Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`facebook/wav2vec2-xls-r-300m`](https://huggingface.co/facebook/wav2vec2-xls-r-300m) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.

How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien).

If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!

README.md

@@ -10,122 +10,123 @@ tags:
 - hf-asr-leaderboard
 datasets:
 - common_voice
+base_model: facebook/wav2vec2-xls-r-300m
 model-index:
 - name: Slovak comodoro Wav2Vec2 XLSR 300M CV8
   results:
   - task:
-      name: Automatic Speech Recognition
       type: automatic-speech-recognition
+      name: Automatic Speech Recognition
     dataset:
       name: Common Voice 8
       type: mozilla-foundation/common_voice_8_0
       args: sk
     metrics:
-    - name: Test WER
-      type: wer
+    - type: wer
       value: 49.6
-    - name: Test CER
-      type: cer
+      name: Test WER
+    - type: cer
       value: 13.3
+      name: Test CER
   - task:
-      name: Automatic Speech Recognition
       type: automatic-speech-recognition
+      name: 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
+    - type: wer
       value: 81.7
+      name: Test WER
   - task:
-      name: Automatic Speech Recognition
       type: automatic-speech-recognition
+      name: Automatic Speech Recognition
     dataset:
       name: Robust Speech Event - Test Data
       type: speech-recognition-community-v2/eval_data
       args: sk
     metrics:
-    - name: Test WER
-      type: wer
+    - type: wer
       value: 80.26
+      name: Test WER
 ---
 
-
-# wav2vec2-xls-r-300m-cs-cv8
-
-This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset.
-It achieves the following results on the evaluation set:
-
-- WER: 0.49575384615384616
-- CER: 0.13333333333333333
-
-## Usage
-
-The model can be used directly (without a language model) as follows:
-
-```python
-import torch
-import torchaudio
-from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-
-test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test[:2%]")
-
-processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
-model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
-
-resampler = torchaudio.transforms.Resample(48_000, 16_000)
-
-# Preprocessing the datasets.
-# We need to read the aduio files as arrays
-def speech_file_to_array_fn(batch):
-	speech_array, sampling_rate = torchaudio.load(batch["path"])
-	batch["speech"] = resampler(speech_array).squeeze().numpy()
-	return batch
-
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
-
-with torch.no_grad():
-	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-
-print("Prediction:", processor.batch_decode(predicted_ids))
-print("Reference:", test_dataset[:2]["sentence"])
-```
-
-## Evaluation
-
-The model can be evaluated using the attached `eval.py` script:
-```
-python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sk-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config sk
-```
-
-## Training and evaluation data
-
-The Common Voice 8.0 `train` and `validation` datasets were used for training
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-
-- learning_rate: 7e-4
-- train_batch_size: 32
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 20
-- total_train_batch_size: 640
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 500
-- num_epochs: 50
-- mixed_precision_training: Native AMP
-
-### Framework versions
-
-- Transformers 4.16.0.dev0
-- Pytorch 1.10.1+cu102
-- Datasets 1.17.1.dev0
-- Tokenizers 0.11.0
+
+# wav2vec2-xls-r-300m-cs-cv8
+
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice 8.0 dataset.
+It achieves the following results on the evaluation set:
+
+- WER: 0.49575384615384616
+- CER: 0.13333333333333333
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
+model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+	speech_array, sampling_rate = torchaudio.load(batch["path"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset[:2]["sentence"])
+```
+
+## Evaluation
+
+The model can be evaluated using the attached `eval.py` script:
+```
+python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sk-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config sk
+```
+
+## Training and evaluation data
+
+The Common Voice 8.0 `train` and `validation` datasets were used for training
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- learning_rate: 7e-4
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 20
+- total_train_batch_size: 640
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 50
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.1+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.11.0