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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() |