Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Armenian
whisper
Eval Results (legacy)
Instructions to use Chillarmo/whisper-base-armenian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chillarmo/whisper-base-armenian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Chillarmo/whisper-base-armenian")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Chillarmo/whisper-base-armenian") model = AutoModelForSpeechSeq2Seq.from_pretrained("Chillarmo/whisper-base-armenian") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - Chillarmo/common_voice_20_armenian | |
| language: | |
| - hy | |
| base_model: | |
| - openai/whisper-base | |
| pipeline_tag: automatic-speech-recognition | |
| library_name: transformers | |
| model-index: | |
| - name: whisper-base-armenian | |
| results: | |
| - task: | |
| type: automatic-speech-recognition | |
| name: Automatic Speech Recognition | |
| dataset: | |
| name: Common Voice 20 Armenian | |
| type: Chillarmo/common_voice_20_armenian | |
| metrics: | |
| - type: wer | |
| value: 33.186880780299205 | |
| name: Word Error Rate | |
| - type: cer | |
| value: 6.983058800639766 | |
| name: Character Error Rate | |
| - type: bleu | |
| value: 47.70616594276946 | |
| name: BLEU Score | |
| - type: exact_match | |
| value: 16.49590163934426 | |
| name: Exact Match | |
| # Whisper-Base Fine-tuned for Armenian ASR | |
| This model is a fine-tuned version of OpenAI's Whisper-base on the Common Voice 20 Armenian dataset for automatic speech recognition. | |
| ## Training Results | |
| The model was trained for 5.34 epochs with the following final results: | |
| | Metric | Value | | |
| |--------|-------| | |
| | **Training Loss** | 0.122 | | |
| | **Training Runtime** | 10,924 seconds (≈3.03 hours) | | |
| | **Training Samples/Second** | 7.32 | | |
| | **Training Steps/Second** | 0.46 | | |
| | **Total Training Steps** | 5,000 | | |
| | **Epochs** | 5.34 | | |
| ## Evaluation Results | |
| | Metric | Value | | |
| |--------|-------| | |
| | **Evaluation Loss** | 0.201 | | |
| | **Word Error Rate (WER)** | 33.19% | | |
| | **Character Error Rate (CER)** | 6.98% | | |
| | **BLEU Score** | 47.71 | | |
| | **Exact Match** | 16.50% | | |
| | **Average Prediction Length** | 7.69 tokens | | |
| | **Average Label Length** | 7.77 tokens | | |
| | **Length Ratio** | 0.989 | | |
| | **Evaluation Runtime** | 1,590 seconds (≈26.5 minutes) | | |
| | **Evaluation Samples/Second** | 3.68 | | |
| | **Evaluation Steps/Second** | 0.46 | | |
| ## Model Details | |
| - **Base Model**: openai/whisper-base | |
| - **Language**: Armenian (hy) | |
| - **Dataset**: Chillarmo/common_voice_20_armenian | |
| - **License**: Apache 2.0 | |
| ## Notes | |
| During model loading, there were missing keys in the checkpoint: `['proj_out.weight']`. This is a common occurrence when fine-tuning Whisper models and typically doesn't affect performance significantly. |