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| # import subprocess | |
| # # Install required libraries | |
| # subprocess.check_call(["pip", "install", "torch>=1.11.0"]) | |
| # subprocess.check_call(["pip", "install", "transformers>=4.31.0"]) | |
| # subprocess.check_call(["pip", "install", "diffusers>=0.14.0"]) | |
| # subprocess.check_call(["pip", "install", "librosa"]) | |
| # subprocess.check_call(["pip", "install", "accelerate>=0.20.1"]) | |
| # subprocess.check_call(["pip", "install", "gradio>=3.35.2"]) | |
| # subprocess.check_call(["pip", "install", "huggingface_hub"]) | |
| # import os | |
| # import threading | |
| # import numpy as np | |
| # import librosa | |
| # import torch | |
| # import gradio as gr | |
| # from functools import lru_cache | |
| # from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
| # from huggingface_hub import login | |
| # from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler | |
| # # Ensure required dependencies are installed | |
| # def install_missing_packages(): | |
| # required_packages = { | |
| # "librosa": None, | |
| # "diffusers": ">=0.14.0", | |
| # "gradio": ">=3.35.2", | |
| # "huggingface_hub": None, | |
| # "accelerate": ">=0.20.1", | |
| # "transformers": ">=4.31.0" | |
| # } | |
| # for package, version in required_packages.items(): | |
| # try: | |
| # __import__(package) | |
| # except ImportError: | |
| # package_name = f"{package}{version}" if version else package | |
| # subprocess.check_call(["pip", "install", package_name]) | |
| # install_missing_packages() | |
| # # Get Hugging Face token for authentication | |
| # hf_token = os.getenv("HF_TOKEN") | |
| # if hf_token: | |
| # login(hf_token) | |
| # else: | |
| # raise ValueError("HF_TOKEN environment variable not set.") | |
| # # Load speech-to-text model (Whisper) | |
| # speech_to_text = pipeline( | |
| # "automatic-speech-recognition", | |
| # model="openai/whisper-tiny", | |
| # return_timestamps=True | |
| # ) | |
| # # Load Stable Diffusion model for text-to-image | |
| # text_to_image = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") | |
| # device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # text_to_image.to(device) | |
| # text_to_image.enable_attention_slicing() | |
| # text_to_image.safety_checker = None | |
| # text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config) | |
| # # Load ChatGPT-like conversational model | |
| # chat_model_name = "microsoft/DialoGPT-medium" | |
| # chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_name) | |
| # chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name) | |
| # # Preprocess audio file into NumPy array | |
| # def preprocess_audio(audio_path): | |
| # try: | |
| # audio, sr = librosa.load(audio_path, sr=16000) # Resample to 16kHz | |
| # return np.array(audio, dtype=np.float32) | |
| # except Exception as e: | |
| # return f"Error in preprocessing audio: {str(e)}" | |
| # # Speech-to-text function with long-form transcription support | |
| # @lru_cache(maxsize=10) | |
| # def transcribe_audio(audio_path): | |
| # try: | |
| # audio_array = preprocess_audio(audio_path) | |
| # if isinstance(audio_array, str): # Error message from preprocessing | |
| # return audio_array | |
| # result = speech_to_text(audio_array) | |
| # # Combine text from multiple segments for long-form transcription | |
| # transcription = " ".join(segment["text"] for segment in result["chunks"]) | |
| # return transcription | |
| # except Exception as e: | |
| # return f"Error in transcription: {str(e)}" | |
| # # Text-to-image function | |
| # @lru_cache(maxsize=10) | |
| # def generate_image_from_text(text): | |
| # try: | |
| # image = text_to_image(text, height=256, width=256).images[0] # Generate smaller images for speed | |
| # return image | |
| # except Exception as e: | |
| # return f"Error in image generation: {str(e)}" | |
| # # ChatGPT-like conversational response | |
| # def chat_with_gpt(prompt): | |
| # try: | |
| # inputs = chat_tokenizer.encode(prompt, return_tensors="pt") | |
| # outputs = chat_model.generate(inputs, max_length=200, pad_token_id=chat_tokenizer.eos_token_id) | |
| # response = chat_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # return response | |
| # except Exception as e: | |
| # return f"Error in chat response: {str(e)}" | |
| # # Combined processing function | |
| # def process_audio_and_generate_results(audio_path): | |
| # transcription_result = {"result": None} | |
| # image_result = {"result": None} | |
| # # Function to run transcription and image generation in parallel | |
| # def transcription_thread(): | |
| # transcription_result["result"] = transcribe_audio(audio_path) | |
| # def image_generation_thread(): | |
| # transcription = transcription_result["result"] | |
| # if transcription and "Error" not in transcription: | |
| # image_result["result"] = generate_image_from_text(transcription) | |
| # # Start both tasks in parallel | |
| # t1 = threading.Thread(target=transcription_thread) | |
| # t2 = threading.Thread(target=image_generation_thread) | |
| # t1.start() | |
| # t2.start() | |
| # t1.join() # Wait for transcription to finish | |
| # t2.join() # Wait for image generation to finish | |
| # transcription = transcription_result["result"] | |
| # image = image_result["result"] | |
| # if "Error" in transcription: | |
| # return None, transcription | |
| # if isinstance(image, str) and "Error" in image: | |
| # return None, image | |
| # return image, transcription | |
| # # Gradio interface for speech-to-text | |
| # speech_to_text_iface = gr.Interface( | |
| # fn=transcribe_audio, | |
| # inputs=gr.Audio(type="filepath", label="Upload audio file for transcription (WAV/MP3)"), | |
| # outputs=gr.Textbox(label="Transcription"), | |
| # title="Speech-to-Text Transcription", | |
| # description="Upload an audio file to transcribe speech into text.", | |
| # ) | |
| # # Gradio interface for voice-to-image and chat | |
| # voice_to_image_and_chat_iface = gr.Interface( | |
| # fn=process_audio_and_generate_results, | |
| # inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"), | |
| # outputs=[gr.Image(label="Generated Image"), gr.Textbox(label="Transcription")], | |
| # title="Voice-to-Image and Chat", | |
| # description="Upload an audio file to transcribe speech to text, generate an image based on the transcription, or chat with GPT.", | |
| # ) | |
| # # Gradio interface for ChatGPT-like functionality | |
| # chat_iface = gr.Interface( | |
| # fn=chat_with_gpt, | |
| # inputs=gr.Textbox(label="Enter your prompt for ChatGPT"), | |
| # outputs=gr.Textbox(label="ChatGPT Response"), | |
| # title="ChatGPT", | |
| # description="Chat with GPT-like conversational AI.", | |
| # ) | |
| # # Combined Gradio app | |
| # iface = gr.TabbedInterface( | |
| # interface_list=[speech_to_text_iface, voice_to_image_and_chat_iface, chat_iface], | |
| # tab_names=["Speech-to-Text", "Voice-to-Image & Chat", "ChatGPT"] | |
| # ) | |
| # # Launch Gradio interface | |
| # iface.launch(debug=True, share=True) | |
| import subprocess | |
| # Install required libraries | |
| subprocess.check_call(["pip", "install", "torch>=1.11.0"]) | |
| subprocess.check_call(["pip", "install", "transformers>=4.31.0"]) | |
| subprocess.check_call(["pip", "install", "diffusers>=0.14.0"]) | |
| subprocess.check_call(["pip", "install", "librosa"]) | |
| subprocess.check_call(["pip", "install", "accelerate>=0.20.1"]) | |
| subprocess.check_call(["pip", "install", "gradio>=3.35.2"]) | |
| subprocess.check_call(["pip", "install", "huggingface_hub"]) | |
| import os | |
| import threading | |
| import numpy as np | |
| import librosa | |
| import torch | |
| import gradio as gr | |
| from functools import lru_cache | |
| from transformers import pipeline | |
| from huggingface_hub import login | |
| from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler | |
| # Ensure required dependencies are installed | |
| def install_missing_packages(): | |
| required_packages = { | |
| "librosa": None, | |
| "diffusers": ">=0.14.0", | |
| "gradio": ">=3.35.2", | |
| "huggingface_hub": None, | |
| "accelerate": ">=0.20.1", | |
| "transformers": ">=4.31.0" | |
| } | |
| for package, version in required_packages.items(): | |
| try: | |
| __import__(package) | |
| except ImportError: | |
| package_name = f"{package}{version}" if version else package | |
| subprocess.check_call(["pip", "install", package_name]) | |
| install_missing_packages() | |
| # Get Hugging Face token for authentication | |
| hf_token = os.getenv("HF_TOKEN") | |
| if hf_token: | |
| login(hf_token) | |
| else: | |
| raise ValueError("HF_TOKEN environment variable not set.") | |
| # Load speech-to-text model (Whisper) | |
| speech_to_text = pipeline( | |
| "automatic-speech-recognition", | |
| model="openai/whisper-tiny", | |
| return_timestamps=True | |
| ) | |
| # Load Stable Diffusion model for text-to-image | |
| text_to_image = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| text_to_image.to(device) | |
| text_to_image.enable_attention_slicing() | |
| text_to_image.safety_checker = None | |
| text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config) | |
| # Preprocess audio file into NumPy array | |
| def preprocess_audio(audio_path): | |
| try: | |
| audio, sr = librosa.load(audio_path, sr=16000) # Resample to 16kHz | |
| return np.array(audio, dtype=np.float32) | |
| except Exception as e: | |
| return f"Error in preprocessing audio: {str(e)}" | |
| # Speech-to-text function with long-form transcription support | |
| def transcribe_audio(audio_path): | |
| try: | |
| audio_array = preprocess_audio(audio_path) | |
| if isinstance(audio_array, str): # Error message from preprocessing | |
| return audio_array | |
| result = speech_to_text(audio_array) | |
| # Combine text from multiple segments for long-form transcription | |
| transcription = " ".join(segment["text"] for segment in result["chunks"]) | |
| return transcription | |
| except Exception as e: | |
| return f"Error in transcription: {str(e)}" | |
| # Text-to-image function | |
| def generate_image_from_text(text): | |
| try: | |
| image = text_to_image(text, height=256, width=256).images[0] # Generate smaller images for speed | |
| return image | |
| except Exception as e: | |
| return f"Error in image generation: {str(e)}" | |
| # Combined processing function | |
| def process_audio_and_generate_results(audio_path): | |
| transcription_result = {"result": None} | |
| image_result = {"result": None} | |
| # Function to run transcription and image generation in parallel | |
| def transcription_thread(): | |
| transcription_result["result"] = transcribe_audio(audio_path) | |
| def image_generation_thread(): | |
| transcription = transcription_result["result"] | |
| if transcription and "Error" not in transcription: | |
| image_result["result"] = generate_image_from_text(transcription) | |
| # Start both tasks in parallel | |
| t1 = threading.Thread(target=transcription_thread) | |
| t2 = threading.Thread(target=image_generation_thread) | |
| t1.start() | |
| t2.start() | |
| t1.join() # Wait for transcription to finish | |
| t2.join() # Wait for image generation to finish | |
| transcription = transcription_result["result"] | |
| image = image_result["result"] | |
| if "Error" in transcription: | |
| return None, transcription | |
| if isinstance(image, str) and "Error" in image: | |
| return None, image | |
| return image, transcription | |
| # Gradio interface for speech-to-text | |
| speech_to_text_iface = gr.Interface( | |
| fn=transcribe_audio, | |
| inputs=gr.Audio(type="filepath", label="Upload audio file for transcription (WAV/MP3)"), | |
| outputs=gr.Textbox(label="Transcription"), | |
| title="Speech-to-Text Transcription", | |
| description="Upload an audio file to transcribe speech into text.", | |
| ) | |
| # Gradio interface for voice-to-image | |
| voice_to_image_iface = gr.Interface( | |
| fn=process_audio_and_generate_results, | |
| inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"), | |
| outputs=[gr.Image(label="Generated Image"), gr.Textbox(label="Transcription")], | |
| title="Voice-to-Image", | |
| description="Upload an audio file to transcribe speech to text and generate an image based on the transcription.", | |
| ) | |
| # Combined Gradio app | |
| iface = gr.TabbedInterface( | |
| interface_list=[speech_to_text_iface, voice_to_image_iface], | |
| tab_names=["Speech-to-Text", "Voice-to-Image"] | |
| ) | |
| # Launch Gradio interface | |
| iface.launch(debug=True, share=True) | |