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--- |
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language: kk |
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license: apache-2.0 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- xlsr-fine-tuning-week |
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datasets: |
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- kazakh_speech_corpus |
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metrics: |
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- wer |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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model-index: |
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- name: Wav2Vec2-XLSR-53 Kazakh by adilism |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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name: Kazakh Speech Corpus v1.1 |
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type: kazakh_speech_corpus |
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args: kk |
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metrics: |
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- type: wer |
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value: 19.65 |
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name: Test WER |
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--- |
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# Wav2Vec2-Large-XLSR-53-Kazakh |
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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) |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import torchaudio |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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from utils import get_test_dataset |
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test_dataset = get_test_dataset("ISSAI_KSC_335RS_v1.1") |
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processor = Wav2Vec2Processor.from_pretrained("wav2vec2-large-xlsr-kazakh") |
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model = Wav2Vec2ForCTC.from_pretrained("wav2vec2-large-xlsr-kazakh") |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array, sampling_rate = torchaudio.load(batch["path"]) |
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batch["speech"] = torchaudio.transforms.Resample(sampling_rate, 16_000)(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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inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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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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## Evaluation |
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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: |
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```python |
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import torch |
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import torchaudio |
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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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from utils import get_test_dataset |
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test_dataset = get_test_dataset("ISSAI_KSC_335RS_v1.1") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("adilism/wav2vec2-large-xlsr-kazakh") |
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model = Wav2Vec2ForCTC.from_pretrained("adilism/wav2vec2-large-xlsr-kazakh") |
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model.to("cuda") |
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# Preprocessing the datasets. |
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# We need to read the audio 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"] = torchaudio.transforms.Resample(sampling_rate, 16_000)(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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def evaluate(batch): |
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inputs = processor(batch["text"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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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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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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**Test Result**: 19.65% |
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## Training |
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The Kazakh Speech Corpus v1.1 `train` dataset was used for training. |
