oruk — Python client for the oruk Speech API

Official Python SDK for oruk, the speech lab building audio models for English transcription, multilabel emotion scores, speaking-style classification, and unified audio analysis.

This SDK calls the file API: send a prerecorded English audio file (WAV, FLAC, MP3, M4A, OGG, or WebM; up to 30 MB / 60 minutes), get structured results back. Resonance is oruk’s flagship speech recognition model. Plans include audio minutes, measured by the second with a one-second minimum. The separate Realtime preview supports 32 locales and phrase-level emotion scores over WebSocket; see the realtime reference.

Install

python -m pip install oruk==0.2.9

Requires Python 3.10 or newer. Versioned wheel mirror.

Quickstart

Create an account at oruk.ai (plans from $12/month, 7-day standard self-serve trial, card required, $0 today) and create an API key in the developer portal.

import os
from oruk import Oruk

with Oruk(api_key=os.environ["ORUK_API_KEY"]) as client:
    result = client.analyze("sample.wav", model="oruk-resonance")

print(result["text"])       # English transcript
print(result["emotions"])   # selected model scores; see interpretation below
print(result["styles"])     # selected speaking-style scores; can be empty

Endpoints

Every method accepts a path, Path, or binary file object and an optional request_id=. The first five endpoints use oruk-resonance by default; oruk-fourier is another file model with its own output scope. Proficiency uses oruk-proficiency-1, not Resonance or Fourier. A supplied proficiency transcript= skips built-in transcription. See the endpoint reference before changing models; their capabilities are not interchangeable. On the first five endpoints, with model="oruk-resonance", pass diarize=True (and optionally num_speakers= when you know the speaker count, 1–32) to label speakers: diarization locates the speaker turns, then Resonance scores each speaker turn, so every segment carries a speaker field. Other fields follow the endpoint: emotion-only output does not gain a transcript. Speaker labels are local to the recording, not identities or roles. Diarization is included in plan minutes.

with Oruk(api_key=os.environ["ORUK_API_KEY"]) as client:
    result = client.analyze("support-call.wav", model="oruk-resonance", diarize=True)
for seg in result["segments"]:
    print(seg["speaker"], seg.get("text"), seg.get("emotions", []))

Emotion only: client.emotions(...) on Resonance runs the encoder and affect head and never invokes the transcription decoder, so nothing is transcribed, the result has no transcript. One audio minute uses one plan minute for either emotion-only or unified analysis; calling both separately processes the audio twice.

with Oruk(api_key=os.environ["ORUK_API_KEY"]) as client:
    result = client.emotions("support-call.wav", model="oruk-resonance")
print(result["emotions"])               # actual selected labels and scores
print(result.get("text"))               # None: no transcript is produced

The client reuses one request ID per call across retries of HTTP 429, 500, 502, 503, and 504 with jittered backoff (two retries by default). Other HTTP errors are not retried; network errors from httpx propagate to the caller. A stable request ID helps tracing; it does not promise exactly-once processing. Errors raise OrukAPIError with status, code, and request_id attributes.

Runnable file workflows

Set ORUK_API_KEY in your environment, download the complete Python example, and use your own recording:

curl --fail -O https://oruk.ai/examples/analyze-file.py
python analyze-file.py sample.wav --task analysis > result.json
python analyze-file.py support-call.wav --task analysis --diarize > speakers.json
python analyze-file.py speaking-sample.wav --task proficiency > proficiency.json

Each command sends one logical request, with bounded HTTP retries if needed. Running multiple commands processes the audio separately. --help describes model selection, known speaker count, and an optional proficiency transcript file. The program writes the complete API response to stdout and errors to stderr. It does not fabricate missing labels or assume every proficiency request was scored. For proficiency, use 30–60 seconds of spontaneous English and inspect check.status; insufficient_audio does not establish a CEFR level. A model estimate is not a language certificate.

Interpreting scores and usage

The 15 emotion labels and 16 speaking-style labels are model vocabularies, not a guarantee that every response contains every label. Outputs are selected by model thresholds. If no emotion clears its threshold, the highest-scoring emotion is returned; styles can be empty. Several labels may be high and scores need not sum to one. These thresholds do not establish calibrated probabilities of a person's private feelings. Evaluate representative audio before choosing application thresholds. See label interpretation and the scope of the evaluations.

usage includes measured and billable audio duration and may carry reference fields such as rate_per_minute_usd, estimated_cost_usd, or pricing_version. Those reference estimates are not your subscription invoice. Actual charges follow the plan allowance, overage terms, and billing records. One minute of audio uses one plan minute per request; separate calls process and meter the file separately. Keep API keys in server-side code. This SDK's file methods do not implement the separate realtime WebSocket workflow.

Links

• Documentation and API reference: https://oruk.ai/docs
• Capabilities and scope: https://oruk.ai/capabilities
• Pricing: https://oruk.ai/pricing
• Benchmarks: https://oruk.ai/benchmarks/methodology
• Service status: https://oruk.ai/status

License

MIT

Orukeet

Use model oruk-orukeet for English transcription up to 60 seconds / 4 MiB. It is included in every subscription at $0.00045/minute ($0.027/audio hour). Optional emotion detection and speaker diarization draw from the same Orukeet allowance at their published task rates. Extra usage shares the plan spending cap. See the Orukeet contract for availability, outputs, streaming, and limits.