Storymation-whisper Fine-Tuned Model
This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset.
Model Usage
!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
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Model tree for Muneeba23/whisper-small-en
Base model
openai/whisper-small