Update README.md

README.md

@@ -1,12 +1,35 @@
 ---
+language: 
+- tt 
+
 license: apache-2.0
 tags:
-- generated_from_trainer
+- automatic-speech-recognition
+- robust-speech-event
 datasets:
-- common_voice
+- mozilla-foundation/common_voice_8_0
+metrics:
+- wer
+- cer
 model-index:
 - name: wav2vec2-large-xls-r-300m-Tatar
-  results: []
+  results:
+  - task: 
+      type: automatic-speech-recognition  # Required. Example: automatic-speech-recognition
+      name: Speech Recognition  # Optional. Example: Speech Recognition
+    dataset:
+      type: mozilla-foundation/common_voice_8_0  # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
+      name: Common Voice tt # Required. Example: Common Voice zh-CN
+      args: tt        # Optional. Example: zh-CN
+    metrics:
+      - type: wer    # Required. Example: wer
+        value: 42.82 # Required. Example: 20.90
+        name: Test WER With LM   # Optional. Example: Test WER
+        
+      - type: cer    # Required. Example: wer
+        value: 11.24  # Required. Example: 20.90
+        name: Test CER With LM   # Optional. Example: Test WER
+        
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -20,19 +43,32 @@ It achieves the following results on the evaluation set:
 - Wer: 0.4263
 - Cer: 0.1117
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
+#### Evaluation Commands
+1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
+
+```bash
+python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Tatar --dataset mozilla-foundation/common_voice_8_0 --config tt --split test
+```
+
+### Inference With LM
+
+```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-Tatar"
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "tt", 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
+
+```
 
 ### Training hyperparameters