
# Wav2Vec2-Large-XLSR-Turkish

This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned 
[cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial)
model on [Turkish Common Voice dataset](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", "tr", split="test[:2%]")

processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")


# 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"])
  resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
  batch["speech"] = resampler(speech_array).squeeze().numpy()
  return batch

test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```


## Evaluation

The model can be evaluated as follows on the Turkish 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", "tr", split="test")
wer = load_metric("wer")

processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv") 
model.to("cuda")

chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'

# 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"])
  resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
  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")).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**: 13.70 %

## Training

The Common Voice `train`, `validation`, other and invalidated 

The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition) 

{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Base Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 13.7, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition audio speech xlsr-fine-tuning-week tr dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 


# Wav2Vec2-Large-XLSR-Turkish

This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned 
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.

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 Turkish test data of Common Voice.



Test Result: 13.70 %

## Training

The Common Voice 'train', 'validation', other and invalidated 

The script used for training can be found here 

# Wav2Vec2-Large-XLSR-Turkish

This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned 
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.

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 Turkish test data of Common Voice.



Test Result: 13.70 % ## Training

The Common Voice 'train', 'validation', other and invalidated 

The script used for training can be found here
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # Wav2Vec2-Large-XLSR-Turkish

This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned 
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.

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 Turkish test data of Common Voice.



Test Result: 13.70 % ## Training

The Common Voice 'train', 'validation', other and invalidated 

The script used for training can be found here
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# Wav2Vec2-Large-XLSR-Turkish

This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned 
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.

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 Turkish test data of Common Voice.



Test Result: 13.70 %## Training

The Common Voice 'train', 'validation', other and invalidated 

The script used for training can be found here