Upload fine-tuned Wav2Vec2BERT CTC model for Czech ASR

README.md

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