litert-community/whisper-large-v3

Whisper large-v3 — 30 s TFLite (LiteRT), quantized

Quantized TFLite (LiteRT) exports of openai/whisper-large-v3: 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 (~6 GB, buffer-offset flatbuffer) is retained by the producing project and available on request.

Files

File	Size	Recipe
whisper_large_v3_30s_i8.tflite	1.6 GB	dynamic-range int8 weights (channelwise), fp32 activations
whisper_large_v3_30s_i4.tflite	1.1 GB	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.

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	7	0.000	0.000	4.04	732
i4	7	0.000	0.000	3.38	582

Both tiers are character-perfect on every clip (CER 0.000 ko + en); the two misses vs exact match are spacing-only / filename artifacts. i4 is both ~30 % smaller and ~20 % faster per step than i8 with identical accuracy on this set.

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.
• large-v3 is the accuracy-reference tier: on this test set it decodes 3–7× slower per step than litert-community/whisper-large-v3-turbo with no accuracy gain over turbo i4 — prefer turbo for latency-sensitive use.
• Requires the 128-mel frontend and 51866-vocab token ids (see integration notes).
• 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.