AiHubMix Documentation Hub

Introdução

The Speech-to-Text (STT) API based on OpenAI’s Whisper models can convert audio files to text. It suporta 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, suporta multiple languages. For Chinese recognition, use with appropriate prompts and low temperature values
whisper-1 —— Original Whisper model, stable and reliable, suporta 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 (p. ex., 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

Model Usage

Speech Transcription

Use /v1/audio/transcriptions endpoint via client.audio.transcriptions.create() method to transcribe audio to text no original language.

Speech Translation

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

Parâmetros da Requisição

Transcription Parameters

file

obrigatório

Audio file object to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav, webm, maximum 25MB

model

string

obrigatório

Model ID to use. Options: whisper-large-v3, whisper-1, distil-whisper-large-v3-en

language

string

Language of the input audio in ISO-639-1 format (p. ex., ‘en’, ‘zh’). Specifying the language can improve accuracy and latency

prompt

string

Optional text prompt to guide the model’s style or continue a previous audio segment. The prompt should match the audio language

response_format

string

Transcription output format. Options: json (default), text, srt, verbose_json, vtt

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

timestamp_granularities[]

array

Timestamp granularities. Options: word, segment. Only available when response_format is verbose_json

Translation Parameters

file

obrigatório

Audio file object to translate. Same formats as transcription

model

string

obrigatório

Model ID to use, same as transcription parameters

prompt

string

Optional English text prompt to guide translation style

response_format

string

Translation output format, same as transcription parameters

temperature

number

Sampling temperature, same as transcription parameters

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"

curl https://aihubmix.com/v1/audio/translations \
  -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 prompt="autocorrect, clean up the stammer, and translate to english" \
  -F response_format="text" \
  -F temperature="0.2"

from openai import OpenAI
import os

client = OpenAI(
  api_key="sk-***", # Replace with your AiHubMix API key
  base_url="https://aihubmix.com/v1"
)

# Open audio file
audio_file = open("path/to/audio.mp3", "rb")

# Transcribe audio
transcript = client.audio.transcriptions.create(
  model="whisper-large-v3",
  file=audio_file,
  language="en",  # Specify English for better accuracy
  prompt="Please transcribe accurately with proper punctuation and grammar",
  response_format="text",
  temperature=0.2  # Lower randomness to reduce hallucinations
)

print(transcript)

from openai import OpenAI
import os

client = OpenAI(
  api_key="sk-***", # Replace with your AiHubMix API key
  base_url="https://aihubmix.com/v1"
)

# Open audio file
audio_file = open("path/to/audio.m4a", "rb")

# Translate audio to English
translation = client.audio.translations.create(
  model="whisper-large-v3",
  file=audio_file,
  prompt="autocorrect, clean up the stammer, and translate to english",
  response_format="text",
  temperature=0.2
)

print(translation)

from openai import OpenAI
import os

client = OpenAI(
  api_key="sk-***", # Replace with your AiHubMix API key
  base_url="https://aihubmix.com/v1"
)

audio_file = open("path/to/audio.wav", "rb")

# Get detailed transcription results with timestamps
transcript = client.audio.transcriptions.create(
  model="whisper-large-v3",
  file=audio_file,
  response_format="verbose_json",
  timestamp_granularities=["word"],
  temperature=0.2
)

# Output results with word-level timestamps
print(f"Text: {transcript.text}")
print(f"Language: {transcript.language}")
for word in transcript.words:
    print(f"'{word.word}' at {word.start}s - {word.end}s")

from openai import OpenAI
import os

client = OpenAI(
  api_key="sk-***", # Replace with your AiHubMix API key
  base_url="https://aihubmix.com/v1"
)

audio_file = open("path/to/video_audio.mp4", "rb")

# Generate SRT subtitle file
srt_transcript = client.audio.transcriptions.create(
  model="whisper-large-v3",
  file=audio_file,
  response_format="srt",
  language="en",
  temperature=0.2
)

# Save as .srt file
with open("subtitles.srt", "w", encoding="utf-8") as f:
    f.write(srt_transcript)

print("SRT subtitle file generated")

Response Formats

JSON Format (Default)

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

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
    }
  ]
}

Text Format

This is the transcribed text content

SRT Format

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

VTT Format

WEBVTT

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

Melhores Práticas

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

Última atualização: 2026-06-01

Começar

Recursos de gateway

Capacidades do modelo

Referência de protocolo

Gestão de plataforma

Privacidade e termos

Speech-to-Text

Introdução

Model Usage

Speech Transcription

Speech Translation

Parâmetros da Requisição

Transcription Parameters

Translation Parameters

Usage Examples

Response Formats

JSON Format (Default)

Verbose JSON Format (verbose_json)

Text Format

SRT Format

VTT Format

Melhores Práticas

​Introdução

​Model Usage

​Speech Transcription

​Speech Translation

​Parâmetros da Requisição

​Transcription Parameters

​Translation Parameters

​Usage Examples

​Response Formats

​JSON Format (Default)

​Verbose JSON Format (verbose_json)

​Text Format

​SRT Format

​VTT Format

​Melhores Práticas

Introdução

Model Usage

Speech Transcription

Speech Translation

Parâmetros da Requisição

Transcription Parameters

Translation Parameters

Usage Examples

Response Formats

JSON Format (Default)

Verbose JSON Format (verbose_json)

Text Format

SRT Format

VTT Format

Melhores Práticas