Nhut DOANNGUYEN
commited on
Commit
•
c038370
1
Parent(s):
cdffa6b
Version 2.27
Browse files- .DS_Store +0 -0
- README.md +223 -14
- added_tokens.json +0 -1
- config.json +5 -5
- pytorch_model.bin +2 -2
- tokenizer_config.json +1 -1
- vocab.json +1 -1
.DS_Store
CHANGED
Binary files a/.DS_Store and b/.DS_Store differ
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README.md
CHANGED
@@ -42,6 +42,115 @@ 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[:2%]")
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processor = Wav2Vec2Processor.from_pretrained("Nhut/wav2vec2-large-xlsr-vietnamese")
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@@ -63,7 +172,7 @@ with torch.no_grad():
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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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@@ -80,26 +189,125 @@ 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(
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model = Wav2Vec2ForCTC.from_pretrained(
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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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# 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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-
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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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@@ -110,10 +318,11 @@ def evaluate(batch):
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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=8)
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-
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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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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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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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predicted_ids = torch.argmax(logits, dim=-1)
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print("Prediction:", decode_string(processor.batch_decode(predicted_ids)))
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print("Reference:", test_dataset["sentence"][:2])
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```
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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import re
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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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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")
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wer = load_metric("wer")
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processor = Wav2Vec2Processor.from_pretrained(MODEL)
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model = Wav2Vec2ForCTC.from_pretrained(MODEL)
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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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# 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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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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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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added_tokens.json
DELETED
@@ -1 +0,0 @@
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-
{"<s>": 91, "</s>": 92}
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config.json
CHANGED
@@ -1,5 +1,5 @@
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{
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-
"_name_or_path": "/content/gdrive/MyDrive/Colab\\ Notebooks/
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"activation_dropout": 0.0,
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"apply_spec_augment": true,
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"architectures": [
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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-
"layerdrop": 0.
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"mask_channel_length": 10,
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"mask_channel_min_space": 1,
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"mask_channel_other": 0.0,
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"mask_time_length": 10,
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63 |
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|
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|
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|
66 |
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|
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|
68 |
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@@ -70,7 +70,7 @@
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|
70 |
"num_conv_pos_embeddings": 128,
|
71 |
"num_feat_extract_layers": 7,
|
72 |
"num_hidden_layers": 24,
|
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75 |
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"vocab_size":
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76 |
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|
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{
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2 |
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"_name_or_path": "/content/gdrive/MyDrive/Colab\\ Notebooks/XLSR_V2_26/wav2vec-large-xlsr-vietnamese-demo",
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3 |
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pytorch_model.bin
CHANGED
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tokenizer_config.json
CHANGED
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{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|"
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{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|"}
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vocab.json
CHANGED
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{"
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