Create app.py

app.py

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+import os
+import torch
+import gradio as gr
+import numpy as np
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from googletrans import Translator
+from gtts import gTTS
+import librosa
+import tempfile
+import soundfile as sf
+
+class RealTimeTranslator:
+    def __init__(self):
+        # Initialize Whisper model for speech recognition
+        self.processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+        self.model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
+
+        # Use GPU if available
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.model = self.model.to(self.device)
+
+        # Initialize translator
+        self.translator = Translator()
+
+        # Supported languages
+        self.languages = {
+            'en': 'English',
+            'fr': 'French',
+            'hi': 'Hindi',
+            'es': 'Spanish',
+            'de': 'German',
+            'ja': 'Japanese'
+        }
+
+    def speech_to_text(self, audio_path, source_lang):
+        """Convert speech to text using Whisper"""
+        # Load and preprocess audio
+        audio, _ = librosa.load(audio_path, sr=16000)
+        input_features = self.processor(audio, sampling_rate=16000, return_tensors="pt").input_features
+        input_features = input_features.to(self.device)
+
+        # Generate token ids
+        predicted_ids = self.model.generate(input_features)
+
+        # Decode token ids to text
+        transcription = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
+        return transcription[0]
+
+    def translate_text(self, text, source_lang, target_lang):
+        """Translate text using Google Translate"""
+        translation = self.translator.translate(text, src=source_lang, dest=target_lang)
+        return translation.text
+
+    def text_to_speech(self, text, target_lang):
+        """Convert text to speech using gTTS"""
+        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as fp:
+            tts = gTTS(text=text, lang=target_lang)
+            tts.save(fp.name)
+            return fp.name
+
+    def process_audio(self, audio, source_lang, target_lang):
+        """Complete pipeline: Speech → Text → Translation → Speech"""
+        try:
+            if audio is None:
+                return None, "No audio input received", "Please provide audio input"
+
+            # Save input audio temporarily
+            with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as fp:
+                sf.write(fp.name, audio[1], audio[0])
+                audio_path = fp.name
+
+            # Speech to text
+            text = self.speech_to_text(audio_path, source_lang)
+
+            # Translate text
+            translated_text = self.translate_text(text, source_lang, target_lang)
+
+            # Text to speech
+            output_audio_path = self.text_to_speech(translated_text, target_lang)
+
+            # Load the generated audio
+            output_audio, sr = librosa.load(output_audio_path)
+
+            # Clean up temporary files
+            os.unlink(audio_path)
+            os.unlink(output_audio_path)
+
+            return (sr, output_audio), text, translated_text
+
+        except Exception as e:
+            return None, f"Error: {str(e)}", f"Error: {str(e)}"
+
+def create_gradio_interface():
+    translator = RealTimeTranslator()
+
+    # Create the Gradio interface with updated Audio component syntax
+    demo = gr.Interface(
+        fn=translator.process_audio,
+        inputs=[
+            gr.Audio(sources=["microphone"], type="numpy", label="Input Audio"),  # Updated syntax
+            gr.Dropdown(choices=list(translator.languages.keys()), value="en", label="Source Language"),
+            gr.Dropdown(choices=list(translator.languages.keys()), value="fr", label="Target Language")
+        ],
+        outputs=[
+            gr.Audio(label="Translated Audio"),
+            gr.Textbox(label="Original Text"),
+            gr.Textbox(label="Translated Text")
+        ],
+        title="Real-time Language Translator",
+        description="Speak in your language and get instant translation in the target language",
+        examples=[
+            [None, "en", "fr"],
+            [None, "hi", "en"],
+            [None, "es", "ja"]
+        ]
+    )
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(share=True, debug=True)