metadata

language: vi
datasets:
  - vivos
  - common_voice
  - FOSD
  - VLSP
metrics:
  - wer
pipeline_tag: automatic-speech-recognition
tags:
  - audio
  - speech
  - Transformer
  - wav2vec2
  - automatic-speech-recognition
  - vietnamese
license: cc-by-nc-4.0
widget:
  - example_title: common_voice_vi_30519758.mp3
    src: >-
      https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/common_voice_vi_30519758.mp3
  - example_title: VIVOSDEV15_020.wav
    src: >-
      https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h/raw/main/examples/VIVOSDEV15_020.wav
model-index:
  - name: Wav2vec2 Base Vietnamese 160h
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common-voice-vietnamese
          type: common_voice
          args: vi
        metrics:
          - name: Test WER
            type: wer
            value: 10.78
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: VIVOS
          type: vivos
          args: vi
        metrics:
          - name: Test WER
            type: wer
            value: 15.05

Vietnamese Speech Recognition using Wav2vec 2.0

Model Description
Implementation
Benchmark Result
Example Usage
Evaluation
Citation
Contact

Model Description

Fine-tuned the Wav2vec2-based model on about 160 hours of Vietnamese speech dataset from different resources, including VIOS, COMMON VOICE, FOSD and VLSP 100h. We have not yet incorporated the Language Model into our ASR system but still gained a promising result.

Implementation

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:

Benchmark WER Result

	VIVOS	COMMON VOICE 8.0
without LM	15.05	10.78
with LM	in progress	in progress

Example Usage

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import librosa
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model.to(device)

def transcribe(wav):
  input_values = processor(wav, sampling_rate=16000, return_tensors="pt").input_values
  logits = model(input_values.to(device)).logits
  pred_ids = torch.argmax(logits, dim=-1)
  pred_transcript = processor.batch_decode(pred_ids)[0]
  return pred_transcript


wav, _ = librosa.load('path/to/your/audio/file', sr = 16000)
print(f"transcript: {transcribe(wav)}")

Evaluation

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
import re
from datasets import load_dataset, load_metric, Audio

wer = load_metric("wer")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# load processor and model
processor = Wav2Vec2Processor.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model = Wav2Vec2ForCTC.from_pretrained("khanhld/wav2vec2-base-vietnamese-160h")
model.to(device)
model.eval()

# Load dataset
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token="your_huggingface_auth_token")
test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16000))
chars_to_ignore = r'[,?.!\-;:"“%\'�]' # ignore special characters

# preprocess data
def preprocess(batch):
  audio = batch["audio"]
  batch["input_values"] = audio["array"]
  batch["transcript"] = re.sub(chars_to_ignore, '', batch["sentence"]).lower()
  return batch

# run inference
def inference(batch):
  input_values = processor(batch["input_values"], 
                            sampling_rate=16000, 
                            return_tensors="pt").input_values
  logits = model(input_values.to(device)).logits
  pred_ids = torch.argmax(logits, dim=-1)
  batch["pred_transcript"] = processor.batch_decode(pred_ids) 
  return batch
  
test_dataset = test_dataset.map(preprocess)
result = test_dataset.map(inference, batched=True, batch_size=1)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_transcript"], references=result["transcript"])))

Test Result: 10.78%

Citation

BibTeX

@mics{Duy_Khanh_Finetune_Wav2vec_2_0_2022,
  author = {Duy Khanh, Le},
  doi = {10.5281/zenodo.6542357},
  license = {CC-BY-NC-4.0},
  month = {5},
  title = {{Finetune Wav2vec 2.0 For Vietnamese Speech Recognition}},
  url = {https://github.com/khanld/ASR-Wa2vec-Finetune},
  year = {2022}
}

APA

Duy Khanh, L. (2022). Finetune Wav2vec 2.0 For Vietnamese Speech Recognition [Data set]. https://doi.org/10.5281/zenodo.6542357

Contact

[email protected]

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wav2vec2-base-vietnamese-160h