from nemo.collections.asr.models import NeuralDiarizer
from nemo.collections.asr.parts.utils.speaker_utils import rttm_to_labels
import gradio as gr
import torch
import pandas as pd

device = "cuda" if torch.cuda.is_available() else "cpu"

model = NeuralDiarizer.from_pretrained("diar_msdd_telephonic").to(device)


def run_diarization(path1):
    annotation = model(path1)
    rttm=annotation.to_rttm()
    df = pd.DataFrame(columns=['start_time', 'end_time', 'speaker'])
    for idx,line in enumerate(rttm.splitlines()):
        split = line.split()
        start_time, end_time, speaker = split[3], split[4], split[7]
        df.loc[idx] = start_time, end_time, speaker
    return df 
	
inputs = [
    gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Input Audio"),
]
output = gr.outputs.Dataframe()


description = (
    "This demonstration will perform offline speaker diarization on an audio file using nemo"
)

article = (
    "<p style='text-align: center'>"
    "<a href='https://huggingface.co/nvidia/speakerverification_en_titanet_large' target='_blank'>🎙️ Learn more about TitaNet model</a> | "
    "<a href='https://arxiv.org/pdf/2110.04410.pdf' target='_blank'>📚 TitaNet paper</a> | "
    "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑‍💻 Repository</a>"
    "</p>"
)
examples = [
    "data/id10270_5r0dWxy17C8-00001.wav",
]

interface = gr.Interface(
    fn=run_diarization,
    inputs=inputs,
    outputs=output,
    title="Offline Speaker Diarization with NeMo",
    description=description,
    article=article,
    layout="horizontal",
    theme="huggingface",
    allow_flagging=False,
    live=False,
    examples=examples,
)
interface.launch(enable_queue=True)