README.md

---
language: ru
datasets:
- SberDevices/Golos
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
widget:
- example_title: test sound with Russian speech
  src: https://huggingface.co/bond005/wav2vec2-large-ru-golos/resolve/main/test_sound_ru.flac
model-index:
- name: XLSR Wav2Vec2 Russian by Ivan Bondarenko
  results:
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Sberdevices Golos (crowd)
      type: SberDevices/Golos
      args: ru
    metrics:
       - name: Test WER
         type: wer
         value: 6.358
       - name: Test CER
         type: cer
         value: 1.711
  - task:
      name: Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Sberdevices Golos (farfield)
      type: SberDevices/Golos
      args: ru
    metrics:
       - name: Test WER
         type: wer
         value: 15.402
       - name: Test CER
         type: cer
         value: 4.315
---

# Wav2Vec2-Large-Ru-Golos

The Wav2Vec2 model is based on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), fine-tuned in Russian using [Sberdevices Golos](https://huggingface.co/datasets/SberDevices/Golos) with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc.

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

## Usage

To transcribe audio files the model can be used as a standalone acoustic model as follows:

```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
 
# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("bond005/wav2vec2-large-ru-golos")
model = Wav2Vec2ForCTC.from_pretrained("bond005/wav2vec2-large-ru-golos")
     
# load test part of Golos dataset and read first soundfile
ds = load_dataset("bond005/sberdevices_golos_10h_crowd", split="test")
 
# tokenize
processed = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest")  # Batch size 1
 
# retrieve logits
logits = model(processed.input_values, attention_mask=processed.attention_mask).logits
 
# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)
```

## Citation
If you want to cite this model you can use this:

```bibtex
@misc{bondarenko2022wav2vec2-large-ru-golos,
  title={XLSR Wav2Vec2 Russian by Ivan Bondarenko},
  author={Bondarenko, Ivan},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/bond005/wav2vec2-large-ru-golos}},
  year={2022}
}
```