---
language:
- ta
metrics:
- wer
library_name: transformers
pipeline_tag: automatic-speech-recognition
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is the fine-tuned version of whisper-large-v2 model for Tamil language.


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <training_args = Seq2SeqTrainingArguments(
    output_dir="./pretrainedwhisper-medium-native-v2",  # change to a repo name of your choice
    per_device_train_batch_size=4,
    gradient_accumulation_steps=1,  # increase by 2x for every 2x decrease in batch size
    learning_rate=1e-5,
    warmup_steps=200,
    max_steps=2000,
    gradient_checkpointing=True,
    fp16=True,
    evaluation_strategy="steps",
    per_device_eval_batch_size=8,
    predict_with_generate=True,
    generation_max_length=225,
    save_steps=500,
    eval_steps=500,
    logging_steps=25,
    report_to=["tensorboard"],
    load_best_model_at_end=True,
    metric_for_best_model="wer",
    greater_is_better=False,
    push_to_hub=True,
    optim="adamw_bnb_8bit"
)>



### Model Architecture and Objective

The model follows the whisper architecture with the encoder-decoder part. Where the encoder used to create the embeddings from the speech input and the decoder used to give the textual outputs.