
# Wav2Vec2-Large-XLSR-53-Estonian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Estonian using the [Common Voice](https://huggingface.co/datasets/common_voice).
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", "et", split="test[:2%]") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.

processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`

resampler = torchaudio.transforms.Resample(48_000, 16_000)

# Preprocessing the datasets.
# We need to read the aduio 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 Estonian 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", "et", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian")
model = Wav2Vec2ForCTC.from_pretrained("vasilis/wav2vec2-large-xlsr-53-Estonian")
model.to("cuda")

chars_to_ignore_regex = "[\,\?\.\!\-\;\:\"\“\%\‘\”\�\']"  # TODO: adapt this list to include all special characters you removed from the data

resampler = {
    48_000: torchaudio.transforms.Resample(48_000, 16_000),
    44100: torchaudio.transforms.Resample(44100, 16_000),
    32000: torchaudio.transforms.Resample(32000, 16_000)
}

# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
    speech_array, sampling_rate = torchaudio.load(batch["path"])
    batch["speech"] = resampler[sampling_rate](speech_array).squeeze().numpy()
    return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)

# Preprocessing the datasets.
# We need to read the aduio 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"])))
print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))

```

**Test Result**:  30.658320 %

## Training

Common voice `train` and `validation` sets were used for finetuning
for 20000 steps (approx. 116 epochs).  Both the `feature extractor` (`Wav2Vec2FeatureExtractor`) and
`feature projection` (`Wav2Vec2FeatureProjection`) layer were frozen. Only the `encoder` layer (`Wav2Vec2EncoderStableLayerNorm`) was finetuned.  




{"language": "et", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "NST Estonian ASR Database"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLSR Wav2Vec2 Large 53 - Estonian by Vasilis", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice et", "type": "common_voice", "args": "et"}, "metrics": [{"type": "wer", "value": 30.65832, "name": "Test WER"}, {"type": "cer", "value": 5.26149, "name": "Test CER"}]}]}]}
transformers pytorch wav2vec2 audio automatic-speech-recognition speech xlsr-fine-tuning-week et license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #et #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Estonian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.
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 Estonian test data of Common Voice.




Test Result:  30.658320 %

## Training

Common voice 'train' and 'validation' sets were used for finetuning
for 20000 steps (approx. 116 epochs).  Both the 'feature extractor' ('Wav2Vec2FeatureExtractor') and
'feature projection' ('Wav2Vec2FeatureProjection') layer were frozen. Only the 'encoder' layer ('Wav2Vec2EncoderStableLayerNorm') was finetuned.  




# Wav2Vec2-Large-XLSR-53-Estonian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.
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 Estonian test data of Common Voice.




Test Result:  30.658320 % ## Training

Common voice 'train' and 'validation' sets were used for finetuning
for 20000 steps (approx. 116 epochs).  Both the 'feature extractor' ('Wav2Vec2FeatureExtractor') and
'feature projection' ('Wav2Vec2FeatureProjection') layer were frozen. Only the 'encoder' layer ('Wav2Vec2EncoderStableLayerNorm') was finetuned.
TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #et #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Estonian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.
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 Estonian test data of Common Voice.




Test Result:  30.658320 % ## Training

Common voice 'train' and 'validation' sets were used for finetuning
for 20000 steps (approx. 116 epochs).  Both the 'feature extractor' ('Wav2Vec2FeatureExtractor') and
'feature projection' ('Wav2Vec2FeatureProjection') layer were frozen. Only the 'encoder' layer ('Wav2Vec2EncoderStableLayerNorm') was finetuned.
passage: TAGS
#transformers #pytorch #wav2vec2 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #et #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Estonian

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice.
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 Estonian test data of Common Voice.




Test Result:  30.658320 %## Training

Common voice 'train' and 'validation' sets were used for finetuning
for 20000 steps (approx. 116 epochs).  Both the 'feature extractor' ('Wav2Vec2FeatureExtractor') and
'feature projection' ('Wav2Vec2FeatureProjection') layer were frozen. Only the 'encoder' layer ('Wav2Vec2EncoderStableLayerNorm') was finetuned.