File size: 2,736 Bytes
2752b4c 414e4b3 2752b4c 71cc0e5 5e06bac 414e4b3 9091f11 414e4b3 9091f11 414e4b3 9091f11 414e4b3 9091f11 2752b4c 414e4b3 f890604 414e4b3 71cc0e5 8f82e5d 71cc0e5 8f82e5d 71cc0e5 8f82e5d 71cc0e5 8f82e5d 71cc0e5 8f82e5d 71cc0e5 8f82e5d 71cc0e5 414e4b3 f890604 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
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/blob/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}
}
```
|