Update README.md

README.md

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----
-license: mpl-2.0
----
+---
+license: mpl-2.0
+language:
+- en
+base_model:
+- KoelLabs/xlsr-timit-b0
+tags:
+- phoneme
+---
+This model is just a conversion of [KoelLabs/xlsr-timit-b0](https://huggingface.co/KoelLabs/xlsr-timit-b0)
+
+The original model card is below
+
+---
+
+# XLSR-TIMIT-B0: Fine-tuned on TIMIT for Phonemic Transcription
+
+This model leverages the pretrained checkpoint [ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa](https://huggingface.co/ginic/data_seed_4_wav2vec2-large-xlsr-buckeye-ipa) and is fine-tuned on the [TIMIT Darpa English Corpus](https://github.com/philipperemy/timit) to transcribe audio into phonemic representations for the English language.
+
+All code is available on [Github](https://github.com/KoelLabs/ML/blob/main/notebooks/TIMIT_Finetune_Ginic.ipynb)
+
+This model outperforms all current xlsr IPA transcription models for English
+
+**Performance**
+- Training Loss: 1.254  
+- Validation Loss: 0.267  
+- Test Results (TIMIT test set):
+  - Average Weighted Distance: 13.309375  
+  - Standard Deviation (Weighted Distance): 9.87
+  - Average Character Error Rate (CER): 0.113 
+  - Standard Deviation (CER): 0.06
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61dd07bafdc070745eed96fd/yXgLgGzlYvTMejmrhu2Y9.png)
+    
+**Model Information**
+- Number of Epochs: 40
+- Learning Rate: 8e-5
+- Optimizer: Adam
+- Datasets Used: TIMIT, Darpa English Corpus
+
+**Example Outputs**
+1. **Prediction**: `lizteɪkðɪsdɹɾiteɪbklɔθiðiklinizfɹmi`  
+   **Ground Truth**: `lizteɪkðɪsdɹɾiteɪbəklɔtiðiklinizfɹmi`  
+   **Weighted Feature Edit Distance**: 7.875  
+   **CER**: 0.0556  
+
+2. **Prediction**: `ɹænmʌðɹʔaʊtɹuhɹʔʌpɹɪŋiɾimpɛɾikoʊts`  
+   **Ground Truth**: `ɹænmʌðɹʔaʊtɹuhɹʔʌpɹɪŋiŋinpɛɾikoʊts`  
+   **Weighted Feature Edit Distance**: 2.375  
+   **CER**: 0.0588  
+
+## Limitations
+
+This phonemic transcription model is fine-tuned on an English speech corpus that does not encompass all dialects and languages. We acknowledge that it may significantly underperform for any unseen languages. We aim to release models and datasets that better serve all populations and languages in the future.
+
+
+---
+
+# Usage
+
+To transcribe audio files, this model can be used as follows:
+
+```python
+from transformers import AutoModelForCTC, AutoProcessor
+import torch
+
+# Load model and processor
+model = AutoModelForCTC.from_pretrained("KoelLabs/xlsr-timit-b0")
+processor = AutoProcessor.from_pretrained("KoelLabs/xlsr-timit-b0")
+
+# Prepare input
+audio_input = "path_to_your_audio_file.wav"  # Replace with your file
+input_values = processor(audio_input, return_tensors="pt", sampling_rate=16000).input_values
+
+# Retrieve logits
+with torch.no_grad():
+    logits = model(input_values).logits
+
+# Decode predictions
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
+print(transcription)