---
language:
- kk
tags:
- audio
- automatic-speech-recognition
- kazakh-asr
widget:
- src: https://drive.google.com/file/d/1udN8ybS7Ih3ESuoYZlaei4RcIPVbJlAf/view?usp=sharing 
  example_title: sample
  
model-index:
- name: whisper-base.kk
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Kazakh Speech Corpus 2 (KSC2)
      type: librispeech_asr
      config: clean
      split: test
      args:
        language: kk
    metrics:
    - name: Test WER
      type: wer
      value: 15.36
pipeline_tag: automatic-speech-recognition
license: apache-2.0
---
# Whisper

Whisper-base for automatic speech recognition (ASR) for the low-resourced Kazakh language. The model was fine-tuned on the [Kazakh Speech Corpus 2](https://issai.nu.edu.kz/2022/12/13/ksc2-an-industrial-scale-open-source-kazakh-speech-corpus/) 
with over 1k hours of labelled data. The model achieved 15.36% WER on the test set.

# Usage

This checkpoint is a *Kazakh-only* model, meaning it can be used *only* for Kazakh speech recognition.

## Transcription

```python
>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
>>> import librosa

>>> # load model and processor
>>> processor = WhisperProcessor.from_pretrained("akuzdeuov/whisper-base.kk")
>>> model = WhisperForConditionalGeneration.from_pretrained("akuzdeuov/whisper-base.kk")

>>> # load your audio
>>> audio, sampling_rate = librosa.load("path_to_audio", sr=16000)
>>> input_features = processor(audio, sampling_rate=sampling_rate, return_tensors="pt").input_features 

>>> # generate token ids
>>> predicted_ids = model.generate(input_features)
>>> # decode token ids to text
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)

>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
```
The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.


## Long-Form Transcription

The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking 
algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 
[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) 
method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline 
can be run with batched inference.

```python
>>> import torch
>>> from transformers import pipeline

>>> device = "cuda:0" if torch.cuda.is_available() else "cpu"

>>> pipe = pipeline(
>>>   "automatic-speech-recognition",
>>>   model="akuzdeuov/whisper-base.kk",
>>>   chunk_length_s=30,
>>>   device=device,
>>> )

>>> prediction = pipe("path_to_audio", batch_size=8)["text"]
```

## References 
1. [Whisper, OpenAI.](https://huggingface.co/openai/whisper-base.en)