
# wav2vec2-xls-r-slavic-pomak

To train a Pomak ASR model, we fine-tuned a Slavic model ([classla/wav2vec2-large-slavic-parlaspeech-hr](https://huggingface.co/classla/wav2vec2-large-slavic-parlaspeech-hr)) on 11h of recorded Pomak speech.

## Recordings

Fours native Pomak speakers (2 female and 2 male) agreed to read Pomak texts at the ILSP audio-visual studio in Xanthi, Greece, resulting in a total of 14h.

|Speaker|Gender|Total recorded hours|
|---|---|---|
|NK9dIF | F | 4h 44m 45s|
|xoVY9q | M | 4h 36m 12s|
|9G75fk | F | 1h 44m 03s|
|n5WzHj | M | 3h 44m 04s|

To fine-tune the model, we split the long recordings into smaller segments of a maximum of 25 seconds each.
This removed the majority of pauses and resulted in a total dataset duration of 11h 8m.

## Metrics

The test set consists of 10% of the dataset recordings.

|Model|CER|WER|
|---|---|---|
|pre-trained|87.31%|31.47%|
|fine-tuned|9.06%|3.12%|

## Training hyperparameters

To fine-tune the wav2vec2-large-slavic-parlaspeech-hr model, we used the following hyperparameters:

| arg                           | value |
|-------------------------------|-------|
| `per_device_train_batch_size` | 8     |
| `gradient_accumulation_steps` | 2     |
| `num_train_epochs`            | 35    |
| `learning_rate`               | 3e-4  |
| `warmup_steps`                | 500   |
{"library_name": "transformers", "tags": ["Pomak", "Slavic"], "metrics": ["wer", "cer"], "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch wav2vec2 automatic-speech-recognition Pomak Slavic endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Pomak #Slavic #endpoints_compatible #region-us 

wav2vec2-xls-r-slavic-pomak
===========================


To train a Pomak ASR model, we fine-tuned a Slavic model (classla/wav2vec2-large-slavic-parlaspeech-hr) on 11h of recorded Pomak speech.


Recordings
----------


Fours native Pomak speakers (2 female and 2 male) agreed to read Pomak texts at the ILSP audio-visual studio in Xanthi, Greece, resulting in a total of 14h.


Speaker: NK9dIF, Gender: F, Total recorded hours: 4h 44m 45s
Speaker: xoVY9q, Gender: M, Total recorded hours: 4h 36m 12s
Speaker: 9G75fk, Gender: F, Total recorded hours: 1h 44m 03s
Speaker: n5WzHj, Gender: M, Total recorded hours: 3h 44m 04s


To fine-tune the model, we split the long recordings into smaller segments of a maximum of 25 seconds each.
This removed the majority of pauses and resulted in a total dataset duration of 11h 8m.


Metrics
-------


The test set consists of 10% of the dataset recordings.


Model: pre-trained, CER: 87.31%, WER: 31.47%
Model: fine-tuned, CER: 9.06%, WER: 3.12%


Training hyperparameters
------------------------


To fine-tune the wav2vec2-large-slavic-parlaspeech-hr model, we used the following hyperparameters:




TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Pomak #Slavic #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #Pomak #Slavic #endpoints_compatible #region-us 
