Create app.py

app.py

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+import gradio as gr
+import whisper
+from transformers import MarianMTModel, MarianTokenizer
+from gtts import gTTS
+from io import BytesIO
+
+# Load Whisper ASR model
+whisper_model = whisper.load_model("small")  # You can choose 'base', 'small', 'medium', 'large'
+
+# Load translation models for Hausa-English and English-Hausa
+model_name_he = 'Helsinki-NLP/opus-mt-ha-en'  # Hausa to English
+model_name_eh = 'Helsinki-NLP/opus-mt-en-ha'  # English to Hausa
+
+tokenizer_he = MarianTokenizer.from_pretrained(model_name_he)
+model_he = MarianMTModel.from_pretrained(model_name_he)
+
+tokenizer_eh = MarianTokenizer.from_pretrained(model_name_eh)
+model_eh = MarianMTModel.from_pretrained(model_name_eh)
+
+# Function to punctuate (simple punctuation for demo)
+def punctuate(text):
+    if text[-1] not in '.!?':
+        text += '.'
+    return text.capitalize()
+
+# Function to translate and punctuate
+def translate_and_punctuate(text, direction):
+    if direction == "Hausa to English":
+        translated = model_he.generate(**tokenizer_he(text, return_tensors="pt", padding=True))
+        result = tokenizer_he.decode(translated[0], skip_special_tokens=True)
+    else:
+        translated = model_eh.generate(**tokenizer_eh(text, return_tensors="pt", padding=True))
+        result = tokenizer_eh.decode(translated[0], skip_special_tokens=True)
+    
+    return punctuate(result)
+
+# Text-to-speech function
+def text_to_speech(text, language):
+    tts = gTTS(text=text, lang=language)
+    audio_fp = BytesIO()
+    tts.save(audio_fp)
+    audio_fp.seek(0)
+    return audio_fp
+
+# Real-time translation function
+def real_time_translation(audio, direction):
+    # Use Whisper model to transcribe the audio (speech to text)
+    result = whisper_model.transcribe(audio)
+    spoken_text = result['text']
+    
+    # Translate and punctuate the transcribed text
+    translated_text = translate_and_punctuate(spoken_text, direction)
+    
+    # Generate speech output from the translated text
+    if direction == "Hausa to English":
+        speech_output = text_to_speech(translated_text, "en")
+    else:
+        speech_output = text_to_speech(translated_text, "ha")
+    
+    return translated_text, speech_output
+
+# Gradio interface
+def translation_app(audio, direction):
+    # Handle real-time translation from audio input
+    translated_text, speech_output = real_time_translation(audio, direction)
+    
+    return translated_text, speech_output
+
+# Define Gradio inputs and outputs
+inputs = [
+    gr.Audio(type="filepath", label="Speak Now"),
+    gr.Radio(choices=["Hausa to English", "English to Hausa"], label="Translation Direction")
+]
+
+outputs = [
+    gr.Textbox(label="Translated and Punctuated Text"),
+    gr.Audio(label="Translated Speech")
+]
+
+# Launch Gradio app
+gr.Interface(fn=translation_app, inputs=inputs, outputs=outputs, title="Real-Time Hausa-English Speech Translator with Whisper").launch()