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

# Realtime STT (Manual)

> Transcript errors, high latency, and server errors for `/stt/websocket`

Tips for other endpoints can be found on the top-level [troubleshooting](/use-the-api/stt/troubleshooting/index) page.

## Transcript errors

<AccordionGroup>
  <Accordion title="Are you joining transcripts correctly?">
    Each `transcript` event carries a **delta** since the last final transcript, not the full transcript for the audio. Append the `text` from every event where `is_final` is `true`:

    ```python theme={null}
    import json

    transcript = ""
    async for message in websocket:
        event = json.loads(message)
        if event["type"] == "transcript" and event["is_final"]:
            # delta, appended exactly as received
            transcript += event["text"]
    ```

    Be sure to include all `transcript` events where `is_final` is true.

    ```json lines theme={null}
    { "type": "transcript", "is_final": false, "text": "Ignore this" }
    { "type": "transcript", "is_final": true, "text": "This is a" }
    { "type": "transcript", "is_final": true, "text": " single sentence." }
    ```

    Do not trim `text`

    ```json lines theme={null}
    "Trimming may"
    " join words."
    ```

    ```json theme={null}
    "Trimming mayjoin words."
    ```

    Do not join `text` with a space in between

    ```json lines theme={null}
    "Insert"
    "ing spaces is not safe"
    ```

    ```json theme={null}
    "Insert ing spaces is not safe"
    ```
  </Accordion>

  <Accordion title="Did you drain all events?">
    Once you are done sending all audio for a session, send `"close"` to tell the model to flush any buffered audio and emit remaining `transcript` events. The server will send `{ "type": "done" }` after all audio has been processed, then close the socket for you.

    The server buffers some audio to improve transcription accuracy. If you don't send the close command or stop reading messages early, that buffered audio will not be processed. This is okay if you don't care about the last second of audio.

    ```python theme={null}
    await websocket.send("close")
    async for message in websocket:
        event = json.loads(message)
        if event["type"] == "transcript" and event["is_final"]:
            transcript += event["text"]
        elif event["type"] == "done":
            print("done! expect the server to close the connection soon with code=1000")
            # optional: stop reading messages and close the socket yourself
    print("server closed the connection now")
    ```
  </Accordion>

  <Accordion title="Did you specify the language?">
    Be sure to include `?language=xx` (replace `xx` with an ISO 639-1 language code) as a query param when establishing your WebSocket connection. This endpoint does not support language detection yet.

    See [Models](/build-with-cartesia/stt/latest) for supported languages.
  </Accordion>

  <Accordion title="Are you using the right sample rate and encoding?">
    The model decodes your bytes using the `encoding` and `sample_rate` you declared in the connection. Our server **might not error** if these parameters are incorrect.

    You can validate your parameters by saving your audio data and playing it back with [ffplay](https://ffmpeg.org/ffplay.html):

    ```bash theme={null}
    # encoding=pcm_s16le
    # sample_rate=16000
    # 1 channel (the API expects mono)
    ffplay -f s16le -ar 16000 -ac 1 audio.raw

    # general format
    ffplay -f <encoding_without_pcm_prefix> -ar <sample_rate> -ac <num_channels_must_be_one> <file_path>
    ```

    If the playback sounds wrong (it should be quite obvious), then your `encoding` or `sample_rate` doesn't match the data. Correct it so your audio plays back cleanly, then send those same values to the API.

    See [Audio Input](/build-with-cartesia/stt/audio-input) for help finding the right parameters.
  </Accordion>

  <Accordion title="Are you finalizing too often?">
    Make sure you're only sending `finalize` after the user is finished speaking. Finalizing mid-speech will produce transcription errors.
  </Accordion>
</AccordionGroup>

## High latency

<AccordionGroup>
  <Accordion title="Are you sending the finalize command?">
    Transcription is triggered by the `finalize` command. Send it after your user signals that they are done speaking or VAD detects that the user stopped speaking to "finalize the turn":

    ```python theme={null}
    await websocket.send("finalize")
    ```

    Without it, the model falls back to silence-based auto-finalization. That's slower by design: it waits out a pause to be sure the user is done.

    You should send `finalize` as many times as necessary, not to be confused with `close`, which closes the session permanently.

    You must only send `finalize` at sensible moments in the audio stream. Finalizing mid-speech will produce transcription errors.
  </Accordion>

  <Accordion title="Are you using the right endpoint?">
    If you don't know when your user starts and stops speaking,
    try [Realtime STT (Auto)](/api-reference/stt/turns/websocket)
    to allow our model to detect turn boundaries and emit final transcripts as soon as your user is done speaking.

    Switching from "manual" to "auto" will improve final transcript latency out-of-the-box
    since the "manual" endpoint will hang onto the last transcript chunk from user speech in expectation that your client will send `finalize`.

    The "auto" endpoint does not expect your client to send anything besides audio and will send the final transcript in a `turn.end` event as soon as it's ready.
  </Accordion>

  <Accordion title="Did you stop sending audio?">
    Our API expects a continuous stream of audio.
    If you stop sending audio, the server will wait for more audio chunks to arrive rather than assuming that the user is silent.

    This is normally desired behavior to handle network lag, but it does mean that your client needs to send silence (all zeros) when your audio input is muted.
  </Accordion>
</AccordionGroup>

## Server errors

<Accordion title="Are you chunking audio?">
  Our realtime WebSocket endpoints expect audio to arrive at roughly the rate it's spoken.
  Pushing a large batch of audio into the socket at once can overload the server-side buffer,
  which may surface as an internal server error.

  Stream in small chunks (\~100 ms each) and pace them to realtime, averaging one second of audio sent per second of wall-clock time. Here's a [JavaScript example](https://github.com/cartesia-ai/cartesia-js/blob/v3.2.0/examples/browser_examples.ts#L29-L66).

  To transcribe a complete file in one shot, consider using [Batch STT](/api-reference/stt/transcribe), which takes the whole file in a single request.
</Accordion>