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. The interface synchronously returns the transcription result.

• 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). Filling this can improve accuracy and speed; leaving it blank will auto-detect.
prompt	string	No	Prompt words to guide writing style or provide proper nouns and terms to improve 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 gpt-transcribe. Mutually exclusive with language, do not send both.
keywords[]	array	No	Proper noun/term hints, only gpt-transcribe, can significantly improve recognition accuracy for brand names and personal names.
stream	boolean	No	Streaming not supported, input will be ignored and complete results will be returned (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

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": "YuJun Platform is testing the speech recognition endpoint. The quick brown fox jumps over the lazy dog."
}

Chinese audio is also supported, no need to specify the language:

{
  "text": "欢迎使用 YuJun 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
YuJun 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": "YuJun 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 }
  ]
}

¶ 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)

¶ Pricing

Model	Platform Price
whisper-1	$0.0078 / minute
gpt-transcribe	$0.0059 / minute

Charged based on actual audio duration, rounded up to the nearest second, with a maximum of 1 hour per single request.

¶ Notes

• The maximum size for a single file is 25 MB. If it exceeds this, please split or compress it (usually lowering the bitrate is sufficient, as speech recognition does not have high audio quality requirements).
• This interface does not support streaming returns (the stream parameter will be ignored).
• 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[], and known_speaker_references[] belong to our unlisted transcription models, and passing them will return 400 instead of silently ignoring them. Model-specific parameters (timestamp_granularities[] for whisper-1, languages[]/keywords[] for gpt-transcribe) will also return 400 when passed to unsupported models.
• The request is relatively time-consuming, and it is recommended that the client timeout setting is 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 exceeds 0–1, timestamp_granularities[] not paired with verbose_json, passed parameters unsupported by whisper-1).
401	authentication_failed	Token is invalid.
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.