Support bark for TTS

lazyllm/common/globals.py

@@ -66,7 +66,7 @@ def __getattr__(self, __name: str) -> Any:
         raise AttributeError(f'Attr {__name} not found in globals')
 
     def clear(self):
-        self.__data.pop(self._sid)
+        self.__data.pop(self._sid, None)
 
     def _clear_all(self):
         self.__data.clear()

lazyllm/components/auto/autodeploy.py

@@ -5,6 +5,7 @@
 from .auto_helper import model_map, get_model_name, check_requirements
 from lazyllm.components.embedding.embed import EmbeddingDeploy
 from lazyllm.components.stable_diffusion.stable_diffusion3 import StableDiffusionDeploy
+from lazyllm.components.text_to_speech.bark import BarkDeploy
 from ..utils.downloader import ModelManager
 
 class AutoDeploy(LazyLLMDeployBase):
@@ -20,6 +21,8 @@ def __new__(cls, base_model, source=lazyllm.config['model_source'], trust_remote
             return EmbeddingDeploy(trust_remote_code, launcher)
         elif type == 'sd' or ModelManager.get_model_type(model_name) == 'sd':
             return StableDiffusionDeploy(launcher)
+        elif type == 'tts' or ModelManager.get_model_type(model_name) == 'tts':
+            return BarkDeploy(launcher)
         map_name = model_map(model_name)
         candidates = get_configer().query_deploy(lazyllm.config['gpu_type'], launcher.ngpus,
                                                  map_name, max_token_num)

lazyllm/components/text_to_speech/bark.py

@@ -0,0 +1,70 @@
+import os
+import json
+
+import lazyllm
+from lazyllm import LOG
+from lazyllm.thirdparty import torch
+from lazyllm.thirdparty import transformers as tf
+from ..utils.downloader import ModelManager
+
+class Bark(object):
+
+    def __init__(self, base_sd, source=None, trust_remote_code=True, init=False):
+        source = lazyllm.config['model_source'] if not source else source
+        self.base_sd = ModelManager(source).download(base_sd)
+        self.trust_remote_code = trust_remote_code
+        self.processor, self.bark = None, None
+        self.init_flag = lazyllm.once_flag()
+        self.device = 'cpu'
+        if init:
+            lazyllm.call_once(self.init_flag, self.load_bark)
+
+    def load_bark(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.processor = tf.AutoProcessor.from_pretrained(self.base_sd)
+        self.processor.speaker_embeddings['repo_or_path'] = self.base_sd
+        self.bark = tf.BarkModel.from_pretrained(self.base_sd,
+                                                 torch_dtype=torch.float16,
+                                                 attn_implementation="flash_attention_2").to(self.device)
+
+    def __call__(self, string):
+        lazyllm.call_once(self.init_flag, self.load_bark)
+        if isinstance(string, str):
+            query = string
+            voice_preset = "v2/zh_speaker_9"
+        elif isinstance(string, dict):
+            query = string['inputs']
+            voice_preset = string['voice_preset']
+        else:
+            raise TypeError(f"Not support input type:{type(string)}, requires str or dict.")
+        inputs = self.processor(query, voice_preset=voice_preset).to(self.device)
+        speech = self.bark.generate(**inputs) * 32767
+        res = {'sounds': (
+            self.bark.generation_config.sample_rate,
+            speech.cpu().numpy().squeeze().tolist()
+        )}
+        return json.dumps(res)
+
+
+class BarkDeploy(object):
+    keys_name_handle = {
+        'inputs': 'inputs',
+    }
+    message_format = {
+        'inputs': 'Who are you ?',
+        'voice_preset': None,
+    }
+    default_headers = {'Content-Type': 'application/json'}
+
+    def __init__(self, launcher=None):
+        self.launcher = launcher
+
+    def __call__(self, finetuned_model=None, base_model=None):
+        if not os.path.exists(finetuned_model) or \
+            not any(filename.endswith('.bin') or filename.endswith('.safetensors')
+                    for _, _, filename in os.walk(finetuned_model) if filename):
+            if not finetuned_model:
+                LOG.warning(f"Note! That finetuned_model({finetuned_model}) is an invalid path, "
+                            f"base_model({base_model}) will be used")
+            finetuned_model = base_model
+        return lazyllm.deploy.RelayServer(func=Bark(finetuned_model), launcher=self.launcher)()

lazyllm/components/utils/downloader/model_mapping.py

@@ -285,7 +285,11 @@
     "stable-diffusion-3-medium": {
         "source": {"huggingface": "stabilityai/stable-diffusion-3-medium", "modelscope": "AI-ModelScope/stable-diffusion-3-medium-diffusers"},
         "type": "sd",
-		},
+	},
+    "bark":{
+        "source": {"huggingface": "suno/bark", "modelscope": "mapjack/bark"},
+        "type": "tts",
+	},
     "llava-1.5-7b": {
 		"source": {"huggingface": "llava-hf/llava-1.5-7b-hf", "modelscope": "huangjintao/llava-1.5-7b-hf"},
         "type": "vlm",

lazyllm/tools/webpages/webmodule.py

@@ -12,6 +12,7 @@
 from PIL import Image
 from io import BytesIO
 from types import GeneratorType
+import numpy as np
 
 import lazyllm
 from lazyllm import LOG, globals
@@ -93,6 +94,9 @@ def init_web(self, component_descs):
                     for _, gname, name, ctype, value in component_descs:
                         if ctype in ('Checkbox', 'Text'):
                             components.append(getattr(gr, ctype)(interactive=True, value=value, label=f'{gname}.{name}'))
+                        elif ctype == 'Dropdown':
+                            components.append(getattr(gr, ctype)(interactive=True, choices=value,
+                                                                 label=f'{gname}.{name}'))
                         else:
                             raise KeyError(f'invalid component type: {ctype}')
                     with gr.Row():
@@ -262,7 +266,11 @@ def get_log_and_message(s):
                         elif 'images_base64' in r:
                             image_data = r.pop('images_base64')[0]
                             image = Image.open(BytesIO(base64.b64decode(image_data)))
-                            return "The image is: ", "".join(log_history), image
+                            return "The image is: ", "".join(log_history), {'img': image}
+                        elif 'sounds' in r:
+                            sound_data = r.pop('sounds')
+                            sound_data = (sound_data[0], np.array(sound_data[1]).astype(np.int16))
+                            return "The Audio is: ", "".join(log_history), {'audio': sound_data}
                         else:
                             s = s
                     except (ValueError, KeyError, TypeError):
@@ -272,11 +280,14 @@ def get_log_and_message(s):
                 return s, "".join(log_history), None
 
             log_history = []
-            image = None
+            file = None
             if isinstance(result, (str, dict)):
-                result, log, image = get_log_and_message(result)
-            if image:
-                chat_history[-1][1] = gr.Image(image)
+                result, log, file = get_log_and_message(result)
+            if file:
+                if 'img' in file:
+                    chat_history[-1][1] = gr.Image(file['img'])
+                if 'audio' in file:
+                    chat_history[-1][1] = gr.Audio(file['audio'])
             elif isinstance(result, str):
                 chat_history[-1][1] = result
             elif isinstance(result, GeneratorType):

tests/advanced_tests/test_deploy.py

@@ -96,6 +96,12 @@ def test_sd3(self):
         res = m('a little cat')
         assert "images_base64" in json.loads(res)
 
+    def test_bark(self):
+        m = lazyllm.TrainableModule('bark')
+        m.update_server()
+        res = m('你好啊，很高兴认识你。')
+        assert "sounds" in json.loads(res)
+
     def test_vlm_and_lmdeploy(self):
         chat = lazyllm.TrainableModule('internvl-chat-2b-v1-5').deploy_method(deploy.LMDeploy)
         m = lazyllm.ServerModule(chat)