OpenAI Speech Recognition API (/v1/audio/transcriptions)

Transcribe audio to text, fully compatible with OpenAI’s /v1/audio/transcriptions. Any OpenAI SDK can directly use it by pointing the base_url to https://api.acedata.cloud and replacing the key with your AceData Token. Supports standard complete responses and also supports gpt-transcribe’s SSE incremental transcription.

Request URL: POST https://api.acedata.cloud/v1/audio/transcriptions (alias POST /openai/audio/transcriptions)
Authentication: Request header Authorization: Bearer {token}
Request format: multipart/form-data
Billing: Charged by audio duration (see table below), rounded up to the nearest second.

Request Parameters

Field	Type	Required	Description
`file`	file	Yes	The audio file to be transcribed, maximum 25 MB. Supports `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, `webm`.
`model`	string	No	`whisper-1` (default) or `gpt-transcribe`, see the table below for capability differences.
`language`	string	No	Audio language, ISO-639-1 code (e.g., `zh`, `en`). Specifying can improve accuracy and speed; leave blank for automatic detection.
`prompt`	string	No	Prompt words to guide writing style or provide proper nouns and terms to enhance recognition accuracy.
`response_format`	string	No	`whisper-1`: `json` (default), `text`, `srt`, `verbose_json`, `vtt`; `gpt-transcribe`: only `json`, `text`.
`temperature`	number	No	Sampling temperature 0–1, default 0.
`timestamp_granularities[]`	array	No	Timestamp granularity, `word` or `segment`, must be used with `response_format=verbose_json`.
`languages[]`	array	No	Candidate languages (ISO-639-1), only for `gpt-transcribe`. Mutually exclusive with `language`, do not send both.
`keywords[]`	array	No	Proper noun/term hints, only for `gpt-transcribe`, can significantly improve recognition accuracy for brand names and personal names.
`stream`	boolean	No	When set to `true` for `gpt-transcribe`, returns SSE incremental events; `whisper-1` will ignore this parameter and return complete results (consistent with OpenAI’s official behavior).

Which Model to Choose

	`whisper-1`	`gpt-transcribe`
Price	$0.0078 / minute	$0.0059 / minute (cheaper)
Recognition Accuracy	Good	Better, especially for brand names and proper nouns
Subtitle Output (`srt`/`vtt`)	✅	❌
Word-level Timestamps	✅	❌
`languages[]` / `keywords[]`	❌	✅
Returns Detected Language	Requires `verbose_json`	Returns by default
SSE Incremental Return	❌ (ignored `stream`)	✅

Need subtitles or word-level timestamps → whisper-1; for other scenarios, recommend gpt-transcribe (more accurate and cheaper).

Example

curl -X POST 'https://api.acedata.cloud/v1/audio/transcriptions' \
  -H 'authorization: Bearer {token}' \
  -F file=@audio.mp3 \
  -F model=whisper-1

Response:

{
  "text": "Ace Data Cloud Platform is testing the speech recognition endpoint. The quick brown fox jumps over the lazy dog."
}

Chinese audio is also supported without specifying the language:

{
  "text": "欢迎使用 AceData Cloud 平台,我们正在测试语音识别接口,今天是 7 月 31 号。"
}

Generate Subtitles

Set response_format to srt or vtt to directly obtain a usable subtitle file:

curl -X POST 'https://api.acedata.cloud/v1/audio/transcriptions' \
  -H 'authorization: Bearer {token}' \
  -F file=@audio.mp3 \
  -F model=whisper-1 \
  -F response_format=srt \
  -o subtitle.srt

Returned content (Content-Type: text/plain):

1
00:00:00,000 --> 00:00:03,800
Ace Data Cloud Platform is testing the speech recognition endpoint.

2
00:00:03,800 --> 00:00:06,280
The quick brown fox jumps over the lazy dog.

Word-level Timestamps

To get the start and end times for each word, use verbose_json with timestamp_granularities[]=word:

curl -X POST 'https://api.acedata.cloud/v1/audio/transcriptions' \
  -H 'authorization: Bearer {token}' \
  -F file=@audio.mp3 \
  -F model=whisper-1 \
  -F response_format=verbose_json \
  -F 'timestamp_granularities[]=word'

Response:

{
  "task": "transcribe",
  "language": "english",
  "duration": 6.29,
  "text": "Ace Data Cloud Platform is testing the speech recognition endpoint. The quick brown fox jumps over the lazy dog.",
  "words": [
    {
      "word": "Ace",
      "start": 0.0,
      "end": 0.32
    },
    {
      "word": "Data",
      "start": 0.32,
      "end": 0.54
    },
    {
      "word": "Cloud",
      "start": 0.54,
      "end": 0.86
    }
  ]
}

Streaming Transcription

gpt-transcribe can return Content-Type: text/event-stream by using stream=true. The service will send OpenAI-compatible events as is: transcript.text.delta carries incremental text, transcript.text.done carries complete text and usage indicating normal completion.

curl -N -X POST 'https://api.acedata.cloud/v1/audio/transcriptions' \
  -H 'authorization: Bearer {token}' \
  -F file=@audio.mp3 \
  -F model=gpt-transcribe \
  -F stream=true

Example of event stream:

data: {"type":"transcript.text.delta","delta":"Hello"}

data: {"type":"transcript.text.done","text":"Hello world","usage":{"type":"tokens","input_tokens":14,"output_tokens":3,"total_tokens":17}}

Receiving transcript.text.done indicates normal completion. If processing fails after the stream is established, the connection will end after the event: error event; if the client disconnects actively, this processing will be canceled and will not continue generating in the background. whisper-1 will still return a normal non-streaming response even if stream=true is passed.

Using the Official SDK

from openai import OpenAI

client = OpenAI(base_url="https://api.acedata.cloud/v1", api_key="{token}")
with open("audio.mp3", "rb") as f:
    result = client.audio.transcriptions.create(model="whisper-1", file=f)
print(result.text)

Price

Model	Price on this platform
`whisper-1`	$0.0078 / minute
`gpt-transcribe`	$0.0059 / minute

Charged based on the actual duration of the audio, with any duration less than 1 second counted as 1 second, and a maximum of 1 hour for a single request.

Notes

The maximum size for a single file is 25 MB. If it exceeds, please segment or compress it (usually lowering the bitrate is sufficient, as speech recognition does not have high audio quality requirements).
gpt-transcribe supports stream=true SSE; whisper-1 will ignore stream and return the complete result.
Parameters are consistent with OpenAI’s official /v1/audio/transcriptions, and the official SDK can be used by simply changing the base_url.
include[], chunking_strategy, known_speaker_names[], known_speaker_references[] belong to transcription models that we have not yet launched, passing them will return 400 instead of silently ignoring. Model-specific parameters (timestamp_granularities[] for whisper-1, languages[]/keywords[] for gpt-transcribe) will also return 400 when passed to unsupported models.
Requests can be time-consuming, it is recommended to set the client timeout to no less than 300 seconds.

Error Codes

Status Code	Code	Description
400	`bad_request`	`file` not provided, file cannot be parsed, or parameters are invalid (`model`/`response_format` values not supported, `temperature` out of 0–1, `timestamp_granularities[]` not paired with `verbose_json`, passing unsupported parameters for `whisper-1`).
401	`authentication_failed`	Invalid token.
403	`used_up`	Insufficient balance.
413	`request_too_large`	Audio file exceeds the 25 MB limit.
429	`too_many_requests`	Requests are too frequent, please try again later.
500	`api_error`	Internal server error, please try again later.

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More guides

OpenAI Speech Recognition API (/v1/audio/transcriptions)

Request Parameters

Which Model to Choose

Example

Generate Subtitles

Word-level Timestamps

Streaming Transcription

Using the Official SDK

Price

Notes

Error Codes

​Request Parameters

​Which Model to Choose

​Example

​Generate Subtitles

​Word-level Timestamps

​Streaming Transcription

​Using the Official SDK

​Price

​Notes

​Error Codes

Request Parameters

Which Model to Choose

Example

Generate Subtitles

Word-level Timestamps

Streaming Transcription

Using the Official SDK

Price

Notes

Error Codes