Deepgram Python Text-to-Speech

What it does

When your agent uses it

• Synthesizing speech from text with the Deepgram Python SDK
• Streaming audio over a WebSocket while an LLM is still generating
• Adding pronunciation or pause controls to Flux batch text
• Reviewing Python code that calls speak.v1

Example prompts

• “Write a Python script that turns this paragraph into an MP3 with Deepgram text to speech.”
• “Stream synthesized audio over a WebSocket as tokens arrive from the model.”
• “Use TextBuilder to add a one-second pause between these two sentences.”

Requirements

• A Deepgram API key
• Python with the Deepgram SDK

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1In-repo reference: reference.md — sections "Speak V1 Audio" (REST) and "Speak V1 Connect" (WSS).
2. 2OpenAPI (REST): https://developers.deepgram.com/openapi.yaml
3. 3AsyncAPI (WSS): https://developers.deepgram.com/asyncapi.yaml
4. 4Context7: library ID /llmstxt/developers_deepgram_llms_txt.
5. 5Product docs

What it can do on your machine

Read from SKILL.md and the folder at commit 5c2f3af. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • npx

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • developers.deepgram.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • DEEPGRAM_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Deepgram Python Text-to-Speech loads about 1.8k tokens when it runs. Until then it costs about 142 tokens; SKILL.md has 492 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~142

When it runs · the whole SKILL.md, loaded when a task matches

~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from deepgram/deepgram-python-sdk at commit 5c2f3af, republished under its MIT licence (© deepgram). 492 words, ~1,805 tokens.

name

deepgram-python-text-to-speech

description

Use when writing or reviewing Python code in this repo that calls Deepgram Text-to-Speech v1 (`/v1/speak`) for audio synthesis. Covers one-shot REST (`client.speak.v1.audio.generate`) and streaming WebSocket (`client.speak.v1.connect`). Also covers the in-repo `deepgram.helpers.TextBuilder` for incremental text assembly before synthesis. Use `deepgram-python-voice-agent` when you need full-duplex STT + LLM + TTS with barge-in. Triggers include "TTS", "speak", "synthesize voice", "aura", "text to speech", "speak.v1", "TextBuilder".

from dotenv import load_dotenv
load_dotenv()

from deepgram import DeepgramClient
client = DeepgramClient()  # reads DEEPGRAM_API_KEY

audio_iter = client.speak.v1.audio.generate(
    text="Hello, this is a text to speech example.",
    model="aura-2-asteria-en",
    encoding="linear16",
    sample_rate=24000,
)

with open("output.raw", "wb") as f:
    for chunk in audio_iter:
        f.write(chunk)

from deepgram.core.events import EventType
from deepgram.speak.v1.types import SpeakV1Text

with client.speak.v1.connect(
    model="aura-2-asteria-en",
    encoding="linear16",
    sample_rate=24000,
) as conn:
    def on_message(m):
        if isinstance(m, bytes):
            # audio chunk — write to file or audio output
            ...
        else:
            print(f"event: {getattr(m, 'type', 'Unknown')}")

    conn.on(EventType.OPEN,    lambda _: print("open"))
    conn.on(EventType.MESSAGE, on_message)
    conn.on(EventType.CLOSE,   lambda _: print("close"))
    conn.on(EventType.ERROR,   lambda e: print(f"err: {e}"))

    conn.send_text(SpeakV1Text(text="Hello, this is streaming TTS."))
    conn.send_flush()
    conn.send_close()
    conn.start_listening()   # blocks until server closes

from deepgram.helpers import TextBuilder

final_text = (
    TextBuilder()
    .text("Hello,")
    .text(" this is built incrementally.")
    .pronunciation("Deepgram", "ˈdiːpɡɹæm")
    .build()
)

from deepgram import AsyncDeepgramClient
client = AsyncDeepgramClient()

# REST
audio_iter = await client.speak.v1.audio.generate(text=..., model="aura-2-asteria-en")
async for chunk in audio_iter:
    ...

# WSS
async with client.speak.v1.connect(model="aura-2-asteria-en", ...) as conn:
    listen_task = asyncio.create_task(conn.start_listening())
    await conn.send_text(SpeakV1Text(text="..."))
    await conn.send_flush()
    await conn.send_close()
    await listen_task

npx skills add deepgram/skills

© deepgram, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/deepgram-python-text-to-speech of deepgram/deepgram-python-sdk.

Open the folder on GitHubat commit 5c2f3af