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--- |
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language: vi |
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datasets: |
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- common_voice |
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- FOSD: https://data.mendeley.com/datasets/k9sxg2twv4/4 |
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- VIVOS: https://ailab.hcmus.edu.vn/vivos |
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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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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: XLSR Wav2Vec2 Vietnamese by Nhut |
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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: 49.59 |
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--- |
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# Wav2Vec2-Large-XLSR-53-Vietnamese |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co./facebook/wav2vec2-large-xlsr-53) on Vietnamese using the [Common Voice](https://huggingface.co./datasets/common_voice), [FOSD](https://data.mendeley.com/datasets/k9sxg2twv4/4) and [VIVOS](https://ailab.hcmus.edu.vn/vivos). |
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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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ENCODER = { |
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"ia ": "iê ", |
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"ìa ": "iề ", |
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"ía ": "iế ", |
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"ỉa ": "iể ", |
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"ĩa ": "iễ ", |
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"ịa ": "iệ ", |
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"ya ": "yê ", |
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"ỳa ": "yề ", |
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"ýa ": "yế ", |
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"ỷa ": "yể ", |
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"ỹa ": "yễ ", |
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"ỵa ": "yệ ", |
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"ua ": "uô ", |
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"ùa ": "uồ ", |
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"úa ": "uố ", |
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"ủa ": "uổ ", |
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"ũa ": "uỗ ", |
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"ụa ": "uộ ", |
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"ưa ": "ươ ", |
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"ừa ": "ườ ", |
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"ứa ": "ướ ", |
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"ửa ": "ưở ", |
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"ữa ": "ưỡ ", |
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"ựa ": "ượ ", |
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"ke": "ce", |
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"kè": "cè", |
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"ké": "cé", |
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"kẻ": "cẻ", |
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"kẽ": "cẽ", |
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"kẹ": "cẹ", |
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"kê": "cê", |
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"kề": "cề", |
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"kế": "cế", |
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"kể": "cể", |
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"kễ": "cễ", |
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"kệ": "cệ", |
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"ki": "ci", |
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"kì": "cì", |
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"kí": "cí", |
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"kỉ": "cỉ", |
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"kĩ": "cĩ", |
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"kị": "cị", |
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"ky": "cy", |
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"kỳ": "cỳ", |
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"ký": "cý", |
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"kỷ": "cỷ", |
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"kỹ": "cỹ", |
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"kỵ": "cỵ", |
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"ghe": "ge", |
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"ghè": "gè", |
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"ghé": "gé", |
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"ghẻ": "gẻ", |
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"ghẽ": "gẽ", |
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"ghẹ": "gẹ", |
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"ghê": "gê", |
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"ghề": "gề", |
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"ghế": "gế", |
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"ghể": "gể", |
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"ghễ": "gễ", |
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"ghệ": "gệ", |
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"ngh": "\x80", |
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"uyê": "\x96", |
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"uyề": "\x97", |
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"uyế": "\x98", |
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"uyể": "\x99", |
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"uyễ": "\x9a", |
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"uyệ": "\x9b", |
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"ng": "\x81", |
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"ch": "\x82", |
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"gh": "\x83", |
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"nh": "\x84", |
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"gi": "\x85", |
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"ph": "\x86", |
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"kh": "\x87", |
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"th": "\x88", |
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"tr": "\x89", |
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"uy": "\x8a", |
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"uỳ": "\x8b", |
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"uý": "\x8c", |
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"uỷ": "\x8d", |
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"uỹ": "\x8e", |
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"uỵ": "\x8f", |
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"iê": "\x90", |
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"iề": "\x91", |
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"iế": "\x92", |
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"iể": "\x93", |
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"iễ": "\x94", |
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"iệ": "\x95", |
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"uô": "\x9c", |
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"uồ": "\x9d", |
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"uố": "\x9e", |
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"uổ": "\x9f", |
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"uỗ": "\xa0", |
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"uộ": "\xa1", |
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"ươ": "\xa2", |
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"ườ": "\xa3", |
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"ướ": "\xa4", |
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"ưở": "\xa5", |
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"ưỡ": "\xa6", |
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"ượ": "\xa7", |
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} |
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|
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def decode_string(x): |
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for k, v in list(reversed(list(ENCODER.items()))): |
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x = x.replace(v, k) |
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return x |
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test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") |
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model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-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:", [decode_string(x) for x in 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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|
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ENCODER = { |
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"ia ": "iê ", |
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"ìa ": "iề ", |
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"ía ": "iế ", |
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"ỉa ": "iể ", |
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"ĩa ": "iễ ", |
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"ịa ": "iệ ", |
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"ya ": "yê ", |
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"ỳa ": "yề ", |
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"ýa ": "yế ", |
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"ỷa ": "yể ", |
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"ỹa ": "yễ ", |
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"ỵa ": "yệ ", |
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"ua ": "uô ", |
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"ùa ": "uồ ", |
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"úa ": "uố ", |
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"ủa ": "uổ ", |
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"ũa ": "uỗ ", |
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"ụa ": "uộ ", |
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"ưa ": "ươ ", |
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"ừa ": "ườ ", |
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"ứa ": "ướ ", |
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"ửa ": "ưở ", |
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"ữa ": "ưỡ ", |
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"ựa ": "ượ ", |
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"ke": "ce", |
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"kè": "cè", |
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"ké": "cé", |
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"kẻ": "cẻ", |
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"kẽ": "cẽ", |
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"kẹ": "cẹ", |
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"kê": "cê", |
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"kề": "cề", |
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"kế": "cế", |
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"kể": "cể", |
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"kễ": "cễ", |
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"kệ": "cệ", |
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"ki": "ci", |
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"kì": "cì", |
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"kí": "cí", |
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"kỉ": "cỉ", |
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"kĩ": "cĩ", |
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"kị": "cị", |
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"ky": "cy", |
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"kỳ": "cỳ", |
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"ký": "cý", |
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"kỷ": "cỷ", |
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"kỹ": "cỹ", |
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"kỵ": "cỵ", |
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"ghe": "ge", |
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"ghè": "gè", |
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"ghé": "gé", |
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"ghẻ": "gẻ", |
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"ghẽ": "gẽ", |
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"ghẹ": "gẹ", |
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"ghê": "gê", |
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"ghề": "gề", |
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"ghế": "gế", |
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"ghể": "gể", |
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"ghễ": "gễ", |
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"ghệ": "gệ", |
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"ngh": "\x80", |
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"uyê": "\x96", |
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"uyề": "\x97", |
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"uyế": "\x98", |
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"uyể": "\x99", |
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"uyễ": "\x9a", |
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"uyệ": "\x9b", |
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"ng": "\x81", |
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"ch": "\x82", |
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"gh": "\x83", |
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"nh": "\x84", |
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"gi": "\x85", |
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"ph": "\x86", |
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"kh": "\x87", |
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"th": "\x88", |
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"tr": "\x89", |
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"uy": "\x8a", |
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"uỳ": "\x8b", |
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"uý": "\x8c", |
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"uỷ": "\x8d", |
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"uỹ": "\x8e", |
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"uỵ": "\x8f", |
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"iê": "\x90", |
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"iề": "\x91", |
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"iế": "\x92", |
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"iể": "\x93", |
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"iễ": "\x94", |
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"iệ": "\x95", |
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"uô": "\x9c", |
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"uồ": "\x9d", |
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"uố": "\x9e", |
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"uổ": "\x9f", |
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"uỗ": "\xa0", |
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"uộ": "\xa1", |
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"ươ": "\xa2", |
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"ườ": "\xa3", |
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"ướ": "\xa4", |
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"ưở": "\xa5", |
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"ưỡ": "\xa6", |
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"ượ": "\xa7", |
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} |
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|
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def decode_string(x): |
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for k, v in list(reversed(list(ENCODER.items()))): |
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x = x.replace(v, k) |
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return x |
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|
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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("Nhut/wav2vec2-large-xlsr-vietnamese") |
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model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") |
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model.to("cuda") |
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chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]' |
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resampler = torchaudio.transforms.Resample(48_000, 16_000) |
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|
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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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|
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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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# decode_string: We replace the encoded letter with the initial letters |
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batch["pred_strings"] = [decode_string(x) for x in batch["pred_strings"]] |
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return batch |
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|
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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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**: 49.59 % |
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## Training |
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The Common Voice `train`, `validation` and FOSD datasets and VIVOS datasets were used for training as well. |
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The script used for training can be found [here](https://colab.research.google.com/drive/11pP4uVJj4SYZTzGjlCUtOHywlhYqs0cPx) |
