# jsnfly /wav2vec2-large-xlsr-53-german-gpt2

anton-l
HF staff
 --- language: - de license: apache-2.0 tags: - automatic-speech-recognition - de - hf-asr-leaderboard - mozilla-foundation/common_voice_7_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_7_0 model-index: - name: Wav2Vec2-Large-XLSR-53-German-GPT2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 7 type: mozilla-foundation/common_voice_7_0 args: de metrics: - name: Test WER type: wer value: 10.02 - name: Test CER type: cer value: 4.7 --- # Wav2Vec2-Large-XLSR-53-German-GPT2 This is an encoder-decoder model for automatic speech recognition trained on on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset. The encoder was initialized from [jonatasgrosman/wav2vec2-large-xlsr-53-german](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-german) and the decoder from [dbmdz/german-gpt2](https://huggingface.co/dbmdz/german-gpt2). It was trained using a two step process: * fine-tuning only the cross-attention weights and the decoder using the pre-computed outputs of the Wav2Vec-Modell * relatively fast training * also works on small GPU (eg. 8 GB) * but may take a lot of disk space * should already yield decent results * fine-tuning the model end-to-end * much slower * needs a bigger GPU There is also one trick, which seemed to improve performance significantly: adding position embeddings to the encoder outputs and initializing them with the pre-trained position embeddings of the GPT2 model (See eval.py). The training notebooks are still early drafts. Also results can probably improved a lot by using for example a learning rate schedule.