Adding info to the Readme file

README.md

@@ -1,3 +1,211 @@
 ---
+language: is
+datasets:
+- samromur
+- samromur_children
+- malromur
+- althingi
+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-ep10-1000h
+  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: ???
+  - 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: ???
+  - 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: ???
+  - 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: ???
+  - 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: ???
+  - 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: ???
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Althingi (Test)
+      type: althingi_test
+      split: test
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: ???
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Althingi (Dev)
+      type: althingi_dev
+      split: validation
+      args: 
+        language: is
+    metrics:
+    - name: WER
+      type: wer
+      value: ???
 ---
+# wav2vec2-large-xlsr-53-icelandic-ep10-1000h
+
+The "wav2vec2-large-xlsr-53-icelandic-ep10-1000h" 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" for 10 epochs with around 1000 hours of Icelandic data developed by the [Language and Voice Laboratory](https://huggingface.co/language-and-voice-lab). Most of the data is available at public repositories such as [LDC](https://www.ldc.upenn.edu/), [OpenSLR](https://openslr.org/) or [Clarin.is](https://clarin.is/)
+
+The specific list of corpora used to fine-tune the model is:
+
+- [Samrómur 21.05 (114h34m)](http://www.openslr.org/112/)
+- [Samrómur Children (127h25m)](https://catalog.ldc.upenn.edu/LDC2022S11)
+- [Malrómur (119hh03m)](https://clarin.is/en/resources/malromur/)
+- [Althingi Parliamentary Speech (514h29m)](https://catalog.ldc.upenn.edu/LDC2021S01)
+- L2-Speakers Data (125h55m) **Unpublished material**
+	
+The fine-tuning process was performed during December (2022) in the servers of the Language and Voice Laboratory (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-icelandic-ep10-1000h"
+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**: ???
+
+# BibTeX entry and citation info
+*When publishing results based on these models please refer to:*
+```bibtex
+@misc{mena2022xlrs53icelandic,
+      title={Acoustic Model in Icelandic: wav2vec2-large-xlsr-53-icelandic-ep10-1000h.}, 
+      author={Hernandez Mena, Carlos Daniel},
+      year={2022},
+      url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-icelandic-ep10-1000h},
+}
+```
+
+# Acknowledgements
+
+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.