
# Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice), 
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here [slack](https://huggingface.slack.com/archives/C01QZ90Q83Z/p1616741140114800)
The text from the common-voice dataset was used to synthesize vocies of female speakers using [Googe's TTS Standard Voice model](https://cloud.google.com/text-to-speech)

Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                          Resuming from checkpoints trained for another 15 epochs >> 34.00%
                            Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               

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", "el", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1") 

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 Greek 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", "el", split="test") 
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1") 
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
model.to("cuda")

chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]' 
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):
  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
  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 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"])))
```

**Test Result**: 45.048955 % 


## Training

The Common Voice `train`, `validation`, datasets were used for training as well as   

The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.

{"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition", "dataset": {"name": "Common Voice el", "type": "common_voice", "args": "el"}, "metrics": [{"name": "Test WER", "type": "wer", "value": 45.048955}]}}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week el dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, 
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack
The text from the common-voice dataset was used to synthesize vocies of female speakers using Googe's TTS Standard Voice model

Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                          Resuming from checkpoints trained for another 15 epochs >> 34.00%
                            Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               

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 Greek test data of Common Voice. 




Test Result: 45.048955 % 


## Training

The Common Voice 'train', 'validation', datasets were used for training as well as   

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.

# Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, 
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack
The text from the common-voice dataset was used to synthesize vocies of female speakers using Googe's TTS Standard Voice model

Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                          Resuming from checkpoints trained for another 15 epochs >> 34.00%
                            Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               

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 Greek test data of Common Voice. 




Test Result: 45.048955 % ## Training

The Common Voice 'train', 'validation', datasets were used for training as well as   

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, 
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack
The text from the common-voice dataset was used to synthesize vocies of female speakers using Googe's TTS Standard Voice model

Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                          Resuming from checkpoints trained for another 15 epochs >> 34.00%
                            Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               

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 Greek test data of Common Voice. 




Test Result: 45.048955 % ## Training

The Common Voice 'train', 'validation', datasets were used for training as well as   

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-53-Greek

Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, 
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack
The text from the common-voice dataset was used to synthesize vocies of female speakers using Googe's TTS Standard Voice model

Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
                          Resuming from checkpoints trained for another 15 epochs >> 34.00%
                            Added synthesised female voices trained for 12 epochs >> 34.00% (no change)               

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 Greek test data of Common Voice. 




Test Result: 45.048955 %## Training

The Common Voice 'train', 'validation', datasets were used for training as well as   

The script used for training can be found here # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.