app.py

# app.py for HF Spaces (ZeroGPU safe pyannote)
import os
import gradio as gr
import spaces
import tempfile
import torch

from torch.serialization import safe_globals
from pyannote.audio.core.model import Model
from pyannote.audio.core.task import Task, Specifications
from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarization
from typing import OrderedDict

from transformers import pipeline
from pyannote.audio import Pipeline

# Required patches for ZeroGPU
os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
torch.serialization.add_safe_globals({
    "OrderedDict": OrderedDict,
})

MODEL_NAME = "palli23/whisper-small-sam_spjall"

@spaces.GPU(duration=120)
def transcribe_with_diarization(audio_path):
    if not audio_path:
        return "Hladdu upp hljóðskrá"
    
    # Fix strict unpickling in torch 2.6 (ZeroGPU)
    with safe_globals([
        torch.torch_version.TorchVersion,
        Model,
        Task,
        Specifications,
        SpeakerDiarization,
        OrderedDict,
    ]):
        diarization = Pipeline.from_pretrained(
            "pyannote/speaker-diarization-3.1",
            use_auth_token=os.getenv("HF_TOKEN")
        ).to("cuda")
    
    # Run diarization
    dia = diarization(audio_path)

    # Whisper model
    asr = pipeline(
        "automatic-speech-recognition",
        model=MODEL_NAME,
        device=0,
        use_auth_token=os.getenv("HF_TOKEN"),
    )

    # segment-by-segment ASR
    result = []
    for turn, _, speaker in dia.itertracks(yield_label=True):
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            diarization.crop(audio_path, turn).export(f.name, format="wav")
            chunk = f.name

        text = asr(chunk)["text"].strip()
        os.unlink(chunk)
        result.append(f"[MÆLENDI {speaker}] {text}")

    return "\n".join(result) or "Enginn texti heyrðist."

with gr.Blocks() as demo:
    gr.Markdown("# Íslenskt ASR + Mælendagreining")
    gr.Markdown("Whisper-small + pyannote 3.1 (ZeroGPU örugg útgáfa)")
    
    audio = gr.Audio(type="filepath", label="Hljóðskrá")
    btn = gr.Button("Transcribe með mælendum")
    out = gr.Textbox(lines=35, label="Úttak")

    btn.click(transcribe_with_diarization, inputs=audio, outputs=out)

demo.launch(auth=("beta", "beta2025"))