Batch transcription - Gnani AI Documentation

Use STT Batch to transcribe long recordings or many files asynchronously. On Batch, Prisma v3.0 is the same model as REST with model=prisma-v3.0: one model for all ten languages, which identifies the spoken language itself. A prisma-v3.0 job also accepts four extra config options: format, keyterms, topic and speaker_names (see Prisma v3.0 options). Everything else about a job is the same on both models: the endpoints, start, cancel, status and file-results calls, speaker diarization, denoising, limits and webhooks. Flow: create a job → start it → poll until it finishes → download transcripts
(or receive them on a webhook).

If you need…	Use
Long files, many files, or offline pipelines	STT Batch (this section)
Short clips (under ~30 seconds), immediate answer	STT REST
Language detection, `codemix`, context hints	Prisma v3.0 (`config.model: prisma-v3.0`)
Live microphone / streaming audio	STT Realtime

How it works

1. CREATE    POST /stt/v3/batch/jobs
             → 201  job_id   status: CREATED

2. START     POST /stt/v3/batch/jobs/{job_id}/start
             → 202             status: STARTING

3. WAIT      GET  /stt/v3/batch/jobs/{job_id}
             poll every 10s until a terminal status
             (live: STARTING → QUEUED → IN_PROGRESS → COMPLETED)

4. FILES     GET  /stt/v3/batch/jobs/{job_id}/files
             → transcript_url for each completed file

5. DOWNLOAD  GET  <transcript_url>
             → JSON with full_transcript + segments

Important: creating a job does not start transcription. You must call /start.

Interactive API reference

These are the same six endpoints as on docs.gnani.ai. Open an endpoint page and use Try it to send a live request with your X-API-Key-ID.

Step	Endpoint	Page	Try it
1	`POST /stt/v3/batch/jobs`	Create job	Try it
2	`POST /stt/v3/batch/jobs/{job_id}/start`	Start job	Try it
3	`GET /stt/v3/batch/jobs/{job_id}`	Get job status	Try it
4	`GET /stt/v3/batch/jobs/{job_id}/files`	Get job files	Try it
—	`GET /stt/v3/batch/jobs`	List jobs	Try it
—	`POST /stt/v3/batch/jobs/{job_id}/cancel`	Cancel job	Try it

For files over 10 MB, create the job with a presigned upload (POST /stt/v3/batch/v2/jobs). It is described on Create job and has no Try it playground. Webhook payload examples: Webhooks — samples for job.completed, job.failed, and job.cancelled.

Before you begin

An API key from the Gnani APIs dashboard
Audio with real speech in a supported format
A supported language code

Base URL

https://api.vachana.ai

Auth — send on every Batch API request:

X-API-Key-ID: <YOUR_API_KEY>

Tone-only / silent audio is accepted into a job but fails later with Empty transcript after 3 retries.

How do I get the transcript text?

Option	When to use	What you do
A. Polling (default)	No webhook	Poll job status → `GET /files` → `GET` each `transcript_url` → read `full_transcript`
B. Webhook	You passed `callback_url`	Receive a POST with `transcripts[]` including `full_transcript`

GET /jobs/{job_id} and GET /jobs/{job_id}/files do not return full_transcript.

Quickstart

1. Create

The Prisma v2.5 request below was live-verified.

curl --request POST \
  --url https://api.vachana.ai/stt/v3/batch/jobs \
  --header 'X-API-Key-ID: <YOUR_API_KEY>' \
  -F 'config={"model":"prisma-v2.5","language_code":"en-IN","mode":"transcribe","with_diarization":false,"is_multi_channel":false};type=application/json' \
  -F "files=@./customer_call.wav"

To use Prisma v3.0, set model to prisma-v3.0 in config. With prisma-v3.0, language_code can be indic-auto, or 1–4 comma-separated codes (see Language identification):

curl --request POST \
  --url https://api.vachana.ai/stt/v3/batch/jobs \
  --header 'X-API-Key-ID: <YOUR_API_KEY>' \
  -F 'config={"model":"prisma-v3.0","language_code":"auto"};type=application/json' \
  -F "files=@./customer_call.wav"

Try it on Create job Example response:

{
  "job_id": "019fa79e-81f9-7a8a-a446-6eff16ddec30",
  "status": "CREATED",
  "total_files_accepted": 1,
  "created_at": "2026-07-28T07:26:50.926489Z",
  "message": "1 file(s) uploaded. Call POST /v1/jobs/{job_id}/start to begin processing."
}

2. Start

curl --request POST \
  --url https://api.vachana.ai/stt/v3/batch/jobs/019fa79e-81f9-7a8a-a446-6eff16ddec30/start \
  --header 'X-API-Key-ID: <YOUR_API_KEY>'

Try it on Start job

{
  "job_id": "019fa79e-81f9-7a8a-a446-6eff16ddec30",
  "status": "STARTING",
  "message": "Job start accepted. File validation and queueing are running in the background. Poll GET /stt/v3/batch/jobs/{job_id} to track progress."
}

3. Poll until `COMPLETED`

curl --request GET \
  --url https://api.vachana.ai/stt/v3/batch/jobs/019fa79e-81f9-7a8a-a446-6eff16ddec30 \
  --header 'X-API-Key-ID: <YOUR_API_KEY>'

Try it on Get job status

4. Get `transcript_url`, then download text

curl --request GET \
  --url 'https://api.vachana.ai/stt/v3/batch/jobs/019fa79e-81f9-7a8a-a446-6eff16ddec30/files?status=COMPLETED' \
  --header 'X-API-Key-ID: <YOUR_API_KEY>'

Try it on Get job files

curl -L "<transcript_url>"

{
  "full_transcript": "hello how can i help you today i am calling about my recent order number 12345",
  "model": "prisma-v2.5",
  "language_code": "en-IN",
  "duration_seconds": 6.01
}

A prisma-v3.0 job returns the same document with model set to prisma-v3.0 and language_detected filled on every segment that has text. See Get job files.

5. List jobs

curl --request GET \
  --url 'https://api.vachana.ai/stt/v3/batch/jobs?limit=3' \
  --header 'X-API-Key-ID: <YOUR_API_KEY>'

Try it on List jobs

6. Cancel a job

curl --request POST \
  --url https://api.vachana.ai/stt/v3/batch/jobs/019fa79c-7880-7d05-a528-edc7892ad9ea/cancel \
  --header 'X-API-Key-ID: <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{"reason":"Docs verification cancel test"}'

Try it on Cancel job

Quickstart — Python SDK

The Python SDK wraps the same four steps — create, start, poll, fetch:

pip install -U "gnani-vachana>=0.8.1"

from gnani.stt import GnaniSTTBatchClient

client = GnaniSTTBatchClient()   # reads GNANI_API_KEY

# 1. Create — POST /stt/v3/batch/jobs
job = client.create_job(
    "customer_call.wav",
    language_code="en-IN",
    start=False,                 # create only; omit to start automatically
)
print(job.id, job.status)        # CREATED

# 2. Start — POST /stt/v3/batch/jobs/{job_id}/start
job.start()
print(job.status)                # STARTING

# 3. Poll until a terminal status — GET /stt/v3/batch/jobs/{job_id}
job.wait()                       # polls every 10 s
print(job.status, job.percent)   # COMPLETED 100

# 4. Fetch transcripts — GET .../files, then each transcript_url
for f in job.files():
    print(f.name, f.status)
    if f.ok:
        print(f.text)            # full_transcript

job.save("./outputs")

Keep job.id to resume later with client.get_job(job_id). For asyncio use AsyncGnaniSTTBatchClient. To do all four steps in one call, use client.transcribe(...) — see the SDK guide.

Configuration

Field	Type	Required	Default	Description
`model`	string	Yes	—	`"prisma-v2.5"` (Prisma v2.5) or `"prisma-v3.0"` (Prisma v3.0, not case-sensitive)
`language_code`	string	Yes	—	One code, or several comma-separated codes. Prisma v3.0 also accepts `"auto"`. See Language identification and Supported languages
`format`	string	No	see Output style	Prisma v3.0 only. `verbatim`, `transcribe` or `codemix`
`keyterms`	string[]	No	—	Prisma v3.0 only. Words or short phrases to favour, up to 200 — see Context
`topic`	string	No	—	Prisma v3.0 only. What the recordings are about, 30–1,600 characters
`speaker_names`	string[]	No	—	Prisma v3.0 only. Names that may be spoken, 1–4
`mode`	string	No	`"transcribe"`	Use `"transcribe"` for now. It is unrelated to `format`.
`with_diarization`	boolean	No	`false`	Enable speaker diarization. With `prisma-v3.0`, speaker labels are produced either way; this flag only decides whether a diarization failure fails the file.
`num_speakers`	integer	No	`5`	Most speakers to separate, `1`–`8`. Speakers are counted automatically; this is an upper limit, not a target
`is_multi_channel`	boolean	No	`false`	`true` only for true multi-channel audio
`with_denoise`	boolean	No	`false`	Enhance (denoise) the audio before transcription — see Speech enhancement
`bias_list`	string[]	No	—	Words to bias recognition towards — see Word boosting. With `prisma-v3.0` it is the older name of `keyterms`.
`bias_score`	number	No	`1.0` when `bias_list` is set	Boost strength: `0.0`, `0.5`, `1.0`, `1.5` or `2.0` — see Word boosting. Ignored, and not stored, with `prisma-v3.0`.

Inverse Text Normalization (ITN) is not supported for STT Batch.

Prisma v3.0 options

format, keyterms, topic and speaker_names exist only for prisma-v3.0. Sending any of them with another model returns 400 INVALID_CONFIG: they are refused, not ignored. They go inside config, for every way of creating a job.

Output style

Style	English words	Numbers and symbols
`verbatim`	written in the native script	spelled out as words
`transcribe`	stay in Latin	digits, symbols, currency signs (`12345`, `%`, `₹`)
`codemix`	stay in Latin	mostly spoken out as words in the native language

When you leave format out, the model picks a style for each segment: transcribe when the only language is hi-IN or en-IN (or a list of languages that are all hi-IN or en-IN), and verbatim otherwise, including indic-auto. English speech is written in Latin in every style. Inverse text normalization is not supported on Batch, so format is what controls how numbers are written. For more on the three styles, see Prisma v3.0.

Context

Context is sent with every segment of every file in the job.

Field	Limits
`keyterms`	Up to 200 items. Words or short phrases, in any script. Duplicates are dropped.
`topic`	30–1,600 characters.
`speaker_names`	1–4 names, each on a single line.

None of them may contain <| or |>. keyterms and speaker_names can also be sent as a comma-separated string, or as a string that holds a JSON array. Use the JSON array form when an item contains a comma. Send one of the combinations below. Anything else returns 400 INVALID_CONFIG when the job is created.

You send	Accepted
nothing	yes
`keyterms`	yes
`topic`	yes
`topic` + `keyterms`	yes
`speaker_names`	yes
any other mix (for example `keyterms` + `speaker_names`, or `topic` + `speaker_names`)	no — `400 INVALID_CONFIG`

bias_list with prisma-v3.0: if keyterms is not sent, bias_list is used as keyterms, and counts as keyterms in the table above. If both are sent, keyterms wins and bias_list is dropped. bias_score is ignored. Get job status shows the result: keyterms filled, bias_list and bias_score null.

Context can make a model less accurate on some audio. Compare a job with and without it on your own recordings before relying on it.

"config": {
  "model": "prisma-v3.0",
  "language_code": "hi-IN,en-IN",
  "format": "transcribe",
  "topic": "Loan collection calls for a retail credit card portfolio",
  "keyterms": ["HDFC Bank", "EMI", "खाता नंबर"]
}

For tips on writing keyterms, see Prisma v3.0.

Not available on Batch

These REST options are not supported on Batch:

REST option	Why
`previous_text`	It describes the clip just before a single request. A batch file is cut into many segments that are transcribed in parallel.
`enable_substitution`, `substitution_map`	Not supported on Batch.

Word boosting

This section describes Prisma v2.5. With prisma-v3.0, bias_list is the older name of keyterms and bias_score is ignored. See Prisma v3.0 options.

bias_list nudges recognition towards words the model tends to get wrong — brand names, product names, Roman-script tokens. Both fields are optional and go inside config, for the public-URL body and for the multipart config field alike:

"config": {
  "model": "prisma-v2.5",
  "language_code": "hi-IN",
  "bias_list": ["HDFC", "Prisma", "EMI"],
  "bias_score": 1.5
}

Field	Rule
`bias_list`	JSON array of up to 100 words. One word per entry — letters only (Latin or Indic script), no spaces, digits or punctuation. A comma-separated string (`"HDFC,Prisma"`) is also accepted. Case does not matter.
`bias_score`	One of the values in the table below. If you send `bias_list` without it, `1.0` is used. A score without a list does nothing.

`bias_score`	Effect
`0.0`	Off (same as leaving it out)
`0.5`	Light nudge
`1.0`	Typical starting point, and the default when `bias_list` is sent
`1.5`	Stronger
`2.0`	Maximum

Start at 1.0 and raise it only if a word is still missed. A stronger score can make the model favour a listed word where it was not spoken. The same list and score are applied to every segment of every file in the job. An invalid list or score is rejected when the job is created (400 INVALID_CONFIG) — unlike the synchronous POST /stt/v3, which would silently skip boosting. Boosting only takes effect for languages served by the v2 model (hi-IN, en-IN, ta-IN, bn-IN, kn-IN, te-IN, ml-IN, mr-IN, gu-IN, pa-IN); it is ignored, not rejected, for anything else. Boosted words are not echoed in the transcript, only in the job’s config.

Speech enhancement (denoising)

Set with_denoise: true to have each file cleaned up before it is segmented and transcribed:

"config": {
  "model": "prisma-v2.5",
  "language_code": "en-IN",
  "with_denoise": true
}

It is off by default, and worth turning on only when your audio needs it:

Your audio	Recommendation
Noisy calls, background hum, street or field recordings	Enable — this is what it is for
Clean studio or headset audio	Leave off — enhancement passes clean speech through nearly unchanged, so you only pay the extra processing time

Notes:

The whole pipeline sees the cleaned audio — speech detection, transcription and diarization all run on it, so timestamps and speaker labels come from the enhanced signal.
It adds processing work per file — on the order of a quarter of the audio’s duration again — so a job with with_denoise takes longer to reach COMPLETED.
It is not conditional on how noisy the file is — every file in the job is enhanced. Set it per job, on the jobs that need it.
Nothing else changes: the transcript JSON, webhook payloads and billing are the same (billing is per audio duration either way).
The setting is echoed back as config.with_denoise on Get job status.

Enhancement is applied where the platform has it enabled. If it is switched off for maintenance, a job that asked for it still runs to COMPLETED — transcribed from the original audio rather than failing.

Limits

Limits depend on how you send the audio. The 10 MB, ZIP and file-count limits below apply to direct upload only — they do not apply to files fetched from cloud storage.

Every file, whichever way it arrives

Limit	Value
Max audio duration	4 hours per file
Min audio duration	0.1 s per file
Max speech segments detected	5,000 per file

A file longer than 4 hours is rejected with AUDIO_TOO_LONG. Split it first.

Direct upload (`multipart/form-data`)

Limit	Value
Max audio files per job	100
Max size per file	10 MB
Max ZIP compressed size	50 MB
Max ZIP decompressed size	200 MB
Max ZIP compression ratio	50:1

The 100-file limit counts everything in the job, including files expanded out of a ZIP.

Presigned upload (`source.type: "direct_upload_url"`)

For files too large for direct upload. See Create job.

Limit	Value
Max size per file	5 GiB

Cloud storage (`source.type: "cloud_storage"`)

Limit	Value
Max size per file	No byte limit — bounded by the 4-hour duration cap above
Max download time per file	30 minutes
Max files per job	Not currently capped

The 10 MB limit is for uploads only. Sending a 200 MB recording as a cloud_storage path is fine — what matters is that it is under 4 hours and downloadable within 30 minutes. Uploading that same file through multipart/form-data would be rejected.

A path ending in / is treated as a folder and expands to every supported audio file under that prefix. Because cloud storage jobs are not file-count capped, a broad prefix can enqueue far more audio than you intended — and every file is billed. Point at explicit file paths, or a narrow prefix you have checked.

Results and polling

Limit	Value
`transcript_url` expiry	1 hour (`X-Amz-Expires=3600`)
Poll interval	≥ 10 s (prefer 30 s for 100+ files)

The poll interval is guidance, not an enforced limit — but the API is rate limited, so polling more tightly than this risks 429 responses on your other calls.

Supported languages

Language	Code	Native Script	Example
Bengali	`bn-IN`	Bengali (বাংলা)	“আমি ভাত খাই”
English	`en-IN`		“I am going to the market”
Gujarati	`gu-IN`	Gujarati (ગુજરાતી)	“હું બજાર જાઉં છું”
Hindi	`hi-IN`	Devanagari (हिन्दी)	“मैं बाज़ार जा रहा हूँ”
Kannada	`kn-IN`	Kannada (ಕನ್ನಡ)	“ನಾನು ಮಾರುಕಟ್ಟೆಗೆ ಹೋಗುತ್ತೇನೆ”
Malayalam	`ml-IN`	Malayalam (മലയാളം)	“ഞാൻ ചന്തയിലേക്ക് പോകുന്നു”
Marathi	`mr-IN`	Devanagari (मराठी)	“मी बाजारात जातोय”
Punjabi	`pa-IN`	Gurmukhi (ਪੰਜਾਬੀ)	“ਮੈਂ ਬਾਜ਼ਾਰ ਜਾ ਰਿਹਾ ਹਾਂ”
Tamil	`ta-IN`	Tamil (தமிழ்)	“நான் சந்தைக்கு செல்கிறேன்”
Telugu	`te-IN`	Telugu (తెలుగు)	“నేను మార్కెట్‌కి వెళ్తున్నాను”

Language identification

How the language is chosen depends on the model. If you know the language, pass a single code on either model.

Prisma v3.0

prisma-v3.0 identifies the spoken language itself, so you can send indic-auto, or one to four language codes.

`language_code`	Mode	What happens
`"indic-auto"`	Auto	The model identifies the language of each segment among all supported languages.
`"hi-IN"` or `"hi-IN,en-IN"` (1–4 codes)	Listed languages	The model listens for your languages first, for each segment. If a segment is clearly in another language, it follows that language.
5 or more codes	—	`400 INVALID_CONFIG`. Use `"indic-auto"` instead.

The language is decided per segment: each stretch of speech between pauses, split again at speaker changes. A call where speakers switch language gets each part in its own language.
Your languages are a hint, not a limit. If a segment is clearly in a language outside your list, it is transcribed and reported in that language.
Order does not matter. Prisma v3.0 has no fallback language.
At most 4 languages.
"auto" is accepted as another spelling of "indic-auto". Either is stored as "auto".
The transcript reports the language of each segment in language_detected, and the language that covers the most speech in language_code. See Get job files.

Prisma v2.5

Pass up to three codes as a comma-separated list. Each file is then identified as one of exactly those languages and transcribed in it:

"language_code": "hi-IN,en-IN"

Rules:

Maximum three codes. More returns 400 UNSUPPORTED_LANGUAGE.
Every code must be from the table above; one unsupported code rejects the request.
Order matters. The first code is the fallback used when identification is not confident enough to choose.
The decision is made per file, not per segment — a file is transcribed entirely in one language. The resolved language is returned as language_code in the transcript JSON, so it always tells you what the text is actually in.
A single code behaves exactly as before, with no identification step.

Supported audio formats

.wav · .mp3 · .mp4 · .flac · .ogg · .opus · .m4a · .aac · .webm · .amr

Job statuses

Status	Terminal	Meaning
`CREATED`	No	Waiting for `/start`
`STARTING`	No	Setup running
`QUEUED`	No	Files queued
`IN_PROGRESS`	No	Transcription running
`COMPLETED`	Yes	All succeeded
`PARTIAL_FAILURE`	Yes	Mixed results
`FAILED`	Yes	Failed
`START_FAILED`	Yes	Setup failed — see `cancel_reason`
`CANCELLING`	No	Cancel in progress
`CANCELLED`	Yes	Cancelled

Webhooks

Pass callback_url at create time to receive a POST when the job reaches a terminal state.

`event`	Triggered when
`job.completed`	All files succeeded
`job.partial_failure`	Mixed success / failure
`job.failed`	All files failed
`job.cancelled`	Job cancelled

The transcripts array includes full per-file transcript JSON (with full_transcript), so webhook users usually do not need /files + transcript_url. Webhooks work the same way for prisma-v3.0: same events, same payload. The entries in transcripts use the same transcript document as Get job files, where the prisma-v3.0 differences are described. Treat webhooks as best-effort. Polling remains the source of truth.

Sample webhook payload — `job.completed`

{
  "event": "job.completed",
  "job_id": "019fa79e-81f9-7a8a-a446-6eff16ddec30",
  "org_id": "org_acme123",
  "user_id": "user_abc456",
  "status": "COMPLETED",
  "total_files": 1,
  "completed_files": 1,
  "failed_files": 0,
  "cancelled_files": 0,
  "completed_at": "2026-07-28T07:27:24Z",
  "callback_url": "https://yourapp.com/webhooks/stt",
  "transcripts": [
    {
      "file_id": "6f5e3bcd-add2-479c-8883-a9c27b221f10",
      "job_id": "019fa79e-81f9-7a8a-a446-6eff16ddec30",
      "original_path": "customer_call.wav",
      "language_code": "en-IN",
      "duration_seconds": 6.01,
      "model": "prisma-v2.5",
      "mode": "transcribe",
      "segments": [
        {
          "segment_id": 0,
          "start_time": 0.0,
          "end_time": 0.71,
          "text": "hello",
          "speaker_id": 1,
          "confidence": null,
          "language_detected": null,
          "sentiment": null,
          "emotion": null
        },
        {
          "segment_id": 0,
          "start_time": 0.71,
          "end_time": 2.36,
          "text": "how can i help you today",
          "speaker_id": 2,
          "confidence": null,
          "language_detected": null,
          "sentiment": null,
          "emotion": null
        },
        {
          "segment_id": 0,
          "start_time": 2.36,
          "end_time": 6.01,
          "text": "i am calling about my recent order number 12345",
          "speaker_id": 2,
          "confidence": null,
          "language_detected": null,
          "sentiment": null,
          "emotion": null
        }
      ],
      "full_transcript": "hello how can i help you today i am calling about my recent order number 12345",
      "created_at": "2026-07-28T07:27:24Z"
    }
  ]
}

Sample webhook payload — `job.failed`

{
  "event": "job.failed",
  "job_id": "019fa79b-a07a-7a3e-93bf-4ee73974dfa2",
  "org_id": "org_acme123",
  "user_id": "user_abc456",
  "status": "FAILED",
  "total_files": 1,
  "completed_files": 0,
  "failed_files": 1,
  "cancelled_files": 0,
  "completed_at": "2026-07-28T07:24:32Z",
  "callback_url": "https://yourapp.com/webhooks/stt",
  "transcripts": []
}

Sample webhook payload — `job.cancelled`

{
  "event": "job.cancelled",
  "job_id": "019fa79c-7880-7d05-a528-edc7892ad9ea",
  "org_id": "org_acme123",
  "user_id": "user_abc456",
  "status": "CANCELLED",
  "total_files": 1,
  "completed_files": 0,
  "failed_files": 0,
  "cancelled_files": 1,
  "completed_at": null,
  "callback_url": "https://yourapp.com/webhooks/stt",
  "transcripts": []
}

Common mistakes

Mistake	What happens	Fix
Forgetting `/start`	Job stays `CREATED`	Call Start job
Expecting `full_transcript` on status/`/files`	Field missing	`GET /files` → then `GET transcript_url`
Using `/v1/jobs/...` from create message	Wrong path	Use `/stt/v3/batch/jobs/{job_id}/start`
Uploading silence / tones	`FAILED` — `Empty transcript after 3 retries`	Use real speech
Invalid public URL	`START_FAILED` / file `SKIPPED`	Use reachable HTTPS URLs
Polling every 1–2 seconds	Wasted calls	Poll every 10s
`with_denoise: true` on clean audio	Job takes longer, transcript barely changes	Enable it only for noisy recordings
Expired `transcript_url`	Download fails	Re-call Get job files
`format`, `keyterms`, `topic` or `speaker_names` with `prisma-v2.5`	`400 INVALID_CONFIG`	Set `model` to `prisma-v3.0`, or remove the field
`keyterms` with `speaker_names`, or `topic` with `speaker_names`	`400 INVALID_CONFIG`	Send one of the accepted combinations
5 or more codes in `language_code` with `prisma-v3.0`	`400 INVALID_CONFIG`	Send up to 4 codes, or `"auto"`
`previous_text` or substitution fields	Not supported on Batch	See Not available on Batch

Support

Docs feedback: Batch STT docs feedback Last verified (Prisma v2.5): 5 August 2026 Live-checked: Create → Start → Poll → Files → Transcript download, List, Cancel, and error codes 401 / 400 / 404 / 409.

​How it works

​Interactive API reference

​Before you begin

​How do I get the transcript text?

​Quickstart

​1. Create

​2. Start

​3. Poll until COMPLETED

​4. Get transcript_url, then download text

​5. List jobs

​6. Cancel a job

​Quickstart — Python SDK

​Configuration

​Prisma v3.0 options

​Output style

​Context

​Not available on Batch

​Word boosting

​Speech enhancement (denoising)

​Limits

​Every file, whichever way it arrives

​Direct upload (multipart/form-data)

​Presigned upload (source.type: "direct_upload_url")

​Cloud storage (source.type: "cloud_storage")

​Results and polling

​Supported languages

​Language identification

​Prisma v3.0

​Prisma v2.5

​Supported audio formats

​Job statuses

​Webhooks

​Sample webhook payload — job.completed

​Sample webhook payload — job.failed

​Sample webhook payload — job.cancelled

​Common mistakes

​Support

How it works

Interactive API reference

Before you begin

How do I get the transcript text?

Quickstart

1. Create

2. Start

3. Poll until `COMPLETED`

4. Get `transcript_url`, then download text

5. List jobs

6. Cancel a job

Quickstart — Python SDK

Configuration

Prisma v3.0 options

Output style

Context

Not available on Batch

Word boosting

Speech enhancement (denoising)

Limits

Every file, whichever way it arrives

Direct upload (`multipart/form-data`)

Presigned upload (`source.type: "direct_upload_url"`)

Cloud storage (`source.type: "cloud_storage"`)

Results and polling

Supported languages

Language identification

Prisma v3.0

Prisma v2.5

Supported audio formats

Job statuses

Webhooks

Sample webhook payload — `job.completed`

Sample webhook payload — `job.failed`

Sample webhook payload — `job.cancelled`

Common mistakes

Support