Update main.py

main.py

@@ -1,96 +1,21 @@
-import torch
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-from datasets import load_dataset
-from googletrans import Translator
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import FastAPI, File, UploadFile
+from pathlib import Path
 
 app = FastAPI()
 
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+@app.post("/uploadfile/")
+async def upload_file(file: UploadFile = File(...)):
 
-model_id = "openai/whisper-large-v3"
+    save_directory = Path("/home/user")
+    save_directory.mkdir(parents=True, exist_ok=True)
 
-model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
-model.to(device)
+    file_location = save_directory / file.filename
 
-processor = AutoProcessor.from_pretrained(model_id)
+    with open(file_location, "wb") as saved_file:
+        content = await file.read()
+        saved_file.write(content)
 
-pipe = pipeline(
-    "automatic-speech-recognition",
-    model=model,
-    tokenizer=processor.tokenizer,
-    feature_extractor=processor.feature_extractor,
-    max_new_tokens=256,
-    chunk_length_s=30,
-    batch_size=16,
-    return_timestamps=True,
-    torch_dtype=torch_dtype,
-    device=device,
-)
-
-dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
-
-@app.post("/voice_recognition")
-async def process_audio(file: UploadFile = File(...)):
-    try:
-        # File
-        file_path = f"/home/user/{file.filename}"
-        with open(file_path, "wb") as f:
-            f.write(file.file.read())
-
-        # JP
-        original = pipe(file_path)
-        original_version = original["text"]
-
-        # EN
-        result = pipe(file_path, generate_kwargs={"task": "translate"})
-        hasil = result["text"]
-
-        # ID
-        detect = detect_google(hasil)
-        id_ver = translate_google(hasil, f"{detect}", "ID")
-
-        # Additional modifications
-        id_ver = modify_text(id_ver)
-
-        return JSONResponse(content={"response": {"jp_text": original_version, "en_text": hasil, "id_text": id_ver}}, status_code=200)
-
-    except Exception as e:
-        return HTTPException(status_code=500, detail=f"Error: {e}")
-
-def detect_google(text):
-    try:
-        translator = Translator()
-        detected_lang = translator.detect(text)
-        return detected_lang.lang.upper()
-    except Exception as e:
-        print(f"Error detect: {e}")
-        return None
-
-def translate_google(text, source, target):
-    try:
-        translator = Translator()
-        translated_text = translator.translate(text, src=source, dest=target)
-        return translated_text.text
-    except Exception as e:
-        print(f"Error translate: {e}")
-        return None
-
-def modify_text(text):
-    # Additional modifications, case-sensitive
-    replacements = {
-        "Tuan": "Master",
-        "tuan": "Master",
-        "Guru": "Master",
-        "guru": "Master",
-        "Monica": "Monika",
-        "monica": "Monika",
-    }
-
-    for original, replacement in replacements.items():
-        text = text.replace(original, replacement)
-
-    return text
+    with open(file_location, "rb") as read_file:
+        file_content = read_file.read()
 
+    return {"info": "File saved successfully", "filename": file.filename, "file_content": file_content.decode("utf-8")}