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--- |
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language: |
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- kk |
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- ru |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- whisper |
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- generated_from_trainer |
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- kazakh |
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- ksc2 |
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- common-voice |
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- gemma-27b |
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datasets: |
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- mozilla-foundation/common_voice_23_0 |
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- InflexionLab/ISSAI-KSC2-Structured |
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metrics: |
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- wer |
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base_model: openai/whisper-large-v3 |
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model-index: |
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- name: whisper-large-v3-kazakh-ksc2 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Kazakh Speech Corpus 2 (KSC2) |
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type: issai/ksc2 |
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metrics: |
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- name: Wer |
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type: wer |
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value: 17.7 |
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--- |
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# Whisper Large V3 Fine-tuned on KSC2 (Sybyrla) |
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This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3). It is designed to provide robust automatic speech recognition (ASR) for the Kazakh language, achieving a Word Error Rate (WER) of approximately **17.7%**. |
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To handle real-world acoustic environments in the region, this model was trained on a strategic mix of Kazakh and Russian data. |
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**Developed by:** Inflexion Lab |
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**License:** Apache License 2.0 |
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## Model Description |
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- **Model type:** Transformer-based sequence-to-sequence model (Whisper Large V3) |
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- **Language(s):** Kazakh (kk), Russian (ru) auxiliary |
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- **Task:** Automatic Speech Recognition (ASR) |
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- **Base Model:** `openai/whisper-large-v3` |
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## Performance |
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The model was evaluated on the held-out test split of the KSC2 dataset. |
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| Metric | Score | |
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|:---:|:---:| |
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| **WER** | **~17.7%** | |
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## Training Data & Methodology |
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The training dataset was curated to address specific challenges in Kazakh ASR, particularly the lack of punctuation in raw datasets and the prevalence of code-switching in daily speech. |
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### Dataset Composition (80/20 Split) |
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We utilized a **80% / 20%** data mixing strategy to prevent model degradation and improve stability when encountering non-Kazakh phonemes. |
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1. **Kazakh Speech Corpus 2 (KSC2) - ~80%** |
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* **Volume:** ~1,200 hours. |
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* **Processing:** The original transcripts are in plain lowercase. We utilized **Gemma 27B** to syntactically restructure the text, restoring proper capitalization and punctuation. |
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* **Sources:** Parliament speeches, TV/Radio broadcasts, podcasts, and crowdsourced recordings. |
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2. **Common Voice Scripted Speech 23.0 (Russian) - ~20%** |
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* **Volume:** ~250 hours. |
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* **Purpose:** Including high-quality Russian speech helps the model distinguish between languages and handle loanwords or code-switching without hallucinating or degrading into gibberish. |
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## Usage |
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### Using with Hugging Face `transformers` |
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You can use this model directly with the Hugging Face `pipeline`. |
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```python |
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from transformers import pipeline |
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# Load the pipeline |
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pipe = pipeline("automatic-speech-recognition", model="InflexionLab/sybyrla") |
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# Transcribe an audio file |
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# The pipeline handles chunking automatically if configured (see batch inference below). |
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result = pipe("path/to/your/audio.mp3") |
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print(result["text"]) |
