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Introduction

The Speech-to-Text (STT) API based on OpenAI’s Whisper models can convert audio files to text. It supports various use cases:
  • Transcribing audio files to text
  • Translating multilingual audio to English
  • Supporting multiple audio format inputs
  • Providing multiple output format options
Available Model List:
  • whisper-large-v3 —— Latest large Whisper model, supports multiple languages. For Chinese recognition, use with appropriate prompts and low temperature values
  • whisper-1 —— Original Whisper model, stable and reliable, supports multiple languages
  • distil-whisper-large-v3-en —— Distilled model, faster processing speed but slightly lower accuracy, recommended with low temperature values
Performance Recommendations:
  • For Chinese audio, recommend using whisper-large-v3 model with appropriate prompts and lower temperature values (e.g., 0.2) to reduce hallucinations
  • For English audio or faster processing, use distil-whisper-large-v3-en model
  • Supported audio formats: mp3, mp4, mpeg, mpga, m4a, wav, webm
  • File size limit: maximum 25MB

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Model Usage

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Speech Transcription

Use /v1/audio/transcriptions endpoint via client.audio.transcriptions.create() method to transcribe audio to text in the original language.

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Speech Translation

Use /v1/audio/translations endpoint via client.audio.translations.create() method to translate audio to English text.

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Request Parameters

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Transcription Parameters

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file
file
required
Audio file object to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav, webm, maximum 25MB
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model
string
required
Model ID to use. Options: whisper-large-v3, whisper-1, distil-whisper-large-v3-en
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language
string
Language of the input audio in ISO-639-1 format (e.g., ‘en’, ‘zh’). Specifying the language can improve accuracy and latency
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prompt
string
Optional text prompt to guide the model’s style or continue a previous audio segment. The prompt should match the audio language
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response_format
string
Transcription output format. Options: json (default), text, srt, verbose_json, vtt
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temperature
number
Sampling temperature between 0 and 1. Higher values make output more random, lower values make it more focused and deterministic. Default is 0
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timestamp_granularities[]
array
Timestamp granularities. Options: word, segment. Only available when response_format is verbose_json

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Translation Parameters

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file
file
required
Audio file object to translate. Same formats as transcription
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model
string
required
Model ID to use, same as transcription parameters
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prompt
string
Optional English text prompt to guide translation style
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response_format
string
Translation output format, same as transcription parameters
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temperature
number
Sampling temperature, same as transcription parameters

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Usage Examples

curl https://aihubmix.com/v1/audio/transcriptions \
  -H "Authorization: Bearer $AIHUBMIX_API_KEY" \
  -H "Content-Type: multipart/form-data" \
  -F file="@/path/to/file/audio.mp3" \
  -F model="whisper-large-v3" \
  -F response_format="text" \
  -F temperature="0.2"

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Response Formats

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JSON Format (Default)

{
  "text": "This is the transcribed text content"
}

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Verbose JSON Format (verbose_json)

{
  "task": "transcribe",
  "language": "english",
  "duration": 8.470000267028809,
  "text": "This is the transcribed text content",
  "segments": [
    {
      "id": 0,
      "seek": 0,
      "start": 0.0,
      "end": 8.470000267028809,
      "text": " This is the transcribed text content",
      "tokens": [50364, 50365, 50365, 50365],
      "temperature": 0.2,
      "avg_logprob": -0.9929364013671875,
      "compression_ratio": 0.8888888888888888,
      "no_speech_prob": 0.0963134765625
    }
  ]
}

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Text Format

This is the transcribed text content

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SRT Format

1
00:00:00,000 --> 00:00:08,470
This is the transcribed text content

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VTT Format

WEBVTT

00:00:00.000 --> 00:00:08.470
This is the transcribed text content

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Best Practices

  1. Chinese Audio Processing: Use whisper-large-v3 model, set language="zh", temperature=0.2, and provide appropriate Chinese prompts
  2. English Audio Processing: Use distil-whisper-large-v3-en for faster processing speed
  3. Noise Handling: Use prompts to instruct the model to ignore background noise or clean up stammering issues
  4. Long Audio Processing: API automatically segments long audio; recommend preprocessing audio quality for best results
  5. Timestamp Requirements: Use verbose_json format and timestamp_granularities when precise timestamps are needed
  6. Subtitle Creation: Use srt or vtt format output directly without additional processing