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--- |
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base_model: facebook/w2v-bert-2.0 |
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datasets: |
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- common_voice_10_0 |
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metrics: |
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- wer |
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model-index: |
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- name: w2v-bert-2.0-uk |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_10_0 |
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type: common_voice_10_0 |
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config: uk |
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split: test |
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args: uk |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.0655 |
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--- |
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# wav2vec2-bert-uk |
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🇺🇦 Join our **Discord server** - https://discord.gg/yVAjkBgmt4 - where we're talking about Data Science, Machine Learning, Deep Learning, and Artificial Intelligence |
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🇺🇦 Join our Speech Recognition Group in Telegram: https://t.me/speech_recognition_uk |
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## Google Colab |
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You can run this model using a Google Colab notebook: https://colab.research.google.com/drive/1QoKw2DWo5a5XYw870cfGE3dJf1WjZgrj?usp=sharing |
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## Metrics |
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- AM: |
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- WER: 0.0727 |
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- CER: 0.0151 |
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- Accuracy: 92.73% |
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- AM + LM: |
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- WER: 0.0655 |
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- CER: 0.0139 |
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- Accuracy: 93.45% |
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## Hyperparameters |
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This model was trained with the following hparams using 2 RTX A4000: |
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``` |
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torchrun --standalone --nnodes=1 --nproc-per-node=2 ../train_w2v2_bert.py \ |
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--custom_set ~/cv10/train.csv \ |
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--custom_set_eval ~/cv10/test.csv \ |
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--num_train_epochs 15 \ |
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--tokenize_config . \ |
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--w2v2_bert_model facebook/w2v-bert-2.0 \ |
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--batch 4 \ |
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--num_proc 5 \ |
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--grad_accum 1 \ |
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--learning_rate 3e-5 \ |
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--logging_steps 20 \ |
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--eval_step 500 \ |
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--group_by_length \ |
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--attention_dropout 0.0 \ |
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--activation_dropout 0.05 \ |
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--feat_proj_dropout 0.05 \ |
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--feat_quantizer_dropout 0.0 \ |
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--hidden_dropout 0.05 \ |
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--layerdrop 0.0 \ |
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--final_dropout 0.0 \ |
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--mask_time_prob 0.0 \ |
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--mask_time_length 10 \ |
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--mask_feature_prob 0.0 \ |
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--mask_feature_length 10 |
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``` |
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## Usage |
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```python |
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# pip install -U torch soundfile transformers |
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import torch |
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import soundfile as sf |
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from transformers import AutoModelForCTC, Wav2Vec2BertProcessor |
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# Config |
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model_name = 'Yehor/w2v-bert-2.0-uk' |
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device = 'cuda:1' # or cpu |
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sampling_rate = 16_000 |
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# Load the model |
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asr_model = AutoModelForCTC.from_pretrained(model_name).to(device) |
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processor = Wav2Vec2BertProcessor.from_pretrained(model_name) |
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paths = [ |
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'sample1.wav', |
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] |
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# Extract audio |
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audio_inputs = [] |
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for path in paths: |
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audio_input, _ = sf.read(path) |
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audio_inputs.append(audio_input) |
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# Transcribe the audio |
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inputs = processor(audio_inputs, sampling_rate=sampling_rate).input_features |
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features = torch.tensor(inputs).to(device) |
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with torch.no_grad(): |
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logits = asr_model(features).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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predictions = processor.batch_decode(predicted_ids) |
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# Log results |
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print('Predictions:') |
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print(predictions) |
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``` |
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### Licenses |
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- Acoustic Model: Apache 2 |
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- Language Model (from https://huggingface.co/Yehor/kenlm-ukrainian): cc-by-nc-sa-4.0 |
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