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--- |
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language: vi |
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datasets: |
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- vlsp |
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- common_voice |
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metrics: |
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- wer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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license: apache-2.0 |
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model-index: |
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- name: Wav2vec2 Base Vietnamese |
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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: Common Voice vi |
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type: common_voice |
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args: vi |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 31.353591 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Vietnamese |
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Fine-tuned [dragonSwing/wav2vec2-base-pretrain-vietnamese](https://huggingface.co/dragonSwing/wav2vec2-base-pretrain-vietnamese) on Vietnamese Speech Recognition task using 100h labelled data from [VSLP dataset](https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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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 datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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test_dataset = load_dataset("common_voice", "vi", split="test") |
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processor = Wav2Vec2Processor.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") |
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model = Wav2Vec2ForCTC.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"][:2], 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, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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print("Prediction:", processor.batch_decode(predicted_ids)) |
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print("Reference:", test_dataset["sentence"][:2]) |
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``` |
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## Evaluation |
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The model can be evaluated as follows on the Vietnamese test data of Common Voice. |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "vi", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") |
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model = Wav2Vec2ForCTC.from_pretrained("dragonSwing/wav2vec2-base-vietnamese") |
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model.to("cuda") |
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chars_to_ignore_regex = r'[,?.!\-;:"“%\'�]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def speech_file_to_array_fn(batch): |
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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = resampler(speech_array).squeeze().numpy() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["speech"], 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.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=1) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 31.353591% |
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