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--- |
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library_name: transformers |
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language: |
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- ta |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small ta - Lingalingeswaran |
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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: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: ta |
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split: None |
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args: 'config: ta, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 43.31959037105998 |
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pipeline_tag: automatic-speech-recognition |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Whisper Small ta - Lingalingeswaran |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2150 |
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- Wer: 43.3196 |
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## Model description |
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This Whisper model has been fine-tuned specifically for the Tamil language using the Common Voice 11.0 dataset. It is designed to handle tasks such as speech-to-text transcription and language identification, making it suitable for applications where Tamil is a primary language of interest. The fine-tuning process focused on enhancing performance for Tamil, aiming to reduce the error rate in transcriptions and improve general accuracy. |
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## Intended uses & limitations |
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Intended Uses: |
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Speech-to-text transcription in Tamil |
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Limitations: |
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May not perform as well on languages or dialects that are not well-represented in the Common Voice dataset. |
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Higher Word Error Rate (WER) in noisy environments or with speakers who have heavy accents not covered in the training data. |
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The model is optimized for Tamil; performance in other languages may be suboptimal. |
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## Training and evaluation data |
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The training data for this model consists of voice recordings in Tamil from the Mozilla-foundation/Common Voice 11.0 dataset. The dataset is a crowd-sourced collection of transcribed speech, ensuring diversity in terms of speaker accents, age groups, and speech styles. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 4000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:-------:| |
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| 0.1753 | 0.2992 | 1000 | 0.2705 | 51.0174 | |
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| 0.1404 | 0.5984 | 2000 | 0.2368 | 46.9969 | |
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| 0.1344 | 0.8977 | 3000 | 0.2196 | 44.5325 | |
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| 0.0947 | 1.1969 | 4000 | 0.2150 | 43.3196 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
