Update README.md

README.md

@@ -1,12 +1,57 @@
 ---
+language: 
+- sv-SE
+
 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-1b-Swedish
-  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 sv-SE # Required. Example: Common Voice zh-CN
+      args: sv-SE       # Optional. Example: zh-CN
+    metrics:
+      - type: wer    # Required. Example: wer
+        value: 14.04  # Required. Example: 20.90
+        name: Test WER Without LM   # Optional. Example: Test WER
+        args: 
+        - learning_rate: 7.5e-05
+        - train_batch_size: 32
+        - eval_batch_size: 8
+        - seed: 42
+        - gradient_accumulation_steps: 4
+        - total_train_batch_size: 128
+        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+        - lr_scheduler_type: linear
+        - lr_scheduler_warmup_steps: 1000
+        - num_epochs: 50
+        - mixed_precision_training: Native AMP    # Optional. Example for BLEU: max_order
+      - type: cer    # Required. Example: wer
+        value: 4.86  # Required. Example: 20.90
+        name: Test CER  Without LM   # Optional. Example: Test WER
+        args: 
+        - learning_rate: 7.5e-05
+        - train_batch_size: 32
+        - eval_batch_size: 8
+        - seed: 42
+        - gradient_accumulation_steps: 4
+        - total_train_batch_size: 128
+        - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+        - lr_scheduler_type: linear
+        - lr_scheduler_warmup_steps: 1000
+        - num_epochs: 50
+        - mixed_precision_training: Native AMP
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -16,23 +61,48 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3232
-- Wer: 0.1844
-- Cer: 0.0575
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
 
-## Training procedure
+**Without LM**
+- Loss: 0.3370
+- Wer: 18.44 
+- Cer: 5.75
+
+**With LM**
+- Loss: 0.3370
+- Wer: 14.04 
+- Cer: 4.86
+
+#### 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-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test
+```
+
+2. To evaluate on `speech-recognition-community-v2/dev_data`
+
+```bash
+python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset speech-recognition-community-v2/dev_data --config sv --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 = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish"
+sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sv-SE", 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