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--- |
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language: |
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- cs |
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- en |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- ctc |
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- wav2vec2-bert |
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- czech |
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license: mit |
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datasets: |
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- common-voice |
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metric: |
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- wer |
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--- |
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# mitkaj/w2v2BERT-CZ-CV-17.0 |
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This is a fine-tuned Wav2Vec2BERT model for Czech Automatic Speech Recognition (ASR) using CTC loss. |
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## Model Details |
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- **Base Model**: facebook/w2v-bert-2.0 |
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- **Architecture**: Wav2Vec2BertForCTC |
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- **Training**: Fine-tuned on Czech Common Voice dataset |
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- **Loss Function**: CTC (Connectionist Temporal Classification) |
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- **Vocab Size**: 51 tokens |
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## Training Summary |
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- **Training Epochs**: 19.97 |
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- **Final Training Loss**: 0.0305 |
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- **Final Evaluation Loss**: 0.1450 |
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- **Final WER**: 0.0583 (5.83%) |
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- **Total Training Time**: 5.1 hours |
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- **Total FLOPS**: 79819834495052513280 GF |
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## Usage |
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```python |
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from transformers import AutoProcessor, AutoModelForCTC |
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import torch |
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# Load model and processor |
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processor = AutoProcessor.from_pretrained("mitkaj/w2v2BERT-CZ-CV-17.0") |
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model = AutoModelForCTC.from_pretrained("mitkaj/w2v2BERT-CZ-CV-17.0") |
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# Process audio |
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inputs = processor(audio, sampling_rate=16000, return_tensors="pt") |
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# Get logits |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# Decode |
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predicted_ids = torch.argmax(logits, dim=-1) |
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transcription = processor.batch_decode(predicted_ids) |
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``` |
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## Training |
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This model was trained using the CTC approach on Czech speech data. |
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## Performance |
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The model was evaluated on Czech test data using WER (Word Error Rate) metric. |
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## Citation |
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If you use this model, please cite the original Wav2Vec2BERT paper and this fine-tuned version. |
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