> ## Documentation Index > Fetch the complete documentation index at: https://polargrid.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Whisper Large V3 Turbo > Fast multilingual speech-to-text on PolarGrid edge nodes Whisper Large V3 Turbo (`whisper-large-v3-turbo`) is OpenAI's 809M-parameter Whisper model, served on PolarGrid edge nodes via Triton's `python` backend using the [faster-whisper](https://github.com/SYSTRAN/faster-whisper) runtime. It is co-resident on the voice pod alongside [`cohere-transcribe-03-2026`](/models/cohere-transcribe-03-2026) and the `kokoro-82m` TTS model — see [`backend/edge-production-setup/CLAUDE.md`](https://github.com/PolarGrid-AI/polargrid-monorepo/blob/main/backend/edge-production-setup/CLAUDE.md) for the pod split rationale. * **HF repo:** [`openai/whisper-large-v3-turbo`](https://huggingface.co/openai/whisper-large-v3-turbo) * **Modality:** Speech-to-Text (streaming + sync) * **Backend:** Triton `python` (voice pod) * **Parameters:** 809M - **Available regions:** all regions except `dfw-02` — see [Model availability](/guides/model-availability) Re-benched 2026-06-11 on the same node, harness, and input corpus as the previous run. Server-only inference dropped **287 → 145 ms p50** (RTF **0.050 → 0.025**, \~40× real-time) after the in-process audio resampling fix on the STT hot path ([PR #612](https://github.com/PolarGrid-AI/polargrid-monorepo/pull/612)) — the gateway previously paid an out-of-process resample on every non-16 kHz upload. Streaming time-to-done improved **639 → 582 ms p50**; TTFT unchanged at \~78 ms. Raw runs: [`benchmarks/yvr-02-2026-06-11/`](https://github.com/PolarGrid-AI/polargrid-monorepo/tree/main/benchmarks/yvr-02-2026-06-11). ## Headline benchmark `POST /v1/audio/transcriptions?stream=true` is the live streaming surface. The server emits a `text/event-stream` of `transcript.text.delta` events as each decode window completes, then closes with a single `transcript.text.done` event. Consumers can render the rolling transcript immediately instead of waiting for the final result. | Measurement | p50 | p95 | | --------------------------------------------------- | --------- | ------- | | **TTFT (response headers → first non-empty delta)** | **78 ms** | 83 ms | | Time to `done` event (response headers → done) | 582 ms | 921 ms | | Interim work (first delta → done) | 504 ms | 841 ms | | Delta events per request | 4 | 7 (max) | | Partial deltas (text != final) | 4 | — | | e2e total (POST → `[DONE]`) | 1304 ms | 1994 ms | | **RTF (e2e ÷ audio duration)** | **0.23** | 0.30 | | well\_formed | 100 / 100 | — | *Bench: 100 streaming transcription runs against `https://api.yvr-02.edge.polargrid.ai`, captured 2026-06-11 from a Vancouver-area laptop. Inputs were the same 5 short utterances (4.2 – 7.7 s, 24 kHz mono WAV, \~200–350 KB each) used by the cohere bench, pre-synthesized via `tada-3b-ml` on the same node. Raw runs: [`benchmarks/yvr-02-2026-06-11/whisper-large-v3-turbo/`](https://github.com/PolarGrid-AI/polargrid-monorepo/blob/main/benchmarks/yvr-02-2026-06-11/whisper-large-v3-turbo/whisper-large-v3-turbo_streaming_bench.json).* > **Delta cadence is chunk-driven.** 4.2 – 5.9 s clips emit 4 deltas, 7.2 – 7.7 s clips emit 7. Deltas are provisional display hints — a delta carries either the new suffix or a full revised hypothesis, and the two are not distinguishable from the event alone, so do not concatenate them; take the final transcript from `done.text` (see [Delta semantics](/api-reference/speech-to-text#delta-semantics)). The first delta arriving at 78 ms after response headers is the meaningful TTFT for live-captioning pipelines. The gateway does not emit `X-Pg-Inference-Ms` on the stream surface yet, so server-only inference cannot be quoted client-side for streaming today. ## Live WebSocket benchmark `wss://…/v1/audio/transcriptions/ws` transcribes **while audio is being captured**: the client streams 16 kHz mono PCM frames at microphone pace and partial transcripts arrive during the utterance — no more waiting for the upload to finish. Benched 2026-06-11 with the same five clips streamed in 100 ms frames at real-time pace, 100 runs after a 5-run warmup. E2e is bounded by the audio duration by design; the numbers that matter are the partial cadence and the end-of-speech lag: | Metric | p50 | p95 | | -------------------------------------------------------- | ---------- | ------------------------ | | First partial (first frame sent → first non-empty delta) | 1023 ms | 1042 ms | | Partials while audio still flowing | 4 | 7 (max, 7.2–7.7 s clips) | | **`stop` → authoritative `done`** | **159 ms** | 205 ms | | well\_formed | 100 / 100 | — | Raw runs: [`whisper-large-v3-turbo/whisper-large-v3-turbo_ws_bench.json`](https://github.com/PolarGrid-AI/polargrid-monorepo/blob/main/benchmarks/yvr-02-2026-06-11/whisper-large-v3-turbo/whisper-large-v3-turbo_ws_bench.json). Harness: [`bench/whisper-large-v3-turbo/bench_ws.py`](https://github.com/PolarGrid-AI/polargrid-monorepo/blob/main/backend/edge-production-setup/bench/whisper-large-v3-turbo/bench_ws.py). The first partial lands at the \~1 s window boundary (partials are emitted per second of new audio) and every run produced partials while audio was still arriving. The **159 ms p50 stop→done** figure is the effective end-of-speech-to-transcript latency — the number to compare against streaming STT providers. The upload surfaces instead pay full-clip transcription after the audio ends (\~580 ms to done on the SSE surface). Sessions cap at 120 s of audio; wire protocol and delta semantics in the [Speech-to-Text API reference](/api-reference/speech-to-text#live-streaming--websocket). ## How this compares Same node, same inputs, same client as the [`cohere-transcribe-03-2026`](/models/cohere-transcribe-03-2026) benches: | Model | Streaming first-partial p50 | ttft\_done p50 | e2e total p50 | RTF (e2e) | | ---------------------------- | --------------------------- | -------------- | ------------- | --------- | | `cohere-transcribe-03-2026` | **44 ms** | **560 ms** | 2510 ms | 0.42 | | **`whisper-large-v3-turbo`** | 77 ms | 639 ms | **1473 ms** | **0.25** | Cohere reaches its first partial sooner; whisper finishes the full streamed transcript materially faster end-to-end (1473 vs 2510 ms p50) at a lower real-time factor. Pick cohere for earliest-possible interim display and broad accented-language accuracy; pick whisper for fastest full-utterance completion. *Both rows are from the pre-PR-#612 runs (2026-05-28 / 2026-06-02) so they remain apples-to-apples. Whisper's post-fix numbers are lower (see the headline table above); cohere has not yet been re-benched on the fixed hot path.* ## Quickstart Edge endpoints accept your raw `pg_*` API key as a bearer token — no token exchange. See [Authentication](/authentication). ```bash cURL theme={null} curl -X POST "https://api.yvr-02.edge.polargrid.ai/v1/audio/transcriptions?sync=true&model=whisper-large-v3-turbo" \ -H "Authorization: Bearer $POLARGRID_API_KEY" \ -F "file=@input.wav" ``` ```typescript JavaScript theme={null} import { PolarGrid } from "@polargrid/polargrid-sdk"; import fs from "node:fs"; const client = await PolarGrid.create({ apiKey: process.env.POLARGRID_API_KEY }); const result = await client.audioTranscriptions({ model: "whisper-large-v3-turbo", file: fs.createReadStream("input.wav"), // Default mode is async (returns job_id). Pass sync to block on the result. sync: true, }); console.log(result.text); ``` ```python Python theme={null} from polargrid import PolarGrid client = await PolarGrid.create(api_key="pg_...") with open("input.wav", "rb") as f: result = await client.audio_transcriptions({ "model": "whisper-large-v3-turbo", "file": f, "sync": True, }) print(result["text"]) ``` ## Endpoint modes `POST /v1/audio/transcriptions` has three upload modes selected by query params (not multipart fields), plus a live WebSocket surface: | Mode | Surface | Response | Use when | | -------------------- | ------------------------------------ | ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------- | | **Live (WebSocket)** | `wss://…/v1/audio/transcriptions/ws` | `transcript.text.delta` events **while audio is still arriving**, `done` after `stop` | Voice agents and live captioning from a microphone — partials during speech, final transcript \~160 ms after end-of-speech. | | **Streaming** | `?stream=true` | `text/event-stream` of delta + done events | Rolling display over a complete file upload. | | **Sync** | `?sync=true` | `200` with the formatted transcript directly | Voice-agent path that needs the answer in one round trip. | | **Async (default)** | *none* | `202` with `{job_id, poll_url}`; poll via `GET ?job_id=...` | Background batch transcription; no caller blocking. | `stream` and `sync` are mutually exclusive — passing both returns `400`. Streaming requires `response_format` in `{json, text}`. The WebSocket surface takes 16 kHz mono int16 PCM frames and caps sessions at 120 s — see the [Speech-to-Text API reference](/api-reference/speech-to-text#live-streaming--websocket) for the wire protocol and delta semantics. ### Sync benchmark (`?sync=true`) The blocking surface holds the connection open until inference completes, then ships the whole JSON response. Client-side TTFB ≈ total wall-clock, and the meaningful split is **server inference time** vs **network leg** (which for STT is dominated by the audio upload). Raw runs: [`benchmarks/yvr-02-2026-06-11/whisper-large-v3-turbo/`](https://github.com/PolarGrid-AI/polargrid-monorepo/blob/main/benchmarks/yvr-02-2026-06-11/whisper-large-v3-turbo/whisper-large-v3-turbo_bench.json). | Measurement | p50 | p95 | | --------------------------------------- | ---------- | ------- | | End-to-end TTFB (with network + upload) | 909 ms | 1329 ms | | **Server-only inference** | **145 ms** | 176 ms | | *Network leg (e2e − server)* | *765 ms* | — | | Body transfer (JSON response) | 1.1 ms | 2.4 ms | | RTF (server inference ÷ audio duration) | 0.025 | 0.031 | Server-only timing comes from the `X-Pg-Inference-Ms` response header (PR #507), available on the sync surface. > **The network leg is upload-dominated.** Each request ships a multi-second WAV file before inference can begin, so the 765 ms p50 network figure is largely the upload time of a \~300 KB body — not POP-to-client RTT. For shorter clips (sub-2 s) the network leg shrinks proportionally. Quote the **server-only RTF** when comparing inference throughput against centralized providers. ## Capabilities | Field | Value | | ----------------- | ------------------------------------------------------------------------------------------------ | | Endpoint | `POST /v1/audio/transcriptions` | | Multipart field | `file` (required) | | Query params | `model`, `language`, `prompt`, `temperature`, `response_format`, `punctuation`, `stream`, `sync` | | `response_format` | `json` (default), `text`, `srt`, `vtt`, `verbose_json` | | Languages | Multilingual — auto-detected, or pin with `language` (e.g. `en`, `fr`, `es`) | | Max batch size | 1 | | Backend pod | `inference-backend-triton-voice` | ## Response timing headers PR #507 added two response headers that bench harnesses and observability tooling can read to get a server-only inference time without inferring it from the body: | Header | Value | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | | `X-Pg-Inference-Ms` | Integer milliseconds — wall-clock around the `transcribe()` call inside the gateway's `_handle_sync` (i.e., excludes the audio upload + response transfer). | | `Server-Timing` | Standard-shape `inference;dur=` entry carrying the same number. | These let callers compute the network leg as `(client wall-clock) − (X-Pg-Inference-Ms)`, the same e2e-vs-server split available for LLM via `pg_metadata`. ## Model identifier Call this model with the canonical id `whisper-large-v3-turbo` at `/v1/audio/transcriptions`. It has no short alias. The HuggingFace repo id `openai/whisper-large-v3-turbo` is accepted at `/v1/models/load` for hot-loading purposes but does not resolve at inference time. ## Notes * License: [Apache 2.0](https://huggingface.co/openai/whisper-large-v3-turbo) (no auth required to pull weights). * Runtime: served via `faster-whisper`, not raw `transformers`, for optimized CTranslate2 inference. * For multilingual coverage and accuracy on accented speech, [`cohere-transcribe-03-2026`](/models/cohere-transcribe-03-2026) is the alternative — whisper-turbo's edge is lowest full-utterance latency. ## See also * [Speech-to-Text API](/api-reference/speech-to-text) — endpoint reference, formats, streaming contract * [Voice AI guide](/guides/voice) — building voice agents on PolarGrid * [Authentication](/authentication) — using your `pg_*` API key * [`/v1/models`](/api-reference/models) — list all available models