# whisper-large-icelandic-10k-steps-1000h

The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" 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- L2-Speakers Data (125h55m) **Unpublished material**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 March (2023) 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 WhisperForConditionalGeneration, WhisperProcessor

#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/whisper-large-icelandic-10k-steps-1000h"
processor = WhisperProcessor.from_pretrained(MODEL_NAME)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda")

#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 map_to_pred(batch):
	audio = batch["audio"]
	input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
	batch["reference"] = processor.tokenizer._normalize(batch['normalized_text'])

	with torch.no_grad():
		predicted_ids = model.generate(input_features.to("cuda"))[0]
	
	transcription = processor.decode(predicted_ids)
	batch["prediction"] = processor.tokenizer._normalize(transcription)
	
	return batch
	
#Do the evaluation
result = ds.map(map_to_pred)

#Compute the overall WER now.
from evaluate import load

wer = load("wer")
WER=100 * wer.compute(references=result["reference"], predictions=result["prediction"])
print(WER)
```
**Test Result**: 12.325364793542379

# BibTeX entry and citation info
*When publishing results based on these models please refer to:*
```bibtex
@misc{mena2023whisperlarge10kicelandic,
      title={Acoustic Model in Icelandic: whisper-large-icelandic-10k-steps-1000h.}, 
      author={Hernandez Mena, Carlos Daniel},
      url={https://huggingface.co/carlosdanielhernandezmena/whisper-large-icelandic-10k-steps-1000h},
      year={2023}
}
```

# 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.

Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.


{"language": "is", "license": "cc-by-4.0", "tags": ["audio", "automatic-speech-recognition", "icelandic", "whisper", "whisper-large", "iceland", "reykjavik", "samromur"], "datasets": ["language-and-voice-lab/samromur_asr", "language-and-voice-lab/samromur_children", "language-and-voice-lab/malromur_asr", "language-and-voice-lab/althingi_asr"], "model-index": [{"name": "whisper-large-icelandic-10k-steps-1000h", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Samr\u00f3mur (Test)", "type": "language-and-voice-lab/samromur_asr", "split": "test", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 11.879, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Samr\u00f3mur (Dev)", "type": "language-and-voice-lab/samromur_asr", "split": "validation", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 10.849, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Samr\u00f3mur Children (Test)", "type": "language-and-voice-lab/samromur_children", "split": "test", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 12.325, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Samr\u00f3mur Children (Dev)", "type": "language-and-voice-lab/samromur_children", "split": "validation", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 8.078, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Malr\u00f3mur (Test)", "type": "language-and-voice-lab/malromur_asr", "split": "test", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 10.132, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Malr\u00f3mur (Dev)", "type": "language-and-voice-lab/malromur_asr", "split": "validation", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 10.157, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Althingi (Test)", "type": "language-and-voice-lab/althingi_asr", "split": "test", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 11.75, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Althingi (Dev)", "type": "language-and-voice-lab/althingi_asr", "split": "validation", "args": {"language": "is"}}, "metrics": [{"type": "wer", "value": 11.141, "name": "WER"}]}]}]}
transformers pytorch whisper automatic-speech-recognition audio icelandic whisper-large iceland reykjavik samromur is dataset:language-and-voice-lab/samromur_asr dataset:language-and-voice-lab/samromur_children dataset:language-and-voice-lab/malromur_asr dataset:language-and-voice-lab/althingi_asr license:cc-by-4.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #icelandic #whisper-large #iceland #reykjavik #samromur #is #dataset-language-and-voice-lab/samromur_asr #dataset-language-and-voice-lab/samromur_children #dataset-language-and-voice-lab/malromur_asr #dataset-language-and-voice-lab/althingi_asr #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 

# whisper-large-icelandic-10k-steps-1000h

The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" with around 1000 hours of Icelandic data developed by the Language and Voice Laboratory. Most of the data is available at public repositories such as LDC, OpenSLR or URL

The specific list of corpora used to fine-tune the model is:

- Samrómur 21.05 (114h34m)
- Samrómur Children (127h25m)
- Malrómur (119hh03m)
- Althingi Parliamentary Speech (514h29m)
- L2-Speakers Data (125h55m) Unpublished material
	
The fine-tuning process was performed during March (2023) in the servers of the Language and Voice Laboratory (URL at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.

# Evaluation

Test Result: 12.325364793542379

# BibTeX entry and citation info
*When publishing results based on these models please refer to:*


# 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.

Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.


# whisper-large-icelandic-10k-steps-1000h

The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" with around 1000 hours of Icelandic data developed by the Language and Voice Laboratory. Most of the data is available at public repositories such as LDC, OpenSLR or URL

The specific list of corpora used to fine-tune the model is:

- Samrómur 21.05 (114h34m)
- Samrómur Children (127h25m)
- Malrómur (119hh03m)
- Althingi Parliamentary Speech (514h29m)
- L2-Speakers Data (125h55m) Unpublished material
	
The fine-tuning process was performed during March (2023) in the servers of the Language and Voice Laboratory (URL at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena. # Evaluation

Test Result: 12.325364793542379 # BibTeX entry and citation info
*When publishing results based on these models please refer to:* # 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.

Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #icelandic #whisper-large #iceland #reykjavik #samromur #is #dataset-language-and-voice-lab/samromur_asr #dataset-language-and-voice-lab/samromur_children #dataset-language-and-voice-lab/malromur_asr #dataset-language-and-voice-lab/althingi_asr #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
 # whisper-large-icelandic-10k-steps-1000h

The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" with around 1000 hours of Icelandic data developed by the Language and Voice Laboratory. Most of the data is available at public repositories such as LDC, OpenSLR or URL

The specific list of corpora used to fine-tune the model is:

- Samrómur 21.05 (114h34m)
- Samrómur Children (127h25m)
- Malrómur (119hh03m)
- Althingi Parliamentary Speech (514h29m)
- L2-Speakers Data (125h55m) Unpublished material
	
The fine-tuning process was performed during March (2023) in the servers of the Language and Voice Laboratory (URL at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena. # Evaluation

Test Result: 12.325364793542379 # BibTeX entry and citation info
*When publishing results based on these models please refer to:* # 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.

Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #icelandic #whisper-large #iceland #reykjavik #samromur #is #dataset-language-and-voice-lab/samromur_asr #dataset-language-and-voice-lab/samromur_children #dataset-language-and-voice-lab/malromur_asr #dataset-language-and-voice-lab/althingi_asr #license-cc-by-4.0 #model-index #endpoints_compatible #region-us 
# whisper-large-icelandic-10k-steps-1000h

The "whisper-large-icelandic-10k-steps-1000h" is an acoustic model suitable for Automatic Speech Recognition in Icelandic. It is the result of fine-tuning the model "openai/whisper-large" with around 1000 hours of Icelandic data developed by the Language and Voice Laboratory. Most of the data is available at public repositories such as LDC, OpenSLR or URL

The specific list of corpora used to fine-tune the model is:

- Samrómur 21.05 (114h34m)
- Samrómur Children (127h25m)
- Malrómur (119hh03m)
- Althingi Parliamentary Speech (514h29m)
- L2-Speakers Data (125h55m) Unpublished material
	
The fine-tuning process was performed during March (2023) in the servers of the Language and Voice Laboratory (URL at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.# Evaluation

Test Result: 12.325364793542379# BibTeX entry and citation info
*When publishing results based on these models please refer to:*# 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.

Special thanks to Björn Ingi Stefánsson for setting up the configuration of the server where this model was trained.