Adding info to the README file.

README.md

@@ -1,3 +1,207 @@
 ---
+language: is
+datasets:
+- language-and-voice-lab/samromur_milljon
+tags:
+- audio
+- automatic-speech-recognition
+- icelandic
+- xlrs-53-icelandic
+- iceland
+- reykjavik
+- samromur
 license: cc-by-4.0
+widget:
+model-index:
+- name: wav2vec2-large-xlsr-53-icelandic-ep30-967h
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Samrómur (Test)
+      type: language-and-voice-lab/samromur_asr
+      split: test
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 7.698
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Samrómur (Dev)
+      type: language-and-voice-lab/samromur_asr
+      split: validation
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 6.786
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Samrómur Children (Test)
+      type: language-and-voice-lab/samromur_children
+      split: test
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 6.467
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Samrómur Children (Dev)
+      type: language-and-voice-lab/samromur_children
+      split: validation
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 4.234
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Malrómur (Test)
+      type: language-and-voice-lab/malromur_asr
+      split: test
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 6.631
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Malrómur (Dev)
+      type: language-and-voice-lab/malromur_asr
+      split: validation
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 5.836
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Althingi (Test)
+      type: language-and-voice-lab/althingi_asr
+      split: test
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 17.904
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Althingi (Dev)
+      type: language-and-voice-lab/althingi_asr
+      split: validation
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: 17.931
 ---
+# wav2vec2-large-xlsr-53-icelandic-ep30-967h
+
+The "wav2vec2-large-xlsr-53-icelandic-ep30-967h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for 30 epochs with 967 hours of Icelandic data collected by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab) through the platform [Samrómur](https://samromur.is/).
+
+The specific data that was used to fine-tune the model is the corpus [Samrómur Milljón](https://huggingface.co/datasets/language-and-voice-lab/samromur_milljon), which is the result of the automatic verification of 1 million of recordings comming from the corpus ["Samromur Unverified 22.07"](http://hdl.handle.net/20.500.12537/265). It has to be pointed out that this model was trained with different data than our previous model [
+wav2vec2-large-xlsr-53-icelandic-ep10-1000h ](https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h).
+	
+The fine-tuning process was performed during July (2023) in the servers of the Language and Voice Laboratory (https://lvl.ru.is/) at Reykjavík University (Iceland) by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena).
+
+# Evaluation
+```python
+import torch
+from transformers import Wav2Vec2Processor
+from transformers import Wav2Vec2ForCTC
+
+#Load the processor and model.
+MODEL_NAME="language-and-voice-lab/wav2vec2-large-xlsr-53-icelandic-ep30-967h"
+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("language-and-voice-lab/samromur_children", split="test")
+
+#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["normalized_text"]).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**: 0.076
+
+# BibTeX entry and citation info
+*When publishing results based on these models please refer to:*
+```bibtex
+@misc{mena2023xlrs53icelandic30ep967h,
+      title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep30-967h.}, 
+      author={Hernandez Mena, Carlos Daniel},
+      year={2023},
+      url={https://huggingface.co/language-and-voice-lab/wav2vec2-large-xlsr-53-icelandic-ep30-967h},
+}
+```
+
+# Acknowledgements
+
+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. This model is an unexpected result of all the resources gathered by the Programme. 
+
+Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.