Update app.py

app.py

@@ -0,0 +1,50 @@
+import os
+import gradio as gr
+import whisper
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from gtts import gTTS
+
+def translate_voice(file, target_lang):
+    # Auto to text (STT) 
+    model = whisper.load_model("base")
+    audio = whisper.load_audio(file.name)
+    audio = whisper.pad_or_trim(audio)
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+    _, probs = model.detect_language(mel)
+
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+
+    text = result.text
+    lang = max(probs, key=probs.get)
+
+    # Translate
+    tokenizer = AutoTokenizer.from_pretrained("alirezamsh/small100")
+    model = AutoModelForSeq2SeqLM.from_pretrained("alirezamsh/small100")
+
+    tokenizer.src_lang = target_lang
+    encoded_bg = tokenizer(text, return_tensors="pt")
+    generated_tokens = model.generate(**encoded_bg)
+    translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+
+    # Text-to-audio (TTS)
+    tts = gTTS(text=translated_text, lang=target_lang)
+    filename = "to_speech.mp3"
+    tts.save(filename)
+
+    return filename, text, translated_text, target_lang
+
+iface = gr.Interface(
+    fn=translate_voice, 
+    inputs=[
+        gr.inputs.File(type="audio", label="Your Audio"), 
+        gr.inputs.Dropdown(choices=['en', 'ru', 'de', 'fr'], label="Target Language")
+    ], 
+    outputs=[
+        gr.outputs.Audio(type="auto", label="Translated Audio"),
+        gr.outputs.Textbox(label="Original Text"),
+        gr.outputs.Textbox(label="Translated Text"),
+        gr.outputs.Textbox(label="Target Language"),
+    ]
+)
+iface.launch()