Open in the interactive docs · markdown for agents: https://neuraldeep.tech/docs/transcribe.md

Transcription · Speech-to-Text

Speech to text, OpenAI-compatible POST /v1/audio/transcriptions. It is synchronous: you send a file and get the text in the same response. For long recordings with speaker labels there is the asynchronous SpeechCore.

model	what it is
`whisper-1`	Whisper large-v3-turbo, 100+ languages, supports every response format
`whisper-podlodka-turbo`	Whisper turbo fine-tuned for Russian
`gigaam-v3`	GigaAM v3, a model built for Russian

Request. multipart/form-data, fields:

file, required: audio or video as a file;
model, required: one of the models above;
language: language code (ru, en...). Without it the language is detected automatically; set it explicitly for short files;
prompt: a hint with terms and names, improves accuracy on special vocabulary;
response_format: json (default), verbose_json, text, srt, vtt;
timestamp_granularities[]=word: per-word timestamps, only together with verbose_json;
temperature: default 0.

curl https://api.neuraldeep.tech/v1/audio/transcriptions \
  -H "Authorization: Bearer $YOUR_KEY" \
  -F [email protected] \
  -F model=whisper-1 \
  -F language=en

from openai import OpenAI
client = OpenAI(api_key="$YOUR_KEY", base_url="https://api.neuraldeep.tech/v1")
with open("meeting.mp3", "rb") as f:
    r = client.audio.transcriptions.create(model="whisper-1", file=f, language="en")
print(r.text)

Response. json and verbose_json return text, language, duration in seconds and segments with timestamps. The gateway wraps text, srt and vtt in JSON as well: the content is a string in the text field. Only whisper-1 builds srt and vtt subtitles. For the other models use verbose_json and build subtitles from segments yourself.

{"text": "Good afternoon, let's start the call.", "language": "en", "duration": 3.0,
 "segments": [{"id": 0, "start": 0.0, "end": 2.9, "text": "Good afternoon, let's start the call."}]}

Input limits.

File size: up to 25 MB per request. The main engine does not accept larger files; such a request goes to a reserve engine and may run slower or fail. Split long recordings into parts under 25 MB.
Duration: there is no separate cap, but the response has to fit into 240 seconds. In practice that is tens of minutes of speech per request. Inside a request the engine splits speech at pauses into short fragments and joins the result; you do not need to cut it into 30-second pieces.
Formats: anything ffmpeg reads. mp3, wav, m4a and mp4, ogg and opus, flac, webm, aac, amr, wma, mov and mkv (the audio track is used).
Sample rate and channels: any, the server converts audio to 16 kHz mono. To stay under 25 MB, send 16 kHz mono mp3 or opus: an hour of speech in 32 kbit/s opus is about 15 MB.

Quota. The free plan has a small daily transcription quota, days are counted in UTC. Once it is used up, a request still goes through and is billed per minute of audio if the account has money in the wallet. Otherwise you get 429 with X-Window: stt-free-daily, X-STT-Limit and Retry-After until 00:00 UTC. Paid plans have no separate transcription quota: each request counts towards the key's common windows together with chat (requests per minute, concurrency, session and week); the remainders are in GET /v1/limits.

Errors.

400: the file is empty or does not look like audio (X-Window: stt-bad-file), ffmpeg could not read it (truncated, corrupted), timestamp_granularities without verbose_json.
429: the free daily quota is used up (see above) or the key's rate or concurrency limit fired, wait for Retry-After.
408 or 504: recognition did not finish in time. Split the file.
503: transcription is temporarily unavailable, retry in a minute.

Transcription · Speech-to-Text

Reference sections (markdown for agents)