
# wav2vec-tr-lite-AG

## 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("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")

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

**Test Result**: 27.30 %


 [here](https://adresgezgini.com)


{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]}
transformers pytorch jax wav2vec2 automatic-speech-recognition audio speech tr dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 


# wav2vec-tr-lite-AG

## 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("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")

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

Test Result: 27.30 %


 here


# wav2vec-tr-lite-AG ## 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("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")

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

Test Result: 27.30 %


 here
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec-tr-lite-AG ## 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("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")

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

Test Result: 27.30 %


 here
passage: TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec-tr-lite-AG## 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("emre/wav2vec-tr-lite-AG")
model = Wav2Vec2ForCTC.from_pretrained("emre/wav2vec-tr-lite-AG")

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

Test Result: 27.30 %


 here