shunyalabs 2.0.0

Shunyalabs Python SDK — ASR, TTS, and Conversational AI

Shunyalabs Python SDK

The official Python SDK for Shunyalabs Speech AI APIs — ASR (speech-to-text) and TTS (text-to-speech).

Supports HTTP batch and WebSocket streaming modes with a fully async client.

Installation

pip install shunyalabs[all]

Install only what you need:

pip install shunyalabs[ASR]     # Speech-to-text only
pip install shunyalabs[TTS]     # Text-to-speech only
pip install shunyalabs[extras]  # Audio playback helpers (sounddevice)

Authentication

All API calls use Authorization: Bearer <api_key> header authentication.

from shunyalabs import AsyncShunyaClient

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

Or set the SHUNYALABS_API_KEY environment variable and omit api_key=.

---

Quick Start

TTS — Batch (HTTP)

import asyncio
from shunyalabs import AsyncShunyaClient
from shunyalabs.tts import TTSConfig

async def main():
    async with AsyncShunyaClient(api_key="your-api-key") as client:
        result = await client.tts.synthesize(
            "Hello, world!",
            config=TTSConfig(model="zero-indic", voice="Varun"),
        )
        result.save("output.mp3")
        print(f"{len(result.audio_data)} bytes saved")

asyncio.run(main())

TTS — Streaming (WebSocket)

import asyncio
from shunyalabs import AsyncShunyaClient
from shunyalabs.tts import TTSConfig

async def main():
    async with AsyncShunyaClient(api_key="your-api-key") as client:
        chunks = []
        async for audio in await client.tts.stream(
            "Hello, world!",
            config=TTSConfig(model="zero-indic", voice="Varun"),
        ):
            chunks.append(audio)
        print(f"{len(chunks)} chunks, {sum(len(c) for c in chunks)} bytes")

asyncio.run(main())

ASR — Batch (HTTP)

import asyncio
from shunyalabs import AsyncShunyaClient
from shunyalabs.asr import TranscriptionConfig

async def main():
    async with AsyncShunyaClient(api_key="your-api-key") as client:
        result = await client.asr.transcribe(
            "audio.wav",
            config=TranscriptionConfig(model="zero-indic"),
        )
        print(result.text)

asyncio.run(main())

ASR — Streaming (WebSocket)

import asyncio, subprocess
from shunyalabs import AsyncShunyaClient
from shunyalabs.asr import StreamingConfig, StreamingMessageType

async def main():
    async with AsyncShunyaClient(api_key="your-api-key") as client:
        conn = await client.asr.stream(
            config=StreamingConfig(language="en", sample_rate=16000),
        )

        @conn.on(StreamingMessageType.FINAL_SEGMENT)
        def on_seg(msg):
            print(f"[seg] {msg.text}")

        @conn.on(StreamingMessageType.FINAL)
        def on_final(msg):
            print(f"[final] {msg.text}")

        # Convert audio to 16kHz mono PCM and stream
        pcm = subprocess.run(
            ["ffmpeg", "-i", "audio.wav", "-ar", "16000", "-ac", "1", "-f", "s16le", "-"],
            capture_output=True,
        ).stdout

        for i in range(0, len(pcm), 4096):
            await conn.send_audio(pcm[i : i + 4096])

        await conn.end()
        await conn.close()

asyncio.run(main())

---

API Reference

Client Configuration

AsyncShunyaClient

Parameter	Type	Default	Description
api_key	str	None	API key. Falls back to SHUNYALABS_API_KEY env var.
timeout	float	60.0	Request timeout in seconds.
max_retries	int	2	Retries for failed requests (5xx, connection errors).
asr_url	str	https://asr.shunyalabs.ai	ASR batch API base URL.
asr_ws_url	str	wss://asr.shunyalabs.ai/ws	ASR streaming WebSocket URL.
tts_url	str	https://tts.shunyalabs.ai	TTS batch API base URL.
tts_ws_url	str	wss://tts.shunyalabs.ai/ws	TTS streaming WebSocket URL.

All URL parameters can also be set via environment variables: SHUNYALABS_ASR_URL, SHUNYALABS_ASR_WS_URL, SHUNYALABS_TTS_URL, SHUNYALABS_TTS_WS_URL.

Examples:

# Default — uses production endpoints
client = AsyncShunyaClient(api_key="your-api-key")

# Custom timeout and retries
client = AsyncShunyaClient(api_key="your-api-key", timeout=120.0, max_retries=5)

# Self-hosted endpoints
client = AsyncShunyaClient(
    api_key="your-api-key",
    asr_url="https://my-asr-server.example.com",
    tts_url="https://my-tts-server.example.com",
    tts_ws_url="wss://my-tts-server.example.com/ws",
)

---

TTS API

TTSConfig

Configuration for synthesis requests. Passed as config= to synthesize() and stream().

Parameter	Type	Default	Description
model	str	required	Model name (e.g. "zero-indic").
voice	str	required	Speaker voice name. See Available Speakers.
response_format	OutputFormat	"mp3"	Output audio format. See Output Formats.
speed	float	1.0	Speaking speed multiplier (0.25–4.0).
trim_silence	bool	False	Trim leading/trailing silence from audio.
volume_normalization	str	None	"peak" or "loudness".
word_timestamps	bool	False	Return word-level timestamps (batch only).
background_audio	str	None	Preset name or base64-encoded background audio.
background_volume	float	0.1	Background volume relative to speech (0.0–1.0).

TTS Parameter Examples

model — Select the TTS model

# Currently available: "zero-indic"
config = TTSConfig(model="zero-indic", voice="Rajesh")
result = await client.tts.synthesize("Hello!", config=config)
# Output: 48000 bytes saved to output.mp3

voice — Choose a speaker

# Male English speaker
config = TTSConfig(model="zero-indic", voice="Varun")

# Female Hindi speaker
config = TTSConfig(model="zero-indic", voice="Sunita")

# Any speaker can speak any language — voice only controls vocal characteristics
config = TTSConfig(model="zero-indic", voice="Murugan")  # Tamil-native male speaking English
result = await client.tts.synthesize("Good morning, how are you?", config=config)

response_format — Output audio format

Values: "pcm", "wav", "mp3", "ogg_opus", "flac", "mulaw", "alaw"

# MP3 (default) — compressed, good for storage
config = TTSConfig(model="zero-indic", voice="Varun", response_format="mp3")
result = await client.tts.synthesize("Hello!", config=config)
result.save("output.mp3")
# Output: 12480 bytes (compressed)

# WAV — uncompressed, good for processing
config = TTSConfig(model="zero-indic", voice="Varun", response_format="wav")
result = await client.tts.synthesize("Hello!", config=config)
result.save("output.wav")
# Output: 96044 bytes (uncompressed with header)

# PCM — raw samples, for real-time pipelines
config = TTSConfig(model="zero-indic", voice="Varun", response_format="pcm")
result = await client.tts.synthesize("Hello!", config=config)
# Output: 96000 bytes (raw 16-bit samples)

# OGG Opus — compressed, good for web streaming
config = TTSConfig(model="zero-indic", voice="Varun", response_format="ogg_opus")

# mu-law / A-law — for telephony systems
config = TTSConfig(model="zero-indic", voice="Varun", response_format="mulaw")
config = TTSConfig(model="zero-indic", voice="Varun", response_format="alaw")

speed — Speaking speed multiplier

Range: 0.25 (very slow) to 4.0 (very fast). Default: 1.0.

# Slow — good for language learning
config = TTSConfig(model="zero-indic", voice="Nisha", speed=0.75)
result = await client.tts.synthesize("Take your time to understand this.", config=config)
# Output: longer audio, ~33% slower than normal

# Normal speed (default)
config = TTSConfig(model="zero-indic", voice="Nisha", speed=1.0)

# Fast — good for notifications or summaries
config = TTSConfig(model="zero-indic", voice="Nisha", speed=1.5)
result = await client.tts.synthesize("Quick update: your order has shipped.", config=config)
# Output: shorter audio, ~50% faster than normal

# Very fast
config = TTSConfig(model="zero-indic", voice="Nisha", speed=2.0)

trim_silence — Remove silence padding

# Without trim (default) — audio may have leading/trailing silence
config = TTSConfig(model="zero-indic", voice="Rajesh", trim_silence=False)
result = await client.tts.synthesize("Hello.", config=config)
# Output: 64000 bytes (includes silence padding)

# With trim — tighter audio, no dead air
config = TTSConfig(model="zero-indic", voice="Rajesh", trim_silence=True)
result = await client.tts.synthesize("Hello.", config=config)
# Output: 48000 bytes (silence stripped)

volume_normalization — Normalize audio loudness

Values: None (off), "peak", "loudness"

# No normalization (default)
config = TTSConfig(model="zero-indic", voice="Rajesh")

# Peak normalization — scale so the loudest sample hits 0 dBFS
config = TTSConfig(model="zero-indic", voice="Rajesh", volume_normalization="peak")
result = await client.tts.synthesize("This audio will have consistent peak levels.", config=config)

# Loudness normalization — perceptually even loudness (EBU R128)
config = TTSConfig(model="zero-indic", voice="Rajesh", volume_normalization="loudness")
result = await client.tts.synthesize("This audio will sound equally loud regardless of content.", config=config)

word_timestamps — Get timing for each word (batch only)

config = TTSConfig(model="zero-indic", voice="Varun", word_timestamps=True)
result = await client.tts.synthesize("Hello world, how are you?", config=config)

for wt in result.word_timestamps:
    print(f"  '{wt.word}' — {wt.start:.2f}s to {wt.end:.2f}s")

# Output:
#   'Hello' — 0.00s to 0.32s
#   'world,' — 0.32s to 0.68s
#   'how' — 0.72s to 0.88s
#   'are' — 0.88s to 1.02s
#   'you?' — 1.02s to 1.28s

background_audio + background_volume — Add background music

import base64

# Using a preset name
config = TTSConfig(
    model="zero-indic",
    voice="Nisha",
    background_audio="cafe-ambience",
    background_volume=0.15,  # 15% volume relative to speech
)
result = await client.tts.synthesize("Welcome to our podcast.", config=config)

# Using custom audio (base64-encoded)
with open("background.mp3", "rb") as f:
    bg_b64 = base64.b64encode(f.read()).decode()

config = TTSConfig(
    model="zero-indic",
    voice="Nisha",
    background_audio=bg_b64,
    background_volume=0.1,  # 10% volume (subtle background)
)
result = await client.tts.synthesize("Welcome to our podcast.", config=config)
result.save("podcast_intro.mp3")

Available Speakers

Each speaker has a native language listed below, but every speaker can speak in any language — the native language only indicates the speaker's voice characteristics and accent.

Language	Male Speaker	Female Speaker
Assamese	Bimal	Anjana
Bengali	Arjun	Priyanka
Bodo	Daimalu	Hasina
Dogri	Vishal	Neelam
English	Varun	Nisha
Gujarati	Rakesh	Pooja
Hindi	Rajesh	Sunita
Kannada	Kiran	Shreya
Kashmiri	Farooq	Habba
Konkani	Mohan	Sarita
Maithili	Suresh	Meera
Malayalam	Krishnan	Deepa
Manipuri	Tomba	Ibemhal
Marathi	Siddharth	Ananya
Nepali	Bikash	Sapana
Odia	Bijay	Sujata
Punjabi	Gurpreet	Simran
Sanskrit	Vedant	Gayatri
Santali	Chandu	Roshni
Sindhi	Amjad	Kavita
Tamil	Murugan	Thangam
Telugu	Vishnu	Lakshmi
Urdu	Salman	Fatima

23 languages, 46 speakers (1 male + 1 female per language).

Expression Styles

Control the emotional tone by passing a style tag in the text prefix (e.g. "Rajesh: <Happy> Hello!").

Style Tag	Description
<Happy>	Joyful, upbeat tone
<Sad>	Somber, melancholic tone
<Angry>	Forceful, intense tone
<Fearful>	Anxious, trembling tone
<Surprised>	Exclamatory, astonished tone
<Disgust>	Repulsed, disapproving tone
<News>	Formal news-anchor style
<Conversational>	Casual, everyday speech
<Narrative>	Storytelling / audiobook style
<Enthusiastic>	Energetic, passionate tone
<Neutral>	Clean read-speech (default, no tag needed)

Expression style examples:

# Happy greeting
config = TTSConfig(model="zero-indic", voice="Rajesh")
result = await client.tts.synthesize("Rajesh: <Happy> Welcome aboard! We're thrilled to have you.", config=config)

# News anchor reading
config = TTSConfig(model="zero-indic", voice="Nisha")
result = await client.tts.synthesize("Nisha: <News> Breaking news: the markets rallied today.", config=config)

# Storytelling
config = TTSConfig(model="zero-indic", voice="Krishnan")
result = await client.tts.synthesize("Krishnan: <Narrative> Once upon a time, in a land far away...", config=config)

# Conversational chatbot
config = TTSConfig(model="zero-indic", voice="Simran")
result = await client.tts.synthesize("Simran: <Conversational> Hey! How's it going?", config=config)

# Neutral (default — no tag needed)
config = TTSConfig(model="zero-indic", voice="Varun")
result = await client.tts.synthesize("Varun: Your account balance is five thousand rupees.", config=config)

Output Formats

Format	Value
PCM (raw)	"pcm"
WAV	"wav"
MP3	"mp3"
OGG Opus	"ogg_opus"
FLAC	"flac"
mu-law	"mulaw"
A-law	"alaw"

TTS Methods

Batch (HTTP)

result = await client.tts.synthesize("text", config=TTSConfig(...))
result.save("output.mp3")       # Save to file
result.audio_data               # Raw bytes
result.duration_seconds          # Audio duration
result.sample_rate               # Sample rate (Hz)
result.word_timestamps           # List[WordTimestamp] if requested

Streaming (WebSocket)

# Iterate audio chunks
async for audio_bytes in await client.tts.stream("text", config=TTSConfig(...)):
    play(audio_bytes)

# With chunk metadata
async for chunk_meta, audio_bytes in await client.tts.stream("text", config=TTSConfig(...), detailed=True):
    print(chunk_meta.chunk_index, len(audio_bytes))

# Collect all and return combined bytes
audio = await client.tts.synthesize_stream("text", config=TTSConfig(...))

# Stream directly to file
await client.tts.stream_to_file("text", "output.pcm", config=TTSConfig(...))

TTSResult

Returned by synthesize().

Attribute	Type	Description
request_id	str	Unique request identifier.
audio_data	bytes	Decoded audio bytes.
sample_rate	int	Audio sample rate in Hz.
duration_seconds	float	Total audio duration.
format	str	Audio format string.
word_timestamps	list[WordTimestamp]	Word-level timestamps (if requested).

---

ASR API

TranscriptionConfig

Configuration for batch transcription. Passed as config= to transcribe().

Parameter	Type	Default	Description
model	str	required	Model name (e.g. "zero-indic").
language_code	str	"auto"	Language code or "auto" for auto-detection.
task	str	"transcribe"	Task type ("transcribe").
output_script	str	"auto"	Output script ("auto", "latin", "native").
enable_diarization	bool	False	Enable speaker diarization.

NLP Features:

Parameter	Type	Default	Description
enable_intent_detection	bool	False	Detect intent from transcript.
intent_choices	list[str]	None	Constrain intent to specific choices.
enable_summarization	bool	False	Generate transcript summary.
summary_max_length	int	150	Maximum summary length.
enable_sentiment_analysis	bool	False	Analyze sentiment.
enable_emotion_diarization	bool	False	Detect emotions per segment.

Post-processing:

Parameter	Type	Default	Description
enable_profanity_hashing	bool	False	Hash profane words.
hash_keywords	list[str]	None	Custom keywords to hash.
enable_keyterm_normalization	bool	False	Normalize key terms.
enable_translation	bool	False	Translate transcript.
target_language	str	None	Target language for translation.
enable_transliteration	bool	False	Transliterate transcript.
project	str	None	Project name for tracking.

ASR Parameter Examples

model + language_code — Basic transcription

# Auto-detect language (default)
config = TranscriptionConfig(model="zero-indic", language_code="auto")
result = await client.asr.transcribe("audio.wav", config=config)
print(result.text)
print(f"Detected: {result.detected_language}")
# Output:
#   "Hello, how are you doing today?"
#   Detected: en

# Specify language for better accuracy
config = TranscriptionConfig(model="zero-indic", language_code="hi")
result = await client.asr.transcribe("hindi_audio.wav", config=config)
print(result.text)
# Output: "नमस्ते, आप कैसे हैं?"

output_script — Control output script

Values: "auto", "latin", "native"

# Native script (default for auto)
config = TranscriptionConfig(model="zero-indic", language_code="hi", output_script="native")
result = await client.asr.transcribe("hindi_audio.wav", config=config)
print(result.text)
# Output: "नमस्ते, आप कैसे हैं?"

# Latin/Roman script — transliterated output
config = TranscriptionConfig(model="zero-indic", language_code="hi", output_script="latin")
result = await client.asr.transcribe("hindi_audio.wav", config=config)
print(result.text)
# Output: "namaste, aap kaise hain?"

# Auto — server decides based on language
config = TranscriptionConfig(model="zero-indic", output_script="auto")

enable_diarization — Speaker identification

config = TranscriptionConfig(model="zero-indic", enable_diarization=True)
result = await client.asr.transcribe("meeting.wav", config=config)
for seg in result.segments:
    print(f"  [{seg.start:.1f}s - {seg.end:.1f}s] {seg.text}")
# Output:
#   [0.0s - 3.2s] [Speaker 1] Good morning, let's begin the meeting.
#   [3.5s - 6.8s] [Speaker 2] Sure, I have the report ready.
#   [7.0s - 10.1s] [Speaker 1] Great, please go ahead.

enable_intent_detection + intent_choices — Detect user intent

# Open intent detection
config = TranscriptionConfig(
    model="zero-indic",
    enable_intent_detection=True,
)
result = await client.asr.transcribe("customer_call.wav", config=config)
print(result.text)
print(result.nlp_analysis.intent)
# Output:
#   "I want to cancel my subscription"
#   {"intent": "cancellation", "confidence": 0.94}

# Constrained intent — pick from specific choices
config = TranscriptionConfig(
    model="zero-indic",
    enable_intent_detection=True,
    intent_choices=["booking", "cancellation", "complaint", "inquiry"],
)
result = await client.asr.transcribe("customer_call.wav", config=config)
print(result.nlp_analysis.intent)
# Output: {"intent": "cancellation", "confidence": 0.97}

enable_summarization + summary_max_length — Summarize transcript

config = TranscriptionConfig(
    model="zero-indic",
    enable_summarization=True,
    summary_max_length=100,  # max 100 characters
)
result = await client.asr.transcribe("meeting_recording.wav", config=config)
print(f"Full transcript: {result.text[:80]}...")
print(f"Summary: {result.nlp_analysis.summary}")
# Output:
#   Full transcript: Good morning everyone. Today we'll review Q3 results. Revenue grew by...
#   Summary: Q3 review meeting covering revenue growth, cost optimization, and next quarter targets.

enable_sentiment_analysis — Detect sentiment

config = TranscriptionConfig(
    model="zero-indic",
    enable_sentiment_analysis=True,
)
result = await client.asr.transcribe("feedback.wav", config=config)
print(result.text)
print(result.nlp_analysis.sentiment)
# Output:
#   "The product is amazing, I absolutely love it!"
#   {"label": "positive", "score": 0.96}

enable_emotion_diarization — Detect emotions per segment

config = TranscriptionConfig(
    model="zero-indic",
    enable_emotion_diarization=True,
)
result = await client.asr.transcribe("conversation.wav", config=config)
print(result.nlp_analysis.emotion)
# Output:
#   {"segments": [
#     {"start": 0.0, "end": 3.2, "emotion": "neutral", "text": "Hello, how can I help?"},
#     {"start": 3.5, "end": 7.1, "emotion": "angry", "text": "I've been waiting for an hour!"},
#     {"start": 7.4, "end": 10.0, "emotion": "empathetic", "text": "I'm sorry about that."}
#   ]}

enable_profanity_hashing + hash_keywords — Redact sensitive words

# Hash common profanity
config = TranscriptionConfig(
    model="zero-indic",
    enable_profanity_hashing=True,
)
result = await client.asr.transcribe("audio.wav", config=config)
print(result.text)
# Output: "What the #### is going on?"

# Hash custom keywords (e.g., names, account numbers)
config = TranscriptionConfig(
    model="zero-indic",
    enable_profanity_hashing=True,
    hash_keywords=["John", "Acme Corp", "Project Alpha"],
)
result = await client.asr.transcribe("meeting.wav", config=config)
print(result.text)
# Output: "#### from ######### said ############# is on track."

enable_translation + target_language — Translate transcript

config = TranscriptionConfig(
    model="zero-indic",
    language_code="hi",
    enable_translation=True,
    target_language="en",
)
result = await client.asr.transcribe("hindi_audio.wav", config=config)
print(f"Original: {result.text}")
print(f"Translation: {result.nlp_analysis.translation}")
# Output:
#   Original: नमस्ते, आज मौसम बहुत अच्छा है।
#   Translation: {"text": "Hello, the weather is very nice today.", "target_language": "en"}

enable_transliteration — Transliterate to Latin script

config = TranscriptionConfig(
    model="zero-indic",
    language_code="hi",
    enable_transliteration=True,
)
result = await client.asr.transcribe("hindi_audio.wav", config=config)
print(f"Native: {result.text}")
print(f"Transliterated: {result.nlp_analysis.transliteration}")
# Output:
#   Native: नमस्ते, आज मौसम बहुत अच्छा है।
#   Transliterated: {"text": "namaste, aaj mausam bahut achha hai."}

enable_keyterm_normalization — Normalize domain terms

config = TranscriptionConfig(
    model="zero-indic",
    enable_keyterm_normalization=True,
)
result = await client.asr.transcribe("tech_audio.wav", config=config)
print(result.text)
# Output: "The API returns JSON over HTTPS." (normalized from "A P I", "jay son", "H T T P S")

project — Tag requests for tracking

config = TranscriptionConfig(
    model="zero-indic",
    project="customer-support-q1",
)
result = await client.asr.transcribe("call.wav", config=config)
# Request is tagged with project name for usage tracking and analytics

ASR Methods

Batch (HTTP)

# From file path
result = await client.asr.transcribe("audio.wav", config=TranscriptionConfig(model="zero-indic"))

# From file object
with open("audio.wav", "rb") as f:
    result = await client.asr.transcribe_file(f, config=TranscriptionConfig(model="zero-indic"))

# From URL
result = await client.asr.transcribe_url("https://example.com/audio.wav", config=TranscriptionConfig(model="zero-indic"))

Streaming (WebSocket)

conn = await client.asr.stream(config=StreamingConfig(language="en"))

StreamingConfig

Configuration for the WebSocket streaming session.

Parameter	Type	Default	Description
language	str	"auto"	Language code or "auto".
sample_rate	int	16000	Audio sample rate in Hz.
dtype	str	"int16"	Audio data type ("int16", "float32").
chunk_size_sec	float	1.0	Processing chunk size in seconds.
silence_threshold_sec	float	0.5	Silence duration to trigger segmentation.

Streaming Parameter Examples

language — Set recognition language

# Auto-detect (default)
conn = await client.asr.stream(config=StreamingConfig(language="auto"))

# Specific language for better accuracy
conn = await client.asr.stream(config=StreamingConfig(language="en"))
conn = await client.asr.stream(config=StreamingConfig(language="hi"))
conn = await client.asr.stream(config=StreamingConfig(language="ta"))

sample_rate + dtype — Match your audio source

# Standard microphone input: 16kHz, 16-bit integer (default)
conn = await client.asr.stream(config=StreamingConfig(
    sample_rate=16000,
    dtype="int16",
))

# High-quality audio: 48kHz, 32-bit float
conn = await client.asr.stream(config=StreamingConfig(
    sample_rate=48000,
    dtype="float32",
))

chunk_size_sec — Processing window size

# Smaller chunks = lower latency, more partial results
conn = await client.asr.stream(config=StreamingConfig(chunk_size_sec=0.5))

# Larger chunks = more context, potentially better accuracy
conn = await client.asr.stream(config=StreamingConfig(chunk_size_sec=2.0))

silence_threshold_sec — Control segment boundaries

# Quick segmentation — short pauses trigger a new segment
conn = await client.asr.stream(config=StreamingConfig(silence_threshold_sec=0.3))
# Good for: fast-paced dialogue, command recognition

# Patient segmentation — only split on longer pauses
conn = await client.asr.stream(config=StreamingConfig(silence_threshold_sec=1.5))
# Good for: lectures, monologues, dictation

Streaming Events

Register event handlers on the connection object:

conn = await client.asr.stream(config=StreamingConfig(language="en"))

@conn.on(StreamingMessageType.PARTIAL)
def on_partial(msg):
    print(f"Interim: {msg.text}")

@conn.on(StreamingMessageType.FINAL_SEGMENT)
def on_segment(msg):
    print(f"Segment: {msg.text}")

@conn.on(StreamingMessageType.FINAL)
def on_final(msg):
    print(f"Final: {msg.text} ({msg.audio_duration_sec}s)")

@conn.on(StreamingMessageType.DONE)
def on_done(msg):
    print(f"Done. {msg.total_segments} segments, {msg.total_audio_duration_sec}s")

@conn.on(StreamingMessageType.ERROR)
def on_error(msg):
    print(f"Error: {msg.message}")

Event	Model	Key Attributes
PARTIAL	StreamingPartial	text, language, segment_id, latency_ms
FINAL_SEGMENT	StreamingFinalSegment	text, language, segment_id, silence_duration_ms
FINAL	StreamingFinal	text, language, audio_duration_sec, inference_time_ms
DONE	StreamingDone	total_segments, total_audio_duration_sec
ERROR	StreamingError	message, code

Streaming Connection Methods

await conn.send_audio(pcm_bytes)   # Send raw PCM audio
await conn.end()                    # Signal end of audio stream
await conn.close()                  # Close WebSocket connection
conn.is_closed                      # Check connection status
conn.session_id                     # Server-assigned session ID

TranscriptionResult

Returned by transcribe().

Attribute	Type	Description
success	bool	Whether transcription succeeded.
request_id	str	Unique request identifier.
text	str	Full transcription text.
segments	list[SegmentResult]	Time-aligned segments (start, end, text).
detected_language	str	Detected language code.
audio_duration	float	Audio duration in seconds.
inference_time_ms	float	Server inference time in ms.
nlp_analysis	NLPAnalysis	NLP results (if any enable_* flags were set).

---

Exceptions

All exceptions inherit from ShunyalabsError.

Exception	Description
AuthenticationError	Invalid or missing API key (401).
PermissionDeniedError	Insufficient permissions (403).
NotFoundError	Resource not found (404).
RateLimitError	Rate limit exceeded (429).
ServerError	Server-side error (5xx).
TimeoutError	Request timed out.
ConnectionError	Network connectivity issue.
TranscriptionError	ASR-specific error.
SynthesisError	TTS-specific error.

---

Framework Plugins

Framework	Package	Install
LiveKit Agents	livekit-plugins-shunyalabs	pip install livekit-plugins-shunyalabs
Pipecat	pipecat-shunyalabs	pip install pipecat-shunyalabs

---

Development

git clone https://github.com/Shunyalabsai/shunyalabs-python-sdk.git
cd shunyalabs-python-sdk

python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check src/
black --check src/
mypy src/

License

MIT