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---
language: fa
datasets:
- common_voice_6_1
tags:
- audio
- automatic-speech-recognition
license: mit
widget:
- example_title: Common Voice Sample 1
src: https://datasets-server.huggingface.co/assets/common_voice/--/fa/train/0/audio/audio.mp3
- example_title: Common Voice Sample 2
src: https://datasets-server.huggingface.co/assets/common_voice/--/fa/train/1/audio/audio.mp3
model-index:
- name: Sharif-wav2vec2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice Corpus 6.1 (clean)
type: common_voice_6_1
config: clean
split: test
args:
language: fa
metrics:
- name: Test WER
type: wer
value: 6.0
---
# Sharif-wav2vec2
This is the fine-tuned version of Sharif Wav2vec2 for Farsi. The base model was fine-tuned on 108 hours of Commonvoice's Farsi samples with a sampling rate equal to 16kHz. Afterward, we trained a 5gram using [kenlm](https://github.com/kpu/kenlm) toolkit and used it in the processor which increased our accuracy on online ASR.
## Usage
When using the model make sure that your speech input is sampled at 16Khz. Prior to the usage, you may need to install the below dependencies:
```shell
pip install pyctcdecode
pip install pypi-kenlm
```
For testing you can use the hosted inference API at the hugging face (There are provided examples from common voice) it may take a while to transcribe the given voice. Or you can use the bellow code for a local run:
```python
import tensorflow
import torchaudio
import torch
import numpy as np
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("SLPL/Sharif-wav2vec2")
model = AutoModelForCTC.from_pretrained("SLPL/Sharif-wav2vec2")
speech_array, sampling_rate = torchaudio.load("path/to/your.wav")
speech_array = speech_array.squeeze().numpy()
features = processor(
speech_array,
sampling_rate=processor.feature_extractor.sampling_rate,
return_tensors="pt",
padding=True)
with torch.no_grad():
logits = model(
features.input_values,
attention_mask=features.attention_mask).logits
prediction = processor.batch_decode(logits.numpy()).text
print(prediction[0])
# تست
```
## Evaluation
For the evaluation use the code below:
```python
?
```
*Result (WER)*:
| clean | other |
|---|---|
| 3.4 | 8.6 |
## Citation
If you want to cite this model you can use this:
```bibtex
?
``` |