app.py

## IMPORTS ##
import os
import tempfile
import time
from pathlib import Path

import gradio as gr
import numpy as np
import spaces
import torch
import torchaudio
from cached_path import cached_path
from huggingface_hub import hf_hub_download
from transformers import pipeline

from infer import DMOInference

## CUDA DEVICE ##
device = "cuda" if torch.cuda.is_available() else "cpu"

## LOAD MODELS ##
asr_pipe = pipeline(
    "automatic-speech-recognition", model="openai/whisper-large-v3-turbo", device=device
)
model = DMOInference(
    student_checkpoint_path=str(cached_path("hf://yl4579/DMOSpeech2/model_85000.pt")),
    duration_predictor_path=str(cached_path("hf://yl4579/DMOSpeech2/model_1500.pt")),
    device=device,
    model_type="F5TTS_Base",
)


def transcribe(ref_audio, language=None):
    """Transcribe audio using the pre-loaded ASR pipeline."""
    return asr_pipe(
        ref_audio,
        chunk_length_s=30,
        batch_size=128,
        generate_kwargs=(
            {"task": "transcribe", "language": language}
            if language
            else {"task": "transcribe"}
        ),
        return_timestamps=False,
    )["text"].strip()


@spaces.GPU(duration=120)
def generate_speech(
    prompt_audio,
    prompt_text,
    target_text,
    mode,
    temperature,
    custom_teacher_steps,
    custom_teacher_stopping_time,
    custom_student_start_step,
    verbose,
):
    if prompt_audio is None:
        raise gr.Error("Please upload a reference audio!")

    if not target_text:
        raise gr.Error("Please enter text to generate!")

    if not prompt_text and prompt_text != "":
        prompt_text = transcribe(prompt_audio)


    if mode == "Student Only (4 steps)":
        teacher_steps = 0
        student_start_step = 0
        teacher_stopping_time = 1.0
    elif mode == "Teacher-Guided (8 steps)":
        teacher_steps = 16
        teacher_stopping_time = 0.07
        student_start_step = 1
    elif mode == "High Diversity (16 steps)":
        teacher_steps = 24
        teacher_stopping_time = 0.3
        student_start_step = 2
    else:  # Custom
        teacher_steps = custom_teacher_steps
        teacher_stopping_time = custom_teacher_stopping_time
        student_start_step = custom_student_start_step

    # Generate speech
    generated_audio = model.generate(
        gen_text=target_text,
        audio_path=prompt_audio,
        prompt_text=prompt_text if prompt_text else None,
        teacher_steps=teacher_steps,
        teacher_stopping_time=teacher_stopping_time,
        student_start_step=student_start_step,
        temperature=temperature,
        verbose=verbose,
    )


    # Save audio
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        output_path = tmp_file.name

    if isinstance(generated_audio, np.ndarray):
        generated_audio = torch.from_numpy(generated_audio)

    if generated_audio.dim() == 1:
        generated_audio = generated_audio.unsqueeze(0)

    torchaudio.save(output_path, generated_audio, 24000)

    return (
        output_path,
        "Success!",
        (
            f"Mode: {mode} | Transcribed: {prompt_text[:50]}..."
            if not prompt_text
            else f"Mode: {mode}"
        ),
    )


# Create Gradio interface
with gr.Blocks(title="DMOSpeech 2 - Zero-Shot TTS") as demo:
    gr.Markdown(
        f"""
    # 🎙️ DMOSpeech 2: Zero-Shot Text-to-Speech
    
    Generate natural speech in any voice with just a short reference audio!     
    """
    )

    with gr.Row():
        with gr.Column(scale=1):
            # Reference audio input
            prompt_audio = gr.Audio(
                label="📎 Reference Audio",
                type="filepath",
                sources=["upload", "microphone"],
            )

            prompt_text = gr.Textbox(
                label="📝 Reference Text (leave empty for auto-transcription)",
                placeholder="The text spoken in the reference audio...",
                lines=2,
            )

            target_text = gr.Textbox(
                label="✍️ Text to Generate",
                placeholder="Enter the text you want to synthesize...",
                lines=4,
            )

            # Generation mode
            mode = gr.Radio(
                choices=[
                    "Student Only (4 steps)",
                    "Teacher-Guided (8 steps)",
                    "High Diversity (16 steps)",
                    "Custom",
                ],
                value="Teacher-Guided (8 steps)",
                label="🚀 Generation Mode",
                info="Choose speed vs quality/diversity tradeoff",
            )

            # Advanced settings (collapsible)
            with gr.Accordion("⚙️ Advanced Settings", open=False):
                temperature = gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    value=0.0,
                    step=0.1,
                    label="Duration Temperature",
                    info="0 = deterministic, >0 = more variation in speech rhythm",
                )

                with gr.Group(visible=False) as custom_settings:
                    gr.Markdown("### Custom Mode Settings")
                    custom_teacher_steps = gr.Slider(
                        minimum=0,
                        maximum=32,
                        value=16,
                        step=1,
                        label="Teacher Steps",
                        info="More steps = higher quality",
                    )

                    custom_teacher_stopping_time = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        value=0.07,
                        step=0.01,
                        label="Teacher Stopping Time",
                        info="When to switch to student",
                    )

                    custom_student_start_step = gr.Slider(
                        minimum=0,
                        maximum=4,
                        value=1,
                        step=1,
                        label="Student Start Step",
                        info="Which student step to start from",
                    )

                verbose = gr.Checkbox(
                    value=False,
                    label="Verbose Output",
                    info="Show detailed generation steps",
                )

            generate_btn = gr.Button("🎵 Generate Speech", variant="primary", size="lg")

        with gr.Column(scale=1):
            # Output
            output_audio = gr.Audio(
                label="🔊 Generated Speech", type="filepath", autoplay=True
            )

            status = gr.Textbox(label="Status", interactive=False)

            metrics = gr.Textbox(label="Performance Metrics", interactive=False)

            info = gr.Textbox(label="Generation Info", interactive=False)

            # Tips
            gr.Markdown(
                """
            ### 💡 Quick Tips:
            
            - **Auto-transcription**: Leave reference text empty to auto-transcribe
            - **Student Only**: Fastest (4 steps), good quality
            - **Teacher-Guided**: Best balance (8 steps), recommended
            - **High Diversity**: More natural prosody (16 steps)
            - **Custom Mode**: Fine-tune all parameters
            
            ### 📊 Expected RTF (Real-Time Factor):
            - Student Only: ~0.05x (20x faster than real-time)
            - Teacher-Guided: ~0.10x (10x faster)
            - High Diversity: ~0.20x (5x faster)
            """
            )

    # Event handler
    generate_btn.click(
        generate_speech,
        inputs=[
            prompt_audio,
            prompt_text,
            target_text,
            mode,
            temperature,
            custom_teacher_steps,
            custom_teacher_stopping_time,
            custom_student_start_step,
            verbose,
        ],
        outputs=[output_audio, status, metrics, info],
    )

    # Update visibility of custom settings based on mode
    def update_custom_visibility(mode):
        is_custom = mode == "Custom"
        return gr.update(visible=is_custom)

    mode.change(update_custom_visibility, inputs=[mode], outputs=[custom_settings])

# Launch the app
if __name__ == "__main__":
    if not model_loaded:
        print(f"Warning: Model failed to load - {status_message}")
    if not asr_pipe:
        print("Warning: ASR pipeline not available - auto-transcription disabled")

    demo.launch()