feat(transformers): add support to Dia (#5991)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>

backend/python/transformers/backend.py

@@ -22,7 +22,7 @@ import torch.cuda
 
 XPU=os.environ.get("XPU", "0") == "1"
 from transformers import AutoTokenizer, AutoModel, set_seed, TextIteratorStreamer, StoppingCriteriaList, StopStringCriteria, MambaConfig, MambaForCausalLM
-from transformers import AutoProcessor, MusicgenForConditionalGeneration
+from transformers import AutoProcessor, MusicgenForConditionalGeneration, DiaForConditionalGeneration
 from scipy.io import wavfile
 import outetts
 from sentence_transformers import SentenceTransformer
@@ -90,6 +90,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         self.CUDA = torch.cuda.is_available()
         self.OV=False
         self.OuteTTS=False
+        self.DiaTTS=False
         self.SentenceTransformer = False
 
         device_map="cpu"
@@ -97,6 +98,30 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         quantization = None
         autoTokenizer = True
 
+        # Parse options from request.Options
+        self.options = {}
+        options = request.Options
+        
+        # The options are a list of strings in this form optname:optvalue
+        # We are storing all the options in a dict so we can use it later when generating
+        # Example options: ["max_new_tokens:3072", "guidance_scale:3.0", "temperature:1.8", "top_p:0.90", "top_k:45"]
+        for opt in options:
+            if ":" not in opt:
+                continue
+            key, value = opt.split(":", 1)
+            # if value is a number, convert it to the appropriate type
+            try:
+                if "." in value:
+                    value = float(value)
+                else:
+                    value = int(value)
+            except ValueError:
+                # Keep as string if conversion fails
+                pass
+            self.options[key] = value
+
+        print(f"Parsed options: {self.options}", file=sys.stderr)
+
         if self.CUDA:
             from transformers import BitsAndBytesConfig, AutoModelForCausalLM
             if request.MainGPU:
@@ -202,6 +227,11 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 autoTokenizer = False
                 self.processor = AutoProcessor.from_pretrained(model_name)
                 self.model = MusicgenForConditionalGeneration.from_pretrained(model_name)
+            elif request.Type == "DiaForConditionalGeneration":
+                autoTokenizer = False
+                self.processor = AutoProcessor.from_pretrained(model_name)
+                self.model = DiaForConditionalGeneration.from_pretrained(model_name)
+                self.DiaTTS = True
             elif request.Type == "OuteTTS":
                 autoTokenizer = False
                 options = request.Options
@@ -262,7 +292,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             elif hasattr(self.model, 'config') and hasattr(self.model.config, 'max_position_embeddings'):
                 self.max_tokens = self.model.config.max_position_embeddings
             else:
-                self.max_tokens = 512
+                self.max_tokens = self.options.get("max_new_tokens", 512)
  
             if autoTokenizer:
                 self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
@@ -485,16 +515,15 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                     return_tensors="pt",
                 )
             
-            tokens = 256
             if request.HasField('duration'):
                 tokens = int(request.duration * 51.2) # 256 tokens = 5 seconds, therefore 51.2 tokens is one second
-            guidance = 3.0
+            guidance = self.options.get("guidance_scale", 3.0)
             if request.HasField('temperature'):
                 guidance = request.temperature
-            dosample = True
+            dosample = self.options.get("do_sample", True)
             if request.HasField('sample'):
                 dosample = request.sample
-            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=tokens)
+            audio_values = self.model.generate(**inputs, do_sample=dosample, guidance_scale=guidance, max_new_tokens=self.max_tokens)
             print("[transformers-musicgen] SoundGeneration generated!", file=sys.stderr)
             sampling_rate = self.model.config.audio_encoder.sampling_rate
             wavfile.write(request.dst, rate=sampling_rate, data=audio_values[0, 0].numpy())
@@ -506,13 +535,59 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
         return backend_pb2.Result(success=True)
 
+
+    def DiaTTS(self, request, context):
+        """
+        Generates dialogue audio using the Dia model.
+        
+        Args:
+            request: A TTSRequest containing text dialogue and generation parameters
+            context: The gRPC context
+            
+        Returns:
+            A Result object indicating success or failure
+        """
+        try:
+            print("[DiaTTS] generating dialogue audio", file=sys.stderr)
+            
+            # Prepare text input - expect dialogue format like [S1] ... [S2] ...
+            text = [request.text]
+            
+            # Process the input
+            inputs = self.processor(text=text, padding=True, return_tensors="pt")
+            
+            # Generate audio with parameters from options or defaults
+            generation_params = {
+                **inputs,
+                "max_new_tokens": self.max_tokens,
+                "guidance_scale": self.options.get("guidance_scale", 3.0),
+                "temperature": self.options.get("temperature", 1.8),
+                "top_p": self.options.get("top_p", 0.90),
+                "top_k": self.options.get("top_k", 45)
+            }
+            
+            outputs = self.model.generate(**generation_params)
+            
+            # Decode and save audio
+            outputs = self.processor.batch_decode(outputs)
+            self.processor.save_audio(outputs, request.dst)
+            
+            print("[DiaTTS] Generated dialogue audio", file=sys.stderr)
+            print("[DiaTTS] Audio saved to", request.dst, file=sys.stderr)
+            print("[DiaTTS] Dialogue generation done", file=sys.stderr)
+            
+        except Exception as err:
+            return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
+        return backend_pb2.Result(success=True)
+
+
     def OuteTTS(self, request, context):
         try:
             print("[OuteTTS] generating TTS", file=sys.stderr)
             gen_cfg = outetts.GenerationConfig(
                 text="Speech synthesis is the artificial production of human speech.",
-                temperature=0.1,
-                repetition_penalty=1.1,
+                temperature=self.options.get("temperature", 0.1),
+                repetition_penalty=self.options.get("repetition_penalty", 1.1),
                 max_length=self.max_tokens,
                 speaker=self.speaker,
                 # voice_characteristics="upbeat enthusiasm, friendliness, clarity, professionalism, and trustworthiness"
@@ -529,6 +604,9 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
     def TTS(self, request, context):
         if self.OuteTTS:
             return self.OuteTTS(request, context)
+        
+        if self.DiaTTS:
+            return self.DiaTTS(request, context)
 
         model_name = request.model
         try: