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Get Started Free →Transcribe audio to text via the OpenAI-compatible transcription endpoint. Supports multiple models, languages, and keyword biasing. Also lists available speech-to-text providers and service types.
.claude/skills/team-telnyx-telnyx-stt-python/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 2% | 0% |
bashpip install telnyx
pythonimport os from telnyx import Telnyx client = Telnyx( api_key=os.environ.get("TELNYX_API_KEY"), )
All examples below assume client is already initialized as shown above.
All API calls can fail with network errors, rate limits (429), validation errors (422), or authentication errors (401). Always handle errors in production code:
pythonimport telnyx try: response = client.ai.audio.transcribe( model="openai/whisper-large-v3-turbo", url="https://example.com/audio.mp3", ) except telnyx.APIConnectionError: print("Network error — check connectivity and retry") except telnyx.RateLimitError: import time time.sleep(1) except telnyx.APIStatusError as e: print(f"API error {e.status_code}: {e.message}")
Common error codes: 401 invalid API key, 403 insufficient permissions, 404 resource not found, 422 validation error, 429 rate limited.
Transcribe an audio file to text. This endpoint is consistent with the OpenAI Transcription API and may be used with the OpenAI JS or Python SDK.
POST /ai/audio/transcriptions
| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | url | string (URL) | Yes | URL of the audio file to transcribe. | | model | string | No | Model ID (e.g., openai/whisper-large-v3-turbo, distil-whisper/distil-large-v2). | | language | string | No | Language code (e.g., en, es, fr). | | prompt | string | No | Optional prompt to guide transcription style. | | response_format | enum | No | json, text, srt, verbose_json, vtt. Default: json. | | temperature | number | No | Sampling temperature (0-1). Default: 0. | | keywords | arraystring] | No | Keyword biasing — improve accuracy for domain-specific terms. |
python# Basic transcription response = client.ai.audio.transcribe( url="https://example.com/audio.mp3", ) print(response.text) # With specific model and language response = client.ai.audio.transcribe( url="https://example.com/audio.mp3", model="openai/whisper-large-v3-turbo", language="es", ) print(response.text) # With keyword biasing for domain-specific terms response = client.ai.audio.transcribe( url="https://example.com/audio.mp3", keywords=["Telnyx", "API", "WebRTC", "SIP"], ) print(response.text) # Verbose JSON with segments response = client.ai.audio.transcribe( url="https://example.com/audio.mp3", response_format="verbose_json", ) for segment in response.segments: print(f"[{segment.start:.1f}s - {segment.end:.1f}s] {segment.text}")
Primary response fields:
response.text — Full transcription textresponse.duration — Audio duration in secondsresponse.segments — Array of segment objects (with start, end, text) when using verbose_json formatRetrieve a list of available speech-to-text providers and their service types.
GET /ai/audio/transcriptions/providers
pythonresponse = client.ai.audio.list_providers() for provider in response.providers: print(f"{provider['name']} — {provider['service_type']}") # Filter by provider name response = client.ai.audio.list_providers(provider="telnyx") for provider in response.providers: print(f"{provider['name']} — {provider['service_type']}") # Filter by service type response = client.ai.audio.list_providers(service_type="transcription") for provider in response.providers: print(f"{provider['name']} — {provider['service_type']}")
Primary response fields:
response.providers — Array of provider objects with name and service_typeThe Telnyx Agent CLI provides composite commands for STT:
bash# Transcribe audio telnyx-agent stt --audio-url https://example.com/audio.mp3 --json # With specific model and language telnyx-agent stt --audio-url https://example.com/audio.mp3 --model openai/whisper-large-v3-turbo --language es --json # List available providers telnyx-agent stt-providers --json # Filter by provider or service type telnyx-agent stt-providers --provider telnyx --service-type transcription --json
https://api.telnyx.com/v2/ai/openai.keywords to improve transcription accuracy for domain-specific terms, product names, or acronyms that generic models may mishear.openai/whisper-large-v3-turbo (fast, accurate) and distil-whisper/distil-large-v2 (lightweight). Check stt-providers for the full list.en, es, fr, de, ja, etc.). Omit to auto-detect.verbose_json to get timestamps and segments. Use srt or vtt for subtitle files.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | fail→pass | 13,942 | 5,705 | -59% | 1 | 1 | 0% | 2,640 | 2,682 | +2% | 0 | 0 | — |
case-04 | fail→pass | 10,145 | 5,722 | -44% | 1 | 1 | 0% | 1,879 | 2,567 | +37% | 0 | 0 | — |
case-02 | fail→pass | 11,306 | 4,924 | -56% | 1 | 1 | 0% | 2,021 | 2,608 | +29% | 0 | 0 | — |
case-01 | fail→pass | 20,476 | 7,171 | -65% | 1 | 1 | 0% | 4,196 | 3,158 | -25% | 0 | 0 | — |
case-05 | fail→pass | 12,093 | 3,210 | -73% | 1 | 1 | 0% | 2,063 | 2,105 | +2% | 0 | 0 | — |
case-06 | fail→pass | 7,567 | 17,522 | +132% | 1 | 1 | 0% | 1,363 | 2,139 | +57% | 0 | 0 | — |
case-07 | fail→pass | 6,733 | 2,584 | -62% | 1 | 1 | 0% | 1,302 | 1,983 | +52% | 0 | 0 | — |
case-08 | fail→pass | 6,444 | 2,100 | -67% | 1 | 1 | 0% | 1,186 | 1,881 | +59% | 0 | 0 | — |
case-09 | fail→pass | 8,900 | 5,714 | -36% | 1 | 1 | 0% | 1,658 | 2,620 | +58% | 0 | 0 | — |
case-10 | pass→pass | 10,118 | 3,546 | -65% | 1 | 1 | 0% | 1,854 | 2,230 | +20% | 0 | 0 | — |
case-11 | fail→pass | 21,278 | 5,144 | -76% | 1 | 1 | 0% | 2,253 | 2,506 | +11% | 0 | 0 | — |
case-12 | pass→pass | 10,625 | 5,120 | -52% | 1 | 1 | 0% | 1,835 | 2,436 | +33% | 0 | 0 | — |
case-13 | pass→pass | 15,616 | 9,768 | -37% | 1 | 1 | 0% | 2,001 | 3,528 | +76% | 0 | 0 | — |
case-14 | fail→pass | 8,063 | 4,165 | -48% | 1 | 1 | 0% | 1,563 | 2,356 | +51% | 0 | 0 | — |
case-15 | fail→pass | 9,282 | 3,211 | -65% | 1 | 1 | 0% | 1,674 | 1,840 | +10% | 0 | 0 | — |
case-16 | fail→pass | 14,810 | 4,186 | -72% | 1 | 1 | 0% | 2,800 | 2,357 | -16% | 0 | 0 | — |
case-17 | fail→pass | 27,404 | 4,328 | -84% | 1 | 1 | 0% | 2,637 | 2,311 | -12% | 0 | 0 | — |
case-18 | fail→pass | 13,280 | 5,515 | -58% | 1 | 1 | 0% | 2,457 | 1,982 | -19% | 0 | 0 | — |
case-19 | fail→pass | 10,033 | 5,949 | -41% | 1 | 1 | 0% | 1,721 | 2,742 | +59% | 0 | 0 | — |
case-20 | pass→pass | 7,552 | 6,327 | -16% | 1 | 1 | 0% | 1,457 | 2,558 | +76% | 0 | 0 | — |
case-21 | fail→pass | 14,849 | 7,736 | -48% | 1 | 1 | 0% | 2,635 | 2,872 | +9% | 0 | 0 | — |
case-22 | pass→pass | 7,884 | 5,521 | -30% | 1 | 1 | 0% | 1,302 | 2,565 | +97% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +77 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.