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import torch
import torchaudio
import numpy as np
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 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
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
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
save_directory = Path("/home/user")
save_directory.mkdir(parents=True, exist_ok=True)
file_location = save_directory / file.filename
with open(file_location, "wb") as saved_file:
content = await file.read()
saved_file.write(content)
# Load audio file using torchaudio
waveform, sample_rate = torchaudio.load(file_location)
# Ensure the waveform is a 1D array
waveform = waveform[0] if waveform.size(0) > 1 else waveform
# ASR
original = pipe(waveform.numpy())
original_version = original[0]["text"]
# EN
result = pipe(waveform.numpy(), generate_kwargs={"task": "translate"})
hasil = result[0]["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 |