app.py

import gradio as gr
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import librosa
import numpy as np
import spaces

MODEL_ID = "userdotcs/whisper-small-turkish-phono-finetuned"
device = "cuda" if torch.cuda.is_available() else "cpu"

# İşlemci ve Modeli yükle
processor = WhisperProcessor.from_pretrained(MODEL_ID)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID).to(device)

@spaces.GPU
def transcribe(audio):
    if audio is None:
        return "Ses dosyası algılanamadı."

    sr, y = audio

    # 1. Float32 Dönüşümü ve Normalizasyon (KRİTİK ADIM)
    y = y.astype(np.float32)
    y /= np.max(np.abs(y)) if np.max(np.abs(y)) > 0 else 1

    # 2. Mono Yapma
    if y.ndim > 1:
        y = y.mean(axis=1)

    # 3. Resampling
    if sr != 16000:
        y = librosa.resample(y, orig_sr=sr, target_sr=16000)
        sr = 16000

    # 4. Input Hazırlama
    input_features = processor(y, sampling_rate=sr, return_tensors="pt").input_features.to(device)

    # 5. Inference
    with torch.no_grad():
        # forced_decoder_ids ile modeli Türkçe'ye zorlayabilirsin (opsiyonel)
        predicted_ids = model.generate(input_features, max_new_tokens=128)

    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
    return transcription

# Arayüz
demo = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="numpy"), # Gradio 4+ kullanıyorsan bu kalsın
    outputs="text",
    title="Whisper Small Turkish Phonetic Trancription"
)

demo.launch()