readme: ---
  license: mit
  tags:
  - audio
  - automatic-speech-recognition
  - endpoints-template
  library_name: generic
  inference: false
  ---
  
  # OpenAI [Whisper](https://github.com/openai/whisper) Inference Endpoint example
  
  > Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
  
  For more information about the model, license and limitations check the original repository at [openai/whisper](https://github.com/openai/whisper).
  
  ---
  
  This repository implements a custom `handler` task for `automatic-speech-recognition` for 🤗 Inference Endpoints using OpenAIs new Whisper model. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/handler.py).
  
  There is also a [notebook](https://huggingface.co/philschmid/openai-whisper-endpoint/blob/main/create_handler.ipynb) included, on how to create the `handler.py`
  
  ###  Request 
  
  The endpoint expects a binary audio file. Below is a cURL example and a Python example using the `requests` library.
  
  **curl**
  
  ```bash
  # 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-flac' \
    --header 'Authorization: Bearer {HF_TOKEN}' \
    --data-binary '@sample1.flac'
  ```
  
  **Python**
  
  ```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="sample1.flac")
  
  prediction
  
  ```
  expected output
  
  ```json
  {"text": " going along slushy country roads and speaking to damp audiences in draughty school rooms day after day for a fortnight. He'll have to put in an appearance at some place of worship on Sunday morning, and he can come to us immediately afterwards."}
  ```
  