Update README.md

README.md

@@ -1,12 +1,33 @@
 ---
+---
+language: 
+- ur
 license: apache-2.0
 tags:
 - generated_from_trainer
+- robust-speech-event
 datasets:
-- common_voice
+- mozilla-foundation/common_voice_8_0
+metrics:
+- wer
 model-index:
 - name: wav2vec2-large-xls-r-300m-Urdu
-  results: []
+  results:
+  - task: 
+      type: automatic-speech-recognition
+      name: Speech Recognition
+    dataset:
+      type: mozilla-foundation/common_voice_8_0
+      name: Common Voice 8
+      args: ur
+    metrics:
+      - type: wer    # Required. Example: wer
+        value: 39.89  # Required. Example: 20.90
+        name: Test WER    # Optional. Example: Test WER
+      - name: Test CER
+        type: cer
+        value: 16.70
+---
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -19,20 +40,35 @@ It achieves the following results on the evaluation set:
 - Loss: 0.9889
 - Wer: 0.5607
 - Cer: 0.2370
+#### Evaluation Commands
+1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
 
-## Model description
-
-More information needed
+```bash
+python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Urdu --dataset mozilla-foundation/common_voice_8_0 --config ur --split test
+```
 
-## Intended uses & limitations
 
-More information needed
+### Inference With LM
 
-## Training and evaluation data
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCTC, AutoProcessor
+import torchaudio.functional as F
+model_id = "kingabzpro/wav2vec2-large-xls-r-300m-Urdu"
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ur", split="test", streaming=True, use_auth_token=True))
+sample = next(sample_iter)
+resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
+model = AutoModelForCTC.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+input_values = processor(resampled_audio, return_tensors="pt").input_values
+with torch.no_grad():
+    logits = model(input_values).logits
+transcription = processor.batch_decode(logits.numpy()).text
+# => "اب نے ٹپیدسون دیتے ہیں"
+```
 
-More information needed
 
-## Training procedure
 
 ### Training hyperparameters
 
@@ -66,3 +102,9 @@ The following hyperparameters were used during training:
 - Pytorch 1.10.2+cu102
 - Datasets 1.18.2.dev0
 - Tokenizers 0.11.0
+
+### Eval results on Common Voice 8 "test" (WER):
+
+| Without LM | With LM (run `./eval.py`) |
+|---|---|
+| 52.03 | 39.89 |