update app.py

app.py

@@ -1,56 +1,200 @@
-#!/usr/bin/env python3
-"""
-Test script to verify that VERSA is installed correctly.
-"""
-
 import os
 import sys
 import subprocess
+import gradio as gr
+import json
+import yaml
+import tempfile
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import time
 from pathlib import Path
 
-# Check if VERSA is installed
+# Check if VERSA is installed, if not, clone and install it
 VERSA_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "versa")
 
-def check_versa():
-    """Check if VERSA is installed and working"""
-    print("Testing VERSA installation...")
-    
+def setup_versa():
+    """Set up VERSA and its dependencies if not already installed"""
     if not os.path.exists(VERSA_ROOT):
-        print("VERSA not found.")
-        return False
-    
-    # Check if the scorer.py exists
-    scorer_path = os.path.join(VERSA_ROOT, "versa", "bin", "scorer.py")
-    if not os.path.exists(scorer_path):
-        print(f"VERSA scorer not found at {scorer_path}")
-        return False
-    
-    # Check if the config directory exists
-    config_dir = os.path.join(VERSA_ROOT, "egs")
-    if not os.path.exists(config_dir):
-        print(f"VERSA config directory not found at {config_dir}")
-        return False
-    
-    # Check for available metrics
+        print("VERSA not found. Installing...")
+        # Clone VERSA repository
+        subprocess.run(
+            ["git", "clone", "https://github.com/shinjiwlab/versa.git", VERSA_ROOT],
+            check=True
+        )
+        
+        # Install VERSA
+        subprocess.run(
+            ["pip", "install", "-e", VERSA_ROOT],
+            check=True
+        )
+        
+        print("VERSA installed successfully!")
+    else:
+        print("VERSA already installed.")
+
+# Install VERSA if not already installed
+setup_versa()
+
+# VERSA paths
+VERSA_BIN = os.path.join(VERSA_ROOT, "versa", "bin", "scorer.py")
+VERSA_CONFIG_DIR = os.path.join(VERSA_ROOT, "egs")
+
+# Create data directory if it doesn't exist
+DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
+UPLOAD_DIR = os.path.join(DATA_DIR, "uploads")
+RESULTS_DIR = os.path.join(DATA_DIR, "results")
+
+for directory in [DATA_DIR, UPLOAD_DIR, RESULTS_DIR]:
+    os.makedirs(directory, exist_ok=True)
+
+# Find available metric configs
+def get_available_metrics():
+    """Get list of available metrics from VERSA config directory"""
     metrics = []
-    for root, _, files in os.walk(config_dir):
+    
+    # Get all YAML files from the egs directory
+    for root, _, files in os.walk(VERSA_CONFIG_DIR):
         for file in files:
             if file.endswith('.yaml'):
-                metrics.append(os.path.join(root, file))
+                path = os.path.join(root, file)
+                # Get relative path from VERSA_CONFIG_DIR
+                rel_path = os.path.relpath(path, VERSA_CONFIG_DIR)
+                metrics.append(rel_path)
     
-    if not metrics:
-        print("No metric configurations found in VERSA.")
-        return False
+    return sorted(metrics)
+
+# Get metric description from YAML file
+def get_metric_description(metric_path):
+    """Get description of a metric from its YAML file"""
+    full_path = os.path.join(VERSA_CONFIG_DIR, metric_path)
+    try:
+        with open(full_path, 'r') as f:
+            config = yaml.safe_load(f)
+            return config.get('description', 'No description available')
+    except Exception as e:
+        return f"Could not load description: {str(e)}"
+
+# Process audio files and run VERSA evaluation
+def evaluate_audio(gt_file, pred_file, metric_config, include_timestamps=False):
+    """Evaluate audio files using VERSA"""
+    if gt_file is None or pred_file is None:
+        return "Please upload both ground truth and prediction audio files."
     
-    print(f"Found {len(metrics)} metric configurations.")
-    for metric in metrics[:5]:  # Print first 5 metrics
-        print(f"- {os.path.relpath(metric, config_dir)}")
+    # Create temp directory for results
+    with tempfile.TemporaryDirectory() as temp_dir:
+        output_file = os.path.join(temp_dir, "result.json")
+        
+        # Full path to metric config
+        metric_config_path = os.path.join(VERSA_CONFIG_DIR, metric_config)
+        
+        # Build command
+        cmd = [
+            sys.executable, VERSA_BIN,
+            "--score_config", metric_config_path,
+            "--gt", gt_file,
+            "--pred", pred_file,
+            "--output_file", output_file
+        ]
+        
+        if include_timestamps:
+            cmd.append("--include_timestamp")
+        
+        # Run VERSA evaluation
+        try:
+            process = subprocess.run(
+                cmd,
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+                text=True
+            )
+            
+            # Read results
+            if os.path.exists(output_file):
+                with open(output_file, 'r') as f:
+                    results = json.load(f)
+                
+                # Format results as DataFrame
+                if results:
+                    results_df = pd.DataFrame(results)
+                    return results_df, json.dumps(results, indent=2)
+                else:
+                    return None, "Evaluation completed but no results were generated."
+            else:
+                return None, "Evaluation completed but no results file was generated."
+        
+        except subprocess.CalledProcessError as e:
+            return None, f"Error running VERSA: {e.stderr}"
+
+# Create the Gradio interface
+def create_gradio_demo():
+    """Create the Gradio demo interface"""
+    available_metrics = get_available_metrics()
+    default_metric = "speech.yaml" if "speech.yaml" in available_metrics else available_metrics[0] if available_metrics else None
     
-    if len(metrics) > 5:
-        print(f"... and {len(metrics) - 5} more.")
+    with gr.Blocks(title="VERSA Speech & Audio Evaluation Demo") as demo:
+        gr.Markdown("# VERSA: Versatile Evaluation of Speech and Audio")
+        gr.Markdown("Upload audio files to evaluate them using VERSA metrics.")
+        
+        with gr.Row():
+            with gr.Column():
+                gt_audio = gr.Audio(label="Ground Truth Audio", type="filepath", sources=["upload", "microphone"])
+                pred_audio = gr.Audio(label="Prediction Audio", type="filepath", sources=["upload", "microphone"])
+                
+                metric_dropdown = gr.Dropdown(
+                    choices=available_metrics,
+                    label="Evaluation Metric",
+                    value=default_metric
+                )
+                
+                metric_description = gr.Textbox(
+                    label="Metric Description",
+                    value=get_metric_description(default_metric) if default_metric else "",
+                    interactive=False
+                )
+                
+                include_timestamps = gr.Checkbox(
+                    label="Include Timestamps in Results",
+                    value=False
+                )
+                
+                eval_button = gr.Button("Evaluate")
+            
+            with gr.Column():
+                results_table = gr.Dataframe(label="Evaluation Results")
+                raw_json = gr.Code(language="json", label="Raw Results")
+        
+        # Event handlers
+        def update_metric_description(metric_path):
+            return get_metric_description(metric_path)
+        
+        metric_dropdown.change(
+            fn=update_metric_description,
+            inputs=[metric_dropdown],
+            outputs=[metric_description]
+        )
+        
+        eval_button.click(
+            fn=evaluate_audio,
+            inputs=[gt_audio, pred_audio, metric_dropdown, include_timestamps],
+            outputs=[results_table, raw_json]
+        )
+        
+        gr.Markdown("""
+        ## About VERSA
+        
+        VERSA (Versatile Evaluation of Speech and Audio) is a toolkit dedicated to collecting evaluation metrics in speech and audio quality. It provides a comprehensive connection to cutting-edge evaluation techniques and is tightly integrated with ESPnet.
+        
+        With full installation, VERSA offers over 60 metrics with 700+ metric variations based on different configurations. These metrics encompass evaluations utilizing diverse external resources, including matching and non-matching reference audio, text transcriptions, and text captions.
+        
+        Learn more at [VERSA GitHub Repository](https://github.com/shinjiwlab/versa)
+        """)
     
-    print("VERSA installation looks good!")
-    return True
+    return demo
 
+# Launch the app
 if __name__ == "__main__":
-    check_versa()
+    demo = create_gradio_demo()
+    demo.launch()