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--- |
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language: Bengali |
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datasets: |
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- OpenSLR |
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metrics: |
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- wer |
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tags: |
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- bn |
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- audio |
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- automatic-speech-recognition |
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- speech |
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license: cc-by-sa-4.0 |
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model-index: |
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- name: XLSR Wav2Vec2 Bengali by Arijit |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: OpenSLR |
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type: OpenSLR |
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args: ben |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 32.45 |
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--- |
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# Wav2Vec2-Large-XLSR-Bengali |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) Bengali using a subset of 40,000 utterances from [Bengali ASR training data set containing ~196K utterances](https://www.openslr.org/53/). Tested WER using ~4200 held out from training. |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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Train Script can be Found at : train.py |
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Data Prep Notebook : https://colab.research.google.com/drive/1JMlZPU-DrezXjZ2t7sOVqn7CJjZhdK2q?usp=sharing |
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Inference Notebook : https://colab.research.google.com/drive/1uKC2cK9JfUPDTUHbrNdOYqKtNozhxqgZ?usp=sharing |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import torchaudio |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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processor = Wav2Vec2Processor.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali") |
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model = Wav2Vec2ForCTC.from_pretrained("arijitx/wav2vec2-large-xlsr-bengali") |
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# model = model.to("cuda") |
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resampler = torchaudio.transforms.Resample(TEST_AUDIO_SR, 16_000) |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch) |
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speech = resampler(speech_array).squeeze().numpy() |
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return speech |
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speech_array = speech_file_to_array_fn("test_file.wav") |
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inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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preds = processor.batch_decode(predicted_ids)[0] |
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print(preds.replace("[PAD]","")) |
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``` |
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**Test Result**: WER on ~4200 utterance : 32.45 % |
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