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

# Translations

> Translate audio into English using the Whisper model

## Create translation

Translates audio into English.

```python theme={null}
from openai import OpenAI

client = OpenAI()

audio_file = open("german_audio.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file
)

print(translation.text)
```

### Parameters

<ParamField path="file" type="file" required>
  The audio file object (not file name) to translate, in one of these formats: `flac`, `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `ogg`, `wav`, or `webm`.
</ParamField>

<ParamField path="model" type="string" required>
  ID of the model to use. Only `whisper-1` (which is powered by OpenAI's open source Whisper V2 model) is currently available.
</ParamField>

<ParamField path="prompt" type="string">
  An optional text to guide the model's style or continue a previous audio segment. The [prompt](https://platform.openai.com/docs/guides/speech-to-text#prompting) should be in English.
</ParamField>

<ParamField path="response_format" type="string" default="json">
  The format of the output, in one of these options: `json`, `text`, `srt`, `verbose_json`, or `vtt`.
</ParamField>

<ParamField path="temperature" type="float" default="0">
  The sampling temperature, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If set to 0, the model will use log probability to automatically increase the temperature until certain thresholds are hit.
</ParamField>

### Response

<ResponseField name="text" type="string">
  The translated text in English.
</ResponseField>

## Examples

### Basic translation

```python theme={null}
from openai import OpenAI

client = OpenAI()

# Translate Spanish audio to English
audio_file = open("spanish_audio.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file
)

print(translation.text)
```

### Get translation as SRT subtitles

```python theme={null}
from openai import OpenAI

client = OpenAI()

audio_file = open("french_video.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file,
    response_format="srt"
)

# Save English SRT file
with open("english_subtitles.srt", "w") as f:
    f.write(translation)
```

### Get translation as VTT

```python theme={null}
from openai import OpenAI

client = OpenAI()

audio_file = open("mandarin_audio.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file,
    response_format="vtt"
)

# Save as WebVTT file
with open("subtitles.vtt", "w") as f:
    f.write(translation)
```

### Translation with verbose JSON

```python theme={null}
from openai import OpenAI

client = OpenAI()

audio_file = open("japanese_audio.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file,
    response_format="verbose_json"
)

# Access detailed information
print(f"Language: {translation.language}")
print(f"Duration: {translation.duration}")
print(f"Text: {translation.text}")
```

### Translation with prompt

```python theme={null}
from openai import OpenAI

client = OpenAI()

audio_file = open("german_presentation.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file,
    prompt="This is a technical presentation about machine learning algorithms."
)

print(translation.text)
```

### Batch translate multiple files

```python theme={null}
from openai import OpenAI
from pathlib import Path

client = OpenAI()

audio_files = Path("audio_files").glob("*.mp3")

for audio_path in audio_files:
    with audio_path.open("rb") as audio_file:
        translation = client.audio.translations.create(
            model="whisper-1",
            file=audio_file
        )
        
        # Save translation
        output_path = audio_path.with_suffix(".txt")
        output_path.write_text(translation.text)
        print(f"Translated {audio_path.name}")
```

## Async usage

```python theme={null}
import asyncio
from openai import AsyncOpenAI

async def translate_audio():
    client = AsyncOpenAI()
    
    audio_file = open("italian_audio.mp3", "rb")
    translation = await client.audio.translations.create(
        model="whisper-1",
        file=audio_file
    )
    
    print(translation.text)

asyncio.run(translate_audio())
```

## Supported audio formats

The translation endpoint supports the following audio formats:

* `flac` - Free Lossless Audio Codec
* `mp3` - MPEG audio format
* `mp4` - MPEG-4 Part 14
* `mpeg` - MPEG audio
* `mpga` - MPEG audio
* `m4a` - MPEG-4 audio
* `ogg` - Ogg Vorbis
* `wav` - Waveform audio
* `webm` - WebM audio

## File uploads

Files are uploaded using multipart/form-data. The file object should be opened in binary mode:

```python theme={null}
# Correct way to open file
audio_file = open("path/to/file.mp3", "rb")

# Or using pathlib
from pathlib import Path
audio_file = Path("path/to/file.mp3").open("rb")
```

## Translation vs Transcription

The key difference between the `/audio/translations` and `/audio/transcriptions` endpoints:

* **Translations** - Always outputs English text, regardless of input language
* **Transcriptions** - Outputs text in the same language as the audio input

### When to use translations

Use the translations endpoint when you need to:

* Convert non-English audio into English text
* Create English subtitles for foreign language videos
* Translate podcasts or audio content into English
* Build multilingual applications that standardize on English output

### When to use transcriptions

Use the transcriptions endpoint when you need to:

* Convert audio to text in the same language
* Create subtitles in the original language
* Preserve the original language of the content

## Example: Multi-language processing

```python theme={null}
from openai import OpenAI

client = OpenAI()

# First transcribe in original language
audio_file = open("spanish_audio.mp3", "rb")
transcription = client.audio.transcriptions.create(
    model="whisper-1",
    file=audio_file,
    language="es"
)
print(f"Spanish: {transcription.text}")

# Then translate to English
audio_file = open("spanish_audio.mp3", "rb")
translation = client.audio.translations.create(
    model="whisper-1",
    file=audio_file
)
print(f"English: {translation.text}")
```

<Info>
  For more information on audio processing and best practices, see the [Speech to text guide](https://platform.openai.com/docs/guides/speech-to-text).
</Info>