model upd
Browse files- README.md +4 -4
- config.json +2 -2
- pytorch_model.bin +1 -1
README.md
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metrics:
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- name: Test WER
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type: wer
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value:
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---
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# Wav2Vec2-Large-XLSR-53-Ukrainian
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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When using this model, make sure that your speech input is sampled at 16kHz.
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```
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**Test Result**:
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## Training
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The Common Voice `train`, `validation
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The script used for training will be available [here](https://github.com/arampacha/hf-sprint-xlsr) soon.
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metrics:
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- name: Test WER
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type: wer
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value: 31.30
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---
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# Wav2Vec2-Large-XLSR-53-Ukrainian
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Ukrainian using the [Common Voice](https://huggingface.co/datasets/common_voice) and sample of [M-AILABS Ukrainian Corpus](https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/) datasets.
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When using this model, make sure that your speech input is sampled at 16kHz.
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```
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**Test Result**: 31.30
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## Training
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The Common Voice `train`, `validation` and sample of the M-AILABS Ukrainian corpus.
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The script used for training will be available [here](https://github.com/arampacha/hf-sprint-xlsr) soon.
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config.json
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{
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"_name_or_path": "
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.
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"final_dropout": 0.0,
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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{
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"_name_or_path": "arampacha/wav2vec2-large-xlsr-ukrainian",
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"activation_dropout": 0.1,
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"apply_spec_augment": true,
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"architectures": [
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.004,
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"final_dropout": 0.0,
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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pytorch_model.bin
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