> ## Documentation Index > Fetch the complete documentation index at: https://docs.gnani.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quick Start > Make your first speech-to-text, text-to-speech or voice-cloned TTS API request in minutes. ## Prerequisites Before you begin, ensure you have: * A valid API key (sign up on the [Gnani API platform](https://app.gnani.ai/voice/) to generate API keys) * cURL installed, or an API client such as Postman Use a test audio file that meets these requirements: * Format: WAV, MP3, OGG, FLAC, AAC, or M4A * Sample rate: 8 kHz to 44.1 kHz * Maximum duration: 60 seconds ### Your First Speech-to-Text Request Minimal example to transcribe a Hindi audio file: ```bash theme={null} curl -X POST https://api.vachana.ai/stt/v3 \ -H 'Content-Type: multipart/form-data' \ -H 'X-API-Key-ID: ' \ -F audio_file='@/path/to/your/audio.wav' \ -F language_code=hi-IN ``` **Replace these values:** * ``: Your Gnani Prisma v2.5 API key * `/path/to/your/audio.wav`: Path to your audio file * `hi-IN`: Language code (see [Language Codes](/api/STT/speech-to-text#language-codes)) ### Expected STT Response On success, you'll receive a JSON response like: ```json theme={null} { "success": true, "request_id": "019fd17d-1106-7265-88ab-3ed12d029292", "transcript": "नमस्ते, आप कैसे हैं?", "model": "gnani-prisma-v2.5" } ``` Stream live audio over WebSocket. See [STT Realtime](/api/STT/stt-websocket) for the full protocol, headers, and PCM requirements. **Endpoint** ```text theme={null} wss://api.vachana.ai/stt/v3/stream ``` **Required headers** | Header | Value | | - | - | | `x-api-key-id` | Your API key | | `lang_code` | BCP-47 code (e.g. `hi-IN`) | Send **binary PCM frames** only (16-bit mono, 8 or 16 kHz, 1024 bytes per frame). The server returns JSON transcript events: ```json theme={null} { "type": "transcript", "text": "Hello, how are you today?", "segment_id": "seg_abc123", "latency": 320 } ``` Timbre v2.5 is now the recommended TTS model. Migrate from Timbre v2.0 to continue receiving the latest improvements. Timbre v2.0 will be deprecated soon. Have your input text ready. Choose a voice from the [Voice Catalog](/api/TTS/available-voices). ### Your First Text-to-Speech Call Minimal example for REST TTS (synchronous audio). This endpoint returns the full synthesized audio as a binary response. ```bash theme={null} curl -X POST https://api.vachana.ai/api/v1/tts/inference \ -H 'Content-Type: application/json' \ -H 'X-API-Key-ID: ' \ -d '{ "text": "नमस्ते, आप कैसे हैं?", "voice": "Nalini", "model": "timbre-v2.5", "language": "hi-IN", "speed": 1.0, "audio_config": { "sample_rate": 48000, "num_channels": 1, "sample_width": 2, "encoding": "linear_pcm", "container": "wav" } }' \ --output response.wav ``` ### Expected TTS Response A successful request will return a `200 OK` HTTP status. The response body will contain raw binary audio data representing the synthesized text, adhering to the format specified in your `audio_config`. ```http theme={null} HTTP/1.1 200 OK Content-Type: audio/wav ``` This endpoint streams synthesized audio using Server-Sent Events (SSE). Audio is generated and delivered incrementally as it becomes available. ### Your First Streaming Call ```bash theme={null} curl -X POST https://api.vachana.ai/api/v1/tts/sse \ -H 'Content-Type: application/json' \ -H 'X-API-Key-ID: ' \ -d '{ "text": "नमस्ते, आप कैसे हैं?", "voice": "Nalini", "model": "timbre-v2.5", "language": "hi-IN", "speed": 1.0, "audio_config": { "sample_rate": 48000, "encoding": "linear_pcm", "container": "wav" } }' ``` ### Expected SSE Response A successful request will return a `200 OK` HTTP status. The response body will contain a stream of server-sent events. Each chunk contains base64 encoded audio fragments. ```http theme={null} HTTP/1.1 200 OK Content-Type: text/event-stream event: start data: {"status": "streaming_started", "text": "नमस्ते, आप कैसे हैं?"} event: chunk data: {"chunk_index": 1, "audio": "", "is_final": false} event: complete data: {"chunk_index": 2, "audio": "", "is_final": true} ``` Connect over WebSocket for lowest latency. See [TTS Realtime](/api/TTS/tts-websocket) for message types and `audio_config` options. **Endpoint** ```text theme={null} wss://api.vachana.ai/api/v1/tts ``` Send a JSON payload after connecting (include `X-API-Key-ID` in the upgrade headers): ```json theme={null} { "text": "नमस्ते, आप कैसे हैं?", "voice": "Nalini", "model": "timbre-v2.5", "language": "hi-IN", "audio_config": { "sample_rate": 48000, "encoding": "linear_pcm", "container": "wav" } } ``` The server streams JSON messages with base64 audio chunks (`start`, then `audio`, then `complete`). Voice cloning works in two steps: 1. **Generate embeddings** — upload a reference audio file to get a `speaker_embedding` 2. **Synthesize** — pass the embedding with your text to any VC TTS endpoint ### Step 1: Generate Voice Embeddings Upload a reference audio file (WAV/MP3, ideally 5–30 seconds of clear speech): ```bash theme={null} curl -X POST https://api.vachana.ai/api/v1/tts/voice-clone/embeddings \ -H 'X-API-Key-ID: ' \ -F audio_file='@/path/to/reference.wav' ``` **Replace these values:** * ``: Your Gnani API key * `/path/to/reference.wav`: Path to your reference audio file ### Expected Embeddings Response ```json theme={null} { "embedding": "", "shape": [1, 768], "dtype": "torch.bfloat16" } ``` ### Step 2: Synthesize with Your Cloned Voice Pass the `speaker_embedding` from Step 1 to synthesize audio in your cloned voice: ```bash theme={null} curl -X POST https://api.vachana.ai/api/v1/tts/inference \ -H 'Content-Type: application/json' \ -H 'X-API-Key-ID: ' \ -d '{ "text": "नमस्ते, आप कैसे हैं?", "model": "vachana-vc-v1", "audio_config": { "sample_rate": 44100, "num_channels": 1, "sample_width": 2, "encoding": "linear_pcm", "container": "wav" }, "speaker_embedding": { "embedding": "", "shape": [1, 768], "dtype": "torch.bfloat16" } }' \ --output cloned_voice.wav ``` A successful request returns a `200 OK` with raw binary audio data in the specified format. Stream cloned voice audio progressively via Server-Sent Events: ```bash theme={null} curl -X POST https://api.vachana.ai/api/v1/tts/sse \ -H 'Content-Type: application/json' \ -H 'X-API-Key-ID: ' \ -d '{ "text": "नमस्ते, आप कैसे हैं?", "model": "vachana-vc-v1", "speaker_embedding": { "embedding": "", "shape": [1, 768], "dtype": "torch.bfloat16" } }' ``` The response streams base64-encoded audio chunks as server-sent events, identical in format to the TTS SSE response. For the lowest latency, stream text and receive cloned voice audio over a WebSocket: ```javascript theme={null} const ws = new WebSocket("wss://api.vachana.ai/api/v1/tts", { headers: { "Content-Type": "application/json", "X-API-Key-ID": "", }, }); ws.on("open", () => { ws.send(JSON.stringify({ text: "नमस्ते, आप कैसे हैं?", model: "vachana-vc-v1", audio_config: { sample_rate: 44100, encoding: "linear_pcm" }, speaker_embedding: { embedding: "", shape: [1, 768], dtype: "torch.bfloat16", }, })); }); ws.on("message", (data) => { // Handle binary PCM audio chunks }); ``` The server streams binary PCM audio chunks over the WebSocket connection. *** ## Next Steps * **Speech-to-Text**: [STT REST](/api/STT/speech-to-text) and [STT Realtime](/api/STT/stt-websocket) * **Text-to-Speech**: [REST](/api/TTS/tts-inference), [Streaming (SSE)](/api/TTS/tts-sse), and [Realtime](/api/TTS/tts-websocket) * **Voice Cloning**: [VC Embeddings](/api/VC/voice-clone-embeddings), [REST](/api/VC/vc-inference), [Streaming](/api/VC/vc-sse), and [Realtime](/api/VC/vc-websocket) * **Batch transcription**: [Batch STT Introduction](/api/STTBatch/Introduction) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.