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--- |
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tags: |
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- espnet |
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- audio |
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- automatic-speech-recognition |
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language: et |
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license: cc-by-4.0 |
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--- |
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# Estonian Espnet2 ASR model |
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## Model description |
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This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech. |
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## Intended uses & limitations |
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This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc. |
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## How to use |
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```python |
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from espnet2.bin.asr_inference import Speech2Text |
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model = Speech2Text.from_pretrained( |
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"TalTechNLP/espnet2_estonian", |
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lm_weight=0.6, ctc_weight=0.4, beam_size=60 |
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) |
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# read a sound file with 16k sample rate |
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import soundfile |
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speech, rate = soundfile.read("speech.wav") |
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assert rate == 16000 |
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text, *_ = model(speech) |
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print(text[0]) |
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``` |
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#### Limitations and bias |
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Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following: |
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* Speech containing technical and other domain-specific terms |
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* Children's speech |
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* Non-native speech |
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* Speech recorded under very noisy conditions or with a microphone far from the speaker |
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* Very spontaneous and overlapping speech |
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## Training data |
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Acoustic training data: |
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| Type | Amount (h) | |
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|-----------------------|:------:| |
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| Broadcast speech | 591 | |
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| Spontaneous speech | 53 | |
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| Elderly speech corpus | 53 | |
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| Talks, lectures | 49 | |
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| Parliament speeches | 31 | |
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| *Total* | *761* | |
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Language model training data: |
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* Estonian National Corpus 2019 |
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* OpenSubtitles |
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* Speech transcripts |
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## Training procedure |
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Standard EspNet2 Conformer recipe. |
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## Evaluation results |
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### WER |
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |
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|---|---|---|---|---|---|---|---|---| |
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|decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/aktuaalne2021.testset|2864|56575|93.1|4.5|2.4|2.0|8.9|63.4| |
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|decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/jutusaated.devset|273|4677|93.9|3.6|2.4|1.2|7.3|46.5| |
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|decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/jutusaated.testset|818|11093|94.7|2.7|2.5|0.9|6.2|45.0| |
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|decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/www-trans.devset|1207|13865|82.3|8.5|9.3|3.4|21.2|74.1| |
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|decode_asr_lm_lm_large_valid.loss.ave_5best_asr_model_valid.acc.ave/www-trans.testset|1648|22707|86.4|7.6|6.0|2.5|16.1|75.7| |
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### BibTeX entry and citation info |
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#### Citing ESPnet |
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```BibTex |
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@inproceedings{watanabe2018espnet, |
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author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, |
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title={{ESPnet}: End-to-End Speech Processing Toolkit}, |
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year={2018}, |
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booktitle={Proceedings of Interspeech}, |
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pages={2207--2211}, |
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doi={10.21437/Interspeech.2018-1456}, |
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456} |
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} |
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``` |
