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Analyze what was said in audio, not just transcribe it.
5 minutes · 1,136 words · 9 sections
Install
npx skills add deepgram/skills --skill audio-intelligencenpx skills add deepgram/skills/plugin marketplace add deepgram/skillsThe first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.
Audio intelligence is not a separate endpoint. It is five query parameters on /v1/listen, the
same endpoint that returns the transcript. You get the transcript and the analysis in one response,
from one API call. This skill gets a verified request working and states the limits precisely.
POST https://api.deepgram.com/v1/listen.POST /v1/read. Open the text-intelligence skill.speech-to-text skill.detect_entities only).| Parameter | Prerecorded | Streaming (wss) | Language |
|---|---|---|---|
summarize=v2 (or summarize=true) | yes | no | English only; an explicit non-English language is a 400 (mistake 3 covers detect_language) |
sentiment=true | yes | no | English only |
topics=true | yes | no | English only |
intents=true | yes | no | English only |
detect_entities=true | yes | yes | English only |
detect_entities is the odd one out twice over: it is the only feature that works on the live
socket, and it is the only one that does not exist on the Read API. Streaming entity detection
runs on Nova, Nova-2, Nova-3, and Enhanced; it is not available on Base models or on Flux STT. [2]
curl -s -X POST 'https://api.deepgram.com/v1/listen?model=nova-3&smart_format=true&summarize=v2&sentiment=true&topics=true&intents=true&detect_entities=true' \
-H "Authorization: Token $DEEPGRAM_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"url":"https://dpgr.am/spacewalk.wav"}'Returns 200. The analysis is scattered across the response, not collected in one place:
| Result | Path |
|---|---|
| Summary | results.summary.short (with results.summary.result = "success") |
| Sentiment per segment | results.sentiments.segments[] — text, start_word, end_word, sentiment, sentiment_score |
| Sentiment overall | results.sentiments.average — sentiment, sentiment_score |
| Sentiment per word | results.channels[0].alternatives[0].words[] gains sentiment and sentiment_score |
| Topics | results.topics.segments[].topics[] — topic, confidence_score |
| Intents | results.intents.segments[].intents[] — intent, confidence_score |
| Entities | results.channels[0].alternatives[0].entities[]: label, value, raw_value, confidence, start_word, end_word |
Note the plural: the parameter is sentiment, the result key is sentiments. metadata gains a
summary_info, sentiment_info, topics_info, and intents_info block per enabled feature, each
with model_uuid, input_tokens, and output_tokens. sentiment_score runs from -1 to 1,
and the break point between neutral and positive or negative is 0.333... either side of
zero; confidence_score on topics and intents runs from 0 to 1. [3] Entity labels come back
uppercased (NAME, ORGANIZATION, LOCATION_CITY, MONEY, DATE_INTERVAL, CARDINAL).
value is the formatted text and raw_value is the text as spoken; raw_value is present when
a formatting feature such as smart_format is on. [4] Deepgram documents 59 entity types, and
redact accepts 56 of them: cardinal, ordinal, and percent are detected but are not valid
redact values, and passing one to redact returns a 400. [6]
summarize needs more than 50 words of speech. For a shorter input, summary.short is the
original input returned as-is, and no tokens in or out are billed as summarization usage, so a
summary_info with zero tokens on a short clip is not a failure. [3]
On the live socket, detect_entities=true adds a top-level entities array to Results
messages, next to channel and not inside channel.alternatives[0]. Read entities only from
messages whose is_final is true. Interim results can carry the key too, usually [] and
sometimes populated, and those values are not final. A final result with nothing detected
carries "entities": []. Same field shape as above, raw_value included when formatting is on. [4]
To return complete entities, the server holds a final result until the speaker moves on to
non-entity speech, 3 seconds of silence pass, or a Finalize message arrives. no_delay=true
forces immediate finalization without that wait, and leaves entities missed or incomplete in
many cases. Send no_delay=true only when latency matters more than entity
accuracy. [4]
custom_topic and custom_intent (repeatable, up to 100 of each) add your own labels;
custom_topic_mode and custom_intent_mode take extended (default, your labels plus the
model’s) or strict (your labels only). strict returns "segments": [] whenever nothing
matches your list, which looks like a broken request but is not. Start with extended. [3]
summarize fails the WebSocket handshake with 400 "Summarization is not available for streaming.". topics and intents fail it with 403
{"err_code":"UNAUTHORIZED_FEATURES_REQUESTED","err_msg":"Project does not have access to the requested feature/s [\"topics\"]."}, which reads as a permissions problem but is not one: the
same key’s prerecorded topics requests succeed, and the docs matrix lists streaming as
unsupported for both. [2] sentiment is worse still: the
handshake succeeds and no sentiment is ever returned. Only detect_entities works.language=es on real
Spanish audio, sentiment, topics, intents, and detect_entities return HTTP 200 with
the transcript, the analysis keys silently absent, and the reason only in metadata.warnings:
[{"parameter":"sentiment","type":"unsupported_language","message":"Sentiment is only supported for English."}], plus "Topics are only supported for English.", "Intents are only supported for English.", and "Entity detection is only supported for English.". Read
metadata.warnings before you conclude the model found nothing.summarize behaves the same way. It is the exception when language is explicit:
language=es is a hard 400,
{"err_code":"Bad Request","err_msg":"Summarization v2 not supported for non-English languages"},
and language=multi gets the same 400. With detect_language=true and non-English speech it
behaves like the others: HTTP 200, a metadata.warnings entry with "parameter":"summarize"
and "type":"unsupported_language", and results.summary present with "result":"failure"
and a short string that says the feature is English only. Check summary.result before you
use summary.short. [3]language=multi as a workaround. It is not one. For sentiment, intents, and topics,
multi returns the analysis when the detected speech is English and drops it with the same
metadata.warnings when it is not, so the same request succeeds or silently degrades
depending on what the caller said. summarize with language=multi is a 400.summarize=v1. Returns 400 "To use the summarize feature, please use 'summarize=true' or 'summarize=v2'. The 'summarize=v1' parameter is deprecated." Use v2; true is accepted and
returns the same summary.short shape.results.summary.text. That is the Read API’s shape. On /v1/listen the
summary is at results.summary.short./v2/listen rejects all five at the handshake with 400
{"err_code":"INVALID_QUERY_PARAMETER","err_msg":"Unknown query parameters: detect_entities"},
and the same message naming summarize, sentiment, topics, or intents. Transcribe with
Flux STT, then send the transcript to /v1/read.redact for that, which is a speech-to-text parameter and rejects cardinal, ordinal, and
percent (see the entity-type counts above). [6]Enabling these features changes what a request costs. Rates and the billing model change, so read https://deepgram.com/pricing (opens in a new tab) rather than any figure quoted in a skill.
text-intelligence
skill (/v1/read).api skill, references/listen.md.speech-to-text skill.starters skill. Note there is no audio-intelligence starter; the
text-intelligence feature (13 frameworks) is the Read API app.recipes skill, “Audio Intelligence v1“ — summarize,
sentiment, topics, intents, entities, in Python, JavaScript, Go, .NET, Java, Rust, and the
CLI. [7]deepgram-{js,python,java,go,rust,dotnet}-audio-intelligence
from the matching SDK repository (npx skills add deepgram/deepgram-python-sdk, and so on).docs skill. You want the docs in your editor: setup-mcp skill.redact values) and https://developers.deepgram.com/docs/redaction (opens in a new tab)v1“ section)Analyze what was said in audio, not just transcribe it. Use when a task mentions "audio intelligence", "sentiment", "sentiment analysis", "summarize a recording", "summarization", "topics", "topic detection", "intents", "intent recognition", "entity detection", "detect entities", "extract names and amounts from a call", or "analyze a call recording". These are five query parameters layered on the speech-to-text endpoint /v1/listen (summarize, sentiment, topics, intents, detect_entities), not a separate API. Covers the prerecorded-only and English-only limits, where each result lives in the JSON, and the errors you get when you cross a limit. Routes to the api, docs, recipes, starters, and per-language SDK skills.
The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.
main, last pushed 5 November 2026.SKILL.md, not by matching a directory convention. One layout observed: skills/*/SKILL.md.h1 and no skipped levels:.claude-plugin/marketplace.json by Deepgram Developer Relations, declaring 7 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree./deepgram/skills.md, and each skill at its own .md URL.