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import gradio as gr | |
import torch | |
import io | |
import base64 | |
import numpy as np | |
import scipy.io.wavfile | |
from typing import Text | |
from pyannote.audio import Pipeline | |
from pyannote.audio import Audio | |
from pyannote.core import Segment | |
import gradio as gr | |
import os | |
import yt_dlp as youtube_dl | |
from gradio_client import Client | |
from transformers.pipelines.audio_utils import ffmpeg_read | |
HF_TOKEN = os.environ.get("HF_TOKEN") | |
# set up the diarization pipeline | |
#diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.0", use_auth_token=HF_TOKEN) | |
#diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=HF_TOKEN) | |
diarization_pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization-3.1", use_auth_token=HF_TOKEN) | |
if torch.cuda.is_available(): | |
diarization_pipeline.to(torch.device("cuda")) | |
import gradio as gr | |
def transcribe(audio_path, num_speakers=2): | |
# Configure the pipeline to use the provided number of speakers | |
#diarization_pipeline.n_speakers = num_speakers | |
# Run diarization | |
diarization = diarization_pipeline(audio_path,num_speakers=2) | |
return diarization | |
title = "SAML Speaker Diarization ⚡️ " | |
description = """ pyannote speaker diarization running locally""" | |
article = """SAMLOne Speaker Segmentation or Diarization""" | |
import gradio as gr | |
def greet(name): | |
return "Hello " + name + "!!" | |
iface = gr.Interface(fn=transcribe, inputs=gr.inputs.Audio(source="upload", optional=True, label="Audio file", type="filepath"), outputs="text") | |
iface.launch() | |