--- language: vi datasets: - common_voice - FOSD: https://data.mendeley.com/datasets/k9sxg2twv4/4 - VIVOS: https://ailab.hcmus.edu.vn/vivos metrics: - wer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 model-index: - name: XLSR Wav2Vec2 Vietnamese by Nhut 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: 52.48 --- # Wav2Vec2-Large-XLSR-53-Vietnamese 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). When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor ENCODER = { "ia ": "iê ", "ìa ": "iề ", "ía ": "iế ", "ỉa ": "iể ", "ĩa ": "iễ ", "ịa ": "iệ ", "ya ": "yê ", "ỳa ": "yề ", "ýa ": "yế ", "ỷa ": "yể ", "ỹa ": "yễ ", "ỵa ": "yệ ", "ua ": "uô ", "ùa ": "uồ ", "úa ": "uố ", "ủa ": "uổ ", "ũa ": "uỗ ", "ụa ": "uộ ", "ưa ": "ươ ", "ừa ": "ườ ", "ứa ": "ướ ", "ửa ": "ưở ", "ữa ": "ưỡ ", "ựa ": "ượ ", "ke": "ce", "kè": "cè", "ké": "cé", "kẻ": "cẻ", "kẽ": "cẽ", "kẹ": "cẹ", "kê": "cê", "kề": "cề", "kế": "cế", "kể": "cể", "kễ": "cễ", "kệ": "cệ", "ki": "ci", "kì": "cì", "kí": "cí", "kỉ": "cỉ", "kĩ": "cĩ", "kị": "cị", "ky": "cy", "kỳ": "cỳ", "ký": "cý", "kỷ": "cỷ", "kỹ": "cỹ", "kỵ": "cỵ", "ghe": "ge", "ghè": "gè", "ghé": "gé", "ghẻ": "gẻ", "ghẽ": "gẽ", "ghẹ": "gẹ", "ghê": "gê", "ghề": "gề", "ghế": "gế", "ghể": "gể", "ghễ": "gễ", "ghệ": "gệ", "ngh": "\x80", "uyê": "\x96", "uyề": "\x97", "uyế": "\x98", "uyể": "\x99", "uyễ": "\x9a", "uyệ": "\x9b", "ng": "\x81", "ch": "\x82", "gh": "\x83", "nh": "\x84", "gi": "\x85", "ph": "\x86", "kh": "\x87", "th": "\x88", "tr": "\x89", "uy": "\x8a", "uỳ": "\x8b", "uý": "\x8c", "uỷ": "\x8d", "uỹ": "\x8e", "uỵ": "\x8f", "iê": "\x90", "iề": "\x91", "iế": "\x92", "iể": "\x93", "iễ": "\x94", "iệ": "\x95", "uô": "\x9c", "uồ": "\x9d", "uố": "\x9e", "uổ": "\x9f", "uỗ": "\xa0", "uộ": "\xa1", "ươ": "\xa2", "ườ": "\xa3", "ướ": "\xa4", "ưở": "\xa5", "ưỡ": "\xa6", "ượ": "\xa7", } def decode_string(x): for k, v in list(reversed(list(ENCODER.items()))): x = x.replace(v, k) return x test_dataset = load_dataset("common_voice", "vi", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", [decode_string(x) for x in processor.batch_decode(predicted_ids)]) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Vietnamese test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re ENCODER = { "ia ": "iê ", "ìa ": "iề ", "ía ": "iế ", "ỉa ": "iể ", "ĩa ": "iễ ", "ịa ": "iệ ", "ya ": "yê ", "ỳa ": "yề ", "ýa ": "yế ", "ỷa ": "yể ", "ỹa ": "yễ ", "ỵa ": "yệ ", "ua ": "uô ", "ùa ": "uồ ", "úa ": "uố ", "ủa ": "uổ ", "ũa ": "uỗ ", "ụa ": "uộ ", "ưa ": "ươ ", "ừa ": "ườ ", "ứa ": "ướ ", "ửa ": "ưở ", "ữa ": "ưỡ ", "ựa ": "ượ ", "ke": "ce", "kè": "cè", "ké": "cé", "kẻ": "cẻ", "kẽ": "cẽ", "kẹ": "cẹ", "kê": "cê", "kề": "cề", "kế": "cế", "kể": "cể", "kễ": "cễ", "kệ": "cệ", "ki": "ci", "kì": "cì", "kí": "cí", "kỉ": "cỉ", "kĩ": "cĩ", "kị": "cị", "ky": "cy", "kỳ": "cỳ", "ký": "cý", "kỷ": "cỷ", "kỹ": "cỹ", "kỵ": "cỵ", "ghe": "ge", "ghè": "gè", "ghé": "gé", "ghẻ": "gẻ", "ghẽ": "gẽ", "ghẹ": "gẹ", "ghê": "gê", "ghề": "gề", "ghế": "gế", "ghể": "gể", "ghễ": "gễ", "ghệ": "gệ", "ngh": "\x80", "uyê": "\x96", "uyề": "\x97", "uyế": "\x98", "uyể": "\x99", "uyễ": "\x9a", "uyệ": "\x9b", "ng": "\x81", "ch": "\x82", "gh": "\x83", "nh": "\x84", "gi": "\x85", "ph": "\x86", "kh": "\x87", "th": "\x88", "tr": "\x89", "uy": "\x8a", "uỳ": "\x8b", "uý": "\x8c", "uỷ": "\x8d", "uỹ": "\x8e", "uỵ": "\x8f", "iê": "\x90", "iề": "\x91", "iế": "\x92", "iể": "\x93", "iễ": "\x94", "iệ": "\x95", "uô": "\x9c", "uồ": "\x9d", "uố": "\x9e", "uổ": "\x9f", "uỗ": "\xa0", "uộ": "\xa1", "ươ": "\xa2", "ườ": "\xa3", "ướ": "\xa4", "ưở": "\xa5", "ưỡ": "\xa6", "ượ": "\xa7", } def decode_string(x): for k, v in list(reversed(list(ENCODER.items()))): x = x.replace(v, k) return x test_dataset = load_dataset("common_voice", "vi", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") model = Wav2Vec2ForCTC.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese") model.to("cuda") chars_to_ignore_regex = '[\\\+\@\ǀ\,\?\.\!\-\;\:\"\“\%\‘\”\�]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) # decode_string: We replace the encoded letter with the initial letters batch["pred_strings"] = [decode_string(x) for x in batch["pred_strings"]] return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 49.58 % ## Training The Common Voice `train`, `validation` and FOSD datasets and VIVOS datasets were used for training as well. The script used for training can be found [here](https://colab.research.google.com/drive/11pP4uVJj4SYZTzGjlCUtOHywlhYqs0cPx)