| | --- |
| | license: cc-by-4.0 |
| | tags: |
| | - audio |
| | - automatic-speech-recognition |
| | - hf-asr-leaderboard |
| | language: et |
| | model-index: |
| | - name: TalTechNLP/whisper-medium-et |
| | results: |
| | - task: |
| | name: Automatic Speech Recognition |
| | type: automatic-speech-recognition |
| | dataset: |
| | name: Common Voice 11 |
| | type: mozilla-foundation/common_voice_11_0 |
| | config: et |
| | split: test |
| | metrics: |
| | - name: Test WER |
| | type: wer |
| | value: 14.66 |
| | - name: Test CER |
| | type: cer |
| | value: 3.76 |
| | - task: |
| | name: Automatic Speech Recognition |
| | type: automatic-speech-recognition |
| | dataset: |
| | name: Common Voice 8 |
| | type: mozilla-foundation/common_voice_8_0 |
| | config: et |
| | split: test |
| | metrics: |
| | - name: Test WER |
| | type: wer |
| | value: 13.793 |
| | - name: Test CER |
| | type: cer |
| | value: 3.194 |
| | --- |
| | |
| |
|
| | # Whisper-medium-et |
| |
|
| | This is a Whisper-medium model [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) finetuned on around 800 hours of diverse Estonian data. |
| |
|
| | ## Model description |
| | This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. |
| |
|
| |
|
| | ## Intended uses & limitations |
| |
|
| | This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc. |
| |
|
| | ## How to use |
| |
|
| | Use as any other Whisper model via HF transformers, or use a faster decoder like [faster-whisper](https://github.com/guillaumekln/faster-whisper). |
| |
|
| |
|
| | #### Limitations and bias |
| |
|
| | Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following: |
| | * Speech containing technical and other domain-specific terms |
| | * Children's speech |
| | * Non-native speech |
| | * Speech recorded under very noisy conditions or with a microphone far from the speaker |
| | * Very spontaneous and overlapping speech |
| |
|
| | ## Training data |
| | Acoustic training data: |
| |
|
| | | Type | Amount (h) | |
| | |-----------------------|:------:| |
| | | Broadcast speech | 591 | |
| | | Spontaneous speech | 53 | |
| | | Elderly speech corpus | 53 | |
| | | Talks, lectures | 49 | |
| | | Parliament speeches | 31 | |
| | | *Total* | *761* | |
| |
|
| |
|
| |
|
| | ## Training procedure |
| |
|
| | Finetuned using Espnet, and then comverted to transformers format using [this](https://gist.github.com/alumae/2dcf473b667cec9d513b80ea24e94672) script. |
| | Finetuning procedure is similar to [this](https://huggingface.co/espnet/shihlun_asr_whisper_medium_finetuned_librispeech100) model. |
| |
|
| | ## Evaluation results |
| |
|
| | ### WER |
| |
|
| | WER results below are obtained using greedy decoding (i.e., beam size 1). |
| |
|
| | |Dataset | WER | |
| | |---|---| |
| | | Common Voice 8.0 | 13.8 | |
| | | Common Voice 11.0 | 14.7 | |
| |
|
