Audio Formats

​

Default format

• Encoding: PCM 16-bit signed little-endian (pcm_s16le)
• Channels: Mono (1 channel)
• Sample rate: 24000 Hz (default; native generation rate)
• Byte order: Little-endian

​

Output formats (output_format)

• Backwards compatible. Omitting output_format is identical to the default behavior — you get pcm_s16le frames. The strict checks below only apply to requests that send output_format.
• Conflicts are rejected. Sending both output_format and an explicitly non-default sample_rate that disagrees with the token’s rate returns a VALIDATION_ERROR (HTTP 400 / WS error frame). The value 24000 is treated as the released SDKs’ serialized wire default rather than evidence of an explicit conflicting choice. Prefer sending only output_format, or matching values.
• Sticky streaming config. On Stream Input and Multi-Context, a format sent on an ordinary config/context message persists until another valid ordinary message changes it. The acknowledged update_settings command accepts only generation parameters and rejects output_format and sample_rate.
• G.711 frame semantics. For ulaw_8000 / alaw_8000, audio frames carry enc: "mulaw" / "alaw", sr: 8000, and samples equals the byte length (1 byte/sample). Decode with the standard G.711 tables (e.g. Python audioop.ulaw2lin(payload, 2); audioop left the standard library in Python 3.13, so install the audioop-lts package there). On REST, the response uses Content-Type: audio/basic and X-Audio-Format: mulaw/alaw.

​

Telephony example (µ-law 8 kHz)

{
  "text": "Your verification code is 4 8 1 5.",
  "voice_id": 1071,
  "output_format": "ulaw_8000",
  "language": "en"
}

​

Audio chunk fields

​

AI-generated audio marking

1. An in-band watermark embedded in the audio signal itself.
2. A disclosure header on every audio response.

​

Disclosure header

​

Watermark

​

Detecting the watermark

pip install "kugelaudio[watermark]"

from kugelaudio.watermark import WatermarkDetector

detector = WatermarkDetector()
result = detector.detect_file("speech.wav")

if result.ai_generated:
    print(result.confidence, result.customer_id)

​

Limitations

• Lossy encoding, added noise, and editing can weaken detection and attribution.
• The detector is trained on speech; a positive result on non-speech audio is unreliable.

​

Related