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--- |
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license: apache-2.0 |
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language: fi |
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tags: |
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- automatic-speech-recognition |
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- fi |
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- finnish |
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--- |
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# Colloquial Finnish Wav2vec2-Base with continued pre-training |
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The base model pre-trained on 16kHz sampled speech audio with [facebook/wav2vec2-base-fi-voxpopuli-v2](https://huggingface.co/facebook/wav2vec2-base-fi-voxpopuli-v2) used as a foundation model for continued pre-training. When using the model make sure that your speech input is also sampled at 16Khz. |
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**Note**: This model does not have a tokenizer as it was pre-trained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more in-detail explanation of how to fine-tune the model. |
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## Model description |
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The Finnish Wav2Vec2 Base has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477). It is pre-trained on 2600 hours of unlabeled colloquial Finnish speech from [Lahjoita puhetta (Donate Speech)](https://link.springer.com/article/10.1007/s10579-022-09606-3). |
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You can read more about the pre-trained model from [this paper](https://www.isca-archive.org/interspeech_2024/getman24_interspeech.html). The training scripts are available on [GitHub](https://github.com/aalto-speech/colloquial-Finnish-wav2vec2) |
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## Intended uses & limitations |
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You can use this model for Finnish ASR (speech-to-text) and SER (Spoken Emotion Recognition) tasks. |
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### How to use |
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See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLS_R_on_Common_Voice.ipynb) for more information on how to fine-tune the model. |
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### Limitations and bias |
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This model was pre-trained with audio samples whose maximum length was 60 seconds so this model most likely works the best for short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking). |
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The model was pre-trained on the data from the [Lahjoita puhetta (Donate Speech) corpus](https://link.springer.com/article/10.1007/s10579-022-09606-3) so this model might have biases towards colloquial Finnish. |
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## Citation |
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If you use our models or scripts, please cite our article as: |
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```bibtex |
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@inproceedings{getman24_interspeech, |
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title = {What happens in continued pre-training? Analysis of self-supervised speech |
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models with continued pre-training for colloquial Finnish ASR}, |
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author = {Yaroslav Getman and Tamas Grosz and Mikko Kurimo}, |
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year = {2024}, |
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booktitle = {Interspeech 2024}, |
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pages = {5043--5047}, |
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doi = {10.21437/Interspeech.2024-476}, |
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} |
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``` |
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## Team Members |
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- Yaroslav Getman, [Hugging Face profile](https://huggingface.co/GetmanY1), [LinkedIn profile](https://www.linkedin.com/in/yaroslav-getman/) |
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- Tamas Grosz, [Hugging Face profile](https://huggingface.co/Grosy), [LinkedIn profile](https://www.linkedin.com/in/tam%C3%A1s-gr%C3%B3sz-950a049a/) |
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Feel free to contact us for more details 🤗 |
