app.py

import gradio as gr
import spaces
import os
import torch
import tempfile
from pathlib import Path

# Try to import DiariZen
try:
    from diarizen.pipelines.inference import DiariZenPipeline
    DIARIZEN_AVAILABLE = True
except ImportError:
    DIARIZEN_AVAILABLE = False
    print("⚠️ DiariZen not available - install from https://github.com/BUTSpeechFIT/DiariZen")

# Model cache
pipeline_cache = {}

def load_diarizen_pipeline(model_id="BUT-FIT/diarizen-wavlm-large-s80-md"):
    """Load DiariZen pipeline with caching"""
    if model_id in pipeline_cache:
        return pipeline_cache[model_id]

    try:
        print(f"Loading DiariZen model: {model_id}")
        pipeline = DiariZenPipeline.from_pretrained(model_id)

        # Move to GPU if available
        if torch.cuda.is_available():
            print("Moving pipeline to CUDA")
            pipeline.to(torch.device("cuda"))

        pipeline_cache[model_id] = pipeline
        print(f"✅ Model loaded successfully")
        return pipeline
    except Exception as e:
        print(f"❌ Error loading model: {e}")
        raise e

def format_diarization_results(annotations):
    """Format diarization results as readable text"""
    results = []
    results.append("# Diarization Results\n\n")
    results.append("| Start Time | End Time | Duration | Speaker |\n")
    results.append("|------------|----------|----------|----------|\n")

    for turn, _, speaker in annotations.itertracks(yield_label=True):
        duration = turn.end - turn.start
        results.append(
            f"| {turn.start:8.2f}s | {turn.end:8.2f}s | {duration:6.2f}s | {speaker} |\n"
        )

    return "".join(results)

def save_rttm(annotations, audio_filename):
    """Save annotations to RTTM format"""
    # Create temporary directory for RTTM
    temp_dir = tempfile.mkdtemp()
    rttm_path = Path(temp_dir) / f"{audio_filename}.rttm"

    with open(rttm_path, 'w') as f:
        for turn, _, speaker in annotations.itertracks(yield_label=True):
            duration = turn.end - turn.start
            # RTTM format: SPEAKER <file> 1 <start> <duration> <NA> <NA> <speaker> <NA> <NA>
            f.write(f"SPEAKER {audio_filename} 1 {turn.start:.3f} {duration:.3f} <NA> <NA> {speaker} <NA> <NA>\n")

    return str(rttm_path)

@spaces.GPU(duration=120)
def diarize_audio(audio_file, model_choice):
    """Main diarization function with GPU acceleration"""
    if not DIARIZEN_AVAILABLE:
        return "❌ Error: DiariZen not installed. Please install from https://github.com/BUTSpeechFIT/DiariZen", None

    if audio_file is None:
        return "⚠️ Please upload an audio file", None

    try:
        # Map model choice to model ID
        model_map = {
            "WavLM Large (Recommended)": "BUT-FIT/diarizen-wavlm-large-s80-md",
            "WavLM Base (Faster)": "BUT-FIT/diarizen-wavlm-base-s80-md",
            "WavLM Large MLC": "BUT-FIT/diarizen-wavlm-large-s80-mlc"
        }
        model_id = model_map[model_choice]

        # Load pipeline
        pipeline = load_diarizen_pipeline(model_id)

        # Get audio filename
        audio_path = Path(audio_file)
        audio_name = audio_path.stem

        print(f"🎤 Processing audio: {audio_file}")

        # Run diarization
        annotations = pipeline(audio_file)

        print(f"✅ Diarization complete")

        # Format results
        results_text = format_diarization_results(annotations)

        # Save RTTM
        rttm_path = save_rttm(annotations, audio_name)

        return results_text, rttm_path

    except Exception as e:
        error_msg = f"❌ Error during diarization:\n{str(e)}"
        print(error_msg)
        import traceback
        traceback.print_exc()
        return error_msg, None

# Build Gradio Interface
with gr.Blocks(title="DiariZen Speaker Diarization") as demo:
    gr.Markdown("""
    # 🎙️ DiariZen - Speaker Diarization

    **Upload audio → Select model → Run diarization → View results & Download RTTM**

    DiariZen: High-performance speaker diarization toolkit from BUT-FIT
    """)

    if not DIARIZEN_AVAILABLE:
        gr.Markdown("""
        ⚠️ **DiariZen not installed**

        To use this Space, DiariZen must be installed. Please see:
        https://github.com/BUTSpeechFIT/DiariZen
        """)

    with gr.Row():
        with gr.Column():
            # Audio input
            audio_input = gr.Audio(
                label="📤 Upload Audio File",
                type="filepath",
                sources=["upload", "microphone"]
            )

            # Model selection
            model_dropdown = gr.Dropdown(
                choices=[
                    "WavLM Large (Recommended)",
                    "WavLM Base (Faster)",
                    "WavLM Large MLC"
                ],
                value="WavLM Large (Recommended)",
                label="🤖 Select Model",
                info="Choose diarization model"
            )

            # Run button
            run_btn = gr.Button("▶️ Run Diarization", variant="primary", size="lg")

        with gr.Column():
            # Results output
            results_output = gr.Textbox(
                label="📊 Diarization Results",
                lines=20,
                max_lines=30,
                show_copy_button=True
            )

            # RTTM download
            rttm_output = gr.File(
                label="📝 Download RTTM",
                interactive=False
            )

    # Model information
    with gr.Accordion("ℹ️ Model Information", open=False):
        gr.Markdown("""
        ### Available Models

        | Model | Parameters | Speed | Quality | Description |
        |-------|-----------|-------|---------|-------------|
        | WavLM Large | 63M | Fast | High | Recommended for most use cases |
        | WavLM Base | - | Very Fast | Good | Faster variant for quick processing |
        | WavLM Large MLC | 63M | Fast | High | Multi-language optimized |

        ### Performance

        DiariZen substantially outperforms Pyannote v3.1:
        - AMI-SDM: 13.9% DER (vs 22.4% Pyannote)
        - VoxConverse: 9.1% DER (vs 11.3% Pyannote)
        - AISHELL-4: 10.1% DER (vs 12.2% Pyannote)

        ### Citation

        ```bibtex
        @inproceedings{diariZen2024,
          title={DiariZen: A toolkit for speaker diarization},
          author={Han, Ivo and Landini, Federico and Burget, Lukáš and Černocký, Jan},
          booktitle={INTERSPEECH},
          year={2024}
        }
        ```
        """)

    # Footer
    gr.Markdown("""
    ---
    **Source**: [github.com/BUTSpeechFIT/DiariZen](https://github.com/BUTSpeechFIT/DiariZen)

    **License**: MIT (Code) | Research/Non-commercial (Models)
    """)

    # Connect button to function
    run_btn.click(
        fn=diarize_audio,
        inputs=[audio_input, model_dropdown],
        outputs=[results_output, rttm_output]
    )

if __name__ == "__main__":
    demo.launch()