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https://github.com/xinnan-tech/xiaozhi-esp32-server.git
synced 2026-07-23 23:53:55 +08:00
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8376b6f0f2 | ||
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490a66d3fe |
@@ -92,9 +92,11 @@
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- **硬件**：一套兼容 `xiaozhi-esp32`
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的硬件设备（具体型号请参考 [此处](https://rcnv1t9vps13.feishu.cn/wiki/DdgIw4BUgivWDPkhMj1cGIYCnRf)）。
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- **电脑或服务器**：至少 4 核 CPU、8G 内存的电脑。
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- **电脑或服务器**：建议 4 核 CPU、8G 内存的电脑。如果开启ASR也使用API，可运行在2核CPU、2G内存的服务器中。
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- **固件编译**：请将本后端服务的接口地址更新至 `xiaozhi-esp32` 项目中，再重新编译`xiaozhi-esp32`固件并烧录到设备上。
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如果你没有esp32相关的硬件设备，但是非常想体验该项目，可以使用以下的项目让你的电脑、手机模拟成esp32设备。
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- [小智安卓端](https://github.com/TOM88812/xiaozhi-android-client)
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@@ -212,10 +214,10 @@ server:
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### Memory 记忆存储
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| 类型 | 平台名称 | 使用方式 | 收费模式 | 备注 |
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|:------:|:---------------:|:----:|:--------:|:--:|
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| Memory | mem0ai | 接口调用 | 100次/月额度 | |
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| Memory | mem_local_short | 本地总结 | 免费 | |
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| 类型 | 平台名称 | 使用方式 | 收费模式 | 备注 |
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|:------:|:---------------:|:----:|:---------:|:--:|
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| Memory | mem0ai | 接口调用 | 1000次/月额度 | |
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| Memory | mem_local_short | 本地总结 | 免费 | |
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---
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@@ -1,3 +1,5 @@
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# 部署方案参考
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# 方式一：docker快速部署
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docker镜像已支持x86架构、arm64架构的CPU，支持在国产操作系统上运行。
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Binary file not shown.
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After Width: | Height: | Size: 576 KiB |
@@ -306,20 +306,24 @@ TTS:
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repetition_penalty: 1.35
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aux_ref_audio_paths: []
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GPT_SOVITS_V3:
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# 定义TTS API类型 GPT-SoVITS-v3lora-20250228
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#启动tts方法：
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#python api.py
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type: gpt_sovits_v3
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url: "http://127.0.0.1:9880/tts"
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url: "http://127.0.0.1:9880"
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output_file: tmp/
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text_lang: "auto"
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ref_audio_path: "caixukun.wav"
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prompt_lang: "zh"
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text_language: "auto"
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refer_wav_path: "caixukun.wav"
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prompt_language: "zh"
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prompt_text: ""
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top_k: 5
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top_p: 1
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temperature: 1
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sample_steps: 16
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media_type: "wav"
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streaming_mode: false
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threshold: 30
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top_k: 15
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top_p: 1.0
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temperature: 1.0
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cut_punc: ""
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speed: 1.0
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inp_refs: []
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sample_steps: 32
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if_sr: false
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MinimaxTTS:
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# Minimax语音合成服务，需要先在minimax平台创建账户充值，并获取登录信息
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# 平台地址：https://platform.minimaxi.com/
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@@ -12,10 +12,10 @@ class LLMProvider(LLMProviderBase):
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self.model_name = config.get("model_name")
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self.base_url = config.get("base_url", "http://localhost:11434")
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# Initialize OpenAI client with Ollama base URL
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#如果没有v1，增加v1
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# 如果没有v1，增加v1
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if not self.base_url.endswith("/v1"):
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self.base_url = f"{self.base_url}/v1"
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self.client = OpenAI(
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base_url=self.base_url,
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api_key="ollama" # Ollama doesn't need an API key but OpenAI client requires one
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@@ -28,13 +28,20 @@ class LLMProvider(LLMProviderBase):
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messages=dialogue,
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stream=True
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)
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is_active=True
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for chunk in responses:
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try:
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delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
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content = delta.content if hasattr(delta, 'content') else ''
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if content:
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yield content
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if '<think>' in content:
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is_active = False
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content = content.split('<think>')[0]
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if '</think>' in content:
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is_active = True
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content = content.split('</think>')[-1]
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if is_active:
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yield content
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error processing chunk: {e}")
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@@ -50,10 +57,10 @@ class LLMProvider(LLMProviderBase):
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stream=True,
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tools=functions,
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)
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for chunk in stream:
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yield chunk.choices[0].delta.content, chunk.choices[0].delta.tool_calls
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in Ollama function call: {e}")
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yield {"type": "content", "content": f"【Ollama服务响应异常: {str(e)}】"}
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yield {"type": "content", "content": f"【Ollama服务响应异常: {str(e)}】"}
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@@ -12,17 +12,18 @@ class TTSProvider(TTSProviderBase):
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def __init__(self, config, delete_audio_file):
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super().__init__(config, delete_audio_file)
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self.url = config.get("url")
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self.text_lang = config.get("text_lang", "audo")
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self.ref_audio_path = config.get("ref_audio_path")
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self.prompt_lang = config.get("prompt_lang")
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self.refer_wav_path = config.get("refer_wav_path")
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self.prompt_text = config.get("prompt_text")
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self.top_k = config.get("top_k", 5)
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self.top_p = config.get("top_p", 1)
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self.temperature = config.get("temperature", 1)
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self.sample_steps = config.get("sample_steps", 16)
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self.media_type = config.get("media_type", "wav")
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self.streaming_mode = config.get("streaming_mode", False)
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self.threshold = config.get("threshold", 30)
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self.prompt_language = config.get("prompt_language")
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self.text_language = config.get("text_language", "audo")
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self.top_k = config.get("top_k", 15)
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self.top_p = config.get("top_p", 1.0)
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self.temperature = config.get("temperature", 1.0)
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self.cut_punc = config.get("cut_punc","")
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self.speed = config.get("speed", 1.0)
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self.inp_refs = config.get("inp_refs",[])
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self.sample_steps = config.get("inp_refs",32)
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self.if_sr = config.get("if_sr",False)
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def generate_filename(self, extension=".wav"):
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@@ -30,18 +31,19 @@ class TTSProvider(TTSProviderBase):
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async def text_to_speak(self, text, output_file):
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request_params = {
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"text": text,
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"text_lang": self.text_lang,
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"ref_audio_path": self.ref_audio_path,
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"prompt_lang": self.prompt_lang,
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"refer_wav_path": self.refer_wav_path,
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"prompt_text": self.prompt_text,
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"prompt_language": self.prompt_language,
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"text": text,
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"text_language": self.text_language,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temperature": self.temperature,
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"cut_punc": self.cut_punc,
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"speed": self.speed,
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"inp_refs": self.inp_refs,
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"sample_steps": self.sample_steps,
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"media_type": self.media_type,
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"streaming_mode": self.streaming_mode,
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"threshold": self.threshold,
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"if_sr": self.if_sr,
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}
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resp = requests.get(self.url, params=request_params)
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