Automatic Speech Recognition
Transformers
Safetensors
Finnish
wav2vec2
finnish
Eval Results (legacy)
Instructions to use GetmanY1/wav2vec2-base-fi-lp-cont-pt-100h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GetmanY1/wav2vec2-base-fi-lp-cont-pt-100h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GetmanY1/wav2vec2-base-fi-lp-cont-pt-100h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("GetmanY1/wav2vec2-base-fi-lp-cont-pt-100h") model = AutoModelForCTC.from_pretrained("GetmanY1/wav2vec2-base-fi-lp-cont-pt-100h", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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).
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You can read more about the pre-trained model from [this paper](
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## Intended uses & limitations
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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{
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year=2024,
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booktitle={
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pages={
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doi={
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issn={XXXX-XXXX}
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```
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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).
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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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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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