Create processing.py

processing.py

@@ -0,0 +1,38 @@
+import whisper
+from transformers import MarianMTModel, MarianTokenizer
+from gtts import gTTS
+import tempfile
+import os
+import certifi
+
+os.environ["SSL_CERT_FILE"] = certifi.where()
+
+def transcribe_translate_speak(audio_path, target_language):
+    # Transcribe
+    model = whisper.load_model("tiny")
+    result = model.transcribe(audio_path)
+    transcription = result["text"]
+
+    # Translate
+    model_map = {
+        'hi': "Helsinki-NLP/opus-mt-en-hi",
+        'es': "Helsinki-NLP/opus-mt-en-es",
+        'fr': "Helsinki-NLP/opus-mt-en-fr",
+        'bn': "shhossain/opus-mt-en-to-bn"
+    }
+
+    if target_language not in model_map:
+        raise ValueError(f"Unsupported language: {target_language}")
+
+    trans_model = MarianMTModel.from_pretrained(model_map[target_language])
+    tokenizer = MarianTokenizer.from_pretrained(model_map[target_language])
+    inputs = tokenizer(transcription, return_tensors="pt", padding=True, truncation=True)
+    outputs = trans_model.generate(**inputs)
+    translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # TTS
+    tts = gTTS(translated_text, lang=target_language)
+    tts_path = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False).name
+    tts.save(tts_path)
+
+    return transcription, translated_text, tts_path