Create process_audio.py

process_audio.py

@@ -0,0 +1,47 @@
+import sys
+import whisper
+from transformers import MarianMTModel, MarianTokenizer
+from gtts import gTTS
+import os
+
+import certifi
+
+os.environ["SSL_CERT_FILE"] = certifi.where()
+
+def process_audio(input_path, output_path, target_language):
+    # Model 1: Speech-to-Text using Whisper
+    model = whisper.load_model("tiny")
+    result = model.transcribe(input_path)
+    text = result["text"]
+
+    # Model 2: Translation (Dynamic model selection based on target_language)
+    if target_language == 'hi':  # Hindi
+        model_name = "Helsinki-NLP/opus-mt-en-hi"
+    elif target_language == 'es':  # Spanish
+        model_name = "Helsinki-NLP/opus-mt-en-es"
+    elif target_language == 'fr':  # French
+        model_name = "Helsinki-NLP/opus-mt-en-fr"
+    elif target_language == 'bn':  # Bengali
+        model_name = "shhossain/opus-mt-en-to-bn"
+    else:
+        raise ValueError(f"Unsupported target language: {target_language}")
+
+    # Load translation model
+    tokenizer = MarianTokenizer.from_pretrained(model_name)
+    model = MarianMTModel.from_pretrained(model_name)
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = model.generate(**inputs)
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Model 3: Text-to-Speech using gTTS
+    tts = gTTS(translation, lang=target_language)
+
+    # Save the translated text as an audio file
+    tts.save(output_path)
+
+
+if __name__ == "__main__":
+    input_file = sys.argv[1]  # Path to input audio file
+    output_file = sys.argv[2]  # Path to output audio file
+    target_language = sys.argv[3]  # Target language passed from backend
+    process_audio(input_file, output_file, target_language)