Adding information to the Readme file

This is the information of the model card that I wrote in my local machine. I hope not to be in the need of adding more information or more make changes in the future.

README.md

@@ -1,3 +1,133 @@
 ---
+language: mt
+datasets:
+- common_voice
+tags:
+- audio
+- automatic-speech-recognition
+- maltese
+- xlrs-53-maltese
+- masri-project
+- malta
+- university-of-malta
 license: cc-by-4.0
+widget:
+model-index:
+- name: wav2vec2-large-xlsr-53-maltese-64h
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice mt
+      type: common_voice
+      split: test
+      args: 
+        language: mt
+    metrics:
+    - name: Test WER
+      type: wer
+      value: 0.011
 ---
+
+# wav2vec2-large-xlsr-53-maltese-64h
+
+The "wav2vec2-large-xlsr-53-maltese-64h" is an acoustic model suitable for Automatic Speech Recognition in Maltese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with around 64 hours of Maltese data developed by the MASRI Project at the University of Malta between 2019 and 2021. Most of the data is available at the the MASRI Project homepage https://www.um.edu.mt/projects/masri/.
+
+The specific list of corpora used to fine-tune the model is:
+
+- MASRI-HEADSET v2 (6h39m)
+- MASRI-Farfield (9h37m)
+- MASRI-Booths (2h27m)
+- MASRI-MEP (1h17m)
+- MASRI-COMVO (7h29m)
+- MASRI-TUBE (13h17m)
+- MASRI-MERLIN (25h18m) *Not available at the MASRI Project homepage
+	
+The fine-tuning process was perform during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.
+
+# Evaluation
+
+```python
+import torch
+from transformers import Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC
+
+#Load the processor and model.
+MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-maltese-64h"
+processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
+
+#Load the dataset
+from datasets import load_dataset, load_metric, Audio
+ds=load_dataset("common_voice", "mt", split="test")
+
+#Normalize the transcriptions
+import re
+chars_to_ignore_regex = '[\\,\\?\\.\\!\\\;\\:\\"\\“\\%\\‘\\”\\�\\)\\(\\*)]'
+def remove_special_characters(batch):
+	batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+	return batch
+ds = ds.map(remove_special_characters)
+
+#Downsample to 16kHz
+ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+
+#Process the dataset
+def prepare_dataset(batch):
+	audio = batch["audio"]
+	#Batched output is "un-batched" to ensure mapping is correct
+	batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
+	with processor.as_target_processor():
+		batch["labels"] = processor(batch["sentence"]).input_ids
+	return batch
+ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)
+
+#Define the evaluation metric
+import numpy as np
+wer_metric = load_metric("wer")
+def compute_metrics(pred):
+	pred_logits = pred.predictions
+	pred_ids = np.argmax(pred_logits, axis=-1)
+	pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
+	pred_str = processor.batch_decode(pred_ids)
+	#We do not want to group tokens when computing the metrics
+	label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
+	wer = wer_metric.compute(predictions=pred_str, references=label_str)
+	return {"wer": wer}
+
+#Do the evaluation (with batch_size=1)
+model = model.to(torch.device("cuda"))
+def map_to_result(batch):
+	with torch.no_grad():
+		input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
+		logits = model(input_values).logits
+	pred_ids = torch.argmax(logits, dim=-1)
+	batch["pred_str"] = processor.batch_decode(pred_ids)[0]
+	batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
+	return batch
+results = ds.map(map_to_result,remove_columns=ds.column_names)
+
+#Compute the overall WER now.
+print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))
+
+```
+**Test Result**: 0.011
+
+# BibTeX entry and citation info
+*When publishing results based on these models please refer to:*
+```bibtex
+@misc{mena2022xlrs53maltese,
+      title={Acustic Model in Maltese: wav2vec2-large-xlsr-53-maltese-64h.}, 
+      author={Hernandez Mena, Carlos Daniel},
+      year={2022},
+      url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-maltese-64h},
+}
+```
+
+# Acknowledgements
+
+The MASRI Project is funded by the University of Malta Research Fund Awards. We want to thank to Merlin Publishers (Malta) for provinding the audiobooks used to create the MASRI-MERLIN Corpus.
+
+Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. We also want to thank to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.
+