victor-upmeet / whisperx-a100-80gb

  • Public
  • 51 runs

Run victor-upmeet/whisperx-a100-80gb with an API

Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.

Input schema

The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.

Field Type Default value Description
audio_file
string
Audio file
language
string
ISO code of the language spoken in the audio, specify None to perform language detection
language_detection_min_prob
number
0
If language is not specified, then the language will be detected recursively on different parts of the file until it reaches the given probability
language_detection_max_tries
integer
5
If language is not specified, then the language will be detected following the logic of language_detection_min_prob parameter, but will stop after the given max retries. If max retries is reached, the most probable language is kept.
initial_prompt
string
Optional text to provide as a prompt for the first window
batch_size
integer
64
Parallelization of input audio transcription
temperature
number
0
Temperature to use for sampling
vad_onset
number
0.5
VAD onset
vad_offset
number
0.363
VAD offset
align_output
boolean
False
Aligns whisper output to get accurate word-level timestamps
diarization
boolean
False
Assign speaker ID labels
huggingface_access_token
string
To enable diarization, please enter your HuggingFace token (read). You need to accept the user agreement for the models specified in the README.
min_speakers
integer
Minimum number of speakers if diarization is activated (leave blank if unknown)
max_speakers
integer
Maximum number of speakers if diarization is activated (leave blank if unknown)
debug
boolean
False
Print out compute/inference times and memory usage information

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{
  "type": "object",
  "title": "Output",
  "required": [
    "detected_language"
  ],
  "properties": {
    "segments": {
      "title": "Segments"
    },
    "detected_language": {
      "type": "string",
      "title": "Detected Language"
    }
  }
}