This is a free to use hosted version of OpenAI Whisper model.

1. How to use our API
2. How to deploy your own API

Access swagger documentation at https://whisper.lablab.ai/docs and https://whisper.lablab.ai/redoc

import requests
url = "https://whisper.lablab.ai/asr"
payload={}
files=[
  ('audio_file',('test1.mp3',open('/C:/Users/pc/Desktop/test1.mp3','rb'),'audio/mpeg'))
]
response = requests.request("POST", url, data=payload, files=files)
print(response.text)

var formdata = new FormData();formdata.append("audio_file", fileInput.files[0], "/C:/Users/pc/Desktop/test1.mp3");
var requestOptions = {
  method: 'POST',
  body: formdata,
  redirect: 'follow'
};
fetch("https://whisper.lablab.ai/asr", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

var client = new RestClient("https://whisper.lablab.ai/asr");
client.Timeout = -1;
var request = new RestRequest(Method.POST);
request.AddFile("audio_file", "/C:/Users/pc/Desktop/test1.mp3");
IRestResponse response = client.Execute(request);
Console.WriteLine(response.Content);

var request = http.MultipartRequest('POST', Uri.parse('https://whisper.lablab.ai/asr'));
request.files.add(await http.MultipartFile.fromPath('audio_file', '/C:/Users/pc/Desktop/test1.mp3'));
 
http.StreamedResponse response = await request.send();
 
if (response.statusCode == 200) {
  print(await response.stream.bytesToString());
}
else {
  print(response.reasonPhrase);
}

For CPU: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice

docker run -d -p 9000:9000 -e ASR_MODEL=base onerahmet/openai-whisper-asr-webservice

For GPU: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice-gpu

docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base onerahmet/openai-whisper-asr-webservice-gpu

You can access the Swagger via http://localhost:9000/docs.

Available ASR_MODELs are tiny, base, small, medium and large

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

# Build Image
docker build -t whisper .

# Run Container
docker run -p 8000:8000 whisper
# or
docker run -p 8000:8000 -e ASR_MODEL=base whisper
# or
docker run \
  --volume /var/lib/nvidia/lib64:/usr/local/nvidia/lib64 \
  --volume /var/lib/nvidia/bin:/usr/local/nvidia/bin \
  --device /dev/nvidia0:/dev/nvidia0 \
  --device /dev/nvidia-uvm:/dev/nvidia-uvm \
  --device /dev/nvidiactl:/dev/nvidiactl \
-p 80:8000 -d whisper-gpu

# Build Image
docker build -f Dockerfile.gpu -t whisper-gpu .

# Run Container
docker run -gpus all -p 8000:8000 whisper-gpu
# or
docker run --gpus all -p 8000:8000 -e ASR_MODEL=base whisper-gpu

If you choose the transcribe task, transcribes the uploaded file. Both audio and video files are supported (as long as ffmpeg supports it).

You can provide the language or it will be automatically recognized.

If you choose the translate task it will provide an English transcript no matter which language was spoken.

Returns a json with following fields:

• text: Contains the full transcript
• segments: Contains an entry per segment. Each entry provides time stamps, transcript, token ids and other metadata
• language: Detected or provided language (as a language code)

These two POST endpoints have the same interface as /asr but they return a subtitle file (either srt or vtt).

Note that you can also upload video formats directly as long as they are supported by ffmpeg.

Detects the language spoken in the uploaded file. For longer files it only processes first 30 seconds.

Returns a json with following fields:

• detected_language
• langauge_code