Together AI provides comprehensive audio transcription and translation capabilities powered by OpenAI’s Whisper models. This guide covers everything you need to know to integrate speech-to-text functionality into your applications.

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Table of Contents

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Quick Start

  1. Register for an account
First, register for an account to get an API key. Once you’ve registered, set your account’s API key to an environment variable named TOGETHER_API_KEY:
Shell
export TOGETHER_API_KEY=xxxxx
  1. Install your preferred library
Together provides an official library for Python and TypeScript: 
pip install together
  1. Run your first transcription
Here’s how to get started with basic transcription and translation: 
from together import Together

## Initialize the client

client = Together()

## Basic transcription

response = client.audio.transcriptions.create(
file="path/to/audio.mp3",
model="openai/whisper-large-v3",
language="en"
)
print(response.text)

## Basic translation

response = client.audio.translations.create(
file="path/to/foreign_audio.mp3",
model="openai/whisper-large-v3"
)
print(response.text)

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Audio Transcription

Audio transcription converts speech to text in the same language as the source audio. 
from together import Together

client = Together()

response = client.audio.transcriptions.create(
file="meeting_recording.mp3",
model="openai/whisper-large-v3",
language="en",
response_format="json"
)

print(f"Transcription: {response.text}")
The API supports the following audio formats:
  • .wav (audio/wav)
  • .mp3 (audio/mpeg)
  • .m4a (audio/mp4)
  • .webm (audio/webm)
  • .flac (audio/flac)

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Input Methods

Local File Path
Python
response = client.audio.transcriptions.create(
    file="/path/to/audio.mp3",
    model="openai/whisper-large-v3"
)
Path Object
Python
from pathlib import Path

audio_file = Path("recordings/interview.wav")
response = client.audio.transcriptions.create(
    file=audio_file,
    model="openai/whisper-large-v3"
)
URL
Python
response = client.audio.transcriptions.create(
    file="https://example.com/audio.mp3",
    model="openai/whisper-large-v3"
)
File-like Object
Python
with open("audio.mp3", "rb") as audio_file:
    response = client.audio.transcriptions.create(
        file=audio_file,
        model="openai/whisper-large-v3"
    )

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Language Support

Specify the audio language using ISO 639-1 language codes:
Python
response = client.audio.transcriptions.create(
    file="spanish_audio.mp3",
    model="openai/whisper-large-v3",
    language="es"  # Spanish
)
Common specifiable language codes:
  • “en” - English
  • “es” - Spanish
  • “fr” - French
  • “de” - German
  • “ja” - Japanese
  • “zh” - Chinese
  • “auto” - Auto-detect (default)

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Custom Prompts

Use prompts to improve transcription accuracy for specific contexts: 
response = client.audio.transcriptions.create(
    file="medical_consultation.mp3",
    model="openai/whisper-large-v3",
    language="en",
    prompt="This is a medical consultation discussing patient symptoms, diagnosis, and treatment options."
)

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Audio Translation

Audio translation converts speech from any language to English text. 
response = client.audio.translations.create(
    file="french_audio.mp3",
    model="openai/whisper-large-v3"
)
print(f"English translation: {response.text}")
Translation with Context 
response = client.audio.translations.create(
    file="business_meeting_spanish.mp3",
    model="openai/whisper-large-v3",
    prompt="This is a business meeting discussing quarterly sales results."
)

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Response Formats

JSON Format (Default) Returns only the transcribed/translated text:
Python
response = client.audio.transcriptions.create(
    file="audio.mp3",
    model="openai/whisper-large-v3",
    response_format="json"
)

print(response.text)  # "Hello, this is a test recording."
Verbose JSON Format Returns detailed information including timestamps: 
response = client.audio.transcriptions.create(
    file="audio.mp3",
    model="openai/whisper-large-v3",
    response_format="verbose_json",
    timestamp_granularities="segment"
)

## Access segments with timestamps

for segment in response.segments:
print(f"[{segment.start:.2f}s - {segment.end:.2f}s]: {segment.text}")
Example Output:
Text
[0.11s - 10.85s]: Call is now being recorded. Parker Scarves, how may I help you? Online for my wife, and it turns out they shipped the wrong... Oh, I am so sorry, sir. I got it for her birthday, which is tonight, and now I'm not 100% sure what I need to do. Okay, let me see if I can help. Do you have the item number of the Parker Scarves? I don't think so. Call the New Yorker, I... Excellent. What color do...

[10.88s - 21.73s]: Blue. The one they shipped was light blue. I wanted the darker one. What's the difference? The royal blue is a bit brighter. What zip code are you located in? One nine.

[22.04s - 32.62s]: Karen's Boutique, Termall. Is that close? I'm in my office. Okay, um, what is your name, sir? Charlie. Charlie Johnson. Is that J-O-H-N-S-O-N? And Mr. Johnson, do you have the Parker scarf in light blue with you now? I do. They shipped it to my office. It came in not that long ago. What I will do is make arrangements with Karen's Boutique for...

[32.62s - 41.03s]: you to Parker Scarf at no additional cost. And in addition, I was able to look up your order in our system, and I'm going to send out a special gift to you to make up for the inconvenience. Thank you. You're welcome. And thank you for calling Parker Scarf, and I hope your wife enjoys her birthday gift. Thank you. You're very welcome. Goodbye.

[43.50s - 44.20s]: you
Word-level Timestamps Get word-level timing information: 
response = client.audio.transcriptions.create(
    file="audio.mp3",
    model="openai/whisper-large-v3",
    response_format="verbose_json",
    timestamp_granularities="word"
)

print(f"Text: {response.text}")
print(f"Language: {response.language}")
print(f"Duration: {response.duration}s")
print(f"Task: {response.task}")

## Access individual words with timestamps

if response.words:
for word in response.words:
print(f"'{word.word}' [{word.start:.2f}s - {word.end:.2f}s]")
Example Output:
Text
Text: It is certain that Jack Pumpkinhead might have had a much finer house to live in.
Language: en
Duration: 7.2562358276643995s
Task: None

'It' [0.00s - 0.36s]
'is' [0.42s - 0.47s]
'certain' [0.51s - 0.74s]
'that' [0.79s - 0.86s]
'Jack' [0.90s - 1.11s]
'Pumpkinhead' [1.15s - 1.66s]
'might' [1.81s - 2.00s]
'have' [2.04s - 2.13s]
'had' [2.16s - 2.26s]
'a' [2.30s - 2.32s]
'much' [2.36s - 2.48s]
'finer' [2.54s - 2.74s]
'house' [2.78s - 2.93s]
'to' [2.96s - 3.03s]
'live' [3.07s - 3.21s]
'in.' [3.26s - 7.27s]

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Advanced Features

Temperature Control Adjust randomness in the output (0.0 = deterministic, 1.0 = creative): 
response = client.audio.transcriptions.create(
    file="audio.mp3",
    model="openai/whisper-large-v3",
    temperature=0.0  # Most deterministic
)

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Async Support

All transcription and translation operations support async/await:

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Async Transcription

Python
import asyncio
from together import AsyncTogether

async def transcribe_audio():
    client = AsyncTogether()

    response = await client.audio.transcriptions.create(
        file="audio.mp3",
        model="openai/whisper-large-v3",
        language="en"
    )

    return response.text

## Run async function
result = asyncio.run(transcribe_audio())
print(result)

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Async Translation

Python
async def translate_audio():
    client = AsyncTogether()

    response = await client.audio.translations.create(
        file="foreign_audio.mp3",
        model="openai/whisper-large-v3"
    )

    return response.text

result = asyncio.run(translate_audio())
print(result)

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Concurrent Processing

Process multiple audio files concurrently:
Python
import asyncio
from together import AsyncTogether

async def process_multiple_files():
    client = AsyncTogether()

    files = ["audio1.mp3", "audio2.mp3", "audio3.mp3"]

    tasks = [
        client.audio.transcriptions.create(
            file=file,
            model="openai/whisper-large-v3"
        )
        for file in files
    ]

    responses = await asyncio.gather(*tasks)

    for i, response in enumerate(responses):
        print(f"File {files[i]}: {response.text}")

asyncio.run(process_multiple_files())

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Best Practices

  • Use high-quality audio files for better transcription accuracy
  • Minimize background noise
  • Ensure clear speech with good volume levels
  • Use appropriate sample rates (16kHz or higher recommended)
  • Consider file size limits for uploads
  • For long audio files, consider splitting into smaller chunks
  • Use streaming for real-time applications when available

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Next Steps