超简单部署离线语音合成TTS和语音识别

一篇文章讲清楚超简单离线语音合成TTS 和离线语音识别系统部署

本文只介绍两个轻量级的

语音合成用piper, 语音识别用vosk

部署简单,效果勉强

语音合成

安装

linux下安装

复制代码

pip install piper-tts

下载模型(63M)

中文模型下载 zh_CN-huayan-medium.onnx 和 zh_CN-huayan-medium.onnx.json两个文件, 放在同一个目录

声音好听, 但是空格,符号等识别不了, 没有停顿, 没有更好的中文模型是个遗憾

如果只是windows系统用微软的TTS效果比这个好

模型下载地址

复制代码

https://hf-mirror.com/rhasspy/piper-voices
https://hf-mirror.com/rhasspy/piper-voices/tree/main/zh/zh_CN/huayan/medium

使用

在当前目录下会输出66.wav这个音频文件

复制代码

echo '今年前5个月，我国货物贸易进出口总值17.94万亿元' | piper --model ./zh_CN-huayan-medium.onnx --output_file 66.wav

对接

下面代码未验证过

python 复制代码

import subprocess
import os
import asyncio
from fastapi import FastAPI, HTTPException, Query
from fastapi.responses import FileResponse
from pydantic import BaseModel
import uuid

app = FastAPI()

# 设置模型路径和输出目录（使用绝对路径）
# MODEL_PATH = './model/zh_CN-huayan-x_low.onnx'
MODEL_PATH = './model/zh_CN-huayan-medium.onnx'
OUTPUT_DIR = os.path.abspath('./output/')  # 使用绝对路径

# 确保输出目录存在
if not os.path.exists(OUTPUT_DIR):
    os.makedirs(OUTPUT_DIR)

class SynthesizeRequest(BaseModel):
    text: str
    mode: str = Query('sync', enum=['sync', 'async'])  # 支持 'sync' 或 'async'

# 同步版本的 TTS 生成
def synthesize_text_sync(text: str, output_file: str):
    try:
        # 调用 piper 命令生成音频
        command = f"echo '{text}' | piper --model {MODEL_PATH} --output_file {output_file}"
        print(command)
        result = subprocess.run(
            #['echo', text, '|', 'piper', '--model', MODEL_PATH, '--output_file', output_file],
            command,
            check=True, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE
        )

        # 打印 piper 命令的输出（标准输出和标准错误）
        print(f"piper stdout: {result.stdout.decode()}")
        print(f"piper stderr: {result.stderr.decode()}")

        if not os.path.exists(output_file):
            raise FileNotFoundError(f"音频文件未生成: {output_file}")
    except subprocess.CalledProcessError as e:
        raise HTTPException(status_code=500, detail=f"Failed to generate speech: {str(e)}")

# 异步版本的 TTS 生成
async def synthesize_text_async(text: str, output_file: str):
    try:
        # 使用异步方式调用 piper 命令生成音频
        process = await asyncio.create_subprocess_exec(
            'echo', text, '|', 'piper', '--model', MODEL_PATH, '--output_file', output_file,
            stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE
        )
        stdout, stderr = await process.communicate()

        # 打印 piper 命令的输出（标准输出和标准错误）
        print(f"piper stdout: {stdout.decode()}")
        print(f"piper stderr: {stderr.decode()}")

        if process.returncode != 0 or not os.path.exists(output_file):
            raise FileNotFoundError(f"音频文件未生成: {output_file}")

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Failed to generate speech: {str(e)}")

@app.post("/synthesize/")
async def synthesize(request: SynthesizeRequest):
    text = request.text
    if not text:
        raise HTTPException(status_code=400, detail="Text is required")

    # 生成音频文件路径（绝对路径）
    # output_file = os.path.join(OUTPUT_DIR, 'welcome.wav')
    # 生成唯一的文件名，避免并发冲突
    unique_id = str(uuid.uuid4())  # 使用 UUID 生成唯一标识符
    output_file = os.path.join(OUTPUT_DIR, f"{unique_id}.wav")  # 生成唯一的文件名

    # 根据请求的模式选择同步或异步
    if request.mode == 'sync':
        synthesize_text_sync(text, output_file)
    elif request.mode == 'async':
        await synthesize_text_async(text, output_file)
    else:
        raise HTTPException(status_code=400, detail="Invalid mode, must be 'sync' or 'async'")

    # 确保文件存在后再返回
    if not os.path.exists(output_file):
        raise HTTPException(status_code=500, detail=f"音频文件生成失败: {output_file}")

    # 返回音频文件
    return FileResponse(output_file, media_type='audio/wav')

语音识别

用vosk

1,nuget安装vosk

2,去官网下载一个中文模型(只有42M) : https://alphacephei.com/vosk/models

42M的模型效果很一般,好在使用简单

还有个1.3G的模型,没有测试,应该不错

csharp 复制代码

using Vosk;

/// <summary>
/// 语音转文字
/// 需要去官网下载模型: https://alphacephei.com/vosk/models
/// 解压模型后放在项目目录下面
/// </summary>
public class VoskDemo
{
   public static void DemoBytes(Model model)
   {
        // Demo byte buffer 基本语音识别
        VoskRecognizer rec = new VoskRecognizer(model, 16000.0f);
        rec.SetMaxAlternatives(0);
        rec.SetWords(true);
        using(Stream source = File.OpenRead("test.wav")) {
            byte[] buffer = new byte[4096];
            int bytesRead;
            while((bytesRead = source.Read(buffer, 0, buffer.Length)) > 0) {
                if (rec.AcceptWaveform(buffer, bytesRead)) {
                    Console.WriteLine(rec.Result());
                } else {
                    Console.WriteLine(rec.PartialResult());
                }
            }
        }
        Console.WriteLine(rec.FinalResult());
   }

   public static void DemoFloats(Model model)
   {
        // Demo float array  流媒体
        VoskRecognizer rec = new VoskRecognizer(model, 16000.0f);
        using(Stream source = File.OpenRead("test.wav")) {
            byte[] buffer = new byte[4096];
            int bytesRead;
            while((bytesRead = source.Read(buffer, 0, buffer.Length)) > 0) {
                float[] fbuffer = new float[bytesRead / 2];
                for (int i = 0, n = 0; i < fbuffer.Length; i++, n+=2) {
                    fbuffer[i] = BitConverter.ToInt16(buffer, n);
                }
                if (rec.AcceptWaveform(fbuffer, fbuffer.Length)) {
                    Console.WriteLine(rec.Result());
                } else {
                    Console.WriteLine(rec.PartialResult());
                }
            }
        }
        Console.WriteLine(rec.FinalResult());
   }

   public static void DemoSpeaker(Model model)
   {
        // Output speakers 说话人识别
        SpkModel spkModel = new SpkModel("model-spk");
        VoskRecognizer rec = new VoskRecognizer(model, 16000.0f);
        rec.SetSpkModel(spkModel);

        using(Stream source = File.OpenRead("test.wav")) {
            byte[] buffer = new byte[4096];
            int bytesRead;
            while((bytesRead = source.Read(buffer, 0, buffer.Length)) > 0) {
                if (rec.AcceptWaveform(buffer, bytesRead)) {
                    Console.WriteLine(rec.Result());
                } else {
                    Console.WriteLine(rec.PartialResult());
                }
            }
        }
        Console.WriteLine(rec.FinalResult());
   }

   public static void Test()
   {
        // You can set to -1 to disable logging messages
        Vosk.Vosk.SetLogLevel(0);

        Model model = new Model("vosk-model-small-cn-0.22"); // 模型目录
        //DemoBytes(model);
        //DemoFloats(model);
        DemoSpeaker(model);
   }
}