Whisper large-v3-turbo — 30 s TFLite (LiteRT), quantized
Quantized TFLite (LiteRT) exports of
openai/whisper-large-v3-turbo: 30 s fixed-window graphs split into
encode and
decode signatures, matching the graph interface of
litert-community/whisper-tiny and
litert-community/whisper-base — drop-in for pipelines built against those graphs (with the 128-mel frontend below).
Exported by the LiteRT-LM-Unity project from the openai checkpoint (transformers TFWhisperForConditionalGeneration → two-signature 30 s graph), then post-training quantized. The f32 source export (~3.2 GB) is retained by the producing project and available on request.
Files
| File | Size | Recipe |
|---|
whisper_large_v3_turbo_30s_i8.tflite | 1.1 GB | dynamic-range int8 weights (channelwise), fp32 activations |
whisper_large_v3_turbo_30s_i4.tflite | 721 MB | dynamic_wi4b64_afp32 mixed ("mixD"): int4 blockwise-64 weights (fp16 scales) with the token-embedding/logits scopes kept at int8 channelwise |
Integration notes (128-mel / vocab 51866)
- 128 mel bins — the large-v3 family needs a 128-mel log-spectrogram frontend, not the 80-mel frontend used by tiny/base/medium. An 80-mel input silently produces wrong output.
- Vocab 51866 — special token ids shift vs the 51865-vocab models:
<|transcribe|> = 50360, <|notimestamps|> = 50364 (language tokens shift accordingly, e.g. <|ko|>/<|en|> differ from the tiny/base ids).
- Two signatures: encode (128-mel spectrogram, 30 s window → encoder hidden states) and decode (token ids + encoder states → logits). The decoder is a fixed-length full re-run per step (no KV cache), matching the tiny/base graph interface.
- Tokenizer: use
tokenizer.json from openai/whisper-large-v3-turbo.
Quantization
Quantizer: ai-edge-quantizer 0.8.0, post-training dynamic-range.
- i8: int8 weights channelwise, fp32 activations (
dynamic_wi8_afp32 scheme).
- i4 ("mixD"):
dynamic_wi4b64_afp32 (int4 weights, blockwise-64, fp16 scales) with int8 overrides on the token-embedding/logits table. Pure full-scope int4 corrupts Korean output on the large family and was rejected. int4 channelwise and blockwise-32 recipes are known-bad (quality collapse / immediate EOS) and were not used.
Validation
9-clip Korean/English test set (Korean tactical-report sentence "2025년 3월 5일 전술평가 결과 보고", English equivalent, weather/status sentences, short Korean voice commands). Greedy decode, desktop CPU (XNNPACK, 8 threads). CER against punctuation-normalized references (space-removed); "exact" = normalized exact match.
| Variant | Exact /9 | CER ko | CER en | Avg RTF | Avg ms/step |
|---|
| i8 | 6 | 0.056 | 0.000 | 1.49 | 94 |
| i4 | 8 | 0.000 | 0.000 | 1.25 | 72 |
i4 was the best tier in the producing project's entire ASR matrix (tiny/base/medium/large-v3/turbo × f32/i8/i4): 8/9 exact, character-perfect in both languages, and the fastest large-family tier per decode step — at 69 % of i8's size it beats i8 on every measured axis. i8's single character error is one short voice-command take heard as 볼륨 억 instead of 볼륨 업 (CER ko 0.000 on the other five Korean clips); its remaining misses vs exact are spacing-only.
i4 is additionally device-validated on Android arm64 (Snapdragon 865-class, shape-based decode tensor binding).
Runtime compatibility
Validated with the LiteRT (ai-edge-litert) interpreter on Windows x86_64 CPU (XNNPACK) and within the LiteRT-LM-Unity v0.14.0 pipeline (LiteRT-LM v0.14.0, Windows x86_64 + Android arm64, Snapdragon 865-class device).
Caveats
- 30 s fixed window; decoder is a full-sequence re-run per step (no KV cache) — a KV-cached runtime will be substantially faster per token.
- Requires the 128-mel frontend and 51866-vocab token ids (see integration notes).
- On Android the decode signature was bound by tensor shape rather than name in the producing project (tensor naming can differ across LiteRT versions) — bind decode inputs by shape if name-based binding fails.
- Language forcing recommended for short clips (e.g.
<|ko|> / <|en|>).
Produced by
LiteRT-LM-Unity (release v0.14.0-unity). Whisper weights are MIT (OpenAI); quantized derivatives inherit the base license.
