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README.md

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----
-license: mit
-title: Multilingual Realtime Translator
-sdk: gradio
-emoji: 🚀
-colorTo: green
-short_description: ranslate between English and Nigerian languages using speech
----
 # Multilingual Realtime Translator
 
-Translate between English and Nigerian languages using speech and text.
+This Gradio app translates speech or text between English and Yoruba (extendable to Hausa and Igbo).
+
+## Usage
+
+- Upload to your Hugging Face Space
+- Ensure you have the proper model weights downloaded (Whisper, MarianMT)

app.py

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+import gradio as gr
+from transformers import pipeline, MarianMTModel, MarianTokenizer
+import edge_tts
+import asyncio
+
+# Load Whisper for English ASR
+asr_en = pipeline("automatic-speech-recognition", model="openai/whisper-base")
+
+# Load MarianMT for Yoruba-English (can switch for Igbo/Hausa)
+mt_model_name = "Helsinki-NLP/opus-mt-yo-en"
+tokenizer = MarianTokenizer.from_pretrained(mt_model_name)
+model = MarianMTModel.from_pretrained(mt_model_name)
+
+def translate_speech(audio):
+    transcription = asr_en(audio)["text"]
+    inputs = tokenizer(transcription, return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**inputs)
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return transcription, translated_text
+
+def translate_text(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**inputs)
+    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return translated_text
+
+with gr.Blocks() as demo:
+    gr.Markdown("### Multilingual Realtime Translator - English ↔ Yoruba (V1)")
+    
+    with gr.Tab("Speech Input"):
+        mic = gr.Audio(source="microphone", type="filepath", label="Speak Now")
+        transcription = gr.Textbox(label="Transcription")
+        translation = gr.Textbox(label="Translation")
+        mic.submit(translate_speech, inputs=mic, outputs=[transcription, translation])
+
+    with gr.Tab("Text Input"):
+        input_text = gr.Textbox(label="Enter text")
+        translated_text = gr.Textbox(label="Translated text")
+        input_text.submit(translate_text, inputs=input_text, outputs=translated_text)
+
+demo.launch()

requirements.txt

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+gradio
 transformers
 torch
-openai-whisper
-gradio
 edge-tts
-speechrecognition