Delete yarngpt_utils.py

yarngpt_utils.py

@@ -1,52 +0,0 @@
-# yarngpt_utils.py
-import torch
-import torchaudio
-import re
-from outetts.wav_tokenizer.decoder import WavTokenizer
-from transformers import AutoTokenizer
-
-class AudioTokenizer:
-    def __init__(self, hf_path, wav_tokenizer_model_path, wav_tokenizer_config_path):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.tokenizer = AutoTokenizer.from_pretrained(hf_path)
-        
-        # Fix: Use the correct parameter names for WavTokenizer
-        self.wav_tokenizer = WavTokenizer(
-            checkpoint_path=wav_tokenizer_model_path,
-            config_path=wav_tokenizer_config_path,
-            device=self.device
-        )
-        
-        self.speakers = ["idera", "emma", "jude", "osagie", "tayo", "zainab", 
-                         "joke", "regina", "remi", "umar", "chinenye"]
-        
-    def create_prompt(self, text, speaker_name=None):
-        if speaker_name is None or speaker_name not in self.speakers:
-            speaker_name = self.speakers[torch.randint(0, len(self.speakers), (1,)).item()]
-        
-        # Create a prompt similar to the original YarnGPT
-        prompt = f"<|system|>\nYou are a helpful assistant that speaks in {speaker_name}'s voice.\n<|user|>\nSpeak this text: {text}\n<|assistant|>"
-        return prompt
-    
-    def tokenize_prompt(self, prompt):
-        input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(self.device)
-        return input_ids
-    
-    def get_codes(self, output):
-        # Decode the sequence
-        decoded_str = self.tokenizer.decode(output[0])
-        
-        # Extract the part after <|assistant|>
-        speech_part = decoded_str.split("<|assistant|>")[-1].strip()
-        
-        # Extract code tokens - assuming format like "<audio_001>"
-        audio_codes = []
-        for match in re.finditer(r"<audio_(\d+)>", speech_part):
-            code = int(match.group(1))
-            audio_codes.append(code)
-        
-        return audio_codes
-    
-    def get_audio(self, codes):
-        audio = self.wav_tokenizer.decode(torch.tensor(codes, device=self.device))
-        return audio