Model overview
_Kotoba-Whisper_ is a collection of distilled Whisper models for Japanese ASR, developed through the collaboration bewteen Asahi Ushio and Kotoba Technologies. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model, and the student model consists the full encoder of the teacher large-v3 model and the decoder with two layers initialized from the first and last layer of the large-v3 model. Kotoba-Whisper is **6.3x faster than large-v3**, while retaining as low error rate as the large-v3. As successor of our first model, kotoba-whisper-v1.0, we release ***kotoba-whisper-v2.0*** trained on the `all` subset of ReazonSpeech (the largest speech-transcription paired dataset in Japanese extracted from Japanese TV audio recordings), which amounts 7,203,957 audio clips (5 sec audio with 18 text tokens in average) after those transcriptions more than 10 WER are removed (see WER Filter for detail). The model was trained for 8 epochs with batch size 256 with sampling rate of 16kHz, and the training and evaluation code to reproduce kotoba-whisper is available at https://github.com/kotoba-tech/kotoba-whisper. Kotoba-whisper-v2.0 achieves better CER and WER than the openai/whisper-large-v3 in the in-domain held-out test set from ReazonSpeech, and achieves competitive CER and WER on the out-of-domain test sets including JSUT basic 5000 and the Japanese subset from CommonVoice 8.0 (see Evaluation for detail).
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。