Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/vietai/ASR
End-to-End Vietnamese Speech Recognition using wav2vec 2.0
https://github.com/vietai/ASR
asr asr-model ctc-loss end-to-end-speech-recognition pretrained-weights wav2vec2
Last synced: 4 months ago
JSON representation
End-to-End Vietnamese Speech Recognition using wav2vec 2.0
- Host: GitHub
- URL: https://github.com/vietai/ASR
- Owner: vietai
- Created: 2021-08-31T15:05:32.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-09-03T14:42:28.000Z (over 3 years ago)
- Last Synced: 2024-08-01T13:27:19.582Z (7 months ago)
- Topics: asr, asr-model, ctc-loss, end-to-end-speech-recognition, pretrained-weights, wav2vec2
- Homepage:
- Size: 10.7 KB
- Stars: 88
- Watchers: 3
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSES
Awesome Lists containing this project
README
# Vietnamese end-to-end speech recognition using wav2vec 2.0
[](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=vietnamese-end-to-end-speech-recognition)
[](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=vietnamese-end-to-end-speech-recognition)
### 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.
[](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
[](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
[email protected] / [email protected]
[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)