---
license: mit
base_model:
- openai/whisper-medium
tags:
- litert
- tflite
- whisper
- asr
- quantized
pipeline_tag: automatic-speech-recognition
---

# Whisper medium — 30 s TFLite (LiteRT), quantized

Quantized TFLite (LiteRT) exports of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium): 30 s fixed-window graphs split into **encode** and **decode** signatures, matching the graph interface of [litert-community/whisper-tiny](https://huggingface.co/litert-community/whisper-tiny) and [litert-community/whisper-base](https://huggingface.co/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

| File | Size | Recipe |
| --- | ---: | --- |
| `whisper_medium_30s_i8.tflite` | 794 MB | dynamic-range int8 weights (channelwise), fp32 activations |
| `whisper_medium_30s_i4.tflite` | 634 MB | `dynamic_wi4b64_afp32` mixed ("L1"): int4 blockwise-64 weights (fp16 scales) with the token-embedding/logits scopes kept at int8 channelwise |

## 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](https://huggingface.co/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.

| Variant | Exact /9 | CER ko | CER en | Avg RTF | Avg ms/step |
| --- | ---: | ---: | ---: | ---: | ---: |
| i8 | 7 | 0.042 | 0.000 | 1.67 | 295 |
| i4 | 7 | 0.042 | 0.000 | 1.74 | 311 |

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](https://github.com/Leuconoe/LiteRT-LM-Unity) (release v0.14.0-unity). Whisper weights are MIT (OpenAI); quantized derivatives inherit the base license.