| --- |
| language: |
| - ja |
| pipeline_tag: automatic-speech-recognition |
| base_model: |
| - KeisukeMiyamoto/zipformer-cpt |
| - KeisukeMiyamoto/lfm2.5-230m-cpt |
| datasets: |
| - KeisukeMiyamoto/whisper-cluster-audio |
| - KeisukeMiyamoto/audio-transcript |
| - KeisukeMiyamoto/lambda-audio-courpus |
| metrics: |
| - cer |
| tags: |
| - japanese |
| - speech-to-text |
| - lambda-audio |
| - zipformer |
| - lfm2 |
| - pytorch |
| model-index: |
| - name: LambdaAudio v6 |
| results: |
| - task: |
| type: automatic-speech-recognition |
| name: Automatic Speech Recognition |
| dataset: |
| type: google/fleurs |
| name: FLEURS Japanese |
| config: ja_jp |
| split: test |
| metrics: |
| - type: cer |
| name: CER (%) |
| value: 6.05 |
| --- |
| |
| # LambdaAudio v6 |
|
|
| LambdaAudio v6 is a Japanese automatic speech recognition (ASR) model that converts speech into Japanese text. It combines a Zipformer audio encoder with an LFM2.5 language decoder, both adapted to Japanese audio or transcripts before joint training. |
|
|
| On Japanese subset of Google FLEURS ASR benchmark, it achieves a character error rate (CER) close to Whisper large-v3-turbo (**6.05% vs. 5.83%**) while transcribing audio **2.3× faster**. |
|
|
| ## Model details |
|
|
| | Field | Value | |
| | --- | --- | |
| | Developer | Keisuke Miyamoto | |
| | Task / language | Speech-to-text / Japanese (`ja`) | |
| | Model type | Encoder-decoder with an audio adapter and gated cross-attention | |
| | Model size | 350M parameters | |
| | Audio encoder | [KeisukeMiyamoto/zipformer-cpt](https://huggingface.co/KeisukeMiyamoto/zipformer-cpt) | |
| | Text decoder / tokenizer | [KeisukeMiyamoto/lfm2.5-230m-cpt](https://huggingface.co/KeisukeMiyamoto/lfm2.5-230m-cpt) | |
| | Input | 16 kHz audio, up to 30 seconds per segment | |
| | Output | Japanese transcription text | |
| | Audio feature interval | 20 ms after the encoder; 40 ms after the adapter | |
|
|
| ## Architecture |
|
|
| The Zipformer encoder extracts features from 16 kHz audio. An audio adapter reduces the sequence length and projects these features into the decoder's hidden dimension. Gated cross-attention in the LFM2 full-attention blocks lets the decoder use audio features to predict the next text token. |
|
|
|  |
|
|
| ## Training data |
|
|
| | Stage | Dataset | Sources and labels | |
| | --- | --- | --- | |
| | Encoder continued pretraining | [Whisper Cluster Audio](https://huggingface.co/datasets/KeisukeMiyamoto/whisper-cluster-audio) | Internet Archive, publicly available podcasts, and radio; 500 cluster labels derived from Whisper large-v3-turbo encoder features | |
| | Decoder continued pretraining | [Japanese Audio Transcripts](https://huggingface.co/datasets/KeisukeMiyamoto/audio-transcript) | Japanese transcripts from NHK archives and technology podcasts | |
| | Joint ASR training | [Lambda Audio Corpus](https://huggingface.co/datasets/KeisukeMiyamoto/lambda-audio-courpus) | NHK archives, technology podcasts, and ReazonSpeech | |
|
|
| ## Benchmarks |
|
|
| Each cell shows **CER (%) / total transcription time (seconds)**. Lower values are better. |
|
|
| | Model | ReazonSpeech | Common Voice 25 Japanese | FLEURS Japanese | |
| | --- | ---: | ---: | ---: | |
| | **[LambdaAudio v6](https://huggingface.co/KeisukeMiyamoto/lambda-audio-v6)** | **10.75 / 121.55** | **23.87 / 167.84** | **6.05 / 20.75** | |
| | [Whisper large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) | 11.42 / 354.04 | 26.40 / 592.25 | 5.83 / 47.82 | |
| | [Kotoba-Whisper v2.2](https://huggingface.co/kotoba-tech/kotoba-whisper-v2.2) | 11.70 / 322.53 | 22.89 / 541.39 | 7.35 / 46.15 | |
| | [ReazonSpeech wav2vec2](https://huggingface.co/reazon-research/japanese-wav2vec2-base-rs35kh) | 14.01 / 49.35 | 31.43 / 68.22 | 15.04 / 9.22 | |
| | [Parakeet tdt_ctc-0.6b-ja](https://huggingface.co/nvidia/parakeet-tdt_ctc-0.6b-ja) | 10.62 / 55.10 | 19.99 / 82.28 | 5.94 / 9.74 | |
| | [Cohere Transcribe 03-2026](https://huggingface.co/CohereLabs/cohere-transcribe-03-2026) | 6.45 / 176.00 | 21.58 / 227.42 | 4.36 / 30.77 | |
|
|
| ### ReazonSpeech |
|
|
|  |
|
|
| ### Common Voice 25 Japanese |
|
|
|  |
|
|
| ### FLEURS Japanese |
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|  |
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|
