Update README.md

README.md

@@ -6,17 +6,46 @@ tags:
 - automatic-speech-recognition
 - mozilla-foundation/common_voice_8_0
 - generated_from_trainer
+- robust-speech-event
 datasets:
-- common_voice
+- mozilla-foundation/common_voice_8_0
 model-index:
-- name: ''
-  results: []
+- name: XLS-R-300M - Slovak
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8
+      type: mozilla-foundation/common_voice_8_0
+      args: sk
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 18.609
+       - name: Test CER
+         type: cer
+         value: 5.488
+  - task: 
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Robust Speech Event - Dev Data
+      type: speech-recognition-community-v2/dev_data
+      args: sk
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 40.548
+       - name: Test CER
+         type: cer
+         value: 17.733
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# 
+# XLS-R-300M - Slovak
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SK dataset.
 It achieves the following results on the evaluation set:
@@ -86,3 +115,42 @@ The following hyperparameters were used during training:
 - Pytorch 1.10.2+cu102
 - Datasets 1.18.4.dev0
 - Tokenizers 0.11.0
+
+#### Evaluation Commands
+1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
+
+```bash
+python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config sk --split test
+```
+
+2. To evaluate on `speech-recognition-community-v2/dev_data`
+
+```bash
+python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config sk --split validation --chunk_length_s 5.0 --stride_length_s 1.0
+```
+
+### Inference With LM
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCTC, AutoProcessor
+import torchaudio.functional as F
+model_id = "anuragshas/wav2vec2-xls-r-300m-sk-cv8-with-lm"
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sk", 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
+# => ""
+```
+
+### Eval results on Common Voice 8 "test" (WER):
+
+| Without LM | With LM (run `./eval.py`) |
+|---|---|
+| 26.707 | 18.609 |