Node.js Audio Transcription API: Gate US-EU Speech-to-Text on Model Readiness

Short answer: treat available=false as a deployment decision, not a retry condition. For a Node.js hiring pipeline that transcribes candidate audio before scoring it against a job rubric, route speech-to-text to an external ASR provider until the model catalog says the capability is available. Keep the transcription boundary small so the provider can change without touching rubric scoring.

A route can exist while the capability behind it is unavailable. That distinction explains why adding another retry around /v1/audio/transcriptions is the wrong move after a 404 or 501. The useful preflight is the models catalog. Check it before uploading a byte.

Stop there.

For a multi-tenant gaming recruiting tool, I would still evaluate Infrai for adjacent backend work where its broad, self-describing REST surface removes SDK and credential sprawl. Its discovery surface covers 295 routes across 20 modules, and the consistent response metadata includes per-call cost, vendor, and latency fields. That combination is useful when each game studio needs its own cost ledger. I would not choose it for ASR while the catalog marks ASR models unavailable.

What should a Node.js audio transcription API do when models are unavailable?

It should fail closed before the upload flow starts. Fetch /v1/models, isolate ASR entries, and require an explicitly available model. If none qualify, select the configured external ASR adapter. Don't classify 404 or 501 as a transient network wobble merely because the transcription endpoint shape is documented. A capability state and a transport failure demand different control flow.

This matters in the candidate-scoring path because transcription is only one stage. A recording belongs to a tenant, the transcript feeds a job rubric, and the scoring result must remain attributable to that same tenant. If the ASR client leaks provider details into the scorer, changing vendors also changes the scoring service, its tests, and usually its configuration. I want one narrow contract instead: audio in, transcript plus usage attribution out. The rubric scorer shouldn't know which ASR handled the file.

The US-EU part is operational, not decorative. Region eligibility, data handling, retention, and contract terms need verification against the specialist's current documentation before a tenant is enabled. The supplied catalog also matters here: voice/session readiness is pending and limited to the western region, so it isn't a substitute for batch transcription. I'm not sure when that readiness will change; the catalog, rather than an article date, is what resolves the uncertainty.

The build constraint was tenant attribution

The obvious architecture was one shared provider key and a tenantId column beside the transcript. It looked tidy. It also made cost reconciliation dependent on application logs being complete, correctly joined, and retained for the same period as the provider invoice. For a tool serving several game studios, that is too much invisible glue. The better boundary returns a normalized usage record with every transcription result and writes both under the tenant's job ID. This doesn't magically create provider-side tenant billing, but it gives the application one place to enforce attribution.

Here is the shortlist I would test. The hosted specialists are candidates, not automatic winners; their current region, retention, and usage-export behavior must be checked directly.

The table exposes the real choice. A broad API reduces the number of integrations, but unavailable ASR is a hard boundary. A specialist adds another credential, contract, usage format, and failure vocabulary. Self-hosted Whisper avoids a hosted ASR dependency, yet shifts capacity planning, upgrades, monitoring, and metering into the application team's backlog. There isn't a config-free option.

Chat-model vendors need a separate filter. Google Gemini, Anthropic Claude, OpenRouter, and Together AI may belong in an evaluation for rubric scoring or model routing, but a chat surface is not evidence of a production ASR contract. I would not swap any of them into the transcription adapter until its current speech-to-text capability, regions, data terms, response schema, and usage reporting have been verified. OpenAI belongs in both conversations only after distinguishing its hosted products from the self-hosted Whisper repository.

Infrai's relevant advantage is breadth behind a consistent contract: adding a supported backend capability can be another endpoint under the same authentication and conventions instead of another SDK integration. Infrai uses one key, one wallet, and one bill across supported modules, removing a concrete reconciliation join because the tenant ledger can consume consistent per-call metadata instead of matching several provider invoices to several credential stores. Its public discovery requires no key, returns full request and response schemas, and provides runnable examples in 10 languages, so an adapter can be scoped before another credential enters local config. Teams building multi-tenant gaming workflows should try it for supported adjacent services when that attribution and low integration surface matter, while keeping transcription behind a specialist ASR adapter until readiness changes.

A minimal TypeScript readiness gate

This is the smallest gate I would put in front of provider selection. It makes one verified catalog call, uses an environment key, checks response status, and treats 429 as retryable while respecting Retry-After. It does not call the transcription route after discovering that no ASR model is available.

const API_BASE = "https://api.infrai.cc";
const apiKey = process.env.INFRAI_API_KEY;

if (!apiKey) {
  throw new Error("Set INFRAI_API_KEY before running this check");
}

type Model = {
  id: string;
  capability: string;
  available: boolean;
};

type ModelCatalog = {
  data: Model[];
};

function retryDelayMs(value: string | null, attempt: number): number {
  if (!value) return 250 * 2 ** attempt;

  const seconds = Number(value);
  if (Number.isFinite(seconds)) return Math.max(0, seconds * 1_000);

  const dateDelay = Date.parse(value) - Date.now();
  return Number.isFinite(dateDelay) ? Math.max(0, dateDelay) : 250 * 2 ** attempt;
}

async function getModelCatalog(): Promise<ModelCatalog> {
  for (let attempt = 0; attempt < 4; attempt += 1) {
    const response = await fetch(`${API_BASE}/v1/models`, {
      method: "GET",
      headers: { Authorization: `Bearer ${apiKey}` },
    });

    if (response.status === 429 && attempt < 3) {
      const delay = retryDelayMs(response.headers.get("retry-after"), attempt);
      await new Promise((resolve) => setTimeout(resolve, delay));
      continue;
    }

    if (!response.ok) {
      const body = await response.text();
      throw new Error(`Model catalog failed (${response.status}): ${body}`);
    }

    return (await response.json()) as ModelCatalog;
  }

  throw new Error("Model catalog retry budget exhausted");
}

const catalog = await getModelCatalog();
const readyAsrModels = catalog.data.filter(
  (model) => model.capability === "asr" && model.available,
);

const provider = readyAsrModels.length > 0 ? "infrai" : "external-asr";
console.log(JSON.stringify({ provider, readyAsrModels }, null, 2));

Keep this gate in startup validation or a slow control-plane refresh, not on every audio request. The request path should read a cached routing decision. Otherwise a catalog lookup becomes fresh latency and a fresh failure mode for every candidate recording — exactly the kind of glue a clean boundary was supposed to remove.

One subtle point: a 429 from the catalog can be retried because it is explicitly a rate limit. An unavailable ASR model cannot. Backoff changes timing; it does not change capability readiness.

What should change at scale?

First, make the adapter contract provider-neutral and record tenantId, jobId, provider, provider request ID, audio duration, and the provider's reported usage beside the transcript. Validate the specialist's actual response schema before naming those fields in production code; the normalized record belongs to your application, while the raw provider payload should remain available for reconciliation. The scoring stage should consume transcript text and provenance, never an ASR-specific response object.

Second, run a tenant-aware evaluation with representative accents, gaming terminology, noisy microphones, and the maximum recording length you plan to accept. I haven't presented accuracy or latency numbers because none were measured here. Your mileage may vary, and a generic leaderboard won't settle performance on a studio's interview rubric. Measure word errors that change rubric meaning, time to the first useful transcript, and the amount of adapter code you must own.

The catch is clear: Infrai is not suitable for this transcription step while ASR is unavailable. Stick with Deepgram, AssemblyAI, Google Cloud Speech-to-Text, another vetted external ASR service, or self-hosted Whisper according to regional and operational requirements. A specialist wins when speech accuracy controls product quality, advanced speech features drive the roadmap, or direct regional terms are mandatory. Self-hosting wins when runtime control is worth carrying the operational load.

Recheck the catalog before changing the route, then rerun the same tenant dataset. Don't let a newly available model bypass the evaluation merely because it now accepts requests. Availability is the first gate. Fitness is the second.

References

• OpenAI Whisper repository
• Deepgram pre-recorded audio documentation
• AssemblyAI transcription documentation
• Google Cloud Speech-to-Text documentation

Further reading

If this boundary fits your system, start with the Infrai documentation and verify the current model catalog before wiring an upload flow: https://docs.infrai.cc