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Model: khanhld/wav2vec2-base-vietnamese-160h 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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- vivos
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- common_voice
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- FOSD
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- VLSP
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metrics:
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- wer
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pipeline_tag: automatic-speech-recognition
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tags:
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- audio
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- speech
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- Transformer
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- wav2vec2
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- automatic-speech-recognition
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- vietnamese
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license: cc-by-nc-4.0
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widget:
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- example_title: common_voice_vi_30519758.mp3
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src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/common_voice_vi_30519758.mp3
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- example_title: VIVOSDEV15_020.wav
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src: https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/VIVOSDEV15_020.wav
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model-index:
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- name: Wav2vec2 Base Vietnamese 160h
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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-vietnamese
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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: 10.78
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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: 15.05
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---
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[](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=wav2vec2-base-vietnamese-160h)
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[](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=wav2vec2-base-vietnamese-160h)
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# Vietnamese Speech Recognition using Wav2vec 2.0
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### Table of contents
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1. [Model Description](#description)
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2. [Implementation](#implementation)
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3. [Benchmark Result](#benchmark)
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4. [Example Usage](#example)
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5. [Evaluation](#evaluation)
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6. [Citation](#citation)
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7. [Contact](#contact)
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<a name = "description" ></a>
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### Model Description
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Fine-tuned the Wav2vec2-based model on about 160 hours of Vietnamese speech dataset from different resources, including [VIOS](https://huggingface.co/datasets/vivos), [COMMON VOICE](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VLSP 100h](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view). We have not yet incorporated the Language Model into our ASR system but still gained a promising result.
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<a name = "implementation" ></a>
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### Implementation
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We also provide code for Pre-training and Fine-tuning the Wav2vec2 model. If you wish to train on your dataset, check it out here:
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- [Pre-train code](https://github.com/khanld/Wav2vec2-Pretraining)
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- [Fine-tune code](https://github.com/khanld/ASR-Wa2vec-Finetune)
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<a name = "benchmark" ></a>
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### Benchmark WER Result
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| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
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|---|---|---|
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|without LM| 15.05 | 10.78 |
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|with LM| in progress | in progress |
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<a name = "example" ></a>
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### Example Usage [](https://colab.research.google.com/drive/1blz1KclnIfbOp8o2fW3WJgObOQ9SMGBo?usp=sharing)
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```python
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
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import librosa
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
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model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
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model.to(device)
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def transcribe(wav):
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input_values = processor(wav, sampling_rate=16000, return_tensors="pt").input_values
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logits = model(input_values.to(device)).logits
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pred_ids = torch.argmax(logits, dim=-1)
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pred_transcript = processor.batch_decode(pred_ids)[0]
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return pred_transcript
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wav, _ = librosa.load('path/to/your/audio/file', sr = 16000)
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print(f"transcript: {transcribe(wav)}")
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```
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<a name = "evaluation"></a>
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### Evaluation [](https://colab.research.google.com/drive/1XQCq4YGLnl23tcKmYeSwaksro4IgC_Yi?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 torch
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import re
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from datasets import load_dataset, load_metric, Audio
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wer = load_metric("wer")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# load processor and model
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processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
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model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
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model.to(device)
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model.eval()
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# Load dataset
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test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token="your_huggingface_auth_token")
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test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000))
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chars_to_ignore = r'[,?.!\-;:"“%\'<EFBFBD>]' # ignore special characters
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# preprocess data
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def preprocess(batch):
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audio = batch["audio"]
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batch["input_values"] = audio["array"]
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batch["transcript"] = re.sub(chars_to_ignore, '', batch["sentence"]).lower()
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return batch
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# run inference
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def inference(batch):
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input_values = processor(batch["input_values"],
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sampling_rate=16000,
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return_tensors="pt").input_values
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logits = model(input_values.to(device)).logits
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pred_ids = torch.argmax(logits, dim=-1)
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batch["pred_transcript"] = processor.batch_decode(pred_ids)
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return batch
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test_dataset = test_dataset.map(preprocess)
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result = test_dataset.map(inference, batched=True, batch_size=1)
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_transcript"], references=result["transcript"])))
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```
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**Test Result**: 10.78%
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<a name = "citation" ></a>
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### Citation
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[](https://zenodo.org/badge/latestdoi/491468343)
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<strong>BibTeX</strong>
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```
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@mics{Duy_Khanh_Finetune_Wav2vec_2_0_2022,
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author = {Duy Khanh, Le},
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doi = {10.5281/zenodo.6542357},
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license = {CC-BY-NC-4.0},
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month = {5},
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title = {{Finetune Wav2vec 2.0 For Vietnamese Speech Recognition}},
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url = {https://github.com/khanld/ASR-Wa2vec-Finetune},
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year = {2022}
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}
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```
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<strong>APA</strong>
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```
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Duy Khanh, L. (2022). Finetune Wav2vec 2.0 For Vietnamese Speech Recognition [Data set]. https://doi.org/10.5281/zenodo.6542357
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```
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<a name = "contact"></a>
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### Contact
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- khanhld218@gmail.com
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- [](https://github.com/)
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- [](https://www.linkedin.com/in/khanhld257/)
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