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| import gradio as gr | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| import torch | |
| # Set up device for torch based on GPU availability | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # Load Whisper model and processor | |
| model_id = "distil-whisper/distil-large-v3" | |
| processor = WhisperProcessor.from_pretrained(model_id) | |
| model = WhisperForConditionalGeneration.from_pretrained(model_id) | |
| model.to(device) | |
| print(f"Model and processor loaded successfully: {model_id}") | |
| def transcribe_speech(file_info): | |
| # Read the audio file | |
| audio_input = file_info["content"] | |
| # Process the audio file with the Whisper processor | |
| inputs = processor(audio_input, return_tensors="pt", sampling_rate=processor.feature_extractor.sampling_rate) | |
| inputs = inputs.to(device) | |
| # Generate transcription using the Whisper model | |
| output = model.generate(inputs["input_values"]) | |
| # Decode the model output to get the transcription text | |
| transcription = processor.batch_decode(output, skip_special_tokens=True)[0] | |
| return transcription | |
| # Set up the Gradio UI | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Transcribe Audio"): | |
| with gr.Row(): | |
| audio_input = gr.Audio(label="Upload audio file or record") | |
| with gr.Row(): | |
| audio_output = gr.Textbox(label="Transcription") | |
| # Setup the interaction - When audio is provided, transcribe it | |
| audio_input.change(transcribe_speech, inputs=audio_input, outputs=audio_output) | |
| demo.launch(share=True) | |