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import nemo | |
from nemo.collections.asr.models.msdd_models import NeuralDiarizer | |
import gradio as gr | |
import pandas as pd | |
import torch | |
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, duration, speaker = split[3], split[4], split[7] | |
end_time = float(start_time) + float(duration) | |
df.loc[idx] = start_time, end_time, speaker | |
return df | |
inputs = [ | |
gr.components.Audio(source="microphone", type="filepath", optional=True, label="Input Audio"), | |
] | |
output = gr.components.Dataframe() | |
description = ( | |
"This demonstration will perform offline speaker diarization on an audio file using nemo" | |
) | |
article = ( | |
"<p style='text-align: center'>" | |
"<a href='https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/diar_msdd_telephonic' target='_blank'>ποΈ Learn more about MSDD model</a> | " | |
"<a href='https://arxiv.org/abs/2203.15974' target='_blank'>π MSDD paper</a> | " | |
"<a href='https://github.com/NVIDIA/NeMo' target='_blank'>π§βπ» Repository</a>" | |
"</p>" | |
) | |
examples = [ | |
["data/sample_interview_conversation.wav"], | |
["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) | |