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| from transformers import MarianTokenizer, MarianMTModel | |
| from gtts import gTTS | |
| import gradio as gr | |
| import gradio as gr | |
| import torch | |
| import torchvision | |
| import torchvision.transforms as transforms | |
| import requests | |
| from einops import rearrange | |
| from transformers import AutoFeatureExtractor, DeiTForImageClassificationWithTeacher | |
| import matplotlib | |
| def imgtrans(img): | |
| feature_extractor = AutoFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-384') | |
| model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-384') | |
| inputs = feature_extractor(images=img, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| # model predicts one of the 21,841 ImageNet-22k classes | |
| predicted_class_idx = logits.argmax(-1).item() | |
| english=model.config.id2label[predicted_class_idx] | |
| english=english.replace("_", " ") | |
| english=english.split(',',1)[0] | |
| src = "en" # source language | |
| trg = "tl" # target language | |
| model_name = f"Helsinki-NLP/opus-mt-{src}-{trg}" | |
| model = MarianMTModel.from_pretrained(model_name) | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| sample_text = english.lower() | |
| batch = tokenizer([sample_text], return_tensors="pt") | |
| generated_ids = model.generate(**batch) | |
| fil=tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]; | |
| tts=gTTS(text=fil,lang='tl') | |
| tts.save('filtrans.wav') | |
| fil_sound='filtrans.wav' | |
| english=english.lower() | |
| tts=gTTS(text=english,lang='en') | |
| tts.save('engtrans.wav') | |
| eng_sound='engtrans.wav' | |
| return fil_sound,fil,eng_sound,english | |
| interface=gr.Interface(fn=imgtrans, | |
| inputs=gr.inputs.Image(shape=(224,224),label='Insert Image'), | |
| outputs=[gr.outputs.Audio(label='Filipino Pronunciation'),gr.outputs.Textbox(label='Filipino Label'), | |
| gr.outputs.Audio(label='English Pronunciation'),gr.outputs.Textbox(label='English label')], | |
| examples = ['220px-Modern_British_LED_Traffic_Light.jpg','aki_dog.jpg','cat.jpg','dog.jpg','plasticbag.jpg', | |
| 'telephone.jpg','vpavic_211006_4796_0061.jpg','watch.jpg','wonder_cat.jpg','hammer.jpg'],description='A Filipino Classifier with Pronunciation using Distilled Data-efficient Image Transformer (base-sized model) for classification and OPUS MT English to Tagalog translation by University of Helsinki. Google Text to Speech module was used for text to speech for English and Filipino. ') | |
| interface.launch() |