home / skills / ratacat / claude-skills / assembly-ai-streaming
This skill enables real-time AssemblyAI streaming transcription and LLM gateway processing to build low-latency voice agents and meeting notetakers.
npx playbooks add skill ratacat/claude-skills --skill assembly-ai-streamingReview the files below or copy the command above to add this skill to your agents.
---
name: assemblyai-streaming
description: This skill should be used when working with AssemblyAI’s Speech-to-Text and LLM Gateway APIs, especially for streaming/live transcription, meeting notetakers, and voice agents that need low-latency transcripts and audio analysis.
license: MIT
allowed-tools:
- Read
- Write
- Edit
- Grep
- Glob
- Bash
- Python
metadata:
skill-version: "1.0.0"
upstream-docs: "https://www.assemblyai.com/docs"
focus: "streaming-stt, meeting-notetaker, voice-agent, llm-gateway"
---
# AssemblyAI Streaming & Live Transcription Skill
## Overview
Use this skill to build and maintain code that talks to AssemblyAI’s:
- **Streaming Speech-to-Text (STT)** via WebSockets (`wss://streaming.assemblyai.com/v3/ws`)
- **Async / pre-recorded STT** via REST (`https://api.assemblyai.com/v2/transcript`)
- **LLM Gateway** for applying Claude/GPT/Gemini-style models to transcripts (`https://llm-gateway.assemblyai.com`)
The emphasis is on **streaming/live transcription**, **meeting notetakers**, and **voice agents**, while still covering async workflows and post-processing.
This skill assumes a Claude Code environment with access to Python (preferred) and Bash.
---
## When to Use
Use this skill when:
- Implementing **real-time transcription** from a microphone, telephony stream, or audio file.
- Building a **live meeting notetaker** (Zoom/Teams/Meet), especially with summaries, action items, and highlights.
- Implementing a **voice agent** where latency and natural turn-taking matter.
- Migrating from other STT providers (OpenAI/Deepgram/Google/AWS/etc.) to AssemblyAI.
- Applying **LLMs to audio** via LLM Gateway for summaries, Q&A, topic tagging, or custom prompts.
Do **not** use this skill when:
- The task is generic HTTP client usage with no AssemblyAI-specific logic.
- The request clearly targets a different STT vendor.
- The environment cannot safely store or use an API key.
---
## AssemblyAI Mental Model
### 1. Products to care about
1. **Pre-recorded Speech-to-Text (Async)**
- REST API: `POST /v2/transcript` → `GET /v2/transcript/{id}`
- Designed for files from URLs, uploads, S3, etc.
- Supports extra models: summarization, topic detection, sentiment, PII redaction, chapters, etc.
2. **Streaming Speech-to-Text**
- WebSocket: `wss://streaming.assemblyai.com/v3/ws`
- Low-latency, immutable transcripts (~300ms).
- Turn detection built in; fits voice agents and live captioning.
3. **LLM Gateway**
- REST API: `POST /v1/chat/completions` at `https://llm-gateway.assemblyai.com`
- Unified access to multiple LLMs (Claude, GPT, Gemini, etc.).
- Designed for “LLM over transcripts” workflows.
### 2. Key model knobs (Async)
- `speech_models`: `["slam-1", "universal"]` etc.
- **Slam-1**: best English accuracy + keyterms_prompt, good for medical/technical conversations.
- **Universal**: multilingual coverage; good default if language is unknown.
- `language_code` vs `language_detection`:
- Use `language_code` when the language is known.
- Use `language_detection: true` when unknown; optionally set `language_confidence_threshold`.
- `keyterms_prompt`:
- Domain words/phrases to boost (med terms, product names, etc.).
- Extra intelligence: `summarization`, `iab_categories`, `content_safety`, `entity_detection`, `auto_chapters`, `sentiment_analysis`, `speaker_labels`, `auto_highlights`, `redact_pii`, etc.
### 3. Key model knobs (Streaming)
Connection URL:
- US: `wss://streaming.assemblyai.com/v3/ws`
- EU: `wss://streaming.eu.assemblyai.com/v3/ws`
Important query parameters:
- `sample_rate` (required): e.g. `16000`
- `format_turns` (bool): return formatted final transcripts; **avoid** for low-latency voice agents.
- `speech_model`: `universal-streaming-english` (default) or `universal-streaming-multi`.
- `keyterms_p
rompt`: JSON-encoded list of terms, e.g. `["AssemblyAI", "Slam-1", "Keanu Reeves"]`.
- Turn detection:
- `end_of_turn_confidence_threshold` (0.0–1.0, default ~0.4)
- `min_end_of_turn_silence_when_confident` (ms, default ~400)
- `max_turn_silence` (ms, default ~1280)
Headers:
- Use either `Authorization: <API_KEY>` or a short-lived `token` query parameter issued by your backend.
Messages:
- Client sends:
- Binary audio chunks (50–1000ms each).
- Optional JSON messages: `{"type": "UpdateConfig", ...}`, `{"type": "Terminate"}`, `{"type": "ForceEndpoint"}`.
- Server sends:
- `Begin` event with `id`, `expires_at`.
- `Turn` events with:
- `transcript` (immutable partials/finals),
- `utterance` (complete semantic chunk),
- `end_of_turn` (bool),
- `turn_is_formatted` (bool),
- `words` array with timestamps/confidences.
- `Termination` event with summary stats.
### 4. Regions and data residency
- Async:
- US: `https://api.assemblyai.com`
- EU: `https://api.eu.assemblyai.com`
- Streaming:
- US: `wss://streaming.assemblyai.com/v3/ws`
- EU: `wss://streaming.eu.assemblyai.com/v3/ws`
Always keep base URLs consistent per project; don’t mix US/EU endpoints for the same data.
---
## Security & API Keys
- Always require an AssemblyAI API key and keep it **out of source** in Claude Code output:
- Use environment variables: `ASSEMBLYAI_API_KEY`.
- Or placeholders (`"<YOUR_API_KEY>"`) in snippets.
- For browser/client code:
- Do **not** embed the API key.
- Instruct the user to generate **temporary streaming tokens** on their backend and pass only the token into the WebSocket connection.
- Never print real keys in logs or comments.
---
## High-Level Workflow Patterns
### Decision tree
1. **Is the audio live?**
- Yes → Use **Streaming STT**.
- No → Use **Async STT**.
2. **Is latency critical (<1s) for responses?**
- Yes → Streaming with `format_turns=false` and careful turn detection.
- No → Async, then Summarization/Chapters/etc.
3. **Do transcripts leave the backend?**
- Yes → Consider `redact_pii` (and optionally `redact_pii_audio`) before sharing.
- No → Use raw transcripts as needed.
4. **Need LLM-based processing (Q&A, structured summaries)?**
- Yes → Pipe transcripts into **LLM Gateway** via `chat/completions`.
---
## How Claude Should Work with This Skill
### General principles
- Prefer **official AssemblyAI SDKs** (Python/JS) when available; fall back to `requests`/`websocket-client` only if SDK cannot be installed.
- Always:
- Validate HTTP responses and WebSocket status.
- Surface useful error messages (`status`, `error` fields in transcript JSON).
- Respect documented min/max chunk sizes (50–1000ms of audio per binary message).
- For voice-agent code, optimize for:
- Immutable partials (`transcript`) and `utterance` field.
- Minimal latency, avoid extra formatting passes.
---
## Recipe 1 – Minimal Streaming from Microphone (Python SDK)
> Goal: Stream mic audio to AssemblyAI and print transcripts in real time.
Use this when the environment has Python and `assemblyai` + `pyaudio` installed, and the user wants a quick streaming demo.
```python
import assemblyai as aai
from assemblyai.streaming import v3 as aai_stream
import pyaudio
API_KEY = "<YOUR_API_KEY>"
aai.settings.api_key = API_KEY
SAMPLE_RATE = 16000
CHUNK_MS = 50
FRAMES_PER_BUFFER = int(SAMPLE_RATE * (CHUNK_MS / 1000.0))
def main():
client = aai_stream.StreamingClient(
aai_stream.StreamingClientOptions(
api_key=API_KEY,
api_host="streaming.assemblyai.com", # or "streaming.eu.assemblyai.com"
)
)
def on_begin(_client, event: aai_stream.BeginEvent):
print(f"Session started: {event.id}, expires at {event.expires_at}")
def on_turn(_client, event: aai_stream.TurnEvent):
# Use immutable transcript text
text = (event.transcript or "").strip()
if not text:
return
# Use formatted finals only for display; keep unformatted for LLMs
if event.turn_is_formatted:
print(f"[FINAL] {text}")
else:
print(f"[PARTIAL] {text}", end="\r")
def on_terminated(_client, event: aai_stream.TerminationEvent):
print(f"\nTerminated. Audio duration={event.audio_duration_seconds}s")
def on_error(_client, error: aai_stream.StreamingError):
print(f"\nStreaming error: {error}")
client.on(aai_stream.StreamingEvents.Begin, on_begin)
client.on(aai_stream.StreamingEvents.Turn, on_turn)
client.on(aai_stream.StreamingEvents.Termination, on_terminated)
client.on(aai_stream.StreamingEvents.Error, on_error)
client.connect(
aai_stream.StreamingParameters(
sample_rate=SAMPLE_RATE,
format_turns=False, # better latency for voice agents
)
)
pa = pyaudio.PyAudio()
stream = pa.open(
format=pyaudio.paInt16,
channels=1,
rate=SAMPLE_RATE,
input=True,
frames_per_buffer=FRAMES_PER_BUFFER,
)
try:
print("Speak into your microphone (Ctrl+C to stop)...")
def audio_gen():
while True:
yield stream.read(FRAMES_PER_BUFFER, exception_on_overflow=False)
client.stream(audio_gen())
except KeyboardInterrupt:
pass
finally:
client.disconnect(terminate=True)
stream.stop_stream()
stream.close()
pa.terminate()
if __name__ == "__main__":
main()
This skill helps you integrate AssemblyAI’s streaming and async Speech-to-Text plus the LLM Gateway into Python projects for low-latency transcription, meeting notetakers, and voice agents. It focuses on live WebSocket streaming, post-recording REST workflows, and piping transcripts into LLMs for summaries and Q&A. It assumes Python tooling and secure handling of API keys.
The skill provides patterns and code to open AssemblyAI streaming WebSockets, send binary audio chunks, receive immutable partials and finalized utterances, and handle turn detection and termination events. It also covers async REST transcription for uploaded or URL-hosted audio and shows how to call the LLM Gateway to run summarization, action-item extraction, or conversational Q&A on transcripts. Emphasis is on low-latency handling, correct sample rates, and safe API-key management.
How should I protect the API key in browser-based streaming?
Never embed the key in client code. Issue short-lived streaming tokens from a secure backend and pass only the token to the browser WebSocket connection.
When should I use streaming versus async transcription?
If latency under ~1s matters or you need turn-level events, use streaming. For longer files, advanced analysis features, or cheaper batch processing use async REST transcription.