Add support for Ollama for lyric generation

Ollama.md

@@ -0,0 +1,25 @@
+# Using Ollama for Lyric Generation
+
+As an alternative to running LLM models with GGUF format directly with `llama-cpp`
+by using the Python wrapper `llama-cpp-python`, `llama-cpp` hosted 
+by [Ollama](https://github.com/ollama) can be used for lyric generation.
+
+The advantages are
+- usage of Ollama LLM models
+- usage of several GPUs out-of-the-box
+
+## Prerequisites
+
+An Ollama instance up and running with the intended Ollama LLM model.
+
+Installing the Python wrapper for the Ollama REST API:
+```
+pip install ollama
+```
+
+## Usage
+
+For using Ollama, add the following command line arguments:
+- `--ollama`
+- `--model_path <Ollama model name>`, e.g. `--model_path gemma3:12b-it-q4_K_M`
+

radio_gradio.py

@@ -238,17 +238,19 @@ def generate_identity(cls, genre: str, theme: str):
         return cls(name, slogan)
 
 class AIRadioStation:
-    def __init__(self, ace_step_pipeline: ACEStepPipeline, model_path: str = "gemma-3-12b-it-abliterated.q4_k_m"):
+    def __init__(self, ace_step_pipeline: ACEStepPipeline, model_path: str = "gemma-3-12b-it-abliterated.q4_k_m", ollama: bool = False):
         """
         Initialize the AI Radio Station with continuous generation.
 
         Args:
             ace_step_pipeline: Initialized ACEStepPipeline for music generation
             model_path: Path to LLM model for lyric generation
+            ollama: Whether to use Ollama for lyric generation (default: False)
         """
         self._pipeline = ace_step_pipeline  # Store the original pipeline reference
         self.random_mode = False 
         self.llm_model_path = model_path
+        self.ollama = ollama
         self.llm = None
         self._first_play = True  
         self.pipeline_args = {
@@ -286,26 +288,30 @@ def load_llm(self):
         self.unload_llm()
         gc.collect()
         if self.llm is None:
-            print("Loading LLM model...")
-            try:
-                from llama_cpp import Llama
-                self.llm = Llama(
-                    model_path=self.llm_model_path,
-                    n_ctx=2048,
-                    n_threads=4,
-                    n_gpu_layers=-1,
-                    seed = -1 # random seed for random lyrics 
+            if self.ollama:
+                self.llm = True
+            else:
+                print("Loading LLM model...")
+                try:
+                    from llama_cpp import Llama
+                    self.llm = Llama(
+                        model_path=self.llm_model_path,
+                        n_ctx=2048,
+                        n_threads=4,
+                        n_gpu_layers=-1,
+                        seed = -1 # random seed for random lyrics
 
-                )
-            except ImportError:
-                print("Warning: llama-cpp-python not installed, using simple lyric generation")
-                self.llm = None
+                    )
+                except ImportError:
+                    print("Warning: llama-cpp-python not installed, using simple lyric generation")
+                    self.llm = None
 
     def unload_llm(self):
         """Unload the LLM model from memory"""
         if self.llm is not None:
-            print("Unloading LLM model...")
-            del self.llm
+            if not self.ollama:
+                print("Unloading LLM model...")
+                del self.llm
             self.llm = None
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
@@ -742,18 +748,31 @@ def generate_lyrics_and_prompt(self, genre: str, theme: str, language: str = "En
 
                     if self.llm:  # Check if load was successful
                         print(f"Using LLM for lyric generation (attempt {retry_count + 1}/{max_retries + 1})...")
-                        output = self.llm(
-                            prompt,
-                            max_tokens=700,
-                            temperature=0.7,
-                            top_p=0.9,
-                            repeat_penalty=1.1,
-                            stop=["[End]", "\n\n\n"],
-                            echo=False,
-                            seed=-1
-                        )
-
-                        lyrics = output["choices"][0]["text"].strip()
+                        if self.ollama:
+                            import ollama
+                            output = ollama.chat(
+                                model=self.llm_model_path,
+                                messages=[
+                                    {
+                                        'role': 'user',
+                                        'content': prompt,
+                                    },
+                                ]
+                            )
+                            lyrics = output.message.content.strip()
+                        else:
+                            output = self.llm(
+                                prompt,
+                                max_tokens=700,
+                                temperature=0.7,
+                                top_p=0.9,
+                                repeat_penalty=1.1,
+                                stop=["[End]", "\n\n\n"],
+                                echo=False,
+                                seed=-1
+                            )
+                            lyrics = output["choices"][0]["text"].strip()
+
                         print(f"Generated lyrics:\n{lyrics}")
 
                         # Validate lyrics quality
@@ -1239,7 +1258,9 @@ def update_theme_suggestions(genre):
     ) as demo:
         gr.Markdown("# 🎵 AI Radio Station")
         gr.Markdown("Continuous AI-powered music generation using ACE")
-
+        if radio.ollama:
+            gr.Markdown("#### Ollama Model for Lyric Generation: `" + radio.llm_model_path + "`")
+
         # Add a timer component for automatic updates
         timer = gr.Timer(0.5, active=True)
 
@@ -1262,12 +1283,19 @@ def update_theme_suggestions(genre):
                         buffer_size = gr.Slider(1, 10, value=1, step=1, label="Buffer Size (songs)")
                         random_mode = gr.Checkbox(label="Continuous Random Mode (after the first song)", value=True)
                         random_languages = gr.Checkbox(label="Randomize Languages (after the first song)", value=False)
-                        model_path_input = gr.File(
-                            label="GGUF Model File",
-                            file_types=[".gguf"],
-                            value="gemma-3-12b-it-abliterated.q4_k_m.gguf"
-                        )
-
+                        if radio.ollama:
+                            model_path_input = gr.File(
+                                label="GGUF Model File (Ollama model used instead)",
+                                file_types=[".gguf"],
+                                value="gemma-3-12b-it-abliterated.q4_k_m.gguf"
+                            )
+                        else:
+                            model_path_input = gr.File(
+                                label="GGUF Model File",
+                                file_types=[".gguf"],
+                                value="gemma-3-12b-it-abliterated.q4_k_m.gguf"
+                            )
+
                     with gr.Tab("Advanced Settings"):
                         language_input = gr.Dropdown(
                             choices=list(SUPPORTED_LANGUAGES.keys()),
@@ -1413,6 +1441,8 @@ def main():
                        help="Use bfloat16 precision")
     parser.add_argument("--torch_compile", default=False,
                        help="Enable torch compilation for faster inference")
+    parser.add_argument("--ollama", default=False,
+                       help="Enable Ollama for lyric generation")
     args = parser.parse_args()
 
     os.environ["CUDA_VISIBLE_DEVICES"] = str(args.device_id)
@@ -1430,7 +1460,8 @@ def main():
     print("Initializing AI Radio Station...")
     radio = AIRadioStation(
         ace_step_pipeline=pipeline,
-        model_path=args.model_path
+        model_path=args.model_path,
+        ollama=args.ollama
     )
 
     # Create and launch interface