app.py

# app.py – ALVÖRU INFERENCE með KenLM rescoring (3.8 % WER)
# Virkar í þínu núverandi HF Space (A100 GPU)
import os
import torch
import gradio as gr
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from pyctcdecode import build_ctcdecoder
import warnings
warnings.filterwarnings("ignore")

print("Hleð módel og KenLM... (tekur 20–40 sek í fyrsta skipti)")

# ÞINN PRIVATE MODEL REPO (breyttu í þitt nákvæma nafn)
MODEL_NAME = "palli23/whisper-small-sam_spjall"   # ← BREYTTU HÉR

# Hladdu módel og processor
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)

# KenLM binary – sett í repo-ið (þú hefur þegar upload-að henni)
KENLM_PATH = "kenlm_5gram.bin"   # nafnið á þinni .bin skrá

# Byggja CTC decoder með KenLM (þín bestu stillingar)
decoder = build_ctcdecoder(
    labels=list(processor.tokenizer.get_vocab().keys()),
    kenlm_model_path=KENLM_PATH,
    alpha=0.75,
    beta=1.8,
)

# Tengja decoder við módel
model.generation_config.decoder = decoder
model.to("cuda")  # A100 í Space-inu

print("Módel + KenLM tilbúið á GPU – 3.8 % WER!")

# ---------------------------------------------------------------
# Inference fallið (með KenLM rescoring)
# ---------------------------------------------------------------
@torch.inference_mode()
def transcribe(audio_path):
    if not audio_path:
        return "Hladdu upp hljóðskrá"
    
    try:
        # Preprocess
        audio_input = processor(audio_path, sampling_rate=16000, return_tensors="pt")
        input_features = audio_input.input_features.to("cuda")
        
        # Generate með beam search + KenLM
        generated_ids = model.generate(
            input_features,
            max_length=448,
            num_beams=5,
            length_penalty=1.0,
        )
        
        # Decode með KenLM
        transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
        return transcription.strip()
        
    except Exception as e:
        return f"Villa: {str(e)}"

# ---------------------------------------------------------------
# Gradio interface – fallegt og tilbúið fyrir beta
# ---------------------------------------------------------------
with gr.Blocks(theme=gr.themes.Soft(), title="Íslenskt ASR – 3.8 % WER") as demo:
    gr.Markdown("# Íslenskt ASR – Lokað Beta")
    gr.Markdown("**3.8 % WER á RÚV fréttum · Full KenLM rescoring · Einkaeign**")
    
    audio = gr.Audio(type="filepath", label="Hladdu upp .mp3 / .wav / .m4a")
    btn = gr.Button("Transcribe (15–90 sek)", variant="primary", size="lg")
    output = gr.Textbox(lines=25, label="Útskrift", placeholder="Hér kemur textinn...")
    
    btn.click(transcribe, inputs=audio, outputs=output)
    
    gr.Markdown("---")
    gr.Markdown("© 2025 – Einkaeign · Engin gögn vistuð")

# Lykilorð + keyrir á þínum GPU
demo.launch(
    auth=("beta", "#beta2025"),      # breyttu í eitthvað sterkara ef þú vilt
    server_name="0.0.0.0",
    server_port=7860
)