3.9 kB
--- | |
language: kk | |
datasets: | |
- kazakh_speech_corpus | |
metrics: | |
- wer | |
tags: | |
- audio | |
- automatic-speech-recognition | |
- speech | |
- xlsr-fine-tuning-week | |
license: apache-2.0 | |
model-index: | |
- name: Wav2Vec2-XLSR-53 Kazakh by adilism | |
results: | |
- task: | |
name: Speech Recognition | |
type: automatic-speech-recognition | |
dataset: | |
name: Kazakh Speech Corpus v1.1 | |
type: kazakh_speech_corpus | |
args: kk | |
metrics: | |
- name: Test WER | |
type: wer | |
value: 19.65 | |
--- | |
# Wav2Vec2-Large-XLSR-53-Kazakh | |
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for Kazakh ASR using the [Kazakh Speech Corpus v1.1](https://issai.nu.edu.kz/kz-speech-corpus/?version=1.1) | |
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 | |
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") | |
# Preprocessing the datasets. | |
# We need to read the audio files as arrays | |
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]) | |
``` | |
## Evaluation | |
The model can be evaluated as follows on the test set of [Kazakh Speech Corpus v1.1](https://issai.nu.edu.kz/kz-speech-corpus/?version=1.1). To evaluate, download the [archive](https://www.openslr.org/resources/102/ISSAI_KSC_335RS_v1.1_flac.tar.gz), untar and pass the path to data to `get_test_dataset` as below: | |
```python | |
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") | |
# Preprocessing the datasets. | |
# We need to read the audio 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"] = 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% | |
## Training | |
The Kazakh Speech Corpus v1.1 `train` dataset was used for training. |