Update app.py

app.py

@@ -4,23 +4,9 @@ import soundfile as sf
 import torch
 import gradio as gr
 import numpy as np
-
+import 
 from transformers import MarianMTModel, MarianTokenizer
 
-model_name = "Helsinki-NLP/opus-mt-en-ar"
-
-# Use MarianTokenizer specifically
-tokenizer = MarianTokenizer.from_pretrained(model_name)
-model = MarianMTModel.from_pretrained(model_name)
-
-def translate_to_arabic(text):
-    inputs = tokenizer(text, return_tensors="pt", padding=True)
-    outputs = model.generate(**inputs, max_length=100)
-    translated = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return translated
-
-# Test it
-print(translate_to_arabic("Hello, how are you?"))
 
 def predict_image(image):
   pipe = pipeline("image-classification", model="google/vit-base-patch16-224")
@@ -34,52 +20,22 @@ def translate_to_arabic(text):
   result=pipe(text , max_length=100)
   return result[0]['translation_text']
 
-# Use a pipeline as a high-level helper
-# Warning: Pipeline type "translation" is no longer supported in transformers v5.
-# You must load the model directly (see below) or downgrade to v4.x with:
-# 'pip install "transformers<5.0.0'
-from transformers import pipeline
-
-pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")
 
 def translate_to_arabic(text):
+    model_name = "Helsinki-NLP/opus-mt-en-ar"
+
+    # Use MarianTokenizer specifically
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    
     inputs = tokenizer(text, return_tensors="pt", padding=True)
     outputs = model.generate(**inputs, max_length=100)
     translated = tokenizer.decode(outputs[0], skip_special_tokens=True)
     return translated
 
-"""✅ Because SpeechT5 requires a speaker embedding
-
-The model MBZUAI/speecht5_tts_clartts_ar is based on SpeechT5, and SpeechT5 needs a speaker embedding to generate speech.
-
-Think of it like this:
-
-The text tells the model what to say.
-
-The speaker embedding tells the model what the voice should sound like.
-
-Without a speaker embedding, the model does not know what voice to use → and the pipeline will fail or produce wrong audio.
-
-What is the dataset?
-
-herwoww/arabic_xvector_embeddings contains pre-computed speaker embeddings (x-vectors) for different Arabic speakers.
-
-Each embedding is like a "voice fingerprint".
-
-You pick one of them to generate speech in that voice.
-
-In your code, you choose speaker number 105:
-"""
-
 def text_to_speech(text):
   pipe = pipeline("text-to-speech", model="MBZUAI/speecht5_tts_clartts_ar")
   embedding_dataset=load_dataset("herwoww/arabic_xvector_embeddings" , split="validation") #Those embeddings represent a speaker’s voice characteristics
-  '''
-   This is a 1D tensor.
-   But most models expect: (batch_size, input_size)
-   torch.Size([1, 3]) 1 sample (batch size = 1)
-   (784,)>> (1, 784)>> (batch_size, 784)
-  '''
   speaker_embedding=torch.tensor(embedding_dataset[100]['speaker_embeddings']).unsqueeze(0) ##It becomes a 2-D tensor
   speech=pipe(text , forward_params={'speaker_embeddings':speaker_embedding})