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| import gradio as gr | |
| import moviepy.video.io.ImageSequenceClip | |
| #image_folder= '/content/gdrive/My Drive/AI/sample_imgs/' | |
| from PIL import Image | |
| #import os, sys | |
| from pydub import AudioSegment | |
| # Import everything needed to edit video clips | |
| from moviepy.editor import * | |
| #path = "/content/gdrive/My Drive/AI/sample_imgs/" | |
| #dirs = os.listdir( path ) | |
| def resize(img_list): | |
| resize_img_list = [] | |
| for item in img_list: | |
| im = Image.open(item) | |
| imResize = im.resize((256,256), Image.ANTIALIAS) | |
| resize_img_list.append(imResize) | |
| return resize_img_list | |
| #def resize(): | |
| # for item in dirs: | |
| # if os.path.isfile(path+item): | |
| # im = Image.open(path+item) | |
| # f, e = os.path.splitext(path+item) | |
| # imResize = im.resize((256,256), Image.ANTIALIAS) | |
| # imResize.save(f + ' resized.jpg', 'JPEG', quality=90) | |
| #resize_img_list = resize(img_list) | |
| #image_files = [os.path.join(image_folder,img) | |
| # for img in resize_img_list | |
| # if img.endswith(".jpg")] | |
| #print(image_files) | |
| def images_to_video(fps, resize_img_list): | |
| clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(resize_img_list, fps=fps) | |
| return clip | |
| #clip.write_videofile('/content/gdrive/My Drive/AI/my_vid_20apr.mp4') | |
| def merge_audio_video(speech, clip): | |
| #convert flac to mp3 audio format | |
| wav_audio = AudioSegment.from_file(speech, "flac") #("/content/gdrive/My Drive/AI/audio1.flac", "flac") | |
| wav_audio.export("audio.mp3", format="mp3") #("/content/gdrive/My Drive/AI/audio1.mp3", format="mp3") | |
| # loading video dsa gfg intro video | |
| clip = VideoFileClip(clip) #("/content/gdrive/My Drive/AI/my_video1.mp4") | |
| # loading audio file | |
| audioclip = AudioFileClip('audio.mp3') #.subclip(0, 15) | |
| # adding audio to the video clip | |
| videoclip = clip.set_audio(audioclip) | |
| # showing video clip | |
| #videoclip.ipython_display() | |
| return videoclip | |
| fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech") | |
| def text2speech(text): | |
| return fastspeech(text) | |
| def engine(text_input): | |
| #Extract entities from text | |
| ner = gr.Interface.load("huggingface/flair/ner-english-ontonotes-large") | |
| entities = ner(text_input) | |
| entities = [tupl for tupl in entities if None not in tupl] | |
| entities_num = len(entities) | |
| #img = run(text_input,'50','256','256','1',10) #entities[0][0] | |
| #Generate images using multimodelart's space for each entity identified above | |
| img_list = [] | |
| for ent in entities: | |
| img = gr.Interface.load("spaces/multimodalart/latentdiffusion")(ent[0],'50','256','256','1',10)[0] | |
| img_list.append(img) | |
| #Resizing all images produced to same size | |
| resize_img_list = resize(img_list) | |
| #Convert text to speech using facebook's latest model from HF hub | |
| speech = text2speech(text_input) | |
| #Calculate the desired frame per second based on given audio length and entities identified | |
| fps= entities_num / 19 #length of audio file #13 / 19 | |
| #Convert string of images into a video | |
| clip = images_to_video(fps, resize_img_list) | |
| #Merge video and audio created above | |
| merged_file = merge_audio_video(speech, clip) | |
| #{'prompt':text_input,'steps':'50','width':'256','height':'256','images':'1','scale':10}).launch() | |
| #img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion", inputs=[gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text")], | |
| #outputs=[gr.outputs.Image(type="pil", label="output image"),gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")], ) | |
| #title="Convert text to image") | |
| #img = img_intfc[0] | |
| #img = img_intfc(text_input,'50','256','256','1',10) | |
| #print(img) | |
| #print(type(img)) | |
| #print(img) | |
| #print(type(img[1][0][0])) | |
| #print(img[1]) | |
| #img = img[0] | |
| #inputs=['George',50,256,256,1,10] | |
| #run(prompt, steps, width, height, images, scale) | |
| return merged_file #img, entities, speech | |
| #image = gr.outputs.Image(type="pil", label="output image") | |
| app = gr.Interface(engine, | |
| gr.inputs.Textbox(lines=5, label="Input Text"), | |
| gradio.outputs.Video(self, type=None, label=None), | |
| #[gr.outputs.Image(type="auto", label="Output"), gr.outputs.Textbox(type="auto", label="Text"), gr.outputs.Audio(type="file", label="Speech Answer") ], | |
| #live=True, | |
| #outputs=[#gr.outputs.Textbox(type="auto", label="Text"),gr.outputs.Audio(type="file", label="Speech Answer"), | |
| #outputs= img, #gr.outputs.Carousel(label="Individual images",components=["image"]), #, gr.outputs.Textbox(label="Error")], | |
| examples = ['Apple'], | |
| description="Takes a text as input and reads it out to you." | |
| #examples=["On April 17th Sunday George celebrated Easter. He is staying at Empire State building with his parents. He is a citizen of Canada and speaks English and French fluently. His role model is former president Obama. He got 1000 dollar from his mother to visit Disney World and to buy new iPhone mobile. George likes watching Game of Thrones."] | |
| ).launch(debug=True) #(enable_queue=True) | |
| #get_audio = gr.Button("generate audio") | |
| #get_audio.click(text2speech, inputs=text, outputs=speech) | |
| #def greet(name): | |
| # return "Hello " + name + "!!" | |
| #iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| #iface.launch() |