Update README.md

README.md

@@ -1,3 +1,76 @@
 ---
+language: 
+ - cy
+ - en
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- automatic-speech-recognition
+- speech
 license: apache-2.0
+model-index:
+- name: wav2vec2-xlsr-ft-en-cy
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice cy
+      type: common_voice
+      args: cy
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 17.70%
 ---
+
+# wav2vec2-xlsr-ft-en-cy
+
+A speech recognition acoustic model for Welsh and English, fine-tuned from [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) using English/Welsh balanced data derived from version 11 of their respective Common Voice datasets (https://commonvoice.mozilla.org/cy/datasets). Custom bilingual Common Voice train/dev and test splits were built using the scripts at https://github.com/techiaith/docker-commonvoice-custom-splits-builder#introduction
+
+Source code and scripts for training wav2vec2-xlsr-ft-en-cy can be found at [https://github.com/techiaith/docker-wav2vec2-cy](https://github.com/techiaith/docker-wav2vec2-cy/blob/main/train/fine-tune/python/run_en_cy.sh). 
+
+
+
+## Usage
+
+The wav2vec2-xlsr-ft-en-cy model can be used directly as follows:
+
+```python
+import torch
+import torchaudio
+import librosa
+
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-ft-en-cy")
+model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-en-cy")
+
+audio, rate = librosa.load(audio_file, sr=16000)
+
+inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+  tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+# greedy decoding
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+
+```
+
+## Evaluation
+
+
+According to a balanced English+Welsh test set derived from Common Voice version 11, the WER of techiaith/wav2vec2-xlsr-ft-en-cy is **17.7%**
+
+However, when evaluated with language specific test sets, the model exhibits a bias to perform better with Welsh.  
+
+| Common Voice Test Set Language | WER | CER | 
+| -------- | --- | --- | 
+| EN+CY | 17.07| 7.32  | 
+| EN | 27.54  | 11.6  |
+| CY | 7.13  | 2.2  |