Deepgram Audio Intelligence for Python

What it does

When your agent uses it

• Adding speaker diarization to a Deepgram transcription call
• Summarizing audio or detecting sentiment, topics and intents
• Redacting sensitive information from a transcript
• Detecting the spoken language of an audio file

Example prompts

• “Transcribe call.wav with Deepgram and label which speaker says each word.”
• “Add summarize and sentiment to our transcribe_url call and print the summary.”
• “Redact personal data from the transcript when we send a file to Deepgram.”
• “Check which of these analytics options work over the live WebSocket connection.”

Requirements

• Python with the Deepgram SDK
• A Deepgram API key

Workflow steps

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

1. 1In-repo reference: reference.md — "Listen V1 Media" (REST params include all analytics flags), "Listen V1 Connect" (WSS-supported subset).
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

  Hosts in commands or code, which the agent is likely to contact:

  • dpgr.am

  Also links to:

  • developers.deepgram.com

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

• Credentials

  Names no API keys, tokens, secrets or passwords.

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

Context cost

Deepgram Audio Intelligence for Python loads about 2.3k tokens when it runs. Until then it costs about 183 tokens; SKILL.md has 612 words of instructions outside code blocks.

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

~183

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

~2.3k

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). 612 words, ~2,323 tokens.

name

deepgram-python-audio-intelligence

description

Use when writing or reviewing Python code in this repo that calls Deepgram audio analytics overlays on `/v1/listen` - summarize, topics, intents, sentiment, diarize, redact, detect_language, entity detection. Same endpoint as plain STT but with analytics params. Covers both REST (`client.listen.v1.media.transcribe_url`/`transcribe_file`) and the WSS-supported subset (`client.listen.v1.connect`). Use `deepgram-python-speech-to-text` for plain transcription, `deepgram-python-text-intelligence` for analytics on already-transcribed text. Triggers include "diarize", "summarize audio", "sentiment from audio", "redact PII", "topic detection audio", "audio intelligence", "detect language audio".

from dotenv import load_dotenv
load_dotenv()

from deepgram import DeepgramClient
client = DeepgramClient()

response = client.listen.v1.media.transcribe_url(
    url="https://dpgr.am/spacewalk.wav",
    model="nova-3",
    smart_format=True,
    punctuate=True,
    diarize=True,              # speaker separation
    summarize="v2",            # "v2" for the current model; True also accepted on /v1/listen
    topics=True,
    intents=True,
    sentiment=True,
    detect_language=True,
    redact=["pci", "pii"],     # or Sequence[str]
    language="en-US",
)

r = response.results
print("transcript:", r.channels[0].alternatives[0].transcript)
print("summary:",    r.summary)
print("topics:",     r.topics)
print("intents:",    r.intents)
print("sentiments:", r.sentiments)
print("detected_language:", r.channels[0].detected_language)

# Speaker diarization
for word in r.channels[0].alternatives[0].words or []:
    speaker = getattr(word, "speaker", None)
    if speaker is not None:
        print(f"Speaker {speaker}: {word.word}")

with open("call.wav", "rb") as f:
    audio = f.read()

response = client.listen.v1.media.transcribe_file(
    request=audio,
    model="nova-3",
    diarize=True,
    redact=["pii"],
    summarize="v2",
    topics=True,
)

response = client.listen.v1.media.transcribe_url(
    url="https://dpgr.am/spacewalk.wav",
    model="nova-3",
    diarize=True,        # tag each word with a speaker id
    smart_format=True,   # punctuated_word for cleaner output
    punctuate=True,
)

words = response.results.channels[0].alternatives[0].words or []

# Per-word: speaker, timestamps, confidence
for w in words:
    speaker = getattr(w, "speaker", None)
    text = w.punctuated_word or w.word
    print(f"[speaker {speaker}] {text}  ({w.start:.2f}s–{w.end:.2f}s, conf={w.confidence:.2f})")

# Group consecutive words by speaker into utterances
from itertools import groupby
for speaker, group in groupby(words, key=lambda w: getattr(w, "speaker", None)):
    text = " ".join((w.punctuated_word or w.word) for w in group)
    print(f"Speaker {speaker}: {text}")

import threading
from deepgram.core.events import EventType

with client.listen.v1.connect(model="nova-3", diarize=True, redact=["pii"]) as conn:
    conn.on(EventType.MESSAGE, lambda m: print(m))
    threading.Thread(target=conn.start_listening, daemon=True).start()
    for chunk in audio_chunks:
        conn.send_media(chunk)
    conn.send_finalize()

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-audio-intelligence of deepgram/deepgram-python-sdk.

Open the folder on GitHubat commit 5c2f3af