Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from jiwer import wer
+import difflib
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load Model 1
+model_name_1 = "maliahson/whisper-agri"
+whisper_pipeline_1 = pipeline("automatic-speech-recognition", model=model_name_1)
+
+# Load Model 2
+model_name_2 = "openai/whisper-large-v3-turbo"
+whisper_pipeline_2 = pipeline("automatic-speech-recognition", model=model_name_2)
+
+def compare_transcriptions(audio, original_transcription=None):
+    """
+    Compares transcriptions from two models and optionally calculates WER.
+    Args:
+        audio: Uploaded audio file.
+        original_transcription: The ground-truth transcription (optional).
+    Returns:
+        Transcriptions from both models, and comparison results.
+    """
+    # Transcribe audio using Model 1
+    transcription_1 = whisper_pipeline_1(audio)["text"]
+    
+    # Transcribe audio using Model 2
+    transcription_2 = whisper_pipeline_2(audio)["text"]
+    
+    result = {
+        "Model 1 Transcription": transcription_1,
+        "Model 2 Transcription": transcription_2
+    }
+    
+    if original_transcription:
+        # Calculate WER for both models
+        wer_1 = wer(original_transcription, transcription_1)
+        wer_2 = wer(original_transcription, transcription_2)
+        result["WER (Model 1)"] = f"{wer_1:.2%}"
+        result["WER (Model 2)"] = f"{wer_2:.2%}"
+        
+        # Create a WER comparison plot
+        fig, ax = plt.subplots()
+        bars = ax.bar(["Model 1", "Model 2"], [wer_1, wer_2], color=["blue", "orange"])
+        ax.set_title("Word Error Rate Comparison")
+        ax.set_ylabel("WER")
+        ax.set_ylim(0, 1)
+        ax.bar_label(bars, fmt="%.2f")
+        plt.tight_layout()
+        return result, fig
+    else:
+        # Calculate and visualize differences between transcriptions
+        diff = difflib.unified_diff(
+            transcription_1.split(),
+            transcription_2.split(),
+            lineterm="",
+            n=0
+        )
+        diff_result = "\n".join(diff)
+        result["Differences"] = diff_result
+        return result, None
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Audio Transcription Comparison with Whisper Models")
+    gr.Markdown("Upload an audio file, and optionally provide the original transcription to compare the outputs.")
+    
+    with gr.Row():
+        audio_input = gr.Audio(source="upload", type="filepath", label="Upload or Record Audio")
+        original_transcription = gr.Textbox(
+            placeholder="Enter original transcription here (optional)",
+            label="Original Transcription"
+        )
+    
+    compare_button = gr.Button("Compare Transcriptions")
+    result_output = gr.JSON(label="Transcriptions and Comparison")
+    wer_plot = gr.Plot(label="Word Error Rate Comparison (if applicable)")
+    
+    compare_button.click(
+        compare_transcriptions,
+        inputs=[audio_input, original_transcription],
+        outputs=[result_output, wer_plot]
+    )
+
+# Launch app
+demo.launch(enable_queue=True)