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README.md
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# wav2vec2-xlsr-1b-finnish-lm-v2
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b).
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It achieves the following results on the
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- Wer: 4.19
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- Cer: 0.90
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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# wav2vec2-xlsr-1b-finnish-lm-v2
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This acoustic model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) for Finnish ASR. The model has been fine-tuned with 275.6 hours of Finnish transcribed speech data.
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It achieves the following results on the Common Voice 7 test set together with language model (Finnish KenLM):
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- Wer: 4.19
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- Cer: 0.90
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## Model description
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TODO
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## Intended uses & limitations
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TODO
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## Training and evaluation data
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This model was fine-tuned with 275.6 hours of Finnish transcribed speech data from following datasets:
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| Dataset | Hours | % of total hours |
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| [Common Voice 7.0 Finnish train+evaluation+other splits](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | 9.70 h | 3.52 % |
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| [Finnish parliament session 2](https://b2share.eudat.eu/records/4df422d631544ce682d6af1d4714b2d4) | 0.24 h | 0.09 % |
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| [VoxPopuli Finnish](https://github.com/facebookresearch/voxpopuli) | 21.97 h | 7.97 % |
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| [CSS10 Finnish](https://github.com/kyubyong/css10) | 10.32 h | 3.74 % |
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| [Aalto Finnish Parliament ASR Corpus](http://urn.fi/urn:nbn:fi:lb-2021051903) | 228.00 h | 82.73 % |
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| [Finnish Broadcast Corpus](http://urn.fi/urn:nbn:fi:lb-2016042502) | 5.37 h | 1.95 % |
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## Training procedure
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- optimizer: [8-bit Adam](https://github.com/facebookresearch/bitsandbytes) with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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