app.py

import gradio as gr
import torch
import torchaudio
import spaces
import os
import tempfile
import subprocess
import shlex
from pathlib import Path
from huggingface_hub import hf_hub_download

# Global cache for model files
model_files_cache = {}

def download_model_files():
    """Download model files once and cache paths."""
    if not model_files_cache:
        print("Downloading model files...")
        model_files_cache["vocab_file"] = hf_hub_download(
            repo_id="IbrahimSalah/Arabic-F5-TTS-v2", 
            filename="vocab.txt"
        )
        model_files_cache["ckpt_file"] = hf_hub_download(
            repo_id="IbrahimSalah/Arabic-F5-TTS-v2", 
            filename="model_547500_8_18.pt"
        )
        model_files_cache["config_file"] = hf_hub_download(
            repo_id="IbrahimSalah/Arabic-F5-TTS-v2", 
            filename="F5TTS_Base_8_18.yaml"
        )
        print("Model files downloaded!")
    return model_files_cache


@spaces.GPU(duration=120)
def generate_speech(
    text: str,
    reference_audio,
    reference_transcript: str,
    nfe_step: int = 32,
    cfg_strength: float = 1.8,
    speed: float = 1.0,
    progress=gr.Progress()
):
    """Generate speech using F5-TTS CLI - exactly like working Colab."""
    try:
        # Validate inputs
        if not text.strip():
            return None, "❌ Please enter text to synthesize."
        
        if reference_audio is None:
            return None, "❌ Please upload a reference audio file."
        
        if not reference_transcript.strip():
            return None, "❌ Please enter the reference transcript."
        
        # Download model files
        progress(0.1, desc="Loading model files...")
        files = download_model_files()
        
        # Create temporary output file
        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav", mode='w') as tmp_file:
            output_path = tmp_file.name
        
        # Build CLI command - EXACTLY like working Colab
        progress(0.3, desc="Generating audio...")
        
        cmd = [
            "python", "-m", "f5_tts.infer.infer_cli",
            "--model_cfg", files["config_file"],
            "--output_file", output_path,
            "--model", "F5TTS_Base",
            "--ckpt_file", files["ckpt_file"],
            "--vocab_file", files["vocab_file"],
            "--ref_audio", reference_audio,
            "--nfe_step", str(nfe_step),
            "--cfg_strength", str(cfg_strength),
            "--speed", str(speed),
            "--ref_text", reference_transcript,
            "--gen_text", text
        ]
        
        print(f"Running command: {' '.join(cmd)}")
        
        # Run the CLI command
        result = subprocess.run(
            cmd,
            capture_output=True,
            text=True,
            timeout=300  # 5 minute timeout
        )
        
        # Print outputs for debugging
        if result.stdout:
            print("STDOUT:", result.stdout)
        if result.stderr:
            print("STDERR:", result.stderr)
        
        # Check for errors
        if result.returncode != 0:
            error_msg = f"❌ CLI failed with return code {result.returncode}\n"
            error_msg += f"STDERR: {result.stderr}\n"
            error_msg += f"STDOUT: {result.stdout}"
            return None, error_msg
        
        # Check if output file was created
        if not os.path.exists(output_path):
            return None, f"❌ Output file not created. Check logs above."
        
        if os.path.getsize(output_path) == 0:
            return None, "❌ Output file is empty."
        
        # Get audio duration
        try:
            audio, sample_rate = torchaudio.load(output_path)
            duration = audio.shape[-1] / sample_rate
            status = f"✅ Generated {duration:.2f}s audio"
        except Exception as e:
            status = f"✅ Audio generated (duration unknown: {str(e)})"
        
        progress(1.0, desc="Complete!")
        return output_path, status
        
    except subprocess.TimeoutExpired:
        return None, "❌ Generation timed out (>5 minutes)"
    except Exception as e:
        import traceback
        error_msg = f"❌ Error: {str(e)}\n{traceback.format_exc()}"
        print(error_msg)
        return None, error_msg


# Default examples
DEFAULT_REFERENCE_TEXT = "لَا يَمُرُّ يَوْمٌ إِلَّا وَأَسْتَقْبِلُ عِدَّةَ رَسَائِلَ، تَتَضَمَّنُ أَسْئِلَةً مُلِحَّةْ."
DEFAULT_TEXT = "تُسَاهِمُ التِّقْنِيَّاتُ الْحَدِيثَةُ فِي تَسْهِيلِ حَيَاةِ الْإِنْسَانِ، وَذَلِكَ مِنْ خِلَالِ تَطْوِيرِ أَنْظِمَةٍ ذَكِيَّةٍ تَعْتَمِدُ عَلَى الذَّكَاءِ الِاصْطِنَاعِيِّ."
DEFAULT_REFERENCE_AUDIO = "reference.wav"

# Create Gradio interface
with gr.Blocks(title="Arabic F5-TTS", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🎙️ Arabic Text-to-Speech | F5-TTS Model
    
    High-quality Arabic TTS with voice cloning. **Diacritized text (تشكيل) required.**
    
    **Model:** [IbrahimSalah/Arabic-F5-TTS-v2](https://huggingface.co/IbrahimSalah/Arabic-F5-TTS-v2)
    """)
    
    with gr.Row():
        with gr.Column(scale=1):
            text_input = gr.Textbox(
                label="📝 Text to Synthesize (Arabic with Tashkeel)",
                placeholder="أَدْخِلْ نَصًّا عَرَبِيًّا مُشَكَّلًا هُنَا...",
                lines=6,
                value=DEFAULT_TEXT
            )
            
            with gr.Row():
                with gr.Column():
                    gr.Markdown("**🎵 Reference Audio**")
                    reference_audio = gr.Audio(
                        label="",
                        type="filepath",
                        value=DEFAULT_REFERENCE_AUDIO
                    )
                
                with gr.Column():
                    reference_transcript = gr.Textbox(
                        label="📄 Reference Transcript (with Tashkeel)",
                        placeholder="النص المقابل للصوت المرجعي...",
                        lines=4,
                        value=DEFAULT_REFERENCE_TEXT
                    )
            
            with gr.Accordion("⚙️ Advanced Settings", open=False):
                with gr.Row():
                    nfe_step = gr.Slider(16, 64, value=32, step=1, label="NFE Steps")
                    cfg_strength = gr.Slider(0.0, 3.0, value=1.8, step=0.1, label="CFG Strength")
                with gr.Row():
                    speed = gr.Slider(0.5, 2.0, value=1.0, step=0.1, label="Speed")
            
            generate_btn = gr.Button("🎤 Generate Speech", variant="primary", size="lg")
        
        with gr.Column(scale=1):
            output_audio = gr.Audio(label="🔊 Generated Speech", type="filepath")
            status_text = gr.Textbox(label="Status", interactive=False, lines=2)
            
            gr.Markdown("""
            ### ℹ️ Requirements
            - **Diacritized text is required** (تشكيل/تشكيل)
            - Reference audio: 5-30 seconds, clear speech
            - Use AI (ChatGPT/Claude) or [online tools](https://tahadz.com/mishkal) to add diacritics
            
            ### 🔗 Resources
            - [Model Card](https://huggingface.co/IbrahimSalah/Arabic-F5-TTS-v2)
            - [Spark TTS](https://huggingface.co/IbrahimSalah/Arabic-TTS-Spark)
            - [Report Issues](https://huggingface.co/IbrahimSalah/Arabic-F5-TTS-v2/discussions)
            """)
    
    # Examples
    with gr.Accordion("📚 Examples", open=False):
        gr.Examples(
            examples=[
                [DEFAULT_TEXT, DEFAULT_REFERENCE_AUDIO, DEFAULT_REFERENCE_TEXT, 32, 1.8, 1.0],
                ["السَّلَامُ عَلَيْكُمْ وَرَحْمَةُ اللَّهِ وَبَرَكَاتُهُ، كَيْفَ حَالُكَ الْيَوْمَ؟", DEFAULT_REFERENCE_AUDIO, DEFAULT_REFERENCE_TEXT, 32, 1.8, 1.0],
                ["الذَّكَاءُ الِاصْطِنَاعِيُّ يُغَيِّرُ الْعَالَمَ بِسُرْعَةٍ كَبِيرَةٍ.", DEFAULT_REFERENCE_AUDIO, DEFAULT_REFERENCE_TEXT, 32, 1.8, 1.0]
            ],
            inputs=[text_input, reference_audio, reference_transcript, nfe_step, cfg_strength, speed]
        )
    
    generate_btn.click(
        fn=generate_speech,
        inputs=[text_input, reference_audio, reference_transcript, nfe_step, cfg_strength, speed],
        outputs=[output_audio, status_text]
    )

if __name__ == "__main__":
    demo.queue(max_size=20)
    demo.launch()