Update README.md

Author: khanld

README.md

@@ -1,2 +1,158 @@
-# Vietnamese-ASR---Released-Model
-Vietnamese Automatic Speech Recognition using Wav2vec 2.0
+---
+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
+---
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wav2vec2-base-vietnamese-160h/speech-recognition-on-common-voice-vi)](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=wav2vec2-base-vietnamese-160h)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wav2vec2-base-vietnamese-160h/speech-recognition-on-vivos)](https://paperswithcode.com/sota/speech-recognition-on-vivos?p=wav2vec2-base-vietnamese-160h)
+# Vietnamese Speech Recognition using Wav2vec 2.0
+### Table of contents
+1. [Model Description](#description)
+2. [Implementation](#implementation)
+3. [Benchmark Result](#benchmark)
+4. [Example Usage](#example)
+5. [Evaluation](#evaluation)
+6. [Citation](#citation)
+7. [Contact](#contact)
+<a name = "description" ></a>
+### Model Description
+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.
+<a name = "implementation" ></a>
+### 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:
+- [Pre-train code](https://github.com/khanld/ASR-Wav2vec-Pretrain) (not available for now but will release soon)
+- [Fine-tune code](https://github.com/khanld/ASR-Wa2vec-Finetune)
+
+<a name = "benchmark" ></a>
+### Benchmark WER Result
+| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) | 
+|---|---|---|
+|without LM| 15.05 | 10.78 |
+|with LM| in progress | in progress |
+
+<a name = "example" ></a>
+### Example Usage [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1blz1KclnIfbOp8o2fW3WJgObOQ9SMGBo?usp=sharing)
+```python
+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)}")
+```
+
+<a name = "evaluation"></a>
+### Evaluation [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1XQCq4YGLnl23tcKmYeSwaksro4IgC_Yi?usp=sharing)
+
+```python
+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%
+
+<a name = "citation" ></a>
+### Citation 
+[![DOI](https://zenodo.org/badge/485623832.svg)](https://github.com/khanld/ASR-Wa2vec-Finetune)
+```text
+@misc{Khanhld_Vietnamese_Wav2vec_Asr_2022,
+  author = {Duy Khanh Le},
+  doi = {10.5281/zenodo.6540979},
+  month = {May},
+  title = {Finetune Wav2vec 2.0 For Vietnamese Speech Recognition},
+  url = {https://github.com/khanld/ASR-Wa2vec-Finetune},
+  year = {2022}
+}
+```
+
+<a name = "contact"></a>
+### Contact
+- khanhld218@uef.edu.vn
+- [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/)
+- [![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/khanhld257/)
+