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This guide covers the three ways to submit audio for batch transcription. For endpoint details and schemas, see the Batch API reference.
The Batch API is served from https://api.batch.slng.ai, not https://api.slng.ai like the rest of the SLNG API.
Prerequisites:
  • An SLNG API key
  • Audio in a supported format: wav, mp3, flac, aac, ogg, m4a, mp4, amr, mpeg

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Input Methods

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Method 1: File Upload

Upload the audio file directly using multipart/form-data. 
curl -X POST https://api.batch.slng.ai/v1/batch/jobs \
  -H "Authorization: Bearer $API_KEY" \
  -F file=@audio.wav \
  -F transcription_config='{"language":"en"}'

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Method 2: URL Input

Provide a publicly accessible HTTPS URL. The system downloads the file (max 1 GB). Supports presigned S3/GCS URLs for private files. 
curl -X POST https://api.batch.slng.ai/v1/batch/jobs \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input_url": "https://example.com/audio.wav",
    "transcription_config": { "language": "en" }
  }'
FieldTypeRequiredDescription
input_urlstringyesHTTPS URL of the audio file
transcription_configobjectyesTranscription settings
job_idstringnoAlphanumeric job ID (auto-generated if omitted)
model_codestringnoModel to use (default: slng/speechmatics/batch:15.0.0)
metadataobjectnoArbitrary key-value metadata

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Method 3: Presigned Upload

Use this method for large files or when you need upload progress tracking. Three steps: request a presigned URL, upload the file directly to S3, then create the job referencing the upload.
1

Request a presigned S3 upload URL

curl -X POST https://api.batch.slng.ai/v1/batch/jobs \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "presign",
    "filename": "audio.wav",
    "transcription_config": { "language": "en" }
  }'
The response contains the job_id and an upload object with url, s3_key, and expires_in (seconds):
{
  "job_id": "job8f3a...",
  "upload": {
    "url": "https://s3.amazonaws.com/...",
    "s3_key": "inputs/job8f3a.../audio.wav",
    "expires_in": 1800
  }
}
2

Upload the file to the presigned URL

curl -X PUT "<presigned_url>" --data-binary @audio.wav
3

Create the job

Reference the job_id and s3_key from step 1.
curl -X POST https://api.batch.slng.ai/v1/batch/jobs \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "job_id": "job8f3a...",
    "s3_key": "inputs/job8f3a.../audio.wav",
    "transcription_config": { "language": "en" }
  }'

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Job Lifecycle

After submission, a job moves through these statuses: QUEUED → IN_PROGRESS → DECODING → DONE / FAILED Poll GET /v1/batch/jobs/{jobId} until the status reaches DONE or FAILED: 
curl https://api.batch.slng.ai/v1/batch/jobs/myjob123 \
  -H "Authorization: Bearer $API_KEY"
Once DONE, retrieve the transcript: 
curl https://api.batch.slng.ai/v1/batch/jobs/myjob123/files \
  -H "Authorization: Bearer $API_KEY"
This returns signed download URLs for both the input audio and output transcript files (JSON and SRT).

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Transcription Config

FieldTypeDefaultDescription
languagestringenLanguage code for the audio
operating_pointstringstandardAccuracy level: standard or enhanced
diarizationstringnoneSpeaker separation: none, speaker, or channel
domainstring—Domain-specific language model
output_localestring—Locale for formatting (dates, numbers)
enable_entitiesboolean—Detect and format entities (dates, times, addresses)
additional_vocabarray—Custom words to improve recognition
Example with diarization and enhanced accuracy: 
{
  "transcription_config": {
    "language": "en",
    "operating_point": "enhanced",
    "diarization": "speaker"
  }
}

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Limits

LimitValue
Max file size1 GB
Presigned upload URL expiry30 minutes
Supported audio formatswav, mp3, flac, aac, ogg, m4a, mp4, amr, mpeg
URL input schemeHTTPS only

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Next Steps

Batch API reference

Endpoint details, request schemas, and response formats

Getting started

Set up your API key and make your first request