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hume

Agents & models

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Updated 10-16 02:36

About this skill

Troubleshooting

If requests fail, run zero doctor check-connector --env-name HUME_TOKEN or zero doctor check-connector --url https://api.hume.ai/v0/batch/jobs --method GET

Expression Measurement (Batch)

Start Inference Job from URLs

Write to /tmp/hume_request.json:

{
  "urls": ["https://example.com/media-file.mp4"],
  "models": {
    "face": {},
    "prosody": {},
    "language": {}
  },
  "notify": true
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/batch/jobs" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

Start Inference Job with Text

Write to /tmp/hume_request.json:

{
  "text": ["I am so excited about this!", "This is really disappointing."],
  "models": {
    "language": {}
  }
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/batch/jobs" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

List Jobs

curl -s "https://api.hume.ai/v0/batch/jobs?limit=10" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

List Jobs by Status

curl -s "https://api.hume.ai/v0/batch/jobs?limit=10&status=COMPLETED" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Get Job Details

curl -s "https://api.hume.ai/v0/batch/jobs/{job-id}" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Get Job Predictions

curl -s "https://api.hume.ai/v0/batch/jobs/{job-id}/predictions" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Download Job Artifacts

curl -s "https://api.hume.ai/v0/batch/jobs/{job-id}/artifacts" --header "X-Hume-Api-Key: $HUME_TOKEN" --output /tmp/hume_artifacts.zip

Text-to-Speech

Synthesize Speech (JSON Response)

Write to /tmp/hume_request.json:

{
  "utterances": [
    {
      "text": "Hello, how are you today?",
      "description": "A warm and friendly greeting"
    }
  ],
  "format": {
    "type": "mp3"
  }
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/tts" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

The response contains base64-encoded audio in .generations[].audio.

Synthesize Speech with Specific Voice

Write to /tmp/hume_request.json:

{
  "utterances": [
    {
      "text": "Welcome to our platform!",
      "speed": 1.0
    }
  ],
  "voice": {
    "name": "{voice-name}"
  },
  "format": {
    "type": "mp3"
  }
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/tts" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

Synthesize and Save Audio File

Write to /tmp/hume_request.json:

{
  "utterances": [
    {
      "text": "This is a test of text-to-speech synthesis."
    }
  ],
  "format": {
    "type": "mp3"
  }
}

Then extract and decode the audio:

curl -s -X POST "https://api.hume.ai/v0/tts" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq -r '.generations[0].audio' | base64 -d > /tmp/hume_output.mp3

Synthesize Multiple Utterances

Write to /tmp/hume_request.json:

{
  "utterances": [
    {
      "text": "First sentence.",
      "description": "calm and measured"
    },
    {
      "text": "Second sentence!",
      "description": "excited and energetic",
      "trailing_silence": 0.5
    }
  ],
  "format": {
    "type": "mp3"
  }
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/tts" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

EVI Configs

List Configs

curl -s "https://api.hume.ai/v0/evi/configs?page_size=20" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Create Config

Write to /tmp/hume_request.json:

{
  "name": "My EVI Config"
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/evi/configs" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

EVI Prompts

List Prompts

curl -s "https://api.hume.ai/v0/evi/prompts?page_size=20" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Create Prompt

Write to /tmp/hume_request.json:

{
  "name": "Customer Support Agent",
  "text": "You are a friendly and empathetic customer support agent. Listen carefully and help resolve issues."
}

Then run:

curl -s -X POST "https://api.hume.ai/v0/evi/prompts" --header "Content-Type: application/json" --header "X-Hume-Api-Key: $HUME_TOKEN" -d @/tmp/hume_request.json | jq .

EVI Chats

List Chat Events

curl -s "https://api.hume.ai/v0/evi/chats/{chat-id}?page_size=50&ascending_order=true" --header "X-Hume-Api-Key: $HUME_TOKEN" | jq .

Available Models for Expression Measurement

Model Description
face Facial expression analysis from images/video
prosody Vocal expression analysis from audio
language Emotion analysis from text
ner Named entity recognition from text
burst Non-speech vocal sounds (laughter, sighs)
facemesh Detailed facial landmark detection

TTS Utterance Parameters

Parameter Type Description
text string Content to convert to speech (required)
description string Acting directions or voice style prompt
speed number Speech rate multiplier (default: 1.0)
trailing_silence number Silence after utterance in seconds (default: 0)

Response Codes

Status Description
200 Success
201 Resource created
400 Invalid request parameters
401 Missing or invalid API key
404 Resource not found
429 Rate limit exceeded
5xx Server error

Guidelines

  1. Authentication: Use X-Hume-Api-Key header (not Bearer token) for all requests
  2. Batch Jobs: Jobs are asynchronous; poll the job details endpoint until status is COMPLETED
  3. Models: Specify only the models you need in batch requests to reduce processing time
  4. TTS Description: Use the description field to control emotional tone and style of generated speech
  5. Pagination: EVI endpoints use zero-based page numbering with configurable page size (1-100)
  6. API Key Types: Organization API Key for TTS and EVI; Personal API Key for Expression Measurement

API Reference