
# Wav2Vec2-Large-XLSR-Upper-Sorbian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Upper Sorbian Common Voice dataset](https://huggingface.co/datasets/common_voice), with an 
extra 28 minutes of audio from an online [Sorbian course](https://sprachkurs.sorbischlernen.de/).

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:

```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "hsb", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-upper-sorbian-mixed")
model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-upper-sorbian-mixed")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```


## Evaluation

The model can be evaluated as follows on the Upper Sorbian test data of Common Voice.


```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "ga-IE", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("jimregan/wav2vec2-large-xlsr-upper-sorbian-mixed")
model = Wav2Vec2ForCTC.from_pretrained("jimregan/wav2vec2-large-xlsr-upper-sorbian-mixed")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\?\\\\\\\\\\\\\\\\.\\\\\\\\\\\\\\\\!\\\\\\\\\\\\\\\\-\\\\\\\\\\\\\\\\;\\\\\\\\\\\\\\\\:\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\“\\\\\\\\\\\\\\\\%\\\\\\\\\\\\\\\\‘\\\\\\\\\\\\\\\\”\\\\\\\\\\\\\\\\�„«»–]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = remove_special_characters(batch["sentence"])
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler(speech_array).squeeze().numpy()
    return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
    with torch.no_grad():
        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits    
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_strings"] = processor.batch_decode(pred_ids)
    return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```

**Test Result**: 48.2 %


## Training

The Common Voice `train` and `validation` datasets were used for training, with the vocabulary from the English A1 lesson from an online [Sorbian course](https://sprachkurs.sorbischlernen.de/)

The script used for training can be found [here](https://github.com/jimregan/wav2vec2-sprint/blob/main/upper_sorbian/fine-tune-xlsr-wav2vec2-on-upper-sorbian-asr-with-transformers.ipynb)

The script used for cleaning the transcripts of the vocabulary data is [here](https://github.com/jimregan/wav2vec2-sprint/blob/main/upper_sorbian/sprachkurs.ipynb)
{"language": "hsb", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Upper Sorbian mixed by Jim O'Regan", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice hsb", "type": "common_voice", "args": "hsb"}, "metrics": [{"type": "wer", "value": 43.48, "name": "Test WER"}]}]}]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week hsb dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-Upper-Sorbian

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Upper Sorbian Common Voice dataset, with an 
extra 28 minutes of audio from an online Sorbian course.

When using this model, make sure that your speech input is sampled at 16kHz.

## Usage

The model can be used directly (without a language model) as follows:




## Evaluation

The model can be evaluated as follows on the Upper Sorbian test data of Common Voice.




Test Result: 48.2 %


## Training

The Common Voice 'train' and 'validation' datasets were used for training, with the vocabulary from the English A1 lesson from an online Sorbian course

The script used for training can be found here

The script used for cleaning the transcripts of the vocabulary data is here
# Wav2Vec2-Large-XLSR-Upper-Sorbian

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Upper Sorbian Common Voice dataset, with an 
extra 28 minutes of audio from an online Sorbian course.

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the Upper Sorbian test data of Common Voice.




Test Result: 48.2 % ## Training

The Common Voice 'train' and 'validation' datasets were used for training, with the vocabulary from the English A1 lesson from an online Sorbian course

The script used for training can be found here

The script used for cleaning the transcripts of the vocabulary data is here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-Upper-Sorbian

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Upper Sorbian Common Voice dataset, with an 
extra 28 minutes of audio from an online Sorbian course.

When using this model, make sure that your speech input is sampled at 16kHz. ## Usage

The model can be used directly (without a language model) as follows: ## Evaluation

The model can be evaluated as follows on the Upper Sorbian test data of Common Voice.




Test Result: 48.2 % ## Training

The Common Voice 'train' and 'validation' datasets were used for training, with the vocabulary from the English A1 lesson from an online Sorbian course

The script used for training can be found here

The script used for cleaning the transcripts of the vocabulary data is here
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hsb #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-Upper-Sorbian

Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Upper Sorbian Common Voice dataset, with an 
extra 28 minutes of audio from an online Sorbian course.

When using this model, make sure that your speech input is sampled at 16kHz.## Usage

The model can be used directly (without a language model) as follows:## Evaluation

The model can be evaluated as follows on the Upper Sorbian test data of Common Voice.




Test Result: 48.2 %## Training

The Common Voice 'train' and 'validation' datasets were used for training, with the vocabulary from the English A1 lesson from an online Sorbian course

The script used for training can be found here

The script used for cleaning the transcripts of the vocabulary data is here