Add application file

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import os
+import whisper
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from gtts import gTTS
+from tempfile import NamedTemporaryFile
+
+# Define translation function
+def translate_audio(input_file, target_language):
+    # Save uploaded audio file to a temporary file
+    with NamedTemporaryFile(suffix=".wav") as temp_audio:
+        temp_audio.write(input_file.read())
+        temp_audio.seek(0)
+    
+    # Auto to text (STT)
+    model = whisper.load_model("base")
+    audio = whisper.load_audio(temp_audio.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_language
+    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_language)
+    output_file = NamedTemporaryFile(suffix=".mp3", delete=False)
+    output_file.close()
+    tts.save(output_file.name)
+    return output_file.name
+
+# Define Gradio interface
+inputs = [
+ gr.File(label="Upload Audio File"),
+ gr.Dropdown(choices=['en', 'es', 'fr', 'de', 'ru'], label="Target Language")
+]
+
+outputs = [
+ gr.File(label="Translated Audio")
+]
+
+title = "Audio Translation"
+description = "Upload an audio file, translate the speech to a target language, and download the translated audio."
+
+gr.Interface(fn=translate_audio, inputs=inputs, outputs=outputs, title=title, description=description).launch(share=True)