Update my model

README.md

@@ -1,142 +1,34 @@
----
-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}
-}
+Convert from model .pt to transformer
+Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
+Bash:
+```bash
+pip install transformers[sentencepiece]
+pip install fairseq -U
+git clone https://github.com/huggingface/transformers.git
+cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .
+wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt
+mkdir dict
+wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
+mkdir outputs
+python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py 
+--pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt
+ --dict_path ./dict/dict.ltr.txt --not_finetuned
+```
+# install and upload model
+```
+curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
+git lfs install
+sudo apt-get install git-lfs
+git lfs install
+git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo
+ls
+cd wav2vec-demo/
+git status
+git add .
+git commit -m "First model version"
+git config --global user.email [yourname]
+git config --global user.name [yourpass]
+git commit -m "First model version"
+git push
 ```
-**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
 
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