Create README.md

README.md

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+---
+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)