> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Speech-to-Text

> Use Whisper models to convert audio files to text, supporting transcription and translation features

## 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

<Tip>
  **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
</Tip>

## Model Usage

### Speech Transcription

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

### Speech Translation

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

### Request Parameters

#### Transcription Parameters

<ParamField body="file" type="file" required>
  Audio file object to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav, webm, maximum 25MB
</ParamField>

<ParamField body="model" type="string" required>
  Model ID to use. Options: `whisper-large-v3`, `whisper-1`, `distil-whisper-large-v3-en`
</ParamField>

<ParamField body="language" type="string">
  Language of the input audio in ISO-639-1 format (e.g., 'en', 'zh'). Specifying the language can improve accuracy and latency
</ParamField>

<ParamField body="prompt" type="string">
  Optional text prompt to guide the model's style or continue a previous audio segment. The prompt should match the audio language
</ParamField>

<ParamField body="response_format" type="string">
  Transcription output format. Options: `json` (default), `text`, `srt`, `verbose_json`, `vtt`
</ParamField>

<ParamField body="temperature" type="number">
  Sampling temperature between 0 and 1. Higher values make output more random, lower values make it more focused and deterministic. Default is 0
</ParamField>

<ParamField body="timestamp_granularities[]" type="array">
  Timestamp granularities. Options: `word`, `segment`. Only available when response\_format is verbose\_json
</ParamField>

#### Translation Parameters

<ParamField body="file" type="file" required>
  Audio file object to translate. Same formats as transcription
</ParamField>

<ParamField body="model" type="string" required>
  Model ID to use, same as transcription parameters
</ParamField>

<ParamField body="prompt" type="string">
  Optional English text prompt to guide translation style
</ParamField>

<ParamField body="response_format" type="string">
  Translation output format, same as transcription parameters
</ParamField>

<ParamField body="temperature" type="number">
  Sampling temperature, same as transcription parameters
</ParamField>

## Usage Examples

<CodeGroup>
  ```shell Curl Transcription theme={null}
  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"
  ```

  ```shell Curl Translation theme={null}
  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"
  ```

  ```py Speech Transcription theme={null}
  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)
  ```

  ```py Speech Translation theme={null}
  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)
  ```

  ```py Verbose Output Format theme={null}
  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")
  ```

  ```py SRT Subtitle Format theme={null}
  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")
  ```
</CodeGroup>

## Response Formats

### JSON Format (Default)

```json theme={null}
{
  "text": "This is the transcribed text content"
}
```

### Verbose JSON Format (verbose\_json)

```json theme={null}
{
  "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

```srt theme={null}
1
00:00:00,000 --> 00:00:08,470
This is the transcribed text content
```

### VTT Format

```vtt theme={null}
WEBVTT

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

## 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

***

Last updated: 2026-06-01