Create app.py

app.py

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+import gradio as gr
+import whisperx
+import torch
+import librosa
+import logging
+import os
+import time
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("whisperx_app")
+
+# Device setup (force CPU)
+device = "cpu"
+torch.set_num_threads(os.cpu_count())
+
+# Pre-load models
+models = {
+    "tiny": whisperx.load_model("tiny", device),
+    "base": whisperx.load_model("base", device),
+    "small": whisperx.load_model("small", device),
+}
+
+def transcribe(audio_file, model_size="base", debug=False):
+    start_time = time.time()
+    result = ""
+    debug_log = []
+    
+    try:
+        # Load audio file
+        audio, sr = librosa.load(audio_file, sr=16000)
+        
+        # Run inference
+        model = models[model_size]
+        batch_size = 8 if model_size == "tiny" else 4
+        transcript = model.transcribe(audio, batch_size=batch_size)
+        
+        # Align whisper output
+        model_a, metadata = whisperx.load_align_model(
+            language_code=transcript["language"], device=device
+        )
+        transcript_aligned = whisperx.align(
+            transcript["segments"], model_a, metadata, audio, device
+        )
+        
+        # Format word-level output
+        for segment in transcript_aligned["segments"]:
+            for word in segment["words"]:
+                result += f"[{word['start']:5.2f}s-{word['end']:5.2f}s] {word['word']}\n"
+        
+        debug_log.append(f"Processed in {time.time()-start_time:.2f}s")
+        debug_log.append(f"Language detected: {transcript['language']}")
+        debug_log.append(f"Batch size: {batch_size}")
+        
+    except Exception as e:
+        logger.error("Error during transcription:", exc_info=True)
+        result = "Error occurred during transcription"
+        debug_log.append(f"ERROR: {str(e)}")
+    
+    if debug:
+        return result, "\n".join(debug_log)
+    return result
+
+# Gradio Interface
+with gr.Blocks(title="WhisperX CPU Transcription") as demo:
+    gr.Markdown("# WhisperX CPU Transcription with Word-Level Timestamps")
+    
+    with gr.Row():
+        with gr.Column():
+            audio_input = gr.Audio(
+                label="Upload Audio File",
+                type="filepath",
+                sources=["upload", "microphone"],
+                interactive=True,
+            )
+            model_selector = gr.Dropdown(
+                choices=["tiny", "base", "small"],
+                value="base",
+                label="Model Size",
+                interactive=True,
+            )
+            debug_checkbox = gr.Checkbox(label="Enable Debug Mode", value=False)
+            transcribe_btn = gr.Button("Transcribe", variant="primary")
+            
+        with gr.Column():
+            output_text = gr.Textbox(
+                label="Transcription Output",
+                lines=20,
+                placeholder="Transcription will appear here...",
+            )
+            debug_output = gr.Textbox(
+                label="Debug Information",
+                lines=10,
+                placeholder="Debug logs will appear here...",
+                visible=False,
+            )
+    
+    # Toggle debug visibility
+    def toggle_debug(debug_enabled):
+        return gr.update(visible=debug_enabled)
+    
+    debug_checkbox.change(
+        toggle_debug,
+        inputs=[debug_checkbox],
+        outputs=[debug_output]
+    )
+    
+    # Process transcription
+    transcribe_btn.click(
+        transcribe,
+        inputs=[audio_input, model_selector, debug_checkbox],
+        outputs=[output_text, debug_output]
+    )
+
+# Launch configuration
+if __name__ == "__main__":
+    demo.queue(max_size=4).launch()