Update README.md

README.md

@@ -123,10 +123,9 @@ base_model:
 #     - name: Test WER
 #       type: wer
 #       value: 2.33
-# new_version: okestro-ai-lab/SYMPHONY-ASR
 ---
 
-* SYMPHONY is an designed for efficient and accurate speech-to-text transcription.
+* FastSLM is an designed for efficient and accurate speech-to-text transcription.
 
 
 <!-- * 🔊 **HFQ-Former**: Hierarchically compresses high-frame-rate audio features while preserving the audio's local and global contextual information. -->
@@ -135,7 +134,7 @@ base_model:
 
 ## 📖 Model Architecture
 
-🚀 **SYMPHONY** is a designed for efficient speech-to-text transcription.  
+🚀 **FastSLM** is a designed for efficient speech-to-text transcription.  
 
 ## 📌 Key Features (Safe Version)
 
@@ -178,7 +177,7 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
 
 # ⬅️ Enter your Hugging Face repository ID here.
-repo_id = "okestro-ai-lab/SYMPHONY" 
+repo_id = "okestro-ai-lab/FastSLM" 
 
 model = AutoModelForCausalLM.from_pretrained(
     repo_id,
@@ -204,7 +203,7 @@ import librosa
 wav_path = "sample_audio/English_audio.wav" 
 wav,sample_rate  = librosa.load(wav_path)
 
-# SYMPHONY-ASR requires 16kHz audio.
+# FastSLM requires 16kHz audio.
 if sample_rate != 16000:
     audio = librosa.resample(wav,orig_sr=sample_rate,target_sr=16000)
 else:
@@ -261,7 +260,7 @@ You can perform different tasks by using the following special tokens in your pr
 
 ## ⚡ GPU Requirements
 
-SYMPHONY inference requires a GPU with sufficient memory.
+FastSLM inference requires a GPU with sufficient memory.
 
 | Task      | Recommended GPU         | Minimum VRAM |
 | --------- | ---------------- | --------- |