Update README.md

# k2_zipformer2_english_v1

- Zipformer2 recipe derived from : https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/zipformer

- Training data : CommonVoice, VoxPopuli (speed-perturb 3x)
- cca 1500 + 3x500 hours of training data

- Output text/symbols include:
- TrueCase capitalization
- punctuation `[,.?!]` as standalone tokens

## Config:
```
--num-epochs 20 \
--base-lr 0.04 \
\
--causal 1 \
--use-transducer 1 \
--use-ctc 0 \
\
--num-encoder-layers 2,2,2,2,2,2 \
--feedforward-dim 512,768,768,768,768,768 \
--encoder-dim 192,256,256,256,256,256 \
--encoder-unmasked-dim 192,192,192,192,192,192 \
```

## Results

| ID | System | cv-dev | cv-test | vp-dev | vp-test | Comment |
|---|-------------------------|--------|---------|--------|---------|-------------------------------|
| A | small (24M) | 18.57 | 21.98 | 13.66 | 13.26 | ep20,avg4 |

- non-streaming results from [decode.py](https://github.com/BUTSpeechFIT/k2_streaming_training/blob/main/training/zipformer/decode.py)
- cv = CommonVoice, vp = VoxPopuli
- exported the model averaging : ep20,avg4

## Note
- Not the best results, this model is for integration tests

README.md

@@ -1,3 +1,11 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- mozilla-foundation/common_voice_15_0
+- facebook/voxpopuli
+language:
+- en
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+---