adjust API support configuration

config.json

@@ -1,16 +0,0 @@
-{
-    "_name_or_path": "kosinebolisa/igbo-tts-model",
-    "model_type": "orpheus_tts",
-    "architecture": "FastLanguageModel+SNAC",
-    "vocoder": "snac",
-    "sample_rate": 24000,
-    "max_text_length": 500,
-    "tokenizer_settings": {
-      "start_of_text": 128000,
-      "end_of_text": 128009,
-      "speech_tokens": {
-        "start": 128257,
-        "end": 128258
-      }
-    }
-  }

custom_pipeline.py

@@ -1,25 +0,0 @@
-from transformers import Pipeline
-import torch
-
-class IgboTTSPipeline(Pipeline):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def _sanitize_parameters(self, **kwargs):
-        return {}, {}, {}
-
-    def preprocess(self, text):
-        return {"inputs": text}
-
-    def _forward(self, model_inputs):
-        # Your existing generate_audio() logic here
-        input_ids = self.tokenizer(model_inputs["inputs"]).input_ids
-        with torch.no_grad():
-            outputs = self.model.generate(input_ids)
-        
-        # Convert to audio using your SNAC vocoder
-        audio = self.snac_vocoder.decode(outputs)
-        return {"audio": audio}
-
-    def postprocess(self, model_outputs):
-        return model_outputs["audio"]

model_index.json

@@ -1,11 +0,0 @@
-{
-    "pipeline_tag": "text-to-speech",
-    "supported_tasks": ["text-to-speech"],
-    "custom_pipelines": {
-      "igbo-tts": {
-        "impl": "custom_pipeline.IgboTTSPipeline",
-        "tf": "",
-        "pt": "custom_pipeline.IgboTTSPipeline"
-      }
-    }
-  }

preprocessor_config.json

@@ -1,6 +0,0 @@
-{
-    "lowercase": false,
-    "normalize_numbers": true,
-    "remove_punctuation": false,
-    "language": "ig"
-  }