Whispering

Press shortcut → speak → get text. Free and open source ❤️

What is Whispering?

Whispering turns your speech into text with a single keyboard shortcut. Press the shortcut, speak, and your words appear wherever you're typing. No window switching, no clicking around.

I built this because I was tired of paying $30/month for transcription apps that are basically API wrappers. With Whispering, you bring your own API key and pay cents directly to providers. I use it 3-4 hours daily and pay about $3/month.

The math is simple: transcription APIs cost $0.02-0.36/hour. Subscription apps charge $30/month. That's a 10-100x markup for a middleman you don't need.

Note: Whispering is designed for quick transcriptions, not long recordings. For extended recording sessions, use a dedicated recording app.

Demo

Click to watch: Complete setup and usage guide (5 min)

Want to see the voice coding workflow? Check out this 3-minute demo showing how I use Whispering with Claude Code for faster development.

Key Features

Choose from multiple transcription providers (Groq at $0.02/hour is my favorite). The app supports voice-activated mode for hands-free operation; just talk and it transcribes. You can set up AI transformations to automatically format your text, fix grammar, or translate languages.

Everything is stored locally on your device. Your audio goes directly from your machine to your chosen API provider. No middleman servers, no data collection, no tracking.

Built with Svelte 5 and Tauri, so it's tiny (~22MB) and starts instantly. The codebase is clean and well-documented if you want to contribute or learn.

Installation

Takes about 2 minutes to get running.

1️⃣ Download Whispering

Choose your operating system below and click the download link:

wget https://github.com/braden-w/whispering/releases/latest/download/Whispering_7.1.1_amd64.AppImage
chmod +x Whispering_7.1.1_amd64.AppImage
./Whispering_7.1.1_amd64.AppImage

wget https://github.com/braden-w/whispering/releases/latest/download/Whispering_7.1.1_amd64.deb
sudo dpkg -i Whispering_7.1.1_amd64.deb

wget https://github.com/braden-w/whispering/releases/latest/download/Whispering-7.1.1-1.x86_64.rpm
sudo rpm -i Whispering-7.1.1-1.x86_64.rpm

Links not working? Find all downloads at GitHub Releases

2️⃣ Get Your API Key

Right now, I personally use Groq for almost all my transcriptions.

💡 Why Groq? The fastest models, super accurate, generous free tier, and unbeatable price (as cheap as $0.02/hour using distil-whisper-large-v3-en)

1. Visit console.groq.com/keys
2. Sign up → Create API key → Copy it

🙌 That's it! No credit card required for the free tier. You can start transcribing immediately.

3️⃣ Connect & Test

1. Open Whispering
2. Click Settings (⚙️) → Transcription
3. Select Groq → Paste your API key where it says Groq API Key
4. Click the recording button (or press Cmd+Shift+; anywhere) and say "Testing Whispering"

🎉 Success! Your words are now in your clipboard. Paste anywhere!

4️⃣ Next Steps: Power User Features

Take your transcription experience to the next level with these advanced features:

Why Whispering Exists

I was paying $30/month for a transcription app. Then I did the math: the actual API calls cost about $0.36/hour. At my usage (3-4 hours/day), I was paying $30 for what should cost $3.

That's when I realized these apps are just middlemen. They take your audio, send it to OpenAI's Whisper API, and charge you 10x markup. Plus your recordings go through their servers, get stored who knows where, and you're locked into their ecosystem.

So I built Whispering to cut out the middleman. You bring your own API key, your audio goes directly to the provider, and you pay actual costs. No subscription, no data collection, no lock-in. Just transcription at cost.

The code is open source because I believe tools this fundamental should be free. Companies pivot, get acquired, or shut down. But open source is forever.

Cost Comparison

With Whispering, you pay providers directly instead of marked-up subscription prices:

How is my data stored?

Whispering stores as much data as possible locally on your device, including recordings and text transcriptions. This approach ensures maximum privacy and data security. Here's an overview of how data is handled:

1. Local Storage: Voice recordings and transcriptions are stored in IndexedDB, which is used as blob storage and a place to store all of your data like text and transcriptions.

2. Transcription Service: The only data sent elsewhere is your recording to an external transcription service—if you choose one. You have the following options:

   • External services like OpenAI, Groq, or ElevenLabs (with your own API keys)
   • A local transcription service such as Speaches, which keeps everything on-device

3. Transformation Service (Optional): Whispering includes configurable transformation settings that allow you to pipe transcription output into custom transformation flows. These flows can leverage:

   • External Large Language Models (LLMs) like OpenAI's GPT-4, Anthropic's Claude, Google's Gemini, or Groq's Llama models
   • Hosted LLMs within your custom workflows for advanced text processing
   • Simple find-and-replace operations for basic text modifications

   When using AI-powered transformations, your transcribed text is sent to your chosen LLM provider using your own API key. All transformation configurations, including prompts and step sequences, are stored locally in your settings.

You can change both the transcription and transformation services in the settings to ensure maximum local functionality and privacy.

Frequently Asked Questions

How is this different from other transcription apps?

Most apps are middlemen charging $30/month for API calls that cost pennies. With Whispering, you bring your own API key and pay providers directly. Your audio goes straight from your device to the API - no servers in between, no data collection, no subscriptions.

What's the catch?

There isn't one. I built this for myself and use it every day. The code is open source so you can verify exactly what it does. No telemetry, no premium tiers, no upsells.

What's it built with?

Svelte 5 + Tauri. The app is tiny (~22MB), starts instantly, and uses minimal resources. The codebase is clean and well-documented if you want to learn or contribute.

Can I use it offline?

Yes - use the Speaches provider for local transcription. No internet, no API keys, completely private.

How much does it actually cost?

With Groq (my favorite): $0.02-$0.06/hour. With OpenAI: $0.18-$0.36/hour. Local transcription: free forever. I use it 3-4 hours daily and pay about $3/month total.

Is it really private?

Your recordings stay on your device in IndexedDB. When you transcribe, audio goes directly to your chosen provider using your API key. No middleman servers. For maximum privacy, use local transcription.

Can I format the output automatically?

Yes - set up AI transformations to fix grammar, translate languages, or reformat text. Works with any LLM provider.

What platforms work?

Desktop: Mac (Intel & Apple Silicon), Windows, Linux. Web: Any modern browser at whispering.bradenwong.com.

Found a bug?

Open an issue on GitHub. I actively maintain this and respond quickly.

Development

Built With Modern Web Technologies

Whispering showcases the power of modern web development as a comprehensive example application:

Web and Desktop

• Svelte 5: The UI reactivity library of choice with cutting-edge runes system
• SvelteKit: For routing and static site generation
• Tauri: The desktop app framework for native performance
• WellCrafted: Lightweight type-safe error handling
• Svelte Sonner: Toast notifications for errors
• TanStack Query: Powerful data synchronization
• TanStack Table: Comprehensive data tables
• IndexedDB & Dexie.js: Local data storage
• shadcn-svelte: Beautiful, accessible components
• TailwindCSS: Utility-first CSS framework
• Turborepo: Monorepo management
• Rust: Native desktop features
• Vercel: Hosting platform
• Zapsplat.com: Royalty-free sound effects

Browser Extension

• React: UI library
• shadcn/ui: Component library
• Chrome API: Extension APIs

Note: The browser extension is temporarily disabled while we stabilize the desktop app.

Architecture Patterns

• Service Layer: Platform-agnostic business logic with Result types
• Query Layer: Reactive data management with caching
• RPC Pattern: Unified API interface (rpc.recordings.getAllRecordings)
• Dependency Injection: Clean separation of concerns

Architecture Deep Dive

Whispering uses a clean three-layer architecture that achieves extensive code sharing between the desktop app (Tauri) and web app. This is possible because of how we handle platform differences and separate business logic from UI concerns.

Quick Navigation: Service Layer | Query Layer | Error Handling

┌─────────────┐     ┌─────────────┐     ┌──────────────┐
│  UI Layer   │ --> │  Query Layer│ --> │ Service Layer│
│ (Svelte 5)  │     │ (TanStack)  │     │   (Pure)     │
└─────────────┘     └─────────────┘     └──────────────┘
      ↑                    │
      └────────────────────┘
         Reactive Updates

Service Layer - Pure Business Logic + Platform Abstraction

The service layer contains all business logic as pure functions with zero UI dependencies. Services don't know about reactive Svelte variables, user settings, or UI state—they only accept explicit parameters and return Result<T, E> types for consistent error handling.

The key innovation is build-time platform detection. Services automatically choose the right implementation based on the target platform:

// Platform abstraction happens at build time
export const ClipboardServiceLive = window.__TAURI_INTERNALS__
  ? createClipboardServiceDesktop() // Uses Tauri clipboard APIs
  : createClipboardServiceWeb();     // Uses browser clipboard APIs

// Same interface, different implementations
export const NotificationServiceLive = window.__TAURI_INTERNALS__
  ? createNotificationServiceDesktop() // Native OS notifications
  : createNotificationServiceWeb();     // Browser notifications

This design enables 97% code sharing between desktop and web versions. The vast majority of the application logic is platform-agnostic, with only the thin service implementation layer varying between platforms. Services are incredibly testable (just pass mock parameters), reusable (work identically anywhere), and maintainable (no hidden dependencies).

Measuring Code Sharing

To calculate the actual code sharing percentage, I analyzed the codebase:

# Count total lines of code in the app
find apps/app/src -name "*.ts" -o -name "*.svelte" -o -name "*.js" | \
  grep -v node_modules | xargs wc -l
# Result: 22,824 lines total

# Count platform-specific implementation code
find apps/app/src/lib/services -name "*desktop.ts" -o -name "*web.ts" | \
  xargs wc -l
# Result: 685 lines (3%)

# Code sharing calculation
# Shared code: 22,824 - 685 = 22,139 lines (97%)

This minimal platform-specific code demonstrates how the architecture maximizes code reuse while maintaining native performance on each platform.

→ Learn more: Services README | Constants Organization

Query Layer - Adding Reactivity and State Management

The query layer is where reactivity gets injected on top of pure services. It wraps service functions with TanStack Query and handles two key responsibilities:

Runtime Dependency Injection - Dynamically switching service implementations based on user settings:

// From transcription query layer
async function transcribeBlob(blob: Blob) {
  const selectedService = settings.value['transcription.selectedTranscriptionService'];

  switch (selectedService) {
    case 'OpenAI':
      return services.transcriptions.openai.transcribe(blob, {
        apiKey: settings.value['apiKeys.openai'],
        model: settings.value['transcription.openai.model'],
      });
    case 'Groq':
      return services.transcriptions.groq.transcribe(blob, {
        apiKey: settings.value['apiKeys.groq'], 
        model: settings.value['transcription.groq.model'],
      });
  }
}

Optimistic Updates - Using the TanStack Query client to manipulate the cache for optimistic UI. By updating the cache, reactivity automatically kicks in and the UI reflects these changes, giving you instant optimistic updates.

It's often unclear where exactly you should mutate the cache with the query client—sometimes at the component level, sometimes elsewhere. By having this dedicated query layer, it becomes very clear: we co-locate three key things in one place: (1) the service call, (2) runtime settings injection based on reactive variables, and (3) cache manipulation (also reactive). This creates a layer that bridges reactivity with services in an intuitive way. It also cleans up our components significantly because we have a consistent place to put this logic—now developers know that all cache manipulation lives in the query folder, making it clear where to find and add this type of functionality:

// From recordings mutations
createRecording: defineMutation({
  resultMutationFn: async (recording: Recording) => {
    const { data, error } = await services.db.createRecording(recording);
    if (error) return Err(error);

    // Optimistically update cache - UI updates instantly
    queryClient.setQueryData(['recordings'], (oldData) => {
      if (!oldData) return [recording];
      return [...oldData, recording];
    });

    return Ok(data);
  },
})

This design keeps all reactive state management isolated in the query layer, allowing services to remain pure and platform-agnostic while the UI gets dynamic behavior and instant updates.

→ Learn more: Query README | RPC Pattern Guide

Error Transformation

The query layer also transforms service-specific errors into WhisperingError types that integrate seamlessly with the toast notification system. This happens inside resultMutationFn or resultQueryFn, creating a clean boundary between business logic errors and UI presentation:

// Service returns domain-specific error
const { data, error: serviceError } = await services.manualRecorder.startRecording(...);

if (serviceError) {
  // Query layer transforms to UI-friendly WhisperingError
  return Err(WhisperingError({
    title: '❌ Failed to start recording',
    description: serviceError.message,  // Preserve detailed message
    action: { type: 'more-details', error: serviceError }
  }));
}

Error Handling with WellCrafted

Whispering uses WellCrafted, a lightweight TypeScript library I created to bring Rust-inspired error handling to JavaScript. I built WellCrafted after using the effect-ts library when it first came out in 2023—I was very excited about the concepts but found it too verbose. WellCrafted distills my takeaways from effect-ts and makes them better by leaning into more native JavaScript syntax, making it perfect for this use case. Unlike traditional try-catch blocks that hide errors, WellCrafted makes all potential failures explicit in function signatures using the Result<T, E> pattern.

wellcrafted ensures robust error handling across the entire codebase, from service layer functions to UI components, while maintaining excellent developer experience with TypeScript's control flow analysis.

Run Whispering in Local Development Mode

1. Clone the repository: git clone https://github.com/braden-w/whispering.git
2. Change into the project directory: cd whispering
3. Install the necessary dependencies: pnpm i

To run the desktop app and website:

cd apps/app
pnpm tauri dev

Build The Executable Yourself

If you have concerns about the installers or want more control, you can build the executable yourself. This requires more setup, but it ensures that you are running the code you expect. Such is the beauty of open-source software!

Desktop

cd apps/app
pnpm i
pnpm tauri build

Find the executable in apps/app/target/release

Contributing

We welcome contributions! Whispering is built with care and attention to clean, maintainable code.

Code Style Guidelines

• Follow existing TypeScript and Svelte patterns throughout
• Use Result types from the WellCrafted library for all error handling
• Follow WellCrafted best practices: explicit errors with Result<T, E>, structured TaggedError objects, and comprehensive error context
• Study the existing patterns in these key directories:
  • Services Architecture - Platform-agnostic business logic
  • Query Layer Patterns - RPC pattern and reactive state
  • Constants Organization - Type-safe configuration

→ New to the codebase? Start with the Architecture Deep Dive to understand how everything fits together.

Note: WellCrafted is a TypeScript utility library I created to bring Rust-inspired error handling to JavaScript. It makes errors explicit in function signatures and ensures robust error handling throughout the codebase.

Contributing New Adapters

We'd love to expand Whispering's capabilities with more transcription and AI service adapters! Here's how to add a new adapter:

Overview of the adapter system:

1. Transcription services (services/transcription/): Convert audio to text
2. Completion services (services/completion/): Power AI transformations in the transformation pipeline
3. Query layer (query/): Provides reactive state management and runtime dependency injection
4. Settings layer: Stores API keys and user preferences

Adding a Transcription Service Adapter

Adding a new transcription service involves four main steps:

1. Create the service implementation in apps/app/src/lib/services/transcription/:

   // apps/app/src/lib/services/transcription/your-service.ts
   import { WhisperingErr, type WhisperingError } from '$lib/result';
   import type { Settings } from '$lib/settings';
   import { Err, Ok, tryAsync, type Result } from 'wellcrafted/result';
      
   // Define your models directly in the service file
   export const YOUR_SERVICE_MODELS = [
     {
       name: 'model-v1',
       description: 'Description of what makes this model special',
       cost: '$0.XX/hour',
     },
     {
       name: 'model-v2',
       description: 'A faster variant with different trade-offs',
       cost: '$0.YY/hour',
     },
   ] as const;
   
   export type YourServiceModel = (typeof YOUR_SERVICE_MODELS)[number];
   
   export function createYourServiceTranscriptionService() {
     return {
       async transcribe(
         audioBlob: Blob,
         options: {
           prompt: string;
           temperature: string;
           outputLanguage: Settings['transcription.outputLanguage'];
           apiKey: string;
           modelName: (string & {}) | YourServiceModel['name'];
           // Add any service-specific options
         }
       ): Promise<Result<string, WhisperingError>> {
         // Validate API key
         if (!options.apiKey) {
           return WhisperingErr({
             title: '🔑 API Key Required',
             description: 'Please enter your YourService API key in settings.',
             action: {
               type: 'link',
               label: 'Add API key',
               href: '/settings/transcription',
             },
           });
         }
         
         // Make the API call
         const { data, error } = await tryAsync({
           try: () => yourServiceClient.transcribe(audioBlob, options),
           mapErr: (error) => WhisperingErr({
             title: '❌ Transcription Failed',
             description: error.message,
             action: { type: 'more-details', error },
           }),
         });
         
         if (error) return Err(error);
         return Ok(data.text.trim());
       }
     };
   }
   
   export const YourServiceTranscriptionServiceLive = createYourServiceTranscriptionService();
   

   Don't forget to export your service in apps/app/src/lib/services/transcription/index.ts:

   import { YourServiceTranscriptionServiceLive } from './your-service';
   
   export {
     // ... existing exports
     YourServiceTranscriptionServiceLive as yourservice,
   };
   

   And add the API key field to the settings schema in apps/app/src/lib/settings/settings.ts:

   'apiKeys.yourservice': z.string().default(''),
   

2. Update the service configuration in apps/app/src/lib/constants/transcription/service-config.ts:

   import { YourServiceIcon } from 'lucide-svelte';
   import {
     YOUR_SERVICE_MODELS,
     type YourServiceModel,
   } from '$lib/services/transcription/your-service';
      
   // Add to the imports at the top
   type TranscriptionModel = OpenAIModel | GroqModel | ElevenLabsModel | YourServiceModel;
   
   // Add to TRANSCRIPTION_SERVICE_IDS
   export const TRANSCRIPTION_SERVICE_IDS = [
     'OpenAI',
     'Groq',
     'speaches',
     'ElevenLabs',
     'YourService', // Add here
   ] as const;
   
   // Add to TRANSCRIPTION_SERVICES array
   {
     id: 'YourService',
     name: 'Your Service Name',
     icon: YourServiceIcon,
     models: YOUR_SERVICE_MODELS,
     defaultModel: YOUR_SERVICE_MODELS[0],
     modelSettingKey: 'transcription.yourservice.model',
     apiKeyField: 'apiKeys.yourservice',
     type: 'api',
   }
   

3. Wire up the query layer in apps/app/src/lib/query/transcription.ts:

   // Add to the switch statement in transcribeBlob function
   case 'YourService':
     return services.transcriptions.yourservice.transcribe(blob, {
       outputLanguage: settings.value['transcription.outputLanguage'],
       prompt: settings.value['transcription.prompt'],
       temperature: settings.value['transcription.temperature'],
       apiKey: settings.value['apiKeys.yourservice'],
       modelName: settings.value['transcription.yourservice.model'],
     });
   

4. Update the settings UI in apps/app/src/routes/(config)/settings/transcription/+page.svelte:

   <!-- Add after other service conditionals -->
   {:else if settings.value['transcription.selectedTranscriptionService'] === 'YourService'}
     <LabeledSelect
       id="yourservice-model"
       label="YourService Model"
       items={YOUR_SERVICE_MODELS.map((model) => ({
         value: model.name,
         label: model.name,
         ...model,
       }))}
       selected={settings.value['transcription.yourservice.model']}
       onSelectedChange={(selected) => {
         settings.value = {
           ...settings.value,
           'transcription.yourservice.model': selected,
         };
       }}
       renderOption={renderModelOption}
     />
     <YourServiceApiKeyInput />
   {/if}
   

   Create the API key input component in apps/app/src/lib/components/settings/api-key-inputs/YourServiceApiKeyInput.svelte:

   <script lang="ts">
     import { LabeledInput } from '$lib/components/labeled/index.js';
     import { Button } from '$lib/components/ui/button/index.js';
     import { settings } from '$lib/stores/settings.svelte';
   </script>
   
   <LabeledInput
     id="yourservice-api-key"
     label="YourService API Key"
     type="password"
     placeholder="Your YourService API Key"
     value={settings.value['apiKeys.yourservice']}
     oninput={({ currentTarget: { value } }) => {
       settings.value = { ...settings.value, 'apiKeys.yourservice': value };
     }}
   >
     {#snippet description()}
       <p class="text-muted-foreground text-sm">
         You can find your YourService API key in your <Button
           variant="link"
           class="px-0.3 py-0.2 h-fit"
           href="https://yourservice.com/api-keys"
           target="_blank"
           rel="noopener noreferrer"
         >
           YourService dashboard
         </Button>.
       </p>
     {/snippet}
   </LabeledInput>
   

   And export it from apps/app/src/lib/components/settings/index.ts:

   export { default as YourServiceApiKeyInput } from './api-key-inputs/YourServiceApiKeyInput.svelte';
   

   Also update apps/app/src/lib/constants/transcription/index.ts to re-export your models:

   export {
     YOUR_SERVICE_MODELS,
     type YourServiceModel,
   } from '$lib/services/transcription/your-service';
   

Adding an AI Transformation Adapter

AI transformations in Whispering use completion services that can be integrated into transformation workflows. Here's how to add a new AI provider:

1. Create the completion service in apps/app/src/lib/services/completion/:

   // apps/app/src/lib/services/completion/your-provider.ts
   import { WhisperingErr, type WhisperingError } from '$lib/result';
   import { Err, Ok, tryAsync, type Result } from 'wellcrafted/result';
      
   export function createYourProviderCompletionService() {
     return {
       async complete(options: {
         apiKey: string;
         model: string;
         systemPrompt: string;
         userPrompt: string;
         temperature?: number;
       }): Promise<Result<string, WhisperingError>> {
         // Validate API key
         if (!options.apiKey) {
           return WhisperingErr({
             title: '🔑 API Key Required',
             description: 'Please add your YourProvider API key.',
           });
         }
         
         // Make the completion request
         const { data, error } = await tryAsync({
           try: () => yourProviderClient.complete(options),
           mapErr: (error) => WhisperingErr({
             title: '❌ Completion Failed',
             description: error.message,
             action: { type: 'more-details', error },
           }),
         });
         
         if (error) return Err(error);
         return Ok(data.text);
       }
     };
   }
   
   export const YourProviderCompletionServiceLive = createYourProviderCompletionService();
   

2. Register the service in apps/app/src/lib/services/completion/index.ts:

   import { YourProviderCompletionServiceLive } from './your-provider';
      
   export {
     // ... existing exports
     YourProviderCompletionServiceLive as yourprovider,
   };
   

3. Wire up the transformation handler in apps/app/src/lib/query/transformer.ts:

   // Add a new case in the handleStep function's prompt_transform switch statement
   case 'YourProvider': {
     const { data: completionResponse, error: completionError } =
       await services.completions.yourprovider.complete({
         apiKey: settings.value['apiKeys.yourprovider'],
         model: step['prompt_transform.inference.provider.YourProvider.model'],
         systemPrompt,
         userPrompt,
       });
      
     if (completionError) {
       return Err(completionError.message);
     }
   
     return Ok(completionResponse);
   }
   

4. Add API key to settings in apps/app/src/lib/settings/settings.ts:

   'apiKeys.yourprovider': z.string().default(''),
   

5. Update transformation types to include your provider models and configuration

Error Handling Best Practices

Always use the WhisperingErr helper for user-facing errors:

// Good: User-friendly error with action
return WhisperingErr({
  title: '⏱️ Rate Limit Reached',
  description: 'Too many requests. Please try again in a few minutes.',
  action: {
    type: 'link',
    label: 'View rate limits',
    href: 'https://yourservice.com/rate-limits',
  },
});

// Handle different error types
if (error.status === 401) {
  return WhisperingErr({
    title: '🔑 Invalid API Key',
    description: 'Your API key appears to be invalid or expired.',
    action: {
      type: 'link',
      label: 'Update API key',
      href: '/settings/transcription',
    },
  });
}

// Use with tryAsync for automatic error mapping
const { data, error } = await tryAsync({
  try: () => apiClient.makeRequest(),
  mapError: (error) => WhisperingErr({
    title: '❌ Request Failed',
    description: error.message,
    action: { type: 'more-details', error },
  }),
});

Testing Your Adapter

Create a test file alongside your service:

// apps/app/src/lib/services/transcription/your-service.test.ts
import { describe, it, expect } from 'vitest';
import { createYourServiceTranscriptionService } from './your-service';

describe('YourService Transcription', () => {
  it('should handle missing API key', async () => {
    const service = createYourServiceTranscriptionService();
    const result = await service.transcribe(new Blob(), {
      apiKey: '',
      // other options
    });
    
    expect(result.error).toBeDefined();
    expect(result.error?.title).toContain('API Key Required');
  });
  
  // Add more tests
});

Example PR Structure

When submitting a PR for a new adapter, include:

• The service implementation with comprehensive error handling
• All type definitions and constants
• Query layer integration
• Settings UI components
• Tests covering success and error cases
• Documentation of any special requirements or limitations
• Example .env entries if needed

We're excited to see what services you'll integrate! Feel free to open an issue first to discuss your adapter idea.

Contributing Process

1. Fork the repository
2. Create a feature branch: git checkout -b feature/your-feature-name
3. Make your changes and commit them
4. Push to your fork: git push origin your-branch-name
5. Create a pull request

Version Bumping

When preparing a new release, use our version bumping script to update all necessary files:

# Update version across all project files
bun run bump-version <new-version>

# Example:
bun run bump-version 7.0.1

This script automatically updates:

• Root package.json
• App package.json
• Tauri configuration (tauri.conf.json)
• Cargo manifest (Cargo.toml)

After running the script, follow the displayed instructions to commit, tag, and push the changes.

Good First Issues

• UI/UX improvements and accessibility enhancements
• Performance optimizations
• New transcription or transformation service integrations

Feel free to suggest and implement any features that improve usability—I'll do my best to integrate contributions that make Whispering better for everyone.

Support and Community

License

Whispering is released under the MIT License. Use it, modify it, learn from it, and build upon it freely.

Support and Feedback

If you encounter any issues or have suggestions for improvements, please open an issue on the GitHub issues tab or contact me via [email protected]. I really appreciate your feedback!

• Community Chat: Discord
• Issues and Bug Reports: GitHub Issues
• Feature Discussions: GitHub Discussions
• Direct Contact: [email protected]

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