Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Tamil
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use deepdml/whisper-medium-ta-mix-norm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepdml/whisper-medium-ta-mix-norm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="deepdml/whisper-medium-ta-mix-norm")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("deepdml/whisper-medium-ta-mix-norm") model = AutoModelForSpeechSeq2Seq.from_pretrained("deepdml/whisper-medium-ta-mix-norm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
language:
- ta
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- deepdml/iisc-mile-tamil-asr
- deepdml/openslr65-tamil
- google/fleurs
- fixie-ai/common_voice_17_0
- ai4bharat/Kathbath
- deepdml/microsoft-speech-corpus-indian
metrics:
- wer
model-index:
- name: Whisper Medium ta
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: deepdml/iisc-mile-tamil-asr
metrics:
- name: Wer
type: wer
value: 28.496348108358777
Whisper Medium ta
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1192
- Wer: 28.4963
- Cer: 4.8178
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- training_steps: 18000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.1425 | 0.0556 | 1000 | 0.2033 | 42.3465 | 8.2575 |
| 0.0992 | 0.1111 | 2000 | 0.1824 | 38.1999 | 7.0789 |
| 0.0969 | 0.1667 | 3000 | 0.1749 | 36.7353 | 6.9087 |
| 0.0724 | 0.2222 | 4000 | 0.1716 | 36.3682 | 6.8174 |
| 0.0605 | 0.2778 | 5000 | 0.1591 | 34.1861 | 6.2048 |
| 0.0544 | 0.3333 | 6000 | 0.1556 | 33.2663 | 5.9622 |
| 0.0544 | 0.3889 | 7000 | 0.1504 | 32.3505 | 5.7064 |
| 0.0459 | 0.4444 | 8000 | 0.1410 | 32.2191 | 5.5940 |
| 0.0533 | 0.5 | 9000 | 0.1434 | 31.6562 | 5.5619 |
| 0.0529 | 0.5556 | 10000 | 0.1386 | 30.9747 | 5.4560 |
| 0.0377 | 0.6111 | 11000 | 0.1435 | 31.1190 | 5.5364 |
| 0.0367 | 0.6667 | 12000 | 0.1457 | 30.4170 | 5.2740 |
| 0.0414 | 0.7222 | 13000 | 0.1375 | 30.3294 | 5.2244 |
| 0.0479 | 0.7778 | 14000 | 0.1338 | 29.7381 | 5.0581 |
| 0.031 | 0.8333 | 15000 | 0.1362 | 29.5707 | 4.9853 |
| 0.026 | 0.8889 | 16000 | 0.1341 | 29.1894 | 4.9600 |
| 0.0399 | 0.9444 | 17000 | 0.1217 | 28.9021 | 4.8740 |
| 0.0454 | 1.0 | 18000 | 0.1192 | 28.4963 | 4.8178 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.0
Citation
Please cite the model using the following BibTeX entry:
@misc{deepdml/whisper-medium-ta-mix-norm,
title={Fine-tuned Whisper medium ASR model for speech recognition in Tamil},
author={Jimenez, David},
howpublished={\url{https://huggingface.co/deepdml/whisper-medium-ta-mix-norm}},
year={2026}
}