fj11
/

ScreenTalk-xs

@@ -11,197 +11,119 @@ base_model:
 - openai/whisper-small
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 - openai/whisper-small
 ---
+# 📌 ScreenTalk-xs: Fine-Tuned Whisper Model for Movie & TV Audio
+## 📜 Model Details
+- **Model Name**: ScreenTalk-xs
+- **Developed by**: DataLabX
+- **Finetuned from**: [`openai/whisper-small`](https://huggingface.co/openai/whisper-small)
+- **Language(s)**: English
+- **License**: Apache-2.0
+- **Repository**: [Hugging Face Model Hub](https://huggingface.co/DataLabX/ScreenTalk-xs)
+## 📌 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**
+---
+## 🚀 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.
+---
+## 🛠 Training Details
+### 📌 Training Data
+The model was fine-tuned using the **ScreenTalk-XS dataset**, a collection of transcribed movie & TV audio.
+### 📌 Training Hyperparameters
+| **Hyperparameter** | **Value** |
+|-------------------|---------|
+| Learning Rate | `5e-5` |
+| Batch Size | `6` |
+| Gradient Accumulation | `4` |
+| Epochs | `5` |
+| LoRA Rank (`r`) | `4` |
+| Optimizer | AdamW |
+### 📌 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).
+---
+## 📊 Evaluation
+### 📌 Testing Data
+- **Dataset**: ScreenTalk-XS
+- **Metrics**: Word Error Rate (WER)
+### 📌 Results
+| **Epoch** | **Training Loss** | **Validation Loss** | **WER (%)** |
+|-----------|-----------------|-----------------|-------------|
+| **1**  | 0.502400	| 0.333292   | 20.870653 |
+| **2**  | 0.244200 | 0.327987	 | 20.580875 |
+| **3**  | 0.523600 | 0.325907	 | 21.924394 |
+| **4**  | 0.445500 | 0.326386	 | 20.508430 |
+| **5**  | 0.285700	| 0.327116   | 20.752107 |
+- **Best Model:** `Epoch 4`, achieving **WER = 20.50%**
+- **Model performance degrades after epoch 4**, suggesting overfitting.
+---
+## 🖥️ 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)
+---
+## 📖 How to Use
+You can use this model for **speech-to-text transcription** with `pipeline`:
+```python
+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"])  # ✅ 输出转录文本
+```
+---
+## 📜 Citation
+If you use this model, please cite:
+```java
+@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}
+}
+```