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| import whisper | |
| import os | |
| import gradio as gr | |
| from groq import Groq | |
| from deep_translator import GoogleTranslator | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| # Set up Groq API key | |
| api_key = os.getenv("GROQ_API_KEY") | |
| client = Groq(api_key=api_key) | |
| # Set device: CUDA if available, else CPU | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # Load Whisper model (if using locally, else use API as in original code) | |
| # This is assuming you're using Whisper locally, if not, the client API is used. | |
| whisper_model = whisper.load_model("base") | |
| # Model IDs for Stable Diffusion pipelines | |
| model_id1 = "dreamlike-art/dreamlike-diffusion-1.0" | |
| model_id2 = "stabilityai/stable-diffusion-xl-base-1.0" | |
| # Initialize Stable Diffusion pipeline based on device | |
| if torch.cuda.is_available(): | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16) | |
| else: | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id1) # Omit torch_dtype for CPU | |
| # Move model to the selected device (either GPU or CPU) | |
| pipe = pipe.to(device) | |
| # Function to transcribe, translate, and analyze sentiment | |
| def process_audio(audio_path, image_option): | |
| if audio_path is None: | |
| return "Please upload an audio file.", None, None, None | |
| # Step 1: Transcribe audio | |
| try: | |
| with open(audio_path, "rb") as file: | |
| transcription = client.audio.transcriptions.create( | |
| file=(os.path.basename(audio_path), file.read()), | |
| model="whisper-large-v3", | |
| language="ta", | |
| response_format="verbose_json", | |
| ) | |
| tamil_text = transcription.text | |
| except Exception as e: | |
| return f"An error occurred during transcription: {str(e)}", None, None, None | |
| # Step 2: Translate Tamil to English | |
| try: | |
| translator = GoogleTranslator(source='ta', target='en') | |
| translation = translator.translate(tamil_text) | |
| except Exception as e: | |
| return tamil_text, f"An error occurred during translation: {str(e)}", None, None | |
| # Step 3: Generate image (if selected) | |
| image = None | |
| if image_option == "Generate Image": | |
| try: | |
| model_id1 = "dreamlike-art/dreamlike-diffusion-1.0" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id1, torch_dtype=torch.float16, use_safetensors=True) | |
| pipe = pipe.to("cuda") | |
| image = pipe(translation).images[0] | |
| except Exception as e: | |
| return tamil_text, translation, f"An error occurred during image generation: {str(e)}" | |
| return tamil_text, translation, image | |
| # Create Gradio interface | |
| with gr.Blocks(theme=gr.themes.Base()) as iface: | |
| gr.Markdown("# Audio Transcription, Translation, and Image Generation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_input = gr.Audio(type="filepath", label="Upload Audio File") | |
| image_option = gr.Dropdown(["Generate Image", "Skip Image"], label="Image Generation", value="Generate Image") | |
| submit_button = gr.Button("Process Audio") | |
| with gr.Column(): | |
| tamil_text_output = gr.Textbox(label="Tamil Transcription") | |
| translation_output = gr.Textbox(label="English Translation") | |
| image_output = gr.Image(label="Generated Image") | |
| submit_button.click( | |
| fn=process_audio, | |
| inputs=[audio_input, image_option], | |
| outputs=[tamil_text_output, translation_output, image_output] | |
| ) | |
| # Launch the interface | |
| iface.launch() | |