("EDIT")

                with gr.Column():
                    with gr.Row():
                        self.edit_type = gr.Dropdown(label="Task", choices=self.edit_type_list, value="clone")
                        self.edit_info = gr.Dropdown(label="Sub-task", choices=[], value=None)
                    self.chat_box = gr.Chatbot(label="History", type="messages", height=480*1)
                    self.clean_history_submit = gr.Button("Clear History")

            gr.Markdown("---")
            gr.Markdown("""
                **Button Description:**
                - CLONE: Synthesizes audio based on uploaded audio and text, only used for clone mode, will clear history information when used.
                - EDIT: Edits based on uploaded audio, or continues to stack edit effects based on the previous round of generated audio.
                """)
            gr.Markdown("""
                **Operation Workflow:**
                - Upload the audio to be edited on the left side and fill in the corresponding text content of the audio;
                - If the task requires modifying text content (such as clone, para-linguistic), fill in the text to be synthesized in the "clone text" field. For all other tasks, keep the uploaded audio text content unchanged;
                - Select tasks and subtasks on the right side (some tasks have no subtasks, such as vad, etc.);
                - Click the "CLONE" or "EDIT" button on the left side, and audio will be generated in the dialog box on the right side.
                """)
            gr.Markdown("""
                **Para-linguistic Description:**
                - Supported tags include: [Breathing] [Laughter] [Surprise-oh] [Confirmation-en] [Uhm] [Surprise-ah] [Surprise-wa] [Sigh] [Question-ei] [Dissatisfaction-hnn]
                - Example:
                    - Fill in "clone text" field: "Great, the weather is so nice today." Click the "CLONE" button to get audio.
                    - Change "clone text" field to: "Great[Laughter], the weather is so nice today[Surprise-ah]." Click the "EDIT" button to get para-linguistic audio.
                """)

    def register_events(self):
        """Register event handlers"""
        # Create independent state for each session
        state = gr.State(self.init_state())

        self.button_tts.click(self.generate_clone,
            inputs=[self.prompt_text_input, self.prompt_audio_input, self.generated_text, self.edit_type, self.edit_info, state],
            outputs=[self.chat_box, state])
        self.button_edit.click(self.generate_edit,
            inputs=[self.prompt_text_input, self.prompt_audio_input, self.generated_text, self.edit_type, self.edit_info, state],
            outputs=[self.chat_box, state])

        self.clean_history_submit.click(self.clear_history, inputs=[state], outputs=[self.chat_box, state])
        self.edit_type.change(
            fn=self.update_edit_info,
            inputs=self.edit_type,
            outputs=self.edit_info,
        )

    def update_edit_info(self, category):
        """Update sub-task dropdown based on main task selection"""
        category_items = get_supported_edit_types()
        choices = category_items.get(category, [])
        value = None if len(choices) == 0 else choices[0]
        return gr.Dropdown(label="Sub-task", choices=choices, value=value)


def launch_demo(args, editx_tab):
    """Launch the gradio demo"""
    with gr.Blocks(title="🎙️ Step-Audio-EditX") as demo:
        gr.Markdown("## 🎙️ Step-Audio-EditX")
        gr.Markdown("Audio editing and voice cloning using Step-Audio-Edit model.")

        # Register components
        editx_tab.register_components()

        # Register events
        editx_tab.register_events()

    # Launch demo
    demo.queue().launch(
        server_name=args.server_name,
        server_port=args.server_port,
        share=args.share if hasattr(args, 'share') else False
    )


if __name__ == "__main__":
    # Parse command line arguments
    parser = argparse.ArgumentParser(description="Step-Audio Edit Demo")
    parser.add_argument("--model-path", type=str, default="stepfun-ai", help="Model path.")
    parser.add_argument("--server-name", type=str, default="0.0.0.0", help="Demo server name.")
    parser.add_argument("--server-port", type=int, default=7860, help="Demo server port.")
    parser.add_argument("--tmp-dir", type=str, default="/tmp/gradio", help="Save path.")
    parser.add_argument("--share", action="store_true", help="Share gradio app.")

    # Multi-source loading support parameters
    parser.add_argument(
        "--model-source",
        type=str,
        default="huggingface",
        choices=["auto", "local", "modelscope", "huggingface"],
        help="Model source: auto (detect automatically), local, modelscope, or huggingface"
    )
    parser.add_argument(
        "--tokenizer-model-id",
        type=str,
        default="dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online",
        help="Tokenizer model ID for online loading"
    )
    parser.add_argument(
        "--tts-model-id",
        type=str,
        default=None,
        help="TTS model ID for online loading (if different from model-path)"
    )

    args = parser.parse_args()

    # Store args globally for model configuration
    args_global = args

    logger.info(f"Configuration loaded:")
    logger.info(f"Model source: {args.model_source}")
    logger.info(f"Model path: {args.model_path}")
    logger.info(f"Tokenizer model ID: {args.tokenizer_model_id}")
    if args.tts_model_id:
        logger.info(f"TTS model ID: {args.tts_model_id}")

    # Models will be initialized on first GPU call to avoid ZeroGPU main process errors

    if ZEROGPU_AVAILABLE:
        logger.info("🎉 ZeroGPU detected - using dynamic GPU duration management!")
        logger.info("💡 First call: 300s (model loading), subsequent calls: 120s (inference only)")
    else:
        logger.info("💻 Running in local mode - models will be loaded on first call")

    # Create EditxTab instance
    editx_tab = EditxTab(args)

    # Launch demo
    launch_demo(args, editx_tab)