## Overview

This guide walks you through transcribing your first audio file with AssemblyAI. You will learn how to submit an audio file for transcription and retrieve the results using the AssemblyAI API.

**Building a medical scribe or clinical documentation app?** Check out the [Medical Scribe guides](/content/docs/medical-scribe-best-practices/index.html) for post-visit and real-time transcription workflows with Medical Mode, HIPAA-compliant configuration, and SOAP note generation.

When transcribing an audio file, there are three main things you will want to specify:

1. The speech models you would like to use (required).
2. The region you would like to use (optional).
3. Other models you would like to use like Speaker Diarization or PII Redaction (optional).

##### speech_models is required

You must include the `speech_models` parameter in every transcription request. There is no default model for pre-recorded transcription. If you omit `speech_models`, the request will fail. See [Model selection](/content/docs/pre-recorded-audio/select-the-speech-model/index.html) to learn about available models.

##### Recommended model

We recommend [Universal-3 Pro](/content/docs/pre-recorded-audio/universal-3-pro/index.html) for pre-recorded audio transcription. It delivers the highest accuracy with support for fine-tuning and customization via prompting. For the broadest language coverage (99 languages), use `['universal-3-pro', 'universal-2']` to automatically fall back to Universal-2 for unsupported languages.

## Prerequisites

Before you begin, make sure you have:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

- An AssemblyAI API key (get one by signing up at [assemblyai.com](/content/site-root.html))
- Python 3.6 or later installed
- The `requests` library (`pip install requests`)

## Step 1: Set up your API credentials

First, configure your API endpoint and authentication:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

```
import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "YOUR_API_KEY"}
```

Replace `YOUR_API_KEY` with your actual AssemblyAI API key.

##### Need EU data residency?

Use our EU endpoint by changing `base_url` to
`"https://api.eu.assemblyai.com".`

## Step 2: Specify your audio source

You can transcribe audio files in two ways:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

**Option A: Use a publicly accessible URL**

```
audio_file = "https://assembly.ai/wildfires.mp3"
```

**Option B: Upload a local file**

If your audio file is stored locally, upload it to AssemblyAI first:

```
with open("./example.mp3", "rb") as f:
    response = requests.post(base_url + "/v2/upload", headers=headers, data=f)

if response.status_code != 200:
        print(f"Error: {response.status_code}, Response: {response.text}")
        response.raise_for_status()

upload_json = response.json()
    audio_file = upload_json["upload_url"]
```

## Step 3: Submit the transcription request

Create a request with your audio URL and desired configuration options:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

```
data = {
    "audio_url": audio_file,
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True,
    "speaker_labels": True
}

response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)

if response.status_code != 200:
    print(f"Error: {response.status_code}, Response: {response.text}")
    response.raise_for_status()

transcript_json = response.json()
transcript_id = transcript_json["id"]
```

This configuration:

- Uses both the `universal-3-pro` and `universal-2` models for broad language coverage. Learn more about our different speech recognition models [here](/content/docs/getting-started/models/index.html).
- Uses [our Automatic Language Detection model](/content/docs/pre-recorded-audio/language-detection/index.html) to detect the dominant language in the spoken audio.
- Uses [our Speaker Diarization model](/content/docs/pre-recorded-audio/label-speakers/index.html) to create turn-by-turn utterances.

##### Model Pricing

Pricing can vary based on the speech model used in the request.

If you already have an account with us, you can find your specific pricing on [the Billing page](/content/dashboard/account/billing/index.html) of your dashboard. If you are a new customer, you can find general pricing information [here](/content/pricing/index.html).

## Step 4: Poll for the transcription result

Transcription happens asynchronously. Poll the API until the transcription is complete:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

```
polling_endpoint = f"{base_url}/v2/transcript/{transcript_id}"

while True:
    transcript = requests.get(polling_endpoint, headers=headers).json()

if transcript["status"] == "completed":
        print(f"\nFull Transcript:\n\n{transcript['text']}")
        break
    elif transcript["status"] == "error":
        raise RuntimeError(f"Transcription failed: {transcript['error']}")
    else:
        time.sleep(3)
```

The polling loop checks the transcription status every 3 seconds and prints the full transcript once processing is complete.

## Step 5: Access speaker diarization (optional)

If you enabled speaker labels, you can access the speaker-separated utterances:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

```
for utterance in transcript['utterances']:
    print(f"Speaker {utterance['speaker']}: {utterance['text']}")
```

## Complete example

Here is the full working code:

###### Python

###### Python SDK

###### JavaScript

###### JavaScript SDK

```
import requests
import time

base_url = "https://api.assemblyai.com"
headers = {"authorization": "YOUR_API_KEY"}

# Use a publicly-accessible URL
audio_file = "https://assembly.ai/wildfires.mp3"

# Or upload a local file:
# with open("./example.mp3", "rb") as f:
#     response = requests.post(base_url + "/v2/upload", headers=headers, data=f)
#     if response.status_code != 200:
#         print(f"Error: {response.status_code}, Response: {response.text}")
#         response.raise_for_status()
#     upload_json = response.json()
#     audio_file = upload_json["upload_url"]

data = {
    "audio_url": audio_file,
    "speech_models": ["universal-3-pro", "universal-2"],
    "language_detection": True,
    "speaker_labels": True
}

response = requests.post(base_url + "/v2/transcript", headers=headers, json=data)

if response.status_code != 200:
    print(f"Error: {response.status_code}, Response: {response.text}")
    response.raise_for_status()

transcript_json = response.json()
transcript_id = transcript_json["id"]
polling_endpoint = f"{base_url}/v2/transcript/{transcript_id}"

while True:
    transcript = requests.get(polling_endpoint, headers=headers).json()
    if transcript["status"] == "completed":
        print(f"\nFull Transcript:\n\n{transcript['text']}")

# Optionally print speaker diarization results
        # for utterance in transcript['utterances']:
        #     print(f"Speaker {utterance['speaker']}: {utterance['text']}")
        break
    elif transcript["status"] == "error":
        raise RuntimeError(f"Transcription failed: {transcript['error']}")
    else:
        time.sleep(3)
```

## Next steps

Now that you have transcribed your first audio file:

- Learn how you can do even more with Universal-3 Pro with [prompting](/content/docs/getting-started/universal-3-pro#prompting/index.html)
- Explore [our Speech Understanding features](/content/products/speech-understanding/index.html) for more ways to analyze your audio data
- Learn more about searching, summarizing, or asking questions on your transcript with [our LLM Gateway feature](/content/docs/llm-gateway/overview/index.html)
- Find out how to use [webhooks](/content/docs/pre-recorded-audio/webhooks/index.html) to get notified when your transcripts are ready

For more information, check out the full [API reference documentation](/content/docs/index.html).