whisper-medium

Whisper medium — 30 s TFLite (LiteRT), quantized

Quantized TFLite (LiteRT) exports of openai/whisper-medium: 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.

Exported by the LiteRT-LM-Unity project from the openai checkpoint (transformers TFWhisperForConditionalGeneration → two-signature 30 s graph), then post-training quantized. f32 source exports (~3 GB) are retained by the producing project and available on request.

Files

Integration notes

• 80 mel bins / vocab 51865 — whisper-medium uses the classic Whisper frontend and token ids (same as tiny/base), not the 128-mel/51866 large-v3 family layout.
• Two signatures: encode (log-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-medium (differs from whisper-base's tokenizer).

Quantization

Quantizer: ai-edge-quantizer 0.8.0, post-training dynamic-range.

• i8: int8 weights channelwise, fp32 activations (dynamic_wi8_afp32 scheme).
• i4: dynamic_wi4b64_afp32 (int4 weights, blockwise-64, fp16 scales) with int8 overrides on the token-embedding/logits table. Pure full-scope int4 was tested and rejected (Korean transcription errors); an additional encoder-at-int8 variant produced identical transcripts and was discarded for size. 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.

i4 reproduces i8's transcripts on every clip. The only content error in either tier is one short voice-command clip heard as 음향 증가 instead of 음량 증가 (CER 0.250 on that clip); all other misses vs exact are spacing-only or filename artifacts.

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) to match the litert-community tiny/base interface — a KV-cached runtime will be substantially faster per token.
• Known content weakness: 음량 → 음향 confusion on one Korean voice-command clip (both tiers; same confusion as whisper-base i4).
• 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.