mirror of
https://git.mirrors.martin98.com/https://github.com/bytedance/deer-flow
synced 2025-08-16 11:45:58 +08:00
feat: integrate volcengine tts functionality
This commit is contained in:
parent
b2f14d1737
commit
a6ab97c970
@ -2,10 +2,14 @@
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DEBUG=True
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DEBUG=True
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APP_ENV=development
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APP_ENV=development
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# Add other environment variables as needed
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# Search Engine
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# tavily, duckduckgo, brave_search, arxiv
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SEARCH_API=tavily
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SEARCH_API=tavily
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TAVILY_API_KEY=tvly-xxx
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TAVILY_API_KEY=tvly-xxx
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BRAVE_SEARCH_API_KEY=brave-xxx
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BRAVE_SEARCH_API_KEY=brave-xxx
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# JINA_API_KEY=jina_xxx # Optional, default is None
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# JINA_API_KEY=jina_xxx # Optional, default is None
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# Volcengine TTS
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VOLCENGINE_TTS_APPID=xxx
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VOLCENGINE_TTS_ACCESS_TOKEN=xxx
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# VOLCENGINE_TTS_CLUSTER=volcano_tts # Optional, default is volcano_tts
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# VOLCENGINE_TTS_VOICE_TYPE=BV700_V2_streaming # Optional, default is BV700_V2_streaming
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33
README.md
33
README.md
@ -17,12 +17,13 @@ cd deer-flow
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# Install dependencies, uv will take care of the python interpreter and venv creation, and install the required packages
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# Install dependencies, uv will take care of the python interpreter and venv creation, and install the required packages
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uv sync
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uv sync
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# Configure .env with your Search Engine API keys
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# Configure .env with your API keys
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# Tavily: https://app.tavily.com/home
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# Tavily: https://app.tavily.com/home
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# Brave_SEARCH: https://brave.com/search/api/
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# Brave_SEARCH: https://brave.com/search/api/
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# volcengine TTS: Add your TTS credentials if you have them
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cp .env.example .env
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cp .env.example .env
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# See the 'Supported Search Engines' section below for all available options
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# See the 'Supported Search Engines' and 'Text-to-Speech Integration' sections below for all available options
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# Configure conf.yaml for your LLM model and API keys
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# Configure conf.yaml for your LLM model and API keys
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# Gemini: https://ai.google.dev/gemini-api/docs/openai
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# Gemini: https://ai.google.dev/gemini-api/docs/openai
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@ -120,6 +121,34 @@ The system employs a streamlined workflow with the following components:
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- Processes and structures the collected information
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- Processes and structures the collected information
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- Generates comprehensive research reports
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- Generates comprehensive research reports
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## Text-to-Speech Integration
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DeerFlow now includes a Text-to-Speech (TTS) feature that allows you to convert research reports to speech. This feature uses the volcengine TTS API to generate high-quality audio from text.
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### Features
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- Convert any text or research report to natural-sounding speech
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- Adjust speech parameters like speed, volume, and pitch
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- Support for multiple voice types
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- Available through both API and web interface
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### Using the TTS API
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You can access the TTS functionality through the `/api/tts` endpoint:
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```bash
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# Example API call using curl
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curl --location 'http://localhost:8000/api/tts' \
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--header 'Content-Type: application/json' \
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--data '{
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"text": "This is a test of the text-to-speech functionality.",
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"speed_ratio": 1.0,
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"volume_ratio": 1.0,
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"pitch_ratio": 1.0
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}' \
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--output speech.mp3
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```
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## Examples
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## Examples
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The following examples demonstrate the capabilities of DeerFlow:
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The following examples demonstrate the capabilities of DeerFlow:
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@ -1,19 +1,22 @@
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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# SPDX-License-Identifier: MIT
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import base64
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import json
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import json
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import logging
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import logging
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import os
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from typing import List, cast
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from typing import List, cast
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from uuid import uuid4
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from uuid import uuid4
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from fastapi import FastAPI
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse
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from fastapi.responses import StreamingResponse, Response
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from langchain_core.messages import AIMessageChunk, ToolMessage
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from langchain_core.messages import AIMessageChunk, ToolMessage
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from langgraph.types import Command
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from langgraph.types import Command
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from src.graph.builder import build_graph
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from src.graph.builder import build_graph
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from src.server.chat_request import ChatMessage, ChatRequest
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from src.server.chat_request import ChatMessage, ChatRequest, TTSRequest
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from src.tools import VolcengineTTS
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logger = logging.getLogger(__name__)
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logger = logging.getLogger(__name__)
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@ -137,3 +140,59 @@ def _make_event(event_type: str, data: dict[str, any]):
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if data.get("content") == "":
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if data.get("content") == "":
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data.pop("content")
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data.pop("content")
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return f"event: {event_type}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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return f"event: {event_type}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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@app.post("/api/tts")
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async def text_to_speech(request: TTSRequest):
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"""Convert text to speech using volcengine TTS API."""
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try:
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app_id = os.getenv("VOLCENGINE_TTS_APPID", "")
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if not app_id:
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raise HTTPException(
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status_code=400, detail="VOLCENGINE_TTS_APPID is not set"
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)
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access_token = os.getenv("VOLCENGINE_TTS_ACCESS_TOKEN", "")
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if not access_token:
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raise HTTPException(
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status_code=400, detail="VOLCENGINE_TTS_ACCESS_TOKEN is not set"
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)
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cluster = os.getenv("VOLCENGINE_TTS_CLUSTER", "volcano_tts")
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voice_type = os.getenv("VOLCENGINE_TTS_VOICE_TYPE", "BV700_V2_streaming")
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tts_client = VolcengineTTS(
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appid=app_id,
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access_token=access_token,
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cluster=cluster,
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voice_type=voice_type,
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)
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# Call the TTS API
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result = tts_client.text_to_speech(
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text=request.text[:1024],
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encoding=request.encoding,
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speed_ratio=request.speed_ratio,
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volume_ratio=request.volume_ratio,
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pitch_ratio=request.pitch_ratio,
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text_type=request.text_type,
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with_frontend=request.with_frontend,
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frontend_type=request.frontend_type,
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)
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if not result["success"]:
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raise HTTPException(status_code=500, detail=str(result["error"]))
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# Decode the base64 audio data
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audio_data = base64.b64decode(result["audio_data"])
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# Return the audio file
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return Response(
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content=audio_data,
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media_type=f"audio/{request.encoding}",
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headers={
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"Content-Disposition": (
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f"attachment; filename=tts_output.{request.encoding}"
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)
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},
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)
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except Exception as e:
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logger.exception(f"Error in TTS endpoint: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@ -1,7 +1,7 @@
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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# SPDX-License-Identifier: MIT
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from typing import List, Optional, Union
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from typing import List, Optional, Union, Dict, Any
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field
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@ -44,3 +44,19 @@ class ChatRequest(BaseModel):
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interrupt_feedback: Optional[str] = Field(
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interrupt_feedback: Optional[str] = Field(
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None, description="Interrupt feedback from the user on the plan"
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None, description="Interrupt feedback from the user on the plan"
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)
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)
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class TTSRequest(BaseModel):
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text: str = Field(..., description="The text to convert to speech")
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voice_type: Optional[str] = Field(
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"BV700_V2_streaming", description="The voice type to use"
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)
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encoding: Optional[str] = Field("mp3", description="The audio encoding format")
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speed_ratio: Optional[float] = Field(1.0, description="Speech speed ratio")
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volume_ratio: Optional[float] = Field(1.0, description="Speech volume ratio")
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pitch_ratio: Optional[float] = Field(1.0, description="Speech pitch ratio")
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text_type: Optional[str] = Field("plain", description="Text type (plain or ssml)")
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with_frontend: Optional[int] = Field(
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1, description="Whether to use frontend processing"
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)
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frontend_type: Optional[str] = Field("unitTson", description="Frontend type")
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@ -1,6 +1,8 @@
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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# SPDX-License-Identifier: MIT
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import os
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from .crawl import crawl_tool
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from .crawl import crawl_tool
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from .python_repl import python_repl_tool
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from .python_repl import python_repl_tool
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from .search import (
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from .search import (
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@ -9,6 +11,7 @@ from .search import (
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brave_search_tool,
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brave_search_tool,
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arxiv_search_tool,
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arxiv_search_tool,
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)
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)
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from .tts import VolcengineTTS
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from src.config import SELECTED_SEARCH_ENGINE, SearchEngine
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from src.config import SELECTED_SEARCH_ENGINE, SearchEngine
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# Map search engine names to their respective tools
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# Map search engine names to their respective tools
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@ -25,4 +28,5 @@ __all__ = [
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"crawl_tool",
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"crawl_tool",
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"web_search_tool",
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"web_search_tool",
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"python_repl_tool",
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"python_repl_tool",
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"VolcengineTTS",
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]
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]
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131
src/tools/tts.py
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131
src/tools/tts.py
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@ -0,0 +1,131 @@
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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
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# SPDX-License-Identifier: MIT
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"""
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Text-to-Speech module using volcengine TTS API.
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"""
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import json
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import uuid
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import logging
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import requests
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from typing import Optional, Dict, Any
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logger = logging.getLogger(__name__)
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class VolcengineTTS:
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"""
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Client for volcengine Text-to-Speech API.
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"""
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def __init__(
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self,
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appid: str,
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access_token: str,
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cluster: str = "volcano_tts",
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voice_type: str = "BV700_V2_streaming",
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host: str = "openspeech.bytedance.com",
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):
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"""
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Initialize the volcengine TTS client.
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Args:
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appid: Platform application ID
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access_token: Access token for authentication
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cluster: TTS cluster name
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voice_type: Voice type to use
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host: API host
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"""
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self.appid = appid
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self.access_token = access_token
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self.cluster = cluster
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self.voice_type = voice_type
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self.host = host
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self.api_url = f"https://{host}/api/v1/tts"
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self.header = {"Authorization": f"Bearer;{access_token}"}
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def text_to_speech(
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self,
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text: str,
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encoding: str = "mp3",
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speed_ratio: float = 1.0,
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volume_ratio: float = 1.0,
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pitch_ratio: float = 1.0,
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text_type: str = "plain",
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with_frontend: int = 1,
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frontend_type: str = "unitTson",
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uid: Optional[str] = None,
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) -> Dict[str, Any]:
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"""
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Convert text to speech using volcengine TTS API.
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Args:
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text: Text to convert to speech
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encoding: Audio encoding format
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speed_ratio: Speech speed ratio
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volume_ratio: Speech volume ratio
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pitch_ratio: Speech pitch ratio
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text_type: Text type (plain or ssml)
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with_frontend: Whether to use frontend processing
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frontend_type: Frontend type
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uid: User ID (generated if not provided)
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Returns:
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Dictionary containing the API response and base64-encoded audio data
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"""
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if not uid:
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uid = str(uuid.uuid4())
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request_json = {
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"app": {
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"appid": self.appid,
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"token": self.access_token,
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"cluster": self.cluster,
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},
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"user": {"uid": uid},
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"audio": {
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"voice_type": self.voice_type,
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"encoding": encoding,
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"speed_ratio": speed_ratio,
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"volume_ratio": volume_ratio,
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"pitch_ratio": pitch_ratio,
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},
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"request": {
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"reqid": str(uuid.uuid4()),
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"text": text,
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"text_type": text_type,
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"operation": "query",
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"with_frontend": with_frontend,
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"frontend_type": frontend_type,
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},
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}
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try:
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logger.debug(f"Sending TTS request for text: {text[:50]}...")
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response = requests.post(
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self.api_url, json.dumps(request_json), headers=self.header
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)
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response_json = response.json()
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if response.status_code != 200:
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logger.error(f"TTS API error: {response_json}")
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return {"success": False, "error": response_json, "audio_data": None}
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if "data" not in response_json:
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logger.error(f"TTS API returned no data: {response_json}")
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return {
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"success": False,
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"error": "No audio data returned",
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"audio_data": None,
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}
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return {
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"success": True,
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"response": response_json,
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"audio_data": response_json["data"], # Base64 encoded audio data
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}
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except Exception as e:
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logger.exception(f"Error in TTS API call: {str(e)}")
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return {"success": False, "error": str(e), "audio_data": None}
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