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---
language: vi
datasets:
- vlsp
- vivos
tags:
- audio
- automatic-speech-recognition
license: cc-by-nc-4.0
widget:
- example_title: VLSP ASR 2020 test T1
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
- example_title: VLSP ASR 2020 test T1
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_utt000000042.wav
- example_title: VLSP ASR 2020 test T2
src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t2_0000006682.wav
model-index:
- name: Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice vi
type: common_voice
args: vi
metrics:
- name: Test WER
type: wer
value: 11.52
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: VIVOS
type: vivos
args: vi
metrics:
- name: Test WER
type: wer
value: 6.15
---
# Vietnamese end-to-end speech recognition using wav2vec 2.0
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vietnamese-end-to-end-speech-recognition/speech-recognition-on-common-voice-vi)](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=vietnamese-end-to-end-speech-recognition)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vietnamese-end-to-end-speech-recognition/speech-recognition-on-vivos)](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=vietnamese-end-to-end-speech-recognition)
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
### Model description
[Our models](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of [VLSP ASR dataset](https://vlsp.org.vn/vlsp2020/eval/asr) on 16kHz sampled speech audio.
We use [wav2vec2 architecture](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) for the pre-trained model. Follow wav2vec2 paper:
>For the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition.
| Model | #params | Pre-training data | Fine-tune data |
|---|---|---|---|
| [base]((https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h)) | 95M | 13k hours | 250 hours |
In a formal ASR system, two components are required: acoustic model and language model. Here ctc-wav2vec fine-tuned model works as an acoustic model. For the language model, we provide a [4-grams model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/blob/main/vi_lm_4grams.bin.zip) trained on 2GB of spoken text.
Detail of training and fine-tuning process, the audience can follow [fairseq github](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec) and [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english).
### Benchmark WER result:
| | [VIVOS](https://ailab.hcmus.edu.vn/vivos) | [COMMON VOICE VI](https://paperswithcode.com/dataset/common-voice) | [VLSP-T1](https://vlsp.org.vn/vlsp2020/eval/asr) | [VLSP-T2](https://vlsp.org.vn/vlsp2020/eval/asr) |
|---|---|---|---|---|
|without LM| 10.77 | 18.34 | 13.33 | 51.45 |
|with 4-grams LM| 6.15 | 11.52 | 9.11 | 40.81 |
### Example usage
When using the model make sure that your speech input is sampled at 16Khz. Audio length should be shorter than 10s. Following the Colab link below to use a combination of CTC-wav2vec and 4-grams LM.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pVBY46gSoWer2vDf0XmZ6uNV3d8lrMxx?usp=sharing)
```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)
```
### Model Parameters License
The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
### Citation
[![CITE](https://zenodo.org/badge/DOI/10.5281/zenodo.5356039.svg)](https://github.com/vietai/ASR)
```text
@misc{Thai_Binh_Nguyen_wav2vec2_vi_2021,
author = {Thai Binh Nguyen},
doi = {10.5281/zenodo.5356039},
month = {09},
title = {{Vietnamese end-to-end speech recognition using wav2vec 2.0}},
url = {https://github.com/vietai/ASR},
year = {2021}
}
```
**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.
# Contact
nguyenvulebinh@gmail.com / binh@vietai.org
[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)

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{
"_name_or_path": "/content/vaw2tmp/model-bin/finetune/base/checkpoint-146596",
"activation_dropout": 0.1,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForCTC"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"codevector_dim": 256,
"contrastive_logits_temperature": 0.1,
"conv_bias": false,
"conv_dim": [
512,
512,
512,
512,
512,
512,
512
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "mean",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": false,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_dropout": 0.0,
"feat_extract_norm": "group",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
"final_dropout": 0.1,
"gradient_checkpointing": true,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_feature_length": 10,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_prob": 0.05,
"model_type": "wav2vec2",
"num_attention_heads": 12,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
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