README.md

metadata

license: mit
tags:
  - pyannote
  - pyannote-audio
  - pyannote-audio-pipeline
  - audio
  - voice
  - speech
  - speaker
  - speaker-diarization
  - speaker-change-detection
  - endpoints-template
library_name: generic

🎹 Speaker diarization with Pyannote and Inference Endpoints

This repository implements a custom handler for speaker-diarization for 🤗 Inference Endpoints using Pyannote. The code for the customized pipeline is in the handler.py.

There is also a notebook included, on how to create the handler.py

Request

The endpoint expects a binary audio file. Below are a cURL and a Python example using the requests library.

curl

# load audio file
wget https://cdn-media.huggingface.co/speech_samples/sample1.flac

# run request
curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: audio/x-wav' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@sample.wav'

Python

import json
from typing import List
import requests as r
import base64
import mimetypes

ENDPOINT_URL=""
HF_TOKEN=""

def predict(path_to_audio:str=None):
    # read audio file
    with open(path_to_audio, "rb") as i:
      b = i.read()
    # get mimetype
    content_type= mimetypes.guess_type(path_to_audio)[0]

    headers= {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": content_type
    }
    response = r.post(ENDPOINT_URL, headers=headers, data=b)
    return response.json()

prediction = predict(path_to_audio="sample.wav")

prediction

expected output

{"diarization": [
{"label": "SPEAKER_01", "start": "0.4978125", "stop": "1.3921875"},
{"label": "SPEAKER_01", "start": "1.8984375", "stop": "2.7590624999999998"},
{"label": "SPEAKER_02", "start": "2.9953125", "stop": "3.5015625000000004"},
{"label": "SPEAKER_01", "start": "3.5690625000000002", "stop": "4.311562500000001"}
...