Wav2Vec2-Large-XLSR-53-Vietnamese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Vietnamese using the Common Voice, FOSD and 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:
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.
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: 49.59 %
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