ScreenTalk-xs

📌 ScreenTalk-xs: Fine-Tuned Whisper Model for Movie & TV Audio

📜 Model Details

• Model Name: ScreenTalk-xs
• Developed by: DataLabX
• Finetuned from: openai/whisper-small
• Language(s): English
• License: Apache-2.0
• Repository: Hugging Face Model Hub

📌 Model Description

ScreenTalk-xs is a fine-tuned version of OpenAI's whisper-small model, optimized for speech-to-text transcription on movies & TV show audio. This model is specifically trained to improve ASR (Automatic Speech Recognition) performance in dialogue-heavy scenarios.

🔹 Key Features

• 📺 Optimized for movie & TV dialogues
• 🎤 Robust to noisy environments
• 🔍 Improved handling of long-form speech
• 🚀 Efficient inference with LoRA fine-tuning

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🚀 Uses

✅ Direct Use

• Speech-to-text transcription for movies, TV shows, and general spoken audio.
• Automatic subtitling & captioning for multimedia content.
• Voice-enabled applications such as AI assistants & transcription services.

🔹 Downstream Use

• Can be used for improving ASR models in entertainment, media, and accessibility applications.

❌ Out-of-Scope Use

• Not optimized for real-time streaming ASR.
• May not generalize well to heavily accented speech outside its training dataset.

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🛠 Training Details

📌 Training Data

The model was fine-tuned using the ScreenTalk-XS dataset, a collection of transcribed movie & TV audio.

📌 Training Hyperparameters

📌 Training Procedure

• Fine-tuned with LoRA to reduce memory consumption while maintaining efficiency.
• Evaluation on a held-out test set to monitor WER (Word Error Rate).

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📊 Evaluation

📌 Training Results

• Best Model: Epoch 4, achieving WER = 20.50%
• Model performance degrades after epoch 4, suggesting overfitting.

📌 Test Results

🔍 Key Observations

• Fine-tuning reduced WER from 30.00% → 27.00% 🎯
• Achieved a 10% relative improvement in ASR accuracy.
• Tested on the ScreenTalk-XS dataset.

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🖥️ Technical Specifications

📌 Model Architecture

• Based on Whisper-small, a transformer-based sequence-to-sequence ASR model.
• Fine-tuned using LoRA to reduce memory footprint.

📌 Hardware & Compute Infrastructure

• Training Hardware: T4 (16GB) GPU
• Training Time: ~5 hours
• Training Environment: PyTorch + Transformers (Hugging Face)

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📖 How to Use

You can use this model for speech-to-text transcription with pipeline:

from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    model="fj11/ScreenTalk-xs",
    device=0  # Run on GPU
)

result = pipe("path/to/audio.wav")
print(result["text"])

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📜 Citation

If you use this model, please cite:

@misc{DataLabX2025ScreenTalkXS,
  author = {DataLabX},
  title = {ScreenTalk-xs: ASR Model Fine-Tuned on Movie & TV Audio},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/DataLabX/ScreenTalk-xs}
}