--- language: vi datasets: - vivos - common_voice metrics: - wer 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 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: 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 --- # 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"]))) ``` **Test Result**: 12.155553% #### Citation ``` @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}}, } ``` #### 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)