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

# Apply LLM Gateway to Streaming

> Learn how to analyze streaming audio transcripts using LLM Gateway.

## Overview

A Large Language Model (LLM) is a machine learning model that uses natural language processing (NLP) to generate text. [LLM Gateway](/llm-gateway/quickstart) is a unified API that provides access to 25+ models from Claude, GPT, Gemini, and more through a single interface. You can use LLM Gateway to analyze streaming audio transcripts in real time, for example to summarize a live conversation or extract action items as they happen.

By the end of this tutorial, you'll be able to use LLM Gateway to analyze a streaming audio transcript from your microphone.

Here's the full sample code for what you'll build in this tutorial:

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python expandable theme={null}
    import pyaudio
    import websocket
    import json
    import threading
    import time
    import wave
    from urllib.parse import urlencode
    from datetime import datetime

    # --- Configuration ---
    YOUR_API_KEY = "<YOUR_API_KEY>"  # Replace with your actual API key

    # LLM Gateway Configuration
    PROMPT = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"
    LLM_GATEWAY_CONFIG = {
        "model": "claude-sonnet-4-6",
        "messages": [
            {"role": "user", "content": PROMPT}
        ],
        "max_tokens": 4000
    }

    CONNECTION_PARAMS = {
        "sample_rate": 16000,
        "format_turns": True,  # Request formatted final transcripts
        "speech_model": "u3-rt-pro",  # USM 3 Pro model
        "llm_gateway": json.dumps(LLM_GATEWAY_CONFIG)  # LLM Gateway configuration
    }
    API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws"
    API_ENDPOINT = f"{API_ENDPOINT_BASE_URL}?{urlencode(CONNECTION_PARAMS)}"

    # Audio Configuration
    FRAMES_PER_BUFFER = 800  # 50ms of audio (0.05s * 16000Hz)
    SAMPLE_RATE = CONNECTION_PARAMS["sample_rate"]
    CHANNELS = 1
    FORMAT = pyaudio.paInt16

    # Global variables for audio stream and websocket
    audio = None
    stream = None
    ws_app = None
    audio_thread = None
    stop_event = threading.Event()  # To signal the audio thread to stop

    # WAV recording variables
    recorded_frames = []  # Store audio frames for WAV file
    recording_lock = threading.Lock()  # Thread-safe access to recorded_frames

    def save_wav_file():
        """Save recorded audio frames to a WAV file."""
        if not recorded_frames:
            print("No audio data recorded.")
            return

        # Generate filename with timestamp
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"recorded_audio_{timestamp}.wav"

        try:
            with wave.open(filename, 'wb') as wf:
                wf.setnchannels(CHANNELS)
                wf.setsampwidth(2)  # 16-bit = 2 bytes
                wf.setframerate(SAMPLE_RATE)

                # Write all recorded frames
                with recording_lock:
                    wf.writeframes(b''.join(recorded_frames))

            print(f"Audio saved to: {filename}")
            print(f"Duration: {len(recorded_frames) * FRAMES_PER_BUFFER / SAMPLE_RATE:.2f} seconds")

        except Exception as e:
            print(f"Error saving WAV file: {e}")

    # --- WebSocket Event Handlers ---

    def on_open(ws):
        """Called when the WebSocket connection is established."""
        print("WebSocket connection opened.")
        print(f"Connected to: {API_ENDPOINT_BASE_URL}")

        # Start sending audio data in a separate thread
        def stream_audio():
            global stream
            print("Starting audio streaming...")
            while not stop_event.is_set():
                try:
                    audio_data = stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)

                    # Store audio data for WAV recording
                    with recording_lock:
                        recorded_frames.append(audio_data)

                    # Send audio data as binary message
                    ws.send(audio_data, websocket.ABNF.OPCODE_BINARY)
                except Exception as e:
                    print(f"Error streaming audio: {e}")
                    # If stream read fails, likely means it's closed, stop the loop
                    break
            print("Audio streaming stopped.")

        global audio_thread
        audio_thread = threading.Thread(target=stream_audio)
        audio_thread.daemon = (
            True  # Allow main thread to exit even if this thread is running
        )
        audio_thread.start()

    def on_message(ws, message):
        try:
            data = json.loads(message)
            msg_type = data.get('type')

            if msg_type == "Begin":
                session_id = data.get('id')
                expires_at = data.get('expires_at')
                print(f"Session started: {session_id}")
            elif msg_type == "Turn":
                end_of_turn = data.get('end_of_turn', False)

                if end_of_turn:
                    transcript = data.get('transcript', '')
                    print(f"\nTranscript:\n{transcript}\n")
            elif msg_type == "LLMGatewayResponse":
                # Extract the LLM response content
                llm_data = data.get('data', {})
                llm_content = llm_data.get("choices", [{}])[0].get("message", {}).get("content", "")
                print(f"LLM Response:\n{llm_content}\n")
            elif msg_type == "Termination":
                audio_duration = data.get('audio_duration_seconds', 0)
                session_duration = data.get('session_duration_seconds', 0)
                print(f"Session terminated: {audio_duration} seconds of audio processed")
        except json.JSONDecodeError as e:
            print(f"Error decoding message: {e}")
        except Exception as e:
            print(f"Error handling message: {e}")

    def on_error(ws, error):
        """Called when a WebSocket error occurs."""
        print(f"\nWebSocket Error: {error}")
        # Attempt to signal stop on error
        stop_event.set()


    def on_close(ws, close_status_code, close_msg):
        """Called when the WebSocket connection is closed."""
        print(f"\nWebSocket Disconnected: Status={close_status_code}, Msg={close_msg}")

        # Save recorded audio to WAV file
        save_wav_file()

        # Ensure audio resources are released
        global stream, audio
        stop_event.set()  # Signal audio thread just in case it's still running

        if stream:
            if stream.is_active():
                stream.stop_stream()
            stream.close()
            stream = None
        if audio:
            audio.terminate()
            audio = None
        # Try to join the audio thread to ensure clean exit
        if audio_thread and audio_thread.is_alive():
            audio_thread.join(timeout=1.0)

    # --- Main Execution ---
    def run():
        global audio, stream, ws_app

        # Initialize PyAudio
        audio = pyaudio.PyAudio()

        # Open microphone stream
        try:
            stream = audio.open(
                input=True,
                frames_per_buffer=FRAMES_PER_BUFFER,
                channels=CHANNELS,
                format=FORMAT,
                rate=SAMPLE_RATE,
            )
            print("Microphone stream opened successfully.")
            print("Speak into your microphone. Press Ctrl+C to stop.")
            print("Audio will be saved to a WAV file when the session ends.")
        except Exception as e:
            print(f"Error opening microphone stream: {e}")
            if audio:
                audio.terminate()
            return  # Exit if microphone cannot be opened

        # Create WebSocketApp
        ws_app = websocket.WebSocketApp(
            API_ENDPOINT,
            header={"Authorization": YOUR_API_KEY},
            on_open=on_open,
            on_message=on_message,
            on_error=on_error,
            on_close=on_close,
        )

        # Run WebSocketApp in a separate thread to allow main thread to catch KeyboardInterrupt
        ws_thread = threading.Thread(target=ws_app.run_forever)
        ws_thread.daemon = True
        ws_thread.start()

        try:
            # Keep main thread alive until interrupted
            while ws_thread.is_alive():
                time.sleep(0.1)
        except KeyboardInterrupt:
            print("\nCtrl+C received. Stopping...")
            stop_event.set()  # Signal audio thread to stop

            # Send termination message to the server
            if ws_app and ws_app.sock and ws_app.sock.connected:
                try:
                    terminate_message = {"type": "Terminate"}
                    print(f"Sending termination message: {json.dumps(terminate_message)}")
                    ws_app.send(json.dumps(terminate_message))
                    # Give a moment for messages to process before forceful close
                    time.sleep(5)
                except Exception as e:
                    print(f"Error sending termination message: {e}")

            # Close the WebSocket connection (will trigger on_close)
            if ws_app:
                ws_app.close()

            # Wait for WebSocket thread to finish
            ws_thread.join(timeout=2.0)

        except Exception as e:
            print(f"\nAn unexpected error occurred: {e}")
            stop_event.set()
            if ws_app:
                ws_app.close()
            ws_thread.join(timeout=2.0)

        finally:
            # Final cleanup (already handled in on_close, but good as a fallback)
            if stream and stream.is_active():
                stream.stop_stream()
            if stream:
                stream.close()
            if audio:
                audio.terminate()
            print("Cleanup complete. Exiting.")


    if __name__ == "__main__":
        run()
    ```
  </Tab>

  <Tab language="python-sdk" title="Python SDK">
    ```python expandable theme={null}
    import logging
    from typing import Type

    import assemblyai as aai
    from assemblyai.streaming.v3 import (
        BeginEvent,
        LLMGatewayResponseEvent,
        StreamingClient,
        StreamingClientOptions,
        StreamingError,
        StreamingEvents,
        StreamingParameters,
        TurnEvent,
        TerminationEvent,
    )
    from assemblyai.streaming.v3.models import LLMGatewayConfig, LLMGatewayMessage

    api_key = "<YOUR_API_KEY>"
    prompt = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"

    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger(__name__)

    def on_begin(self: Type[StreamingClient], event: BeginEvent):
        print(f"Session started: {event.id}")

    def on_turn(self: Type[StreamingClient], event: TurnEvent):
        if event.end_of_turn:
            print(f"\nTranscript:\n{event.transcript}\n")

    def on_llm_response(self: Type[StreamingClient], event: LLMGatewayResponseEvent):
        # Extract the actual LLM response content from the data
        llm_content = event.data.get("choices", [{}])[0].get("message", {}).get("content", "")
        print(f"LLM Response:\n{llm_content}\n")

    def on_terminated(self: Type[StreamingClient], event: TerminationEvent):
        print(
            f"Session terminated: {event.audio_duration_seconds} seconds of audio processed"
        )

    def on_error(self: Type[StreamingClient], error: StreamingError):
        print(f"Error occurred: {error}")

    def main():
        client = StreamingClient(
            StreamingClientOptions(
                api_key=api_key,
                api_host="streaming.assemblyai.com",
            )
        )

        client.on(StreamingEvents.Begin, on_begin)
        client.on(StreamingEvents.Turn, on_turn)
        client.on(StreamingEvents.LLMGatewayResponse, on_llm_response)
        client.on(StreamingEvents.Termination, on_terminated)
        client.on(StreamingEvents.Error, on_error)

        client.connect(
            StreamingParameters(
                sample_rate=16000,
                speech_model="u3-rt-pro",
                format_turns=True,
                llm_gateway=LLMGatewayConfig(
                    model="claude-sonnet-4-6",
                    messages=[
                        LLMGatewayMessage(role="user", content=prompt)
                    ],
                    max_tokens=4000
                )
            )
        )

        try:
            client.stream(
                aai.extras.MicrophoneStream(sample_rate=16000)
            )
        finally:
            client.disconnect(terminate=True)

    if __name__ == "__main__":
        main()
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript expandable theme={null}
    const WebSocket = require("ws");
    const mic = require("mic");
    const querystring = require("querystring");
    const fs = require("fs");

    // --- Configuration ---
    const YOUR_API_KEY = "<YOUR_API_KEY>"; // Replace with your actual API key

    // LLM Gateway Configuration
    const PROMPT =
      "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
    const LLM_GATEWAY_CONFIG = {
      model: "claude-sonnet-4-6",
      messages: [{ role: "user", content: PROMPT }],
      max_tokens: 4000,
    };

    const CONNECTION_PARAMS = {
      sample_rate: 16000,
      format_turns: true, // Request formatted final transcripts
      speech_model: "u3-rt-pro", // USM 3 Pro model
      llm_gateway: JSON.stringify(LLM_GATEWAY_CONFIG), // LLM Gateway configuration
    };
    const API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws";
    const API_ENDPOINT = `${API_ENDPOINT_BASE_URL}?${querystring.stringify(CONNECTION_PARAMS)}`;

    // Audio Configuration
    const SAMPLE_RATE = CONNECTION_PARAMS.sample_rate;
    const CHANNELS = 1;

    // Global variables
    let micInstance = null;
    let micInputStream = null;
    let ws = null;
    let stopRequested = false;

    // WAV recording variables
    let recordedFrames = []; // Store audio frames for WAV file

    // --- Helper functions ---
    function formatTimestamp(timestamp) {
      return new Date(timestamp * 1000).toISOString();
    }

    function createWavHeader(sampleRate, channels, dataLength) {
      const buffer = Buffer.alloc(44);

      // RIFF header
      buffer.write("RIFF", 0);
      buffer.writeUInt32LE(36 + dataLength, 4);
      buffer.write("WAVE", 8);

      // fmt chunk
      buffer.write("fmt ", 12);
      buffer.writeUInt32LE(16, 16); // fmt chunk size
      buffer.writeUInt16LE(1, 20); // PCM format
      buffer.writeUInt16LE(channels, 22);
      buffer.writeUInt32LE(sampleRate, 24);
      buffer.writeUInt32LE(sampleRate * channels * 2, 28); // byte rate
      buffer.writeUInt16LE(channels * 2, 32); // block align
      buffer.writeUInt16LE(16, 34); // bits per sample

      // data chunk
      buffer.write("data", 36);
      buffer.writeUInt32LE(dataLength, 40);

      return buffer;
    }

    function saveWavFile() {
      if (recordedFrames.length === 0) {
        console.log("No audio data recorded.");
        return;
      }

      // Generate filename with timestamp
      const timestamp = new Date().toISOString().replace(/[:.]/g, "-").slice(0, 19);
      const filename = `recorded_audio_${timestamp}.wav`;

      try {
        // Combine all recorded frames
        const audioData = Buffer.concat(recordedFrames);
        const dataLength = audioData.length;

        // Create WAV header
        const wavHeader = createWavHeader(SAMPLE_RATE, CHANNELS, dataLength);

        // Write WAV file
        const wavFile = Buffer.concat([wavHeader, audioData]);
        fs.writeFileSync(filename, wavFile);

        console.log(`Audio saved to: ${filename}`);
        console.log(
          `Duration: ${(dataLength / (SAMPLE_RATE * CHANNELS * 2)).toFixed(2)} seconds`
        );
      } catch (error) {
        console.error(`Error saving WAV file: ${error}`);
      }
    }

    // --- Main function ---
    async function run() {
      console.log(
        "Starting AssemblyAI streaming transcription with LLM Gateway..."
      );
      console.log("Audio will be saved to a WAV file when the session ends.");

      // Initialize WebSocket connection
      ws = new WebSocket(API_ENDPOINT, {
        headers: {
          Authorization: YOUR_API_KEY,
        },
      });

      // Setup WebSocket event handlers
      ws.on("open", () => {
        console.log("WebSocket connection opened.");
        console.log(`Connected to: ${API_ENDPOINT_BASE_URL}`);
        // Start the microphone
        startMicrophone();
      });

      ws.on("message", (message) => {
        try {
          const data = JSON.parse(message);
          const msgType = data.type;

          if (msgType === "Begin") {
            const sessionId = data.id;
            console.log(`Session started: ${sessionId}`);
          } else if (msgType === "Turn") {
            const endOfTurn = data.end_of_turn;

            if (endOfTurn) {
              const transcript = data.transcript || "";
              console.log(`\nTranscript:\n${transcript}\n`);
            }
          } else if (msgType === "LLMGatewayResponse") {
            // Extract the LLM response content
            const llmData = data.data || {};
            const llmContent = llmData.choices?.[0]?.message?.content || "";
            console.log(`LLM Response:\n${llmContent}\n`);
          } else if (msgType === "Termination") {
            const audioDuration = data.audio_duration_seconds;
            console.log(
              `Session terminated: ${audioDuration} seconds of audio processed`
            );
          }
        } catch (error) {
          console.error(`\nError handling message: ${error}`);
          console.error(`Message data: ${message}`);
        }
      });

      ws.on("error", (error) => {
        console.error(`\nWebSocket Error: ${error}`);
        cleanup();
      });

      ws.on("close", (code, reason) => {
        console.log(`\nWebSocket Disconnected: Status=${code}, Msg=${reason}`);
        cleanup();
      });

      // Handle process termination
      setupTerminationHandlers();
    }

    function startMicrophone() {
      try {
        micInstance = mic({
          rate: SAMPLE_RATE.toString(),
          channels: CHANNELS.toString(),
          debug: false,
          exitOnSilence: 6, // This won't actually exit, just a parameter for mic
        });

        micInputStream = micInstance.getAudioStream();

        micInputStream.on("data", (data) => {
          if (ws && ws.readyState === WebSocket.OPEN && !stopRequested) {
            // Store audio data for WAV recording
            recordedFrames.push(Buffer.from(data));

            // Send audio data to WebSocket
            ws.send(data);
          }
        });

        micInputStream.on("error", (err) => {
          console.error(`Microphone Error: ${err}`);
          cleanup();
        });

        micInstance.start();
        console.log("Microphone stream opened successfully.");
        console.log("Speak into your microphone. Press Ctrl+C to stop.");
      } catch (error) {
        console.error(`Error opening microphone stream: ${error}`);
        cleanup();
      }
    }

    function cleanup() {
      stopRequested = true;

      // Save recorded audio to WAV file
      saveWavFile();

      // Stop microphone if it's running
      if (micInstance) {
        try {
          micInstance.stop();
        } catch (error) {
          console.error(`Error stopping microphone: ${error}`);
        }
        micInstance = null;
      }

      // Close WebSocket connection if it's open
      if (ws && [WebSocket.OPEN, WebSocket.CONNECTING].includes(ws.readyState)) {
        try {
          // Send termination message if possible
          if (ws.readyState === WebSocket.OPEN) {
            const terminateMessage = { type: "Terminate" };
            console.log(
              `Sending termination message: ${JSON.stringify(terminateMessage)}`
            );
            ws.send(JSON.stringify(terminateMessage));
          }
          ws.close();
        } catch (error) {
          console.error(`Error closing WebSocket: ${error}`);
        }
        ws = null;
      }

      console.log("Cleanup complete.");
    }

    function setupTerminationHandlers() {
      // Handle Ctrl+C and other termination signals
      process.on("SIGINT", () => {
        console.log("\nCtrl+C received. Stopping...");
        cleanup();
        // Give time for cleanup before exiting
        setTimeout(() => process.exit(0), 1000);
      });

      process.on("SIGTERM", () => {
        console.log("\nTermination signal received. Stopping...");
        cleanup();
        // Give time for cleanup before exiting
        setTimeout(() => process.exit(0), 1000);
      });

      // Handle uncaught exceptions
      process.on("uncaughtException", (error) => {
        console.error(`\nUncaught exception: ${error}`);
        cleanup();
        // Give time for cleanup before exiting
        setTimeout(() => process.exit(1), 1000);
      });
    }

    // Start the application
    run();
    ```
  </Tab>

  <Tab language="javascript-sdk" title="JavaScript SDK">
    ```javascript expandable theme={null}
    import { AssemblyAI } from "assemblyai";
    import recorder from "node-record-lpcm16";
    import { Readable } from "stream";

    // --- Configuration ---
    const API_KEY = "<YOUR_API_KEY>";

    // LLM Gateway Configuration
    const PROMPT =
      "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
    const LLM_GATEWAY_CONFIG = {
      model: "claude-sonnet-4-6",
      messages: [{ role: "user", content: PROMPT }],
      max_tokens: 4000,
    };

    const run = async () => {
      const client = new AssemblyAI({
        apiKey: API_KEY,
      });

      const transcriber = client.streaming.transcriber({
        sampleRate: 16000,
        formatTurns: true,
        speechModel: "u3-rt-pro",
        llmGateway: LLM_GATEWAY_CONFIG,
      });

      // Session started event
      transcriber.on("open", ({ id }) => {
        console.log(`Session started: ${id}`);
      });

      // Error event
      transcriber.on("error", (error) => {
        console.error(`\nError: ${error}`);
      });

      // Close event
      transcriber.on("close", (code, reason) => {
        console.log("Session closed:", code, reason || "");
      });

      // Turn event - displays transcript when turn ends
      transcriber.on("turn", (turn) => {
        if (turn.end_of_turn) {
          console.log(`\nTranscript:\n${turn.transcript}\n`);
        }
      });

      // LLM Gateway Response event - displays LLM summary
      transcriber.on("llmGatewayResponse", (response) => {
        const llmContent = response.data?.choices?.[0]?.message?.content || "";
        console.log(`\nLLM Response:\n${llmContent}\n`);
      });

      // Termination event
      transcriber.on("termination", (event) => {
        console.log(
          `Session terminated: ${event.audio_duration_seconds} seconds of audio processed`
        );
      });

      try {
        await transcriber.connect();

        console.log("Connecting to streaming transcript service");
        console.log("Starting recording");

        const recording = recorder.record({
          channels: 1,
          sampleRate: 16000,
          audioType: "wav", // Linear PCM
        });

        Readable.toWeb(recording.stream()).pipeTo(transcriber.stream());

        // Stop recording and close connection using Ctrl-C
        process.on("SIGINT", async function () {
          console.log();
          console.log("Stopping recording");
          recording.stop();

          console.log("Closing streaming transcript connection");
          await transcriber.close();

          process.exit();
        });
      } catch (error) {
        console.error(`Error: ${error}`);
      }
    };

    run();
    ```
  </Tab>
</Tabs>

## Before you begin

To complete this tutorial, you need:

* [Python](https://www.python.org/) or [Node](https://nodejs.org/en) installed.
* An <a href="https://www.assemblyai.com/dashboard/signup" target="_blank">AssemblyAI account with a credit card set up</a>.
* A microphone connected to your computer.
* Basic understanding of how to [Transcribe streaming audio](/streaming/getting-started/transcribe-streaming-audio).

## Step 1: Install prerequisites

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    Install the required packages via pip:

    ```bash theme={null}
    pip install pyaudio websocket-client
    ```
  </Tab>

  <Tab language="python-sdk" title="Python SDK">
    Install the AssemblyAI Python SDK via pip:

    ```bash theme={null}
    pip install "assemblyai[extras]"
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    Install the required packages via NPM:

    ```bash theme={null}
    npm install ws mic
    ```
  </Tab>

  <Tab language="javascript-sdk" title="JavaScript SDK">
    Install the AssemblyAI JavaScript SDK and a recording package via NPM:

    ```bash theme={null}
    npm install assemblyai node-record-lpcm16
    ```
  </Tab>
</Tabs>

## Step 2: Connect to Universal Streaming

In this step, you'll set up a connection to the Universal Streaming API with the `llm_gateway` parameter. This parameter configures LLM Gateway to process your streaming transcripts.

For more information about streaming transcription, see [Transcribe streaming audio](/streaming/getting-started/transcribe-streaming-audio).

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python expandable theme={null}
    import pyaudio
    import websocket
    import json
    import threading
    import time
    import wave
    from urllib.parse import urlencode
    from datetime import datetime

    # --- Configuration ---
    YOUR_API_KEY = "<YOUR_API_KEY>"  # Replace with your actual API key

    # LLM Gateway Configuration
    PROMPT = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"
    LLM_GATEWAY_CONFIG = {
        "model": "claude-sonnet-4-6",
        "messages": [
            {"role": "user", "content": PROMPT}
        ],
        "max_tokens": 4000
    }

    CONNECTION_PARAMS = {
        "sample_rate": 16000,
        "format_turns": True,  # Request formatted final transcripts
        "speech_model": "u3-rt-pro",  # USM 3 Pro model
        "llm_gateway": json.dumps(LLM_GATEWAY_CONFIG)  # LLM Gateway configuration
    }
    API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws"
    API_ENDPOINT = f"{API_ENDPOINT_BASE_URL}?{urlencode(CONNECTION_PARAMS)}"

    # Audio Configuration
    FRAMES_PER_BUFFER = 800  # 50ms of audio (0.05s * 16000Hz)
    SAMPLE_RATE = CONNECTION_PARAMS["sample_rate"]
    CHANNELS = 1
    FORMAT = pyaudio.paInt16

    # Global variables for audio stream and websocket
    audio = None
    stream = None
    ws_app = None
    audio_thread = None
    stop_event = threading.Event()  # To signal the audio thread to stop

    # WAV recording variables
    recorded_frames = []  # Store audio frames for WAV file
    recording_lock = threading.Lock()  # Thread-safe access to recorded_frames
    ```
  </Tab>

  <Tab language="python-sdk" title="Python SDK">
    ```python expandable theme={null}
    import logging
    from typing import Type

    import assemblyai as aai
    from assemblyai.streaming.v3 import (
        BeginEvent,
        LLMGatewayResponseEvent,
        StreamingClient,
        StreamingClientOptions,
        StreamingError,
        StreamingEvents,
        StreamingParameters,
        TurnEvent,
        TerminationEvent,
    )
    from assemblyai.streaming.v3.models import LLMGatewayConfig, LLMGatewayMessage

    api_key = "<YOUR_API_KEY>"
    prompt = "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}"

    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger(__name__)
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript expandable theme={null}
    const WebSocket = require("ws");
    const mic = require("mic");
    const querystring = require("querystring");
    const fs = require("fs");

    // --- Configuration ---
    const YOUR_API_KEY = "<YOUR_API_KEY>"; // Replace with your actual API key

    // LLM Gateway Configuration
    const PROMPT =
      "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
    const LLM_GATEWAY_CONFIG = {
      model: "claude-sonnet-4-6",
      messages: [{ role: "user", content: PROMPT }],
      max_tokens: 4000,
    };

    const CONNECTION_PARAMS = {
      sample_rate: 16000,
      format_turns: true, // Request formatted final transcripts
      speech_model: "u3-rt-pro", // USM 3 Pro model
      llm_gateway: JSON.stringify(LLM_GATEWAY_CONFIG), // LLM Gateway configuration
    };
    const API_ENDPOINT_BASE_URL = "wss://streaming.assemblyai.com/v3/ws";
    const API_ENDPOINT = `${API_ENDPOINT_BASE_URL}?${querystring.stringify(CONNECTION_PARAMS)}`;

    // Audio Configuration
    const SAMPLE_RATE = CONNECTION_PARAMS.sample_rate;
    const CHANNELS = 1;

    // Global variables
    let micInstance = null;
    let micInputStream = null;
    let ws = null;
    let stopRequested = false;

    // WAV recording variables
    let recordedFrames = []; // Store audio frames for WAV file
    ```
  </Tab>

  <Tab language="javascript-sdk" title="JavaScript SDK">
    ```javascript theme={null}
    import { AssemblyAI } from "assemblyai";
    import recorder from "node-record-lpcm16";
    import { Readable } from "stream";

    // --- Configuration ---
    const API_KEY = "<YOUR_API_KEY>";

    // LLM Gateway Configuration
    const PROMPT =
      "Provide a brief summary of the transcript.\n\nTranscript: {{turn}}";
    const LLM_GATEWAY_CONFIG = {
      model: "claude-sonnet-4-6",
      messages: [{ role: "user", content: PROMPT }],
      max_tokens: 4000,
    };
    ```
  </Tab>
</Tabs>

The `llm_gateway` parameterisa JSON-stringified object that follows the same interface as the [LLM Gateway chat completions API](/llm-gateway/chat-completions). It accepts the following fields:

| Key          | Type   | Description                                                                         |
| ------------ | ------ | ----------------------------------------------------------------------------------- |
| `model`      | string | The model to use. See [Available models](/llm-gateway/quickstart#available-models). |
| `messages`   | array  | An array of message objects. The `content` field contains your prompt.              |
| `max_tokens` | number | The maximum number of tokens to generate.                                           |

## Step 3: Stream audio and analyze with LLM Gateway

In this step, you'll stream audio from your microphone, collect the transcribed text from completed turns, and then send the accumulated transcript to LLM Gateway for analysis when the session ends.

<Steps>
  <Step>
    Set up the event handlers to stream audio and collect transcripts from completed turns.

    <Tabs groupId="language">
      <Tab language="python" title="Python" default>
        ```python expandable theme={null}
        def save_wav_file():
            """Save recorded audio frames to a WAV file."""
            if not recorded_frames:
                print("No audio data recorded.")
                return

            # Generate filename with timestamp
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"recorded_audio_{timestamp}.wav"

            try:
                with wave.open(filename, 'wb') as wf:
                    wf.setnchannels(CHANNELS)
                    wf.setsampwidth(2)  # 16-bit = 2 bytes
                    wf.setframerate(SAMPLE_RATE)

                    # Write all recorded frames
                    with recording_lock:
                        wf.writeframes(b''.join(recorded_frames))

                print(f"Audio saved to: {filename}")
                print(f"Duration: {len(recorded_frames) * FRAMES_PER_BUFFER / SAMPLE_RATE:.2f} seconds")

            except Exception as e:
                print(f"Error saving WAV file: {e}")

        # --- WebSocket Event Handlers ---

        def on_open(ws):
            """Called when the WebSocket connection is established."""
            print("WebSocket connection opened.")
            print(f"Connected to: {API_ENDPOINT_BASE_URL}")

            # Start sending audio data in a separate thread
            def stream_audio():
                global stream
                print("Starting audio streaming...")
                while not stop_event.is_set():
                    try:
                        audio_data = stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)

                        # Store audio data for WAV recording
                        with recording_lock:
                            recorded_frames.append(audio_data)

                        # Send audio data as binary message
                        ws.send(audio_data, websocket.ABNF.OPCODE_BINARY)
                    except Exception as e:
                        print(f"Error streaming audio: {e}")
                        # If stream read fails, likely means it's closed, stop the loop
                        break
                print("Audio streaming stopped.")

            global audio_thread
            audio_thread = threading.Thread(target=stream_audio)
            audio_thread.daemon = (
                True  # Allow main thread to exit even if this thread is running
            )
            audio_thread.start()

        def on_message(ws, message):
            try:
                data = json.loads(message)
                msg_type = data.get('type')

                if msg_type == "Begin":
                    session_id = data.get('id')
                    expires_at = data.get('expires_at')
                    print(f"Session started: {session_id}")
                elif msg_type == "Turn":
                    end_of_turn = data.get('end_of_turn', False)

                    if end_of_turn:
                        transcript = data.get('transcript', '')
                        print(f"\nTranscript:\n{transcript}\n")
                elif msg_type == "LLMGatewayResponse":
                    # Extract the LLM response content
                    llm_data = data.get('data', {})
                    llm_content = llm_data.get("choices", [{}])[0].get("message", {}).get("content", "")
                    print(f"LLM Response:\n{llm_content}\n")
                elif msg_type == "Termination":
                    audio_duration = data.get('audio_duration_seconds', 0)
                    session_duration = data.get('session_duration_seconds', 0)
                    print(f"Session terminated: {audio_duration} seconds of audio processed")
            except json.JSONDecodeError as e:
                print(f"Error decoding message: {e}")
            except Exception as e:
                print(f"Error handling message: {e}")

        def on_error(ws, error):
            """Called when a WebSocket error occurs."""
            print(f"\nWebSocket Error: {error}")
            # Attempt to signal stop on error
            stop_event.set()


        def on_close(ws, close_status_code, close_msg):
            """Called when the WebSocket connection is closed."""
            print(f"\nWebSocket Disconnected: Status={close_status_code}, Msg={close_msg}")

            # Save recorded audio to WAV file
            save_wav_file()

            # Ensure audio resources are released
            global stream, audio
            stop_event.set()  # Signal audio thread just in case it's still running

            if stream:
                if stream.is_active():
                    stream.stop_stream()
                stream.close()
                stream = None
            if audio:
                audio.terminate()
                audio = None
            # Try to join the audio thread to ensure clean exit
            if audio_thread and audio_thread.is_alive():
                audio_thread.join(timeout=1.0)
        ```
      </Tab>

      <Tab language="python-sdk" title="Python SDK">
        ```python theme={null}
        def on_begin(self: Type[StreamingClient], event: BeginEvent):
            print(f"Session started: {event.id}")

        def on_turn(self: Type[StreamingClient], event: TurnEvent):
            if event.end_of_turn:
                print(f"\nTranscript:\n{event.transcript}\n")

        def on_llm_response(self: Type[StreamingClient], event: LLMGatewayResponseEvent):
            # Extract the actual LLM response content from the data
            llm_content = event.data.get("choices", [{}])[0].get("message", {}).get("content", "")
            print(f"LLM Response:\n{llm_content}\n")

        def on_terminated(self: Type[StreamingClient], event: TerminationEvent):
            print(
                f"Session terminated: {event.audio_duration_seconds} seconds of audio processed"
            )

        def on_error(self: Type[StreamingClient], error: StreamingError):
            print(f"Error occurred: {error}")
        ```
      </Tab>

      <Tab language="javascript" title="JavaScript">
        ```javascript expandable theme={null}
        // --- Helper functions ---
        function formatTimestamp(timestamp) {
          return new Date(timestamp * 1000).toISOString();
        }

        function createWavHeader(sampleRate, channels, dataLength) {
          const buffer = Buffer.alloc(44);

          // RIFF header
          buffer.write("RIFF", 0);
          buffer.writeUInt32LE(36 + dataLength, 4);
          buffer.write("WAVE", 8);

          // fmt chunk
          buffer.write("fmt ", 12);
          buffer.writeUInt32LE(16, 16); // fmt chunk size
          buffer.writeUInt16LE(1, 20); // PCM format
          buffer.writeUInt16LE(channels, 22);
          buffer.writeUInt32LE(sampleRate, 24);
          buffer.writeUInt32LE(sampleRate * channels * 2, 28); // byte rate
          buffer.writeUInt16LE(channels * 2, 32); // block align
          buffer.writeUInt16LE(16, 34); // bits per sample

          // data chunk
          buffer.write("data", 36);
          buffer.writeUInt32LE(dataLength, 40);

          return buffer;
        }

        function saveWavFile() {
          if (recordedFrames.length === 0) {
            console.log("No audio data recorded.");
            return;
          }

          // Generate filename with timestamp
          const timestamp = new Date().toISOString().replace(/[:.]/g, "-").slice(0, 19);
          const filename = `recorded_audio_${timestamp}.wav`;

          try {
            // Combine all recorded frames
            const audioData = Buffer.concat(recordedFrames);
            const dataLength = audioData.length;

            // Create WAV header
            const wavHeader = createWavHeader(SAMPLE_RATE, CHANNELS, dataLength);

            // Write WAV file
            const wavFile = Buffer.concat([wavHeader, audioData]);
            fs.writeFileSync(filename, wavFile);

            console.log(`Audio saved to: ${filename}`);
            console.log(
              `Duration: ${(dataLength / (SAMPLE_RATE * CHANNELS * 2)).toFixed(2)} seconds`
            );
          } catch (error) {
            console.error(`Error saving WAV file: ${error}`);
          }
        }

        function startMicrophone() {
          try {
            micInstance = mic({
              rate: SAMPLE_RATE.toString(),
              channels: CHANNELS.toString(),
              debug: false,
              exitOnSilence: 6, // This won't actually exit, just a parameter for mic
            });

            micInputStream = micInstance.getAudioStream();

            micInputStream.on("data", (data) => {
              if (ws && ws.readyState === WebSocket.OPEN && !stopRequested) {
                // Store audio data for WAV recording
                recordedFrames.push(Buffer.from(data));

                // Send audio data to WebSocket
                ws.send(data);
              }
            });

            micInputStream.on("error", (err) => {
              console.error(`Microphone Error: ${err}`);
              cleanup();
            });

            micInstance.start();
            console.log("Microphone stream opened successfully.");
            console.log("Speak into your microphone. Press Ctrl+C to stop.");
          } catch (error) {
            console.error(`Error opening microphone stream: ${error}`);
            cleanup();
          }
        }
        ```
      </Tab>

      <Tab language="javascript-sdk" title="JavaScript SDK">
        ```javascript expandable theme={null}
        // Session started event
        transcriber.on("open", ({ id }) => {
          console.log(`Session started: ${id}`);
        });

        // Error event
        transcriber.on("error", (error) => {
          console.error(`\nError: ${error}`);
        });

        // Close event
        transcriber.on("close", (code, reason) => {
          console.log("Session closed:", code, reason || "");
        });

        // Turn event - displays transcript when turn ends
        transcriber.on("turn", (turn) => {
          if (turn.end_of_turn) {
            console.log(`\nTranscript:\n${turn.transcript}\n`);
          }
        });

        // LLM Gateway Response event - displays LLM summary
        transcriber.on("llmGatewayResponse", (response) => {
          const llmContent = response.data?.choices?.[0]?.message?.content || "";
          console.log(`\nLLM Response:\n${llmContent}\n`);
        });

        // Termination event
        transcriber.on("termination", (event) => {
          console.log(
            `Session terminated: ${event.audio_duration_seconds} seconds of audio processed`
          );
        });
        ```
      </Tab>
    </Tabs>
  </Step>

  <Step>
    Define a function to send the accumulated transcript to LLM Gateway for analysis. This function uses the [LLM Gateway chat completions API](/llm-gateway/chat-completions) to process the transcript with your prompt.

    <Tabs groupId="language">
      <Tab language="python" title="Python" default>
        When using the raw WebSocket approach with `llm_gateway` in the connection parameters, LLM Gateway responses are received as `LLMGatewayResponse` messages through the WebSocket, handled by the `on_message` callback registered in the previous step. No separate API call is needed.
      </Tab>

      <Tab language="python-sdk" title="Python SDK">
        When using the Python SDK with `LLMGatewayConfig`, analysis responses are received automatically through the `LLMGatewayResponseEvent` event handler registered in the previous step. No separate API call is needed.
      </Tab>

      <Tab language="javascript" title="JavaScript">
        When using the raw WebSocket approach with `llm_gateway` in the connection parameters, LLM Gateway responses are received as `LLMGatewayResponse` messages through the WebSocket, handled by the `on_message` callback in the `run()` function. No separate API call is needed.
      </Tab>

      <Tab language="javascript-sdk" title="JavaScript SDK">
        When using the JavaScript SDK with `llmGateway` in the transcriber options, LLM Gateway responses are received automatically through the `llmGatewayResponse` event handler registered in the previous step. No separate API call is needed.
      </Tab>
    </Tabs>
  </Step>

  <Step>
    Run the streaming session and analyze the transcript with LLM Gateway when the session ends.

    <Tabs groupId="language">
      <Tab language="python" title="Python" default>
        ```python expandable theme={null}
        # --- Main Execution ---
        def run():
            global audio, stream, ws_app

            # Initialize PyAudio
            audio = pyaudio.PyAudio()

            # Open microphone stream
            try:
                stream = audio.open(
                    input=True,
                    frames_per_buffer=FRAMES_PER_BUFFER,
                    channels=CHANNELS,
                    format=FORMAT,
                    rate=SAMPLE_RATE,
                )
                print("Microphone stream opened successfully.")
                print("Speak into your microphone. Press Ctrl+C to stop.")
                print("Audio will be saved to a WAV file when the session ends.")
            except Exception as e:
                print(f"Error opening microphone stream: {e}")
                if audio:
                    audio.terminate()
                return  # Exit if microphone cannot be opened

            # Create WebSocketApp
            ws_app = websocket.WebSocketApp(
                API_ENDPOINT,
                header={"Authorization": YOUR_API_KEY},
                on_open=on_open,
                on_message=on_message,
                on_error=on_error,
                on_close=on_close,
            )

            # Run WebSocketApp in a separate thread to allow main thread to catch KeyboardInterrupt
            ws_thread = threading.Thread(target=ws_app.run_forever)
            ws_thread.daemon = True
            ws_thread.start()

            try:
                # Keep main thread alive until interrupted
                while ws_thread.is_alive():
                    time.sleep(0.1)
            except KeyboardInterrupt:
                print("\nCtrl+C received. Stopping...")
                stop_event.set()  # Signal audio thread to stop

                # Send termination message to the server
                if ws_app and ws_app.sock and ws_app.sock.connected:
                    try:
                        terminate_message = {"type": "Terminate"}
                        print(f"Sending termination message: {json.dumps(terminate_message)}")
                        ws_app.send(json.dumps(terminate_message))
                        # Give a moment for messages to process before forceful close
                        time.sleep(5)
                    except Exception as e:
                        print(f"Error sending termination message: {e}")

                # Close the WebSocket connection (will trigger on_close)
                if ws_app:
                    ws_app.close()

                # Wait for WebSocket thread to finish
                ws_thread.join(timeout=2.0)

            except Exception as e:
                print(f"\nAn unexpected error occurred: {e}")
                stop_event.set()
                if ws_app:
                    ws_app.close()
                ws_thread.join(timeout=2.0)

            finally:
                # Final cleanup (already handled in on_close, but good as a fallback)
                if stream and stream.is_active():
                    stream.stop_stream()
                if stream:
                    stream.close()
                if audio:
                    audio.terminate()
                print("Cleanup complete. Exiting.")


        if __name__ == "__main__":
            run()
        ```
      </Tab>

      <Tab language="python-sdk" title="Python SDK">
        ```python expandable theme={null}
        def main():
            client = StreamingClient(
                StreamingClientOptions(
                    api_key=api_key,
                    api_host="streaming.assemblyai.com",
                )
            )

            client.on(StreamingEvents.Begin, on_begin)
            client.on(StreamingEvents.Turn, on_turn)
            client.on(StreamingEvents.LLMGatewayResponse, on_llm_response)
            client.on(StreamingEvents.Termination, on_terminated)
            client.on(StreamingEvents.Error, on_error)

            client.connect(
                StreamingParameters(
                    sample_rate=16000,
                    speech_model="u3-rt-pro",
                    format_turns=True,
                    llm_gateway=LLMGatewayConfig(
                        model="claude-sonnet-4-6",
                        messages=[
                            LLMGatewayMessage(role="user", content=prompt)
                        ],
                        max_tokens=4000
                    )
                )
            )

            try:
                client.stream(
                    aai.extras.MicrophoneStream(sample_rate=16000)
                )
            finally:
                client.disconnect(terminate=True)

        if __name__ == "__main__":
            main()
        ```
      </Tab>

      <Tab language="javascript" title="JavaScript">
        ```javascript expandable theme={null}
        // --- Main function ---
        async function run() {
          console.log(
            "Starting AssemblyAI streaming transcription with LLM Gateway..."
          );
          console.log("Audio will be saved to a WAV file when the session ends.");

          // Initialize WebSocket connection
          ws = new WebSocket(API_ENDPOINT, {
            headers: {
              Authorization: YOUR_API_KEY,
            },
          });

          // Setup WebSocket event handlers
          ws.on("open", () => {
            console.log("WebSocket connection opened.");
            console.log(`Connected to: ${API_ENDPOINT_BASE_URL}`);
            // Start the microphone
            startMicrophone();
          });

          ws.on("message", (message) => {
            try {
              const data = JSON.parse(message);
              const msgType = data.type;

              if (msgType === "Begin") {
                const sessionId = data.id;
                console.log(`Session started: ${sessionId}`);
              } else if (msgType === "Turn") {
                const endOfTurn = data.end_of_turn;

                if (endOfTurn) {
                  const transcript = data.transcript || "";
                  console.log(`\nTranscript:\n${transcript}\n`);
                }
              } else if (msgType === "LLMGatewayResponse") {
                // Extract the LLM response content
                const llmData = data.data || {};
                const llmContent = llmData.choices?.[0]?.message?.content || "";
                console.log(`LLM Response:\n${llmContent}\n`);
              } else if (msgType === "Termination") {
                const audioDuration = data.audio_duration_seconds;
                console.log(
                  `Session terminated: ${audioDuration} seconds of audio processed`
                );
              }
            } catch (error) {
              console.error(`\nError handling message: ${error}`);
              console.error(`Message data: ${message}`);
            }
          });

          ws.on("error", (error) => {
            console.error(`\nWebSocket Error: ${error}`);
            cleanup();
          });

          ws.on("close", (code, reason) => {
            console.log(`\nWebSocket Disconnected: Status=${code}, Msg=${reason}`);
            cleanup();
          });

          // Handle process termination
          setupTerminationHandlers();
        }

        function cleanup() {
          stopRequested = true;

          // Save recorded audio to WAV file
          saveWavFile();

          // Stop microphone if it's running
          if (micInstance) {
            try {
              micInstance.stop();
            } catch (error) {
              console.error(`Error stopping microphone: ${error}`);
            }
            micInstance = null;
          }

          // Close WebSocket connection if it's open
          if (ws && [WebSocket.OPEN, WebSocket.CONNECTING].includes(ws.readyState)) {
            try {
              // Send termination message if possible
              if (ws.readyState === WebSocket.OPEN) {
                const terminateMessage = { type: "Terminate" };
                console.log(
                  `Sending termination message: ${JSON.stringify(terminateMessage)}`
                );
                ws.send(JSON.stringify(terminateMessage));
              }
              ws.close();
            } catch (error) {
              console.error(`Error closing WebSocket: ${error}`);
            }
            ws = null;
          }

          console.log("Cleanup complete.");
        }

        function setupTerminationHandlers() {
          // Handle Ctrl+C and other termination signals
          process.on("SIGINT", () => {
            console.log("\nCtrl+C received. Stopping...");
            cleanup();
            // Give time for cleanup before exiting
            setTimeout(() => process.exit(0), 1000);
          });

          process.on("SIGTERM", () => {
            console.log("\nTermination signal received. Stopping...");
            cleanup();
            // Give time for cleanup before exiting
            setTimeout(() => process.exit(0), 1000);
          });

          // Handle uncaught exceptions
          process.on("uncaughtException", (error) => {
            console.error(`\nUncaught exception: ${error}`);
            cleanup();
            // Give time for cleanup before exiting
            setTimeout(() => process.exit(1), 1000);
          });
        }

        // Start the application
        run();
        ```
      </Tab>

      <Tab language="javascript-sdk" title="JavaScript SDK">
        ```javascript expandable theme={null}
        const run = async () => {
          const client = new AssemblyAI({
            apiKey: API_KEY,
          });

          const transcriber = client.streaming.transcriber({
            sampleRate: 16000,
            formatTurns: true,
            speechModel: "u3-rt-pro",
            llmGateway: LLM_GATEWAY_CONFIG,
          });

          // ... (event handlers from Step 1)

          try {
            await transcriber.connect();

            console.log("Connecting to streaming transcript service");
            console.log("Starting recording");

            const recording = recorder.record({
              channels: 1,
              sampleRate: 16000,
              audioType: "wav", // Linear PCM
            });

            Readable.toWeb(recording.stream()).pipeTo(transcriber.stream());

            // Stop recording and close connection using Ctrl-C
            process.on("SIGINT", async function () {
              console.log();
              console.log("Stopping recording");
              recording.stop();

              console.log("Closing streaming transcript connection");
              await transcriber.close();

              process.exit();
            });
          } catch (error) {
            console.error(`Error: ${error}`);
          }
        };

        run();
        ```
      </Tab>
    </Tabs>

    The output will look something like this:

    ```
    Session started: de5d9927-73a6-4be8-b52d-b4c07be37e6b

    Transcript: Hi, my name is Sonny.

    Transcript: I am a voice agent.

    Stopping...

    Session terminated: 12s of audio processed

    Analyzing conversation with LLM Gateway...
    The speaker introduces themselves as Sonny and identifies as a voice agent.
    ```
  </Step>
</Steps>

<Tip>
  Want to make your LLM requests more resilient? Use [fallback models](/llm-gateway/fallback) to automatically switch to a backup model if your primary model is unavailable.
</Tip>

## Next steps

In this tutorial, you've learned how to analyze streaming audio transcripts using LLM Gateway. The type of output depends on your prompt, so try exploring different prompts to see how they affect the output. Here are a few more prompts to try:

* "Provide an analysis of the transcript and offer areas to improve with exact quotes."
* "What's the main take-away from the transcript?"
* "Generate a set of action items from this transcript."

To learn more about LLM Gateway and streaming, see the following resources:

* [LLM Gateway Overview](/llm-gateway/quickstart)
* [Basic Chat Completions](/llm-gateway/chat-completions)
* [Model Selection](/streaming)
* [Transcribe streaming audio](/streaming/getting-started/transcribe-streaming-audio)

## Need some help?

If you get stuck, or have any other questions, we'd love to help you out. Contact our support team at [support@assemblyai.com](mailto:support@assemblyai.com) or create a [support ticket](https://www.assemblyai.com/contact/support).