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Model: nguyenvulebinh/wav2vec2-base-vietnamese-250h Source: Original Platform
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README.md
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README.md
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---
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language: vi
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datasets:
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- vlsp
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- vivos
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tags:
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- audio
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- automatic-speech-recognition
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license: cc-by-nc-4.0
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widget:
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- example_title: VLSP ASR 2020 test T1
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src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav
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- example_title: VLSP ASR 2020 test T1
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src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_utt000000042.wav
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- example_title: VLSP ASR 2020 test T2
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src: https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t2_0000006682.wav
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model-index:
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- name: Vietnamese end-to-end speech recognition using wav2vec 2.0 by VietAI
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice vi
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type: common_voice
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args: vi
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metrics:
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- name: Test WER
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type: wer
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value: 11.52
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: VIVOS
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type: vivos
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args: vi
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metrics:
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- name: Test WER
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type: wer
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value: 6.15
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---
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# Vietnamese end-to-end speech recognition using wav2vec 2.0
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[](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=vietnamese-end-to-end-speech-recognition)
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[](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=vietnamese-end-to-end-speech-recognition)
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[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
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### Model description
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[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.
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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:
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>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.
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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.
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| Model | #params | Pre-training data | Fine-tune data |
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|---|---|---|---|
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| [base]((https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h)) | 95M | 13k hours | 250 hours |
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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.
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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).
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### Benchmark WER result:
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| | [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) |
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|---|---|---|---|---|
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|without LM| 10.77 | 18.34 | 13.33 | 51.45 |
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|with 4-grams LM| 6.15 | 11.52 | 9.11 | 40.81 |
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### Example usage
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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.
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[](https://colab.research.google.com/drive/1pVBY46gSoWer2vDf0XmZ6uNV3d8lrMxx?usp=sharing)
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```python
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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from datasets import load_dataset
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import soundfile as sf
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import torch
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# load model and tokenizer
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processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
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model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h")
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# define function to read in sound file
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def map_to_array(batch):
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speech, _ = sf.read(batch["file"])
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batch["speech"] = speech
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return batch
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# load dummy dataset and read soundfiles
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ds = map_to_array({
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"file": 'audio-test/t1_0001-00010.wav'
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})
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# tokenize
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input_values = processor(ds["speech"], return_tensors="pt", padding="longest").input_values # Batch size 1
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# retrieve logits
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logits = model(input_values).logits
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# take argmax and decode
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predicted_ids = torch.argmax(logits, dim=-1)
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transcription = processor.batch_decode(predicted_ids)
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```
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### Model Parameters License
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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
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### Citation
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[](https://github.com/vietai/ASR)
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```text
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@misc{Thai_Binh_Nguyen_wav2vec2_vi_2021,
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author = {Thai Binh Nguyen},
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doi = {10.5281/zenodo.5356039},
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month = {09},
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title = {{Vietnamese end-to-end speech recognition using wav2vec 2.0}},
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url = {https://github.com/vietai/ASR},
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year = {2021}
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}
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```
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**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.
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# Contact
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nguyenvulebinh@gmail.com / binh@vietai.org
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[](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
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