Update README.md

README.md

@@ -16,7 +16,42 @@ The model was trained using fairseq with [this config](https://github.com/centre
 
 
 ## Usage
+```Python
+import torch
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# load model and tokenizer
+processor = Wav2Vec2Processor.from_pretrained(
+    "chcaa/xls-r-300m-danish-nst-cv9")
+model = Wav2Vec2ForCTC.from_pretrained(
+    "chcaa/xls-r-300m-danish-nst-cv9")
+
+# load dataset and read soundfiles
+ds = load_dataset("Alvenir/alvenir_asr_da_eval", split="test")
+
+# tokenize
+input_values = processor(
+    ds[0]["audio"]["array"], return_tensors="pt", padding="longest"
+).input_values  # Batch size 1
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)
+print(transcription)
+```
 
 ## Performance
+The table below shows the WER rate of four different Danish ASR models on three datasets.
+
+|Model                                  | [Alvenir](https://huggingface.co/datasets/Alvenir/alvenir_asr_da_eval)|   [NST](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-19/)|     [CV9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0)|
+|:--------------------------------------|------:|-----:|-----:|
+|Alvenir/wav2vec2-base-da-ft-nst        |  0.202| 0.099| 0.238|
+|chcaa/alvenir-wav2vec2-base-da-nst-cv9 |  0.233| 0.126| 0.256|
+|chcaa/xls-r-300m-nst-cv9-da            |  0.105| 0.060| 0.119|
+|chcaa/xls-r-300m-danish-nst-cv9        |  0.082| 0.051| 0.108|
 
 The model was finetuned in collaboration with [Alvenir](https://alvenir.ai).