--- 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)