Update README.md

README.md

@@ -9,16 +9,10 @@ pipeline_tag: automatic-speech-recognition
 tags:
 - audio
 - speech
-- speechbrain
 - Transformer
 license: cc-by-nc-4.0
-widget:
-- example_title: Example 1
-  src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example.mp3
-- example_title: Example 2
-  src: https://huggingface.co/dragonSwing/wav2vec2-base-vn-270h/raw/main/example2.mp3
 model-index:
-- name: Wav2vec2 Base Vietnamese 270h
+- name: Wav2vec2 NCKH Vietnamese 2022
   results:
   - task: 
       name: Speech Recognition
@@ -30,127 +24,39 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 9.66
-  - task: 
-      name: Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 7.0
-      type: mozilla-foundation/common_voice_7_0
-      args: vi
-    metrics:
-       - name: Test WER
-         type: wer
-         value: 5.57
-  - task: 
-      name: Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 8.0
-      type: mozilla-foundation/common_voice_8_0
-      args: vi
-    metrics:
-       - name: Test WER
-         type: wer
-         value: 5.76
-  - task: 
-      name: Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: VIVOS
-      type: vivos
-      args: vi
-    metrics:
-       - name: Test WER
-         type: wer
-         value: 3.70
+         value: No
 ---
-# Wav2Vec2-Base-Vietnamese-270h
-Fine-tuned Wav2Vec2 model on Vietnamese Speech Recognition task using about 270h labelled data combined from multiple datasets including [Common Voice](https://huggingface.co/datasets/common_voice), [VIVOS](https://huggingface.co/datasets/vivos), [VLSP2020](https://vlsp.org.vn/vlsp2020/eval/asr). The model was fine-tuned using SpeechBrain toolkit with a custom tokenizer. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io/).  
-When using this model, make sure that your speech input is sampled at 16kHz.  
-Please refer to [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) or [speechbrain](https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonVoice/ASR/CTC) on how to fine-tune Wav2Vec2 model on a specific language.
-
-### Benchmark WER result:
-| | [VIVOS](https://huggingface.co/datasets/vivos) | [COMMON VOICE 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) | [COMMON VOICE 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) |
-|---|---|---|---|
-|without LM| 8.23 | 12.15 | 12.15 |
-|with 4-grams LM| 3.70 | 5.57 | 5.76 |
-
-The language model was trained using [OSCAR](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109) dataset on about 32GB of crawled text.
 
-### Install SpeechBrain
-To use this model, you should install speechbrain > 0.5.10
-
-### Usage
-The model can be used directly (without a language model) as follows:
-```python
-from speechbrain.pretrained import EncoderASR
-
-model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi")
-model.transcribe_file('dragonSwing/wav2vec2-base-vn-270h/example.mp3')
-# Output: được hồ chí minh coi là một động lực lớn của sự phát triển đất nước
-```
-
-### Inference on GPU
-To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
-
-### Evaluation
-The model can be evaluated as follows on the Vietnamese test data of Common Voice 8.0.
-```python
-import torch
-import torchaudio
-from datasets import load_dataset, load_metric, Audio
-from transformers import Wav2Vec2FeatureExtractor
-from speechbrain.pretrained import EncoderASR
-import re
-test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "vi", split="test", use_auth_token=True)
-test_dataset = test_dataset.cast_column("audio", Audio(sampling_rate=16_000))
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-wer = load_metric("wer")
-extractor = Wav2Vec2FeatureExtractor.from_pretrained("dragonSwing/wav2vec2-base-vn-270h")
-model = EncoderASR.from_hparams(source="dragonSwing/wav2vec2-base-vn-270h", savedir="pretrained_models/asr-wav2vec2-vi", run_opts={'device': device})
-chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]'
-# Preprocessing the datasets.
-# We need to read the audio files as arrays
-def speech_file_to_array_fn(batch):
-  audio = batch["audio"]
-  batch["target_text"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
-  batch['speech'] = audio['array']
-  return batch
-test_dataset = test_dataset.map(speech_file_to_array_fn)
-
-def evaluate(batch):
-  # For padding inputs only
-  inputs = extractor(
-    batch['speech'], 
-    sampling_rate=16000, 
-    return_tensors="pt", 
-    padding=True, 
-    do_normalize=False
-  ).input_values
-  input_lens = torch.ones(inputs.shape[0])
-  pred_str, pred_tokens = model.transcribe_batch(inputs, input_lens)
-  batch["pred_strings"] = pred_str
-  
-  return batch
-result = test_dataset.map(evaluate, batched=True, batch_size=1)
-print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["target_text"])))
+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
 ```
-**Test Result**: 12.155553%
-
-#### Citation
+# install and upload model
 ```
-@misc{SB2021,
-    author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua },
-    title = {SpeechBrain},
-    year = {2021},
-    publisher = {GitHub},
-    journal = {GitHub repository},
-    howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}},
-  }
+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
 ```
-
-#### About SpeechBrain
-SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains.  
-Website: [https://speechbrain.github.io](https://speechbrain.github.io/)  
-GitHub: [https://github.com/speechbrain/speechbrain](https://github.com/speechbrain/speechbrain)