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---
language: ga
datasets:
- common_voice
- living-audio-Irish
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- ga-IE
- speech
- Irish
- Gaelic
model-index:
- name: Wav2vec 2.0 large 300m XLS-R
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 10.0
type: common_voice
args: ga-IE
metrics:
- name: Test WER
type: wer
value: 25.94
---
# Irish-Gaelic Automatic Speech Recognition
This is the model for Irish ASR. It has been trained on the Common-voice dataset and living Irish audio dataset. The Common-voice code for the Irish language is ga-IE. From the Common voice dataset, all the Validated audio clips and all the living audio clips were taken into account and after a random train-test split, 90% of the total dataset (5156 utterances) were taken for training, and the rest of the 10% of real data (579 utterances) were taken for testing.
This dataset was finetuned on wav2vec2-large-xls-r-300m. On the testing dataset, 25.94% of WER could be achieved.
### How to use
Example of transcribing the Common Voice audio clip from the invalidated dataset, using GPU if available. The model expects 16kHz audio.
```python
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
model = Wav2Vec2ForCTC.from_pretrained("Aditya3107/wav2vec2-large-xls-r-1b-ga-ie")
processor = Wav2Vec2Processor.from_pretrained("Aditya3107/wav2vec2-large-xls-r-1b-ga-ie")
# Reading taken audio clip
import librosa, torch
audio, rate = librosa.load("common-voice-irish/common_voice/cv-corpus-10.0-2022-07-04/ga-IE/clips/common_voice_ga-IE_1818627.mp3", sr = 16000)
# Taking an input value
input_values = processor(audio, sampling_rate=16_000, return_tensors = "pt", padding="longest").input_values
# Storing logits (non-normalized prediction values)
logits = model(input_values).logits
# Storing predicted ids
prediction = torch.argmax(logits, dim = -1)
# Passing the prediction to the tokenizer decode to get the transcription
transcription = processor.batch_decode(prediction)[0]
print(transcription)
```
### Results
Example of the transcribed audio clips and testing on SCLITE.
```
Speaker sentences 0: #utts: 1
id: (common_voice_ga-IE_17401296.mp3)
Scores: (#C #S #D #I) 4 1 0 0
Attributes: Case_sensitve
REF: an bhfuil cóta bán óir
HYP: an bhfuil cóta bán air
Eval: S
id: (common_voice_ga-IE_17410244.mp3)
Scores: (#C #S #D #I) 3 1 0 2
Attributes: Case_sensitve
REF: *** ** an bud é sin
HYP: cad é an rud é sin
Eval: I I S
id: (common_voice_ga-IE_17410257.mp3)
Scores: (#C #S #D #I) 9 2 1 2
Attributes: Case_sensitve
REF: i gabhaim buíochas libh a chairde ******* ** támindéagtstruth le tuilleadh uaibh ar baá
HYP: * gabhaim buíochas libh a chairde táimid ag tsnúth le tuilleadh uaibh ar ball
Eval: D I I S S
id: (common_voice_ga-IE_17410401.mp3)
Scores: (#C #S #D #I) 6 1 0 0
Attributes: Case_sensitve
REF: níl ach tá peann ina phóca uige
HYP: níl ach tá peann ina phóca aige
Eval: S
id: (common_voice_ga-IE_17410403.mp3)
Scores: (#C #S #D #I) 5 1 0 1
Attributes: Case_sensitve
REF: agus *** cadé an dath atá air
HYP: agus cad é an dath atá air
Eval: I S
id: (common_voice_ga-IE_17410412.mp3)
Scores: (#C #S #D #I) 6 2 0 0
Attributes: Case_sensitve
REF: is lá é seo chun ceiliúradh a dhéan
HYP: is lá é seo chun céiliúradh a dhéanamh
Eval: S S
id: (common_voice_ga-IE_17444712.mp3)
Scores: (#C #S #D #I) 4 6 0 0
Attributes: Case_sensitve
REF: don chathaoileach mirín de brom don stiúrdhóirat liam ón maoladha
HYP: don chathaoirleach máirín de brún don stiúrthóir liam ó maolaodha
Eval: S S S S S S
id: (common_voice_ga-IE_17449454.mp3)
Scores: (#C #S #D #I) 4 0 0 0
Attributes: Case_sensitve
REF: ceacht a trí déag
HYP: ceacht a trí déag
Eval:
```
### Future Tasks
The language model with KenLM will be added if any good resource of Irish text is found.
### Citation
If you want to cite this model you can use this:
```
@MISC {,
author = "Aditya Parikh",
title = "Finetuned XLS-R model for Irish (Ga-IE) language for Automatic Speech Recognition",
howpublished = "{\url{https://huggingface.co/Aditya3107/wav2vec2-large-xls-r-1b-ga-ie}}",
month = "aug",
year = "2022"
}
``` |