nicknaskida/whisper-diarization

nicknaskida / whisper-diarization

Public
78 runs
GitHub

Run with an API

Playground API Examples README Versions

Run nicknaskida/whisper-diarization 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
file_string	string		Either provide: Base64 encoded audio file,
file_url	string		Or provide: A direct audio file URL
file	string		Or an audio file
hf_token	string		Provide a hf.co/settings/token for Pyannote.audio to diarise the audio clips. You need to agree to the terms in 'https://huggingface.co/pyannote/speaker-diarization-3.1' and 'https://huggingface.co/pyannote/segmentation-3.0' first.
group_segments	boolean	True	Group segments of same speaker shorter apart than 2 seconds
transcript_output_format	string (enum)	both Options: words_only, segments_only, both	Specify the format of the transcript output: individual words with timestamps, full text of segments, or a combination of both.
num_speakers	integer	2 Min: 1 Max: 50	Number of speakers, leave empty to autodetect.
translate	boolean	False	Translate the speech into English.
language	string		Language of the spoken words as a language code like 'en'. Leave empty to auto detect language.
prompt	string		Vocabulary: provide names, acronyms and loanwords in a list. Use punctuation for best accuracy.
batch_size	integer	64 Min: 1	Batch size for inference. (Reduce if face OOM error)
offset_seconds	integer	0	Offset in seconds, used for chunked inputs

{
  "type": "object",
  "title": "Input",
  "properties": {
    "file": {
      "type": "string",
      "title": "File",
      "format": "uri",
      "x-order": 2,
      "description": "Or an audio file"
    },
    "prompt": {
      "type": "string",
      "title": "Prompt",
      "x-order": 9,
      "description": "Vocabulary: provide names, acronyms and loanwords in a list. Use punctuation for best accuracy."
    },
    "file_url": {
      "type": "string",
      "title": "File Url",
      "x-order": 1,
      "description": "Or provide: A direct audio file URL"
    },
    "hf_token": {
      "type": "string",
      "title": "Hf Token",
      "x-order": 3,
      "description": "Provide a hf.co/settings/token for Pyannote.audio to diarise the audio clips. You need to agree to the terms in 'https://huggingface.co/pyannote/speaker-diarization-3.1' and 'https://huggingface.co/pyannote/segmentation-3.0' first."
    },
    "language": {
      "type": "string",
      "title": "Language",
      "x-order": 8,
      "description": "Language of the spoken words as a language code like 'en'. Leave empty to auto detect language."
    },
    "translate": {
      "type": "boolean",
      "title": "Translate",
      "default": false,
      "x-order": 7,
      "description": "Translate the speech into English."
    },
    "batch_size": {
      "type": "integer",
      "title": "Batch Size",
      "default": 64,
      "minimum": 1,
      "x-order": 10,
      "description": "Batch size for inference. (Reduce if face OOM error)"
    },
    "file_string": {
      "type": "string",
      "title": "File String",
      "x-order": 0,
      "description": "Either provide: Base64 encoded audio file,"
    },
    "num_speakers": {
      "type": "integer",
      "title": "Num Speakers",
      "default": 2,
      "maximum": 50,
      "minimum": 1,
      "x-order": 6,
      "description": "Number of speakers, leave empty to autodetect."
    },
    "group_segments": {
      "type": "boolean",
      "title": "Group Segments",
      "default": true,
      "x-order": 4,
      "description": "Group segments of same speaker shorter apart than 2 seconds"
    },
    "offset_seconds": {
      "type": "integer",
      "title": "Offset Seconds",
      "default": 0,
      "minimum": 0,
      "x-order": 11,
      "description": "Offset in seconds, used for chunked inputs"
    },
    "transcript_output_format": {
      "enum": [
        "words_only",
        "segments_only",
        "both"
      ],
      "type": "string",
      "title": "transcript_output_format",
      "description": "Specify the format of the transcript output: individual words with timestamps, full text of segments, or a combination of both.",
      "default": "both",
      "x-order": 5
    }
  }
}

Output schema

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

Schema

{
  "type": "object",
  "title": "Output",
  "required": [
    "segments"
  ],
  "properties": {
    "language": {
      "type": "string",
      "title": "Language"
    },
    "segments": {
      "type": "array",
      "items": {},
      "title": "Segments"
    },
    "num_speakers": {
      "type": "integer",
      "title": "Num Speakers"
    }
  }
}