Errors

Configuration errors

Configuration errors indicate that something about the API call was not configured correctly. The error message you get from the Hume APIs will often contain more information than we’re able to provide on this page. For example if an audio file is too long, the error message from the API will specify the limit as well as the length of the audio received.

WebSocket status codes

Service errors

If you encounter an error code starting with I (for example, error code I0100), it indicates an outage or a bug in a Hume service. Our team will already have been alerted of the internal error, but if you need immediate assistance please reach out to our support team.

Warnings

Warnings indicate that the payload was configured correctly, but no results could be returned.

Common errors

Some errors will not have an associated error code, but are documented here.

Transcript confidence below threshold value

This error indicates that our transcription service had difficulty identifying the language spoken in your audio file or the quality was too low. We prioritize quality and accuracy, so if it cannot transcribe with confidence, our models won’t be able to process it further.

By default, we use an automated language detection method for our Speech Prosody, Language, and NER models. However, if you know what language is being spoken in your media samples, you can specify it via its BCP-47 tag and potentially obtain more accurate results.

If you see the message above there are few steps you can do to resolve the issue:

• Verify we support the language
• Ensure you are providing clear, high-quality audio files.
• Specify the language within your request if you know the language in the audio.

1
import asyncio
2
from hume import AsyncHumeClient
3
from hume.expression_measurement.batch import Prosody, Transcription, Models
4
from hume.expression_measurement.batch.types import InferenceBaseRequest
5
6
async def main():
7
    # Initialize an authenticated client
8
    client = AsyncHumeClient(api_key="<YOUR_API_KEY>")
9
10
    # Define the filepath(s) of the file(s) you would like to analyze
11
    local_filepaths = [
12
        open("<YOUR_FILE_PATH>", mode="rb"),
13
    ]
14
15
    # Create a default configuration for the prosody model
16
    prosody_config = Prosody()
17
18
    # Create a transcription coniguration with the language set to English
19
    transcription_config = Transcription(language="en")
20
21
    # Create a Models object
22
    models_chosen = Models(prosody=prosody_config)
23
    
24
    # Create a stringified object containing the configuration
25
    stringified_configs = InferenceBaseRequest(models=models_chosen, transcription=transcription_config)
26
27
    # Start an inference job and print the job_id
28
    job_id = await client.expression_measurement.batch.start_inference_job_from_local_file(
29
        json=stringified_configs, file=local_filepaths
30
    )
31
    print(job_id)
32
33
if __name__ == "__main__":
34
    asyncio.run(main())