---
language: vi
datasets:
- VLSP 2020 ASR dataset
- VIVOS
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
widget:
- label: VLSP ASR 2020 test T1
  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
- label: VLSP ASR 2020 test T1
  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_utt000000042.wav
- label: VLSP ASR 2020 test T2
  src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t2_0000006682.wav
---

# Wav2Vec2-Base-250h for the Vietnamese language

[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)

The base model pretrained and fine-tuned on 250 hours of VLSP ASR dataset on 16kHz sampled speech audio. When using the model
make sure that your speech input is also 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 soundfile as sf
import torch

# load model and tokenizer
processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")

# define function to read in sound file
def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch

# load dummy dataset and read soundfiles
ds = map_to_array({
    "file": 'audio-test/t1_0001-00010.wav'
})

# tokenize
input_values = processor(ds["speech"], return_tensors="pt", padding="longest").input_values  # Batch size 1

# retrieve logits
logits = model(input_values).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
 ```
 
*Result WER (with 4-grams LM)*:

| "VIVOS" | "VLSP-T1" | "VLSP-T2" |
|---|---|---|
| 6.1 | 9.1 | 40.8 |