| 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'>🎙️ Learn more about MSDD model</a> | " | |
| "<a href='https://arxiv.org/abs/2203.15974' target='_blank'>📚 MSDD paper</a> | " | |
| "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑💻 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) | |