đ Wav2Vec2-Large-XLSR-53-Kazakh
This model is fine - tuned from facebook/wav2vec2-large-xlsr-53 for Kazakh Automatic Speech Recognition (ASR) using the Kazakh Speech Corpus v1.1.
Key Information
Property |
Details |
Model Type |
Audio, Automatic Speech Recognition |
Training Data |
Kazakh Speech Corpus v1.1 |
Base Model |
facebook/wav2vec2-large-xlsr-53 |
Metrics |
Word Error Rate (WER) |
Model Index
- Name: Wav2Vec2 - XLSR - 53 Kazakh by adilism
- Results:
- Task:
- Type: Automatic Speech Recognition
- Name: Speech Recognition
- Dataset:
- Name: Kazakh Speech Corpus v1.1
- Type: kazakh_speech_corpus
- Args: kk
- Metrics:
- Type: WER
- Value: 19.65
- Name: Test WER
Important Note
â ī¸ Important Note
When using this model, make sure that your speech input is sampled at 16kHz.
đ Quick Start
The model can be used directly (without a language model) as described below.
đģ Usage Examples
Basic Usage
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from utils import get_test_dataset
test_dataset = get_test_dataset("ISSAI_KSC_335RS_v1.1")
processor = Wav2Vec2Processor.from_pretrained("wav2vec2-large-xlsr-kazakh")
model = Wav2Vec2ForCTC.from_pretrained("wav2vec2-large-xlsr-kazakh")
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = torchaudio.transforms.Resample(sampling_rate, 16_000)(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:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
đ§ Evaluation
The model can be evaluated on the test set of Kazakh Speech Corpus v1.1. To evaluate, download the archive, untar and pass the path to data to get_test_dataset
as shown below:
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from utils import get_test_dataset
test_dataset = get_test_dataset("ISSAI_KSC_335RS_v1.1")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("adilism/wav2vec2-large-xlsr-kazakh")
model = Wav2Vec2ForCTC.from_pretrained("adilism/wav2vec2-large-xlsr-kazakh")
model.to("cuda")
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"] = torchaudio.transforms.Resample(sampling_rate, 16_000)(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
def evaluate(batch):
inputs = processor(batch["text"], 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)
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: 19.65%
đ§ Training
The Kazakh Speech Corpus v1.1 train
dataset was used for training.
đ License
This project is licensed under the Apache - 2.0 license.