---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Storymation-whisper Fine-Tuned Model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Storymation-whisper Fine-Tuned Model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.

## Model Usage
```python
!pip install transformers accelerate gradio
from transformers import pipeline
import gradio as gr

# Load the Whisper model
model = "Muneeba23/whisper-small-en"
pipe = pipeline(model=model)

# Define the transcribe function
def transcribe(audio):
    text = pipe(audio)["text"]
    return text

# Create the Gradio interface
iface = gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(type="filepath"), 
    outputs="text",
    title="Whisper Small",
    description="Real-time Demo. Hurrah!!"
)

# Launch the interface
iface.launch()
```

## Intended uses & limitations

For a average audio prompt of 5 secs the latency observed was 40 secs.

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3

### Training results
- global_step=3, 
- training_loss=5.196450551350911,
- WER = 30% for 8 hours of training


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0