fix(llama.cpp): fix eos without cache

Signed-off-by: GitHub <noreply@github.com>
Co-authored-by: mudler <mudler@users.noreply.github.com>

Makefile

@@ -4,11 +4,11 @@ GOVET=$(GOCMD) vet
 BINARY_NAME=local-ai
 
 # llama.cpp versions
-GOLLAMA_VERSION?=aeba71ee842819da681ea537e78846dc75949ac0
+GOLLAMA_VERSION?=6a8041ef6b46d4712afc3ae791d1c2d73da0ad1c
 
 GOLLAMA_STABLE_VERSION?=50cee7712066d9e38306eccadcfbb44ea87df4b7
 
-CPPLLAMA_VERSION?=19885d205e768579ab090d1e99281cae58c21b54
+CPPLLAMA_VERSION?=4755afd1cbd40d93c017e5b98c39796f52345314
 
 # gpt4all version
 GPT4ALL_REPO?=https://github.com/nomic-ai/gpt4all
@@ -19,13 +19,13 @@ RWKV_REPO?=https://github.com/donomii/go-rwkv.cpp
 RWKV_VERSION?=661e7ae26d442f5cfebd2a0881b44e8c55949ec6
 
 # whisper.cpp version
-WHISPER_CPP_VERSION?=37a709f6558c6d9783199e2b8cbb136e1c41d346
+WHISPER_CPP_VERSION?=a56f435fd475afd7edf02bfbf9f8c77f527198c2
 
 # bert.cpp version
 BERT_VERSION?=6abe312cded14042f6b7c3cd8edf082713334a4d
 
 # go-piper version
-PIPER_VERSION?=d6b6275ba037dabdba4a8b65dfdf6b2a73a67f07
+PIPER_VERSION?=9d0100873a7dbb0824dfea40e8cec70a1b110759
 
 # stablediffusion version
 STABLEDIFFUSION_VERSION?=362df9da29f882dbf09ade61972d16a1f53c3485
@@ -91,10 +91,13 @@ ifeq ($(BUILD_TYPE),openblas)
 	export WHISPER_OPENBLAS=1
 endif
 
+
 ifeq ($(BUILD_TYPE),cublas)
-	CGO_LDFLAGS+=-lcublas -lcudart -L$(CUDA_LIBPATH)
+	CGO_LDFLAGS+=-lcublas -lcudart -lculibos -lcublasLt -L$(CUDA_LIBPATH)
 	export LLAMA_CUBLAS=1
+# required by whisper.cpp
 	export WHISPER_CUBLAS=1
+	CGO_LDFLAGS+=-L$(CUDA_PATH)/stubs -lcuda
 endif
 
 ifeq ($(BUILD_TYPE),hipblas)
@@ -462,9 +465,6 @@ backend-assets/grpc/llama: backend-assets/grpc sources/go-llama/libbinding.a
 	CGO_LDFLAGS="$(CGO_LDFLAGS)" C_INCLUDE_PATH=$(CURDIR)/sources/go-llama LIBRARY_PATH=$(CURDIR)/sources/go-llama \
 	$(GOCMD) build -ldflags "$(LD_FLAGS)" -tags "$(GO_TAGS)" -o backend-assets/grpc/llama ./backend/go/llm/llama/
 # TODO: every binary should have its own folder instead, so can have different  implementations
-ifeq ($(BUILD_TYPE),metal)
-	cp backend/cpp/llama/llama.cpp/ggml-metal.metal backend-assets/grpc/
-endif
 
 ## BACKEND CPP LLAMA START
 # Sets the variables in case it has to build the gRPC locally.
@@ -494,7 +494,7 @@ backend-assets/grpc/llama-cpp: backend-assets/grpc backend/cpp/llama/grpc-server
 	cp -rfv backend/cpp/llama/grpc-server backend-assets/grpc/llama-cpp
 # TODO: every binary should have its own folder instead, so can have different metal implementations
 ifeq ($(BUILD_TYPE),metal)
-	cp backend/cpp/llama/llama.cpp/build/bin/ggml-metal.metal backend-assets/grpc/
+	cp backend/cpp/llama/llama.cpp/build/bin/default.metallib backend-assets/grpc/
 endif
 
 backend-assets/grpc/llama-ggml: backend-assets/grpc sources/go-llama-ggml/libbinding.a

backend/backend.proto

@@ -191,6 +191,7 @@ message TTSRequest {
   string text = 1;
   string model = 2;
   string dst = 3;
+  string voice = 4;
 }
 
 message TokenizationResponse {

backend/cpp/llama/Makefile

@@ -18,6 +18,7 @@ else ifeq ($(BUILD_TYPE),clblas)
 # If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ 
 else ifeq ($(BUILD_TYPE),hipblas)
 	CMAKE_ARGS+=-DLLAMA_HIPBLAS=ON
+# If it's OSX, DO NOT embed the metal library - -DLLAMA_METAL_EMBED_LIBRARY=ON requires further investigation
 endif
 
 ifeq ($(BUILD_TYPE),sycl_f16)

backend/cpp/llama/grpc-server.cpp

@@ -1084,7 +1084,7 @@ struct llama_server_context
             slot.has_next_token = false;
         }
 
-        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
+        if (result.tok == llama_token_eos(model))
         {
             slot.stopped_eos = true;
             slot.has_next_token = false;

backend/python/common-env/transformers/transformers-nvidia.yml

@@ -30,6 +30,7 @@ dependencies:
       - async-timeout==4.0.3
       - attrs==23.1.0
       - bark==0.1.5
+      - bitsandbytes==0.43.0
       - boto3==1.28.61
       - botocore==1.31.61
       - certifi==2023.7.22

backend/python/transformers/transformers_server.py

@@ -23,7 +23,7 @@ if XPU:
     from intel_extension_for_transformers.transformers.modeling import AutoModelForCausalLM
     from transformers import AutoTokenizer, AutoModel, set_seed
 else:
-    from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed
+    from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM, set_seed, BitsAndBytesConfig
 
 
 _ONE_DAY_IN_SECONDS = 60 * 60 * 24
@@ -75,18 +75,50 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
             A Result object that contains the result of the LoadModel operation.
         """
         model_name = request.Model
+
+        compute = "auto"
+        if request.F16Memory == True:
+            compute=torch.bfloat16
+
+        self.CUDA = request.CUDA
+
+        device_map="cpu"
+
+        quantization = None
+
+        if self.CUDA:
+            if request.Device:
+                device_map=request.Device
+            else:
+                device_map="cuda:0"
+            if request.Quantization == "bnb_4bit":
+                quantization = BitsAndBytesConfig(
+                    load_in_4bit = True,
+                    bnb_4bit_compute_dtype = compute,
+                    bnb_4bit_quant_type = "nf4",
+                    bnb_4bit_use_double_quant = True,
+                    load_in_8bit = False,
+                )
+            elif request.Quantization == "bnb_8bit":
+                quantization = BitsAndBytesConfig(
+                    load_in_4bit=False,
+                    bnb_4bit_compute_dtype = None,
+                    load_in_8bit=True,                                   
+                )
+                                                   
+    
         try:
             if request.Type == "AutoModelForCausalLM":
                 if XPU:
+                    if quantization == "xpu_4bit":
+                        xpu_4bit = True
                     self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode,
-                                              device_map="xpu", load_in_4bit=True)
+                                              device_map="xpu", load_in_4bit=xpu_4bit)
                 else:
-                    self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
+                    self.model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode, use_safetensors=True, quantization_config=quantization, device_map=device_map, torch_dtype=compute)
             else:
-                self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode)
-
-            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-            self.CUDA = False
+                self.model = AutoModel.from_pretrained(model_name, trust_remote_code=request.TrustRemoteCode,  use_safetensors=True,  quantization_config=quantization, device_map=device_map, torch_dtype=compute)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name, use_safetensors=True)
             self.XPU = False
 
             if XPU:
@@ -97,13 +129,6 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
                 except Exception as err:
                     print("Not using XPU:", err, file=sys.stderr)
 
-            if request.CUDA or torch.cuda.is_available():
-                try:
-                    print("Loading model", model_name, "to CUDA.", file=sys.stderr)
-                    self.model = self.model.to("cuda")
-                    self.CUDA = True
-                except Exception as err:
-                    print("Not using CUDA:", err, file=sys.stderr)
         except Exception as err:
             return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
         # Implement your logic here for the LoadModel service
@@ -130,13 +155,17 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         encoded_input = self.tokenizer(request.Embeddings, padding=True, truncation=True, max_length=max_length, return_tensors="pt")    
 
         # Create word embeddings
-        model_output = self.model(**encoded_input)
+        if self.CUDA:
+            encoded_input = encoded_input.to("cuda")
+
+        with torch.no_grad():    
+            model_output = self.model(**encoded_input)
 
         # Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
-        sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']).detach().numpy()
+        sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
         print("Calculated embeddings for: " + request.Embeddings, file=sys.stderr)
         print("Embeddings:", sentence_embeddings, file=sys.stderr)
-        return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings)
+        return backend_pb2.EmbeddingResult(embeddings=sentence_embeddings[0])
 
     def Predict(self, request, context):
         """
@@ -163,12 +192,8 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
         if XPU:
             inputs = inputs.to("xpu")
 
-        outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP)
-
-        generated_text = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
-        # Remove prompt from response if present
-        if request.Prompt in generated_text:
-            generated_text = generated_text.replace(request.Prompt, "")
+        outputs = self.model.generate(inputs,max_new_tokens=max_tokens, temperature=request.Temperature, top_p=request.TopP, do_sample=True, pad_token_id=self.tokenizer.eos_token_id)
+        generated_text = self.tokenizer.batch_decode(outputs[:, inputs.shape[1]:], skip_special_tokens=True)[0]
 
         return backend_pb2.Reply(message=bytes(generated_text, encoding='utf-8'))
 

core/backend/embeddings.go

@@ -10,10 +10,6 @@ import (
 )
 
 func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, backendConfig config.BackendConfig, appConfig *config.ApplicationConfig) (func() ([]float32, error), error) {
-	if !backendConfig.Embeddings {
-		return nil, fmt.Errorf("endpoint disabled for this model by API configuration")
-	}
-
 	modelFile := backendConfig.Model
 
 	grpcOpts := gRPCModelOpts(backendConfig)

core/backend/tts.go

@@ -29,7 +29,7 @@ func generateUniqueFileName(dir, baseName, ext string) string {
 	}
 }
 
-func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appConfig *config.ApplicationConfig, backendConfig config.BackendConfig) (string, *proto.Result, error) {
+func ModelTTS(backend, text, modelFile, voice string, loader *model.ModelLoader, appConfig *config.ApplicationConfig, backendConfig config.BackendConfig) (string, *proto.Result, error) {
 	bb := backend
 	if bb == "" {
 		bb = model.PiperBackend
@@ -44,12 +44,12 @@ func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appCon
 		model.WithAssetDir(appConfig.AssetsDestination),
 		model.WithLoadGRPCLoadModelOpts(grpcOpts),
 	})
-	piperModel, err := loader.BackendLoader(opts...)
+	ttsModel, err := loader.BackendLoader(opts...)
 	if err != nil {
 		return "", nil, err
 	}
 
-	if piperModel == nil {
+	if ttsModel == nil {
 		return "", nil, fmt.Errorf("could not load piper model")
 	}
 
@@ -57,25 +57,31 @@ func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, appCon
 		return "", nil, fmt.Errorf("failed creating audio directory: %s", err)
 	}
 
-	fileName := generateUniqueFileName(appConfig.AudioDir, "piper", ".wav")
+	fileName := generateUniqueFileName(appConfig.AudioDir, "tts", ".wav")
 	filePath := filepath.Join(appConfig.AudioDir, fileName)
 
 	// If the model file is not empty, we pass it joined with the model path
 	modelPath := ""
 	if modelFile != "" {
-		if bb != model.TransformersMusicGen {
-			modelPath = filepath.Join(loader.ModelPath, modelFile)
-			if err := utils.VerifyPath(modelPath, appConfig.ModelPath); err != nil {
+		// If the model file is not empty, we pass it joined with the model path
+		// Checking first that it exists and is not outside ModelPath
+		// TODO: we should actually first check if the modelFile is looking like
+		// a FS path
+		mp := filepath.Join(loader.ModelPath, modelFile)
+		if _, err := os.Stat(mp); err == nil {
+			if err := utils.VerifyPath(mp, appConfig.ModelPath); err != nil {
 				return "", nil, err
 			}
+			modelPath = mp
 		} else {
 			modelPath = modelFile
 		}
 	}
 
-	res, err := piperModel.TTS(context.Background(), &proto.TTSRequest{
+	res, err := ttsModel.TTS(context.Background(), &proto.TTSRequest{
 		Text:  text,
 		Model: modelPath,
+		Voice: voice,
 		Dst:   filePath,
 	})
 

core/http/api.go

@@ -6,6 +6,7 @@ import (
 	"os"
 	"strings"
 
+	"github.com/go-skynet/LocalAI/core/http/endpoints/elevenlabs"
 	"github.com/go-skynet/LocalAI/core/http/endpoints/localai"
 	"github.com/go-skynet/LocalAI/core/http/endpoints/openai"
 
@@ -21,6 +22,24 @@ import (
 	"github.com/gofiber/fiber/v2/middleware/recover"
 )
 
+func readAuthHeader(c *fiber.Ctx) string {
+	authHeader := c.Get("Authorization")
+
+	// elevenlabs
+	xApiKey := c.Get("xi-api-key")
+	if xApiKey != "" {
+		authHeader = "Bearer " + xApiKey
+	}
+
+	// anthropic
+	xApiKey = c.Get("x-api-key")
+	if xApiKey != "" {
+		authHeader = "Bearer " + xApiKey
+	}
+
+	return authHeader
+}
+
 func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) (*fiber.App, error) {
 	// Return errors as JSON responses
 	app := fiber.New(fiber.Config{
@@ -94,10 +113,12 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
 			return c.Next()
 		}
 
-		authHeader := c.Get("Authorization")
+		authHeader := readAuthHeader(c)
 		if authHeader == "" {
 			return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Authorization header missing"})
 		}
+
+		// If it's a bearer token
 		authHeaderParts := strings.Split(authHeader, " ")
 		if len(authHeaderParts) != 2 || authHeaderParts[0] != "Bearer" {
 			return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid Authorization header format"})
@@ -111,7 +132,6 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
 		}
 
 		return c.Status(fiber.StatusUnauthorized).JSON(fiber.Map{"message": "Invalid API key"})
-
 	}
 
 	if appConfig.CORS {
@@ -147,6 +167,11 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
 	app.Get("/models/jobs/:uuid", auth, modelGalleryEndpointService.GetOpStatusEndpoint())
 	app.Get("/models/jobs", auth, modelGalleryEndpointService.GetAllStatusEndpoint())
 
+	app.Post("/tts", auth, localai.TTSEndpoint(cl, ml, appConfig))
+
+	// Elevenlabs
+	app.Post("/v1/text-to-speech/:voice-id", auth, elevenlabs.TTSEndpoint(cl, ml, appConfig))
+
 	// openAI compatible API endpoint
 
 	// chat
@@ -181,7 +206,7 @@ func App(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *confi
 
 	// audio
 	app.Post("/v1/audio/transcriptions", auth, openai.TranscriptEndpoint(cl, ml, appConfig))
-	app.Post("/tts", auth, localai.TTSEndpoint(cl, ml, appConfig))
+	app.Post("/v1/audio/speech", auth, localai.TTSEndpoint(cl, ml, appConfig))
 
 	// images
 	app.Post("/v1/images/generations", auth, openai.ImageEndpoint(cl, ml, appConfig))

core/http/endpoints/elevenlabs/tts.go

@@ -0,0 +1,55 @@
+package elevenlabs
+
+import (
+	"github.com/go-skynet/LocalAI/core/backend"
+	"github.com/go-skynet/LocalAI/core/config"
+	fiberContext "github.com/go-skynet/LocalAI/core/http/ctx"
+	"github.com/go-skynet/LocalAI/pkg/model"
+
+	"github.com/go-skynet/LocalAI/core/schema"
+	"github.com/gofiber/fiber/v2"
+	"github.com/rs/zerolog/log"
+)
+
+func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfig *config.ApplicationConfig) func(c *fiber.Ctx) error {
+	return func(c *fiber.Ctx) error {
+
+		input := new(schema.ElevenLabsTTSRequest)
+		voiceID := c.Params("voice-id")
+
+		// Get input data from the request body
+		if err := c.BodyParser(input); err != nil {
+			return err
+		}
+
+		modelFile, err := fiberContext.ModelFromContext(c, ml, input.ModelID, false)
+		if err != nil {
+			modelFile = input.ModelID
+			log.Warn().Msgf("Model not found in context: %s", input.ModelID)
+		}
+
+		cfg, err := cl.LoadBackendConfigFileByName(modelFile, appConfig.ModelPath,
+			config.LoadOptionDebug(appConfig.Debug),
+			config.LoadOptionThreads(appConfig.Threads),
+			config.LoadOptionContextSize(appConfig.ContextSize),
+			config.LoadOptionF16(appConfig.F16),
+		)
+		if err != nil {
+			modelFile = input.ModelID
+			log.Warn().Msgf("Model not found in context: %s", input.ModelID)
+		} else {
+			if input.ModelID != "" {
+				modelFile = input.ModelID
+			} else {
+				modelFile = cfg.Model
+			}
+		}
+		log.Debug().Msgf("Request for model: %s", modelFile)
+
+		filePath, _, err := backend.ModelTTS(cfg.Backend, input.Text, modelFile, voiceID, ml, appConfig, *cfg)
+		if err != nil {
+			return err
+		}
+		return c.Download(filePath)
+	}
+}

core/http/endpoints/localai/tts.go

@@ -46,7 +46,7 @@ func TTSEndpoint(cl *config.BackendConfigLoader, ml *model.ModelLoader, appConfi
 			cfg.Backend = input.Backend
 		}
 
-		filePath, _, err := backend.ModelTTS(cfg.Backend, input.Input, modelFile, ml, appConfig, *cfg)
+		filePath, _, err := backend.ModelTTS(cfg.Backend, input.Input, modelFile, input.Voice, ml, appConfig, *cfg)
 		if err != nil {
 			return err
 		}

core/schema/elevenlabs.go

@@ -0,0 +1,6 @@
+package schema
+
+type ElevenLabsTTSRequest struct {
+	Text    string `json:"text" yaml:"text"`
+	ModelID string `json:"model_id" yaml:"model_id"`
+}

core/schema/localai.go

@@ -17,5 +17,6 @@ type BackendMonitorResponse struct {
 type TTSRequest struct {
 	Model   string `json:"model" yaml:"model"`
 	Input   string `json:"input" yaml:"input"`
+	Voice   string `json:"voice" yaml:"voice"`
 	Backend string `json:"backend" yaml:"backend"`
 }

docs/content/docs/features/text-generation.md

@@ -272,3 +272,56 @@ curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d
    "temperature": 0.1, "top_p": 0.1
  }'
 ```
+
+### Transformers
+
+[Transformers](https://huggingface.co/docs/transformers/index) is a State-of-the-art Machine Learning library for PyTorch, TensorFlow, and JAX.
+
+LocalAI has a built-in integration with Transformers, and it can be used to run models.
+
+This is an extra backend - in the container images (the `extra` images already contains python dependencies for Transformers) is already available and there is nothing to do for the setup.
+
+#### Setup
+
+Create a YAML file for the model you want to use with `transformers`.
+
+To setup a model, you need to just specify the model name in the YAML config file:
+```yaml
+name: transformers
+backend: transformers
+parameters:
+    model: "facebook/opt-125m"
+type: AutoModelForCausalLM
+quantization: bnb_4bit # One of: bnb_8bit, bnb_4bit, xpu_4bit (optional)
+```
+
+The backend will automatically download the required files in order to run the model.
+
+#### Parameters
+
+##### Type
+
+| Type | Description |
+| --- | --- |
+| `AutoModelForCausalLM` | `AutoModelForCausalLM` is a model that can be used to generate sequences. |
+| N/A | Defaults to `AutoModel` |
+
+
+##### Quantization
+
+| Quantization | Description |
+| --- | --- |
+| `bnb_8bit` | 8-bit quantization |
+| `bnb_4bit` | 4-bit quantization |
+| `xpu_4bit` | 4-bit quantization for Intel XPUs |
+
+#### Usage
+
+Use the `completions` endpoint by specifying the `transformers` model:
+```
+curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{   
+   "model": "transformers",
+   "prompt": "Hello, my name is",
+   "temperature": 0.1, "top_p": 0.1
+ }'
+```

docs/content/docs/features/text-to-audio.md

@@ -6,7 +6,13 @@ weight = 11
 url = "/features/text-to-audio/"
 +++
 
-The `/tts` endpoint can be used to generate speech from text.
+## API Compatibility
+
+The LocalAI TTS API is compatible with the [OpenAI TTS API](https://platform.openai.com/docs/guides/text-to-speech) and the [Elevenlabs](https://api.elevenlabs.io/docs) API.
+
+## LocalAI API
+
+The `/tts` endpoint can also be used to generate speech from text.
 
 ## Usage
 

main.go

@@ -50,7 +50,7 @@ func main() {
 	app := &cli.App{
 		Name:    "LocalAI",
 		Version: internal.PrintableVersion(),
-		Usage:   "OpenAI compatible API for running LLaMA/GPT models locally on CPU with consumer grade hardware.",
+		Usage:   "OpenAI, OSS alternative. Drop-in compatible API for running LLM, GPT and genAI models locally on CPU, GPUs with consumer grade hardware. Supported server endpoints: OpenAI, Elevenlabs",
 		Flags: []cli.Flag{
 			&cli.BoolFlag{
 				Name:    "f16",
@@ -394,6 +394,12 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
 						Usage:    "Model name to run the TTS",
 						Required: true,
 					},
+					&cli.StringFlag{
+						Name:     "voice",
+						Aliases:  []string{"v"},
+						Usage:    "Voice name to run the TTS (optional)",
+						Required: true,
+					},
 					&cli.StringFlag{
 						Name:    "output-file",
 						Aliases: []string{"o"},
@@ -427,7 +433,7 @@ For a list of compatible model, check out: https://localai.io/model-compatibilit
 
 					defer ml.StopAllGRPC()
 
-					filePath, _, err := backend.ModelTTS(backendOption, text, modelOption, ml, opts, config.BackendConfig{})
+					filePath, _, err := backend.ModelTTS(backendOption, text, modelOption, ctx.String("voice"), ml, opts, config.BackendConfig{})
 					if err != nil {
 						return err
 					}

pkg/grpc/proto/backend.pb.go

@@ -1,7 +1,7 @@
 // Code generated by protoc-gen-go. DO NOT EDIT.
 // versions:
 // 	protoc-gen-go v1.26.0
-// 	protoc        v4.25.3
+// 	protoc        v4.23.4
 // source: backend.proto
 
 package proto
@@ -1436,6 +1436,7 @@ type TTSRequest struct {
 	Text  string `protobuf:"bytes,1,opt,name=text,proto3" json:"text,omitempty"`
 	Model string `protobuf:"bytes,2,opt,name=model,proto3" json:"model,omitempty"`
 	Dst   string `protobuf:"bytes,3,opt,name=dst,proto3" json:"dst,omitempty"`
+	Voice string `protobuf:"bytes,4,opt,name=voice,proto3" json:"voice,omitempty"`
 }
 
 func (x *TTSRequest) Reset() {
@@ -1491,6 +1492,13 @@ func (x *TTSRequest) GetDst() string {
 	return ""
 }
 
+func (x *TTSRequest) GetVoice() string {
+	if x != nil {
+		return x.Voice
+	}
+	return ""
+}
+
 type TokenizationResponse struct {
 	state         protoimpl.MessageState
 	sizeCache     protoimpl.SizeCache
@@ -1896,84 +1904,86 @@ var file_backend_proto_rawDesc = []byte{
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 }
 
 var (

pkg/grpc/proto/backend_grpc.pb.go

@@ -1,7 +1,7 @@
 // Code generated by protoc-gen-go-grpc. DO NOT EDIT.
 // versions:
 // - protoc-gen-go-grpc v1.2.0
-// - protoc             v4.25.3
+// - protoc             v4.23.4
 // source: backend.proto
 
 package proto