gnani-vachana 0.4.3

Python client for Gnani's Vachana speech AI platform (STT, TTS, and more)

gnani-vachana

Official Python client for Vachana Speech APIs by Gnani.ai. Build multilingual voice workflows with Speech-to-Text (STT) and Text-to-Speech (TTS) across REST, SSE streaming, and real-time WebSockets.

Vachana is a production-ready speech platform with high-accuracy STT and low-latency TTS for 10+ Indian languages, with 6 voices, multilingual and code-switching scenarios.

Installation

pip install gnani-vachana

Requires Python 3.9+.

Quick Start

STT REST (file-based transcription)

from gnani.stt import GnaniSTTClient

client = GnaniSTTClient(api_key="your-api-key")

result = client.transcribe("audio.wav", language_code="hi-IN")
print(result["transcript"])

Realtime Streaming (WebSocket)

import asyncio
from gnani.stt import GnaniSTTStreamClient, StreamTranscriptEvent

async def main():
    async with GnaniSTTStreamClient(api_key="your-api-key", language_code="hi-IN") as stream:
        # Send audio chunks (raw PCM, 16-bit LE, 16 kHz, mono)
        with open("audio.pcm", "rb") as f:
            while chunk := f.read(1024):
                await stream.send_audio(chunk)
                await asyncio.sleep(0.032)  # real-time pacing (32 ms per frame)

        # Iterate over events
        async for event in stream:
            if isinstance(event, StreamTranscriptEvent):
                print(event.text)

asyncio.run(main())

TTS REST (single response)

from gnani.tts import GnaniTTSClient, AudioConfig

client = GnaniTTSClient(api_key="your-api-key")
audio = client.synthesize(
    "नमस्ते, आप कैसे हैं?",
    voice="Karan",
    audio_config=AudioConfig(sample_rate=44100, encoding="linear_pcm", container="wav"),
)

with open("tts_output.wav", "wb") as f:
    f.write(audio)

TTS SSE Streaming (lower latency)

from gnani.tts import GnaniTTSStreamClient

client = GnaniTTSStreamClient(api_key="your-api-key")
audio = client.synthesize("Hello from Gnani TTS", voice="Karan", output_file="tts_sse.wav")

TTS Realtime WebSocket (lowest latency)

import asyncio
from gnani.tts import GnaniTTSRealtimeClient

async def main():
    async with GnaniTTSRealtimeClient(api_key="your-api-key") as client:
        audio = await client.synthesize_and_collect(
            "Hello from Gnani TTS", voice="Karan", output_file="tts_realtime.wav",
        )

asyncio.run(main())

Authentication

All APIs (STT REST, STT Realtime, TTS) require a single API key:

Parameter	Header	Description
api_key	X-API-Key-ID	API key identifier for authentication

Obtaining Credentials

Email [e-mail] with your name, company, and use case. Credentials are typically provisioned within 1 business day, and all new accounts receive free credits -- no credit card required.

Passing Credentials

Option 1 -- Constructor argument:

from gnani.stt import GnaniSTTClient, GnaniSTTStreamClient
from gnani.tts import GnaniTTSClient, GnaniTTSRealtimeClient, GnaniTTSStreamClient

client = GnaniSTTClient(api_key="your-api-key")
stream = GnaniSTTStreamClient(api_key="your-api-key")
tts_rest = GnaniTTSClient(api_key="your-api-key")
tts_stream = GnaniTTSStreamClient(api_key="your-api-key")
tts_realtime = GnaniTTSRealtimeClient(api_key="your-api-key")

Option 2 -- Environment variable:

export GNANI_API_KEY="your-api-key"

from gnani.stt import GnaniSTTClient, GnaniSTTStreamClient
from gnani.tts import GnaniTTSClient

client = GnaniSTTClient()           # picks up GNANI_API_KEY
stream = GnaniSTTStreamClient()     # picks up GNANI_API_KEY
tts = GnaniTTSClient()              # picks up GNANI_API_KEY

Supported Languages

STT Languages (Speech-to-Text)

STT uses BCP-47 locale codes (e.g. hi-IN). For the full list of supported languages, see:

• STT REST — Supported Languages
• STT Realtime — Supported Languages

Code-switching — pass comma-separated codes for multilingual audio:

result = client.transcribe("meeting.wav", language_code="en-IN,hi-IN")

Auto-detect (streaming only):

from gnani.stt import GnaniSTTStreamClient

stream = GnaniSTTStreamClient(api_key="key", language_code=GnaniSTTStreamClient.AUTO_DETECT)

---

TTS Voices

Voice	Gender	Description
Karan	Male	Bold, Trustworthy
Simran	Female	Confident, Bright
Nara	Female	Gentle, Expressive
Riya	Female	Cheerful, Energetic
Viraj	Male	Commanding, Dynamic
Raju	Male	Grounded, Conversational

TTS Languages (Text-to-Speech)

TTS uses ISO 639 language codes (e.g. hi, bn). Note: TTS does not use the -IN suffix.

For the full list of supported languages, see TTS — Supported Languages.

REST Usage

Transcribe a file by path

result = client.transcribe("meeting.wav", language_code="en-IN")
print(result["transcript"])

Transcribe from a file object

with open("meeting.mp3", "rb") as f:
    result = client.transcribe(f, language_code="ta-IN")

Transcribe raw bytes

audio_bytes = download_audio_from_somewhere()
result = client.transcribe_bytes(
    audio_bytes, filename="clip.wav", language_code="kn-IN"
)

Custom request ID

result = client.transcribe(
    "call.flac", language_code="hi-IN", request_id="my-trace-123"
)

List supported languages

for code, name in GnaniSTTClient.supported_languages().items():
    print(f"{code}: {name}")

Realtime Streaming Usage

Connection Flow

1. Client opens a WebSocket connection to wss://api.vachana.ai/stt/v3/stream with auth headers.
2. Server sends a connected event with the active configuration.
3. Client sends binary PCM audio frames (1024 bytes each = 512 samples at 16-bit).
4. Server detects speech via VAD and responds with processing and transcript events.
5. Either side may close the connection at any time.

Audio Format

Property	16 kHz	8 kHz
Encoding	PCM signed 16-bit LE	PCM signed 16-bit LE
Sample Rate	16,000 Hz	8,000 Hz
Channels	1 (mono)	1 (mono)
Chunk Size	512 samples (32 ms)	512 samples (64 ms)
Frame Bytes	1,024	1,024

Using the async context manager

import asyncio
from gnani.stt import GnaniSTTStreamClient, StreamTranscriptEvent

async def main():
    async with GnaniSTTStreamClient(
        api_key="your-api-key",
        language_code="hi-IN",
        sample_rate=16000,
    ) as stream:
        print(f"Connected! Sample rate: {stream.connected_config.sample_rate}")

        with open("audio.pcm", "rb") as f:
            while chunk := f.read(1024):
                await stream.send_audio(chunk)
                await asyncio.sleep(0.032)

        async for event in stream:
            if isinstance(event, StreamTranscriptEvent):
                print(f"[{event.segment_index}] {event.text}")
                print(f"  Duration: {event.audio_duration_ms}ms, Latency: {event.latency}ms")

asyncio.run(main())

Manual connect / close

import asyncio
from gnani.stt import GnaniSTTStreamClient, StreamTranscriptEvent

async def main():
    stream = GnaniSTTStreamClient(api_key="your-api-key")
    config = await stream.connect()
    print(f"Server ready: {config.message}")

    await stream.send_audio(audio_chunk)
    transcripts = await stream.close()

    for t in transcripts:
        print(t.text)

asyncio.run(main())

High-level stream_audio helper with callbacks

import asyncio
from gnani.stt import GnaniSTTStreamClient, StreamTranscriptEvent, StreamProcessingEvent

async def main():
    async with GnaniSTTStreamClient(api_key="your-api-key") as stream:
        with open("audio.pcm", "rb") as f:
            transcripts = await stream.stream_audio(
                f,
                on_transcript=lambda t: print(f"Transcript: {t.text}"),
                on_processing=lambda p: print(f"Processing at {p.timestamp}..."),
                realtime_pace=True,
            )

    print(f"Total segments: {len(transcripts)}")

asyncio.run(main())

Using 8 kHz audio

stream = GnaniSTTStreamClient(
    api_key="your-api-key",
    language_code="en-IN",
    sample_rate=8000,
)

Event Types

All events are typed dataclasses:

Event	Fields	Description
StreamConnectedEvent	message, timestamp, sample_rate, chunk_size, raw	Handshake confirmation with server config
StreamProcessingEvent	timestamp, raw	VAD detected end-of-speech, transcribing
StreamTranscriptEvent	text, audio_duration_ms, segment_id, segment_index, latency, timestamp, raw	Completed transcription for a speech segment
StreamErrorEvent	message, timestamp, raw	Server-side error

Accessing the raw JSON payload

Every event includes a raw field with the full server JSON:

async for event in stream:
    print(event.raw)  # dict with the complete server response

Text-to-Speech Usage

TTS REST

from gnani.tts import GnaniTTSClient

client = GnaniTTSClient(api_key="your-api-key")
audio = client.synthesize("यह एक टेस्ट है", voice="Karan")
with open("tts_rest.wav", "wb") as f:
    f.write(audio)

TTS Streaming (SSE)

Lower latency than REST — audio is streamed via Server-Sent Events.

from gnani.tts import GnaniTTSStreamClient, AudioConfig

client = GnaniTTSStreamClient(api_key="your-api-key")

# synthesize() collects all SSE chunks and returns a valid WAV file
audio = client.synthesize(
    "Streaming TTS response",
    voice="Raju",
    audio_config=AudioConfig(sample_rate=16000, encoding="linear_pcm", container="wav"),
    output_file="tts_sse.wav",
)

For raw PCM streaming (e.g. real-time playback), use synthesize_stream():

for pcm_chunk in client.synthesize_stream("Hello!", voice="Karan"):
    play_audio(pcm_chunk)  # raw PCM, no WAV header

TTS Realtime (WebSocket)

Lowest latency — audio is streamed over a persistent WebSocket connection.

import asyncio
from gnani.tts import GnaniTTSRealtimeClient, AudioConfig

async def main():
    async with GnaniTTSRealtimeClient(api_key="your-api-key") as client:
        # synthesize_and_collect() returns a valid WAV file
        audio = await client.synthesize_and_collect(
            "Realtime TTS response",
            voice="Simran",
            audio_config=AudioConfig(sample_rate=16000, encoding="linear_pcm", container="wav"),
            output_file="tts_realtime.wav",
        )

asyncio.run(main())

For raw PCM streaming (e.g. real-time playback):

async with GnaniTTSRealtimeClient(api_key="your-api-key") as client:
    async for pcm_chunk in client.synthesize("Hello!", voice="Karan"):
        play_audio(pcm_chunk)  # raw PCM, no WAV header

TTS Voices

6 voices are available. List them programmatically:

from gnani.tts import GnaniTTSClient

print(GnaniTTSClient.supported_voices())

Available voices: Karan, Simran, Nara, Riya, Viraj, Raju.

Audio Requirements

REST API

Constraint	Value
Formats	WAV, MP3, FLAC, OGG, M4A
Max duration	60 seconds
Channels	Mono or stereo
Sample rate	Automatically converted to 16 kHz mono

Realtime Streaming

Constraint	Value
Encoding	Raw PCM, signed 16-bit little-endian
Sample rate	16,000 Hz or 8,000 Hz
Channels	1 (mono)
Frame size	1,024 bytes (512 samples)
Pacing	Send frames at real-time cadence for best VAD accuracy

Response Format

REST

{
  "success": true,
  "request_id": "req_abc123",
  "timestamp": "20251226_143052.123",
  "transcript": "नमस्ते, आप कैसे हैं?"
}

Realtime Streaming

Connected:

{
  "type": "connected",
  "message": "STT service ready — VAD service connected",
  "timestamp": "2024-01-15T10:30:00.000Z",
  "config": { "sample_rate": 16000, "chunk_size": 512 }
}

Transcript:

{
  "type": "transcript",
  "timestamp": "2024-01-15T10:30:05.987Z",
  "text": "Hello, how are you today?",
  "audio_duration_ms": 2340,
  "segment_id": "<segment_id>",
  "segment_index": "<segment_index>",
  "latency": 320
}

Error Handling

from gnani.stt import (
    AuthenticationError,
    InvalidAudioError,
    APIError,
    StreamConnectionError,
    StreamClosedError,
    StreamError,
)

# REST errors
try:
    result = client.transcribe("audio.wav", language_code="hi-IN")
except AuthenticationError:
    print("Check your credentials")
except InvalidAudioError as e:
    print(f"Bad audio file: {e}")
except APIError as e:
    print(f"API error {e.status_code}: {e}")

# Streaming errors
try:
    async with GnaniSTTStreamClient(api_key="key") as stream:
        await stream.send_audio(chunk)
except StreamConnectionError as e:
    print(f"Connection failed: {e}")
except StreamClosedError as e:
    print(f"Stream already closed: {e}")
except StreamError as e:
    print(f"Server error: {e} (at {e.timestamp})")

Documentation

Full API reference and guides are available at docs.inya.ai/vachana.

• STT REST API
• STT Realtime WebSocket
• TTS REST API
• TTS Streaming (SSE)
• TTS Realtime WebSocket

License

This project is licensed under the MIT License -- see the LICENSE file for details.