pipecat-shunyalabs 1.1.0

Shunyalabs ASR & TTS services for Pipecat

pipecat-shunyalabs

Shunyalabs STT and TTS services for Pipecat.

Provides ShunyalabsSTTService and ShunyalabsTTSService that integrate with Pipecat's pipeline framework, backed by the Shunyalabs Python SDK.

Key capabilities:

• Real-time streaming ASR with interim and final transcription frames
• High-fidelity voice synthesis with 46 speakers across 23 languages
• 11 emotion/delivery style tags for expressive voice responses
• Native Pipecat frame protocol — drop-in with any Pipecat pipeline
• Persistent WebSocket for STT; per-request WebSocket for TTS
• Output formats: PCM, WAV, MP3, OGG Opus, FLAC, mu-law, A-law

---

⚠️ Upgrading from 1.0.0 → 1.0.1

If you're already using pipecat-shunyalabs, read this first. There are two breaking changes that affect TTS:

1. language is now required

Old (1.0.0) silently accepted missing language; the gateway now returns HTTP 422 if it's missing. Always pass an ISO 639 code:

  tts = ShunyalabsTTSService(
      voice="Rajesh",
+     language="en",        # required — pass "en", "hi", "ta", etc.
  )

2. speaker parameter removed; _format_text no longer prepends speaker name

Old behaviour produced text like "Rajesh: <Neutral> Hello" — but the gateway already prepends the speaker name server-side, which caused "Rajesh: Rajesh: <Neutral> Hello" in the LLM prompt and resulted in muddied output. Fixed in 1.0.1.

  tts = ShunyalabsTTSService(
      voice="Rajesh",
-     speaker="Rajesh",     # remove — was a duplicate of `voice`
-     style="<Neutral>",    # optional now — gateway defaults to <Conversational>
+     style="<Happy>",      # only set this if you want a non-default style
      language="en",
  )

3. style is now optional

If you don't pass style, the gateway automatically applies <Conversational>. You only need to set style when you want a specific emotion (e.g. <Happy>, <Sad>, <News>).

Quick install upgrade

pip install --upgrade pipecat-shunyalabs

That's it for migrations. Everything else works as before.

---

Installation (New Users)

Requirements: Python 3.9+, Pipecat framework, a valid Shunyalabs API key.

pip install pipecat-shunyalabs

Install with a transport:

# Daily WebRTC transport
pip install pipecat-shunyalabs pipecat-ai[daily]

Authentication

Set your API key as an environment variable (recommended):

export SHUNYALABS_API_KEY="your-api-key"

Or pass it directly:

stt = ShunyalabsSTTService(api_key="your-api-key")
tts = ShunyalabsTTSService(api_key="your-api-key")

Security: Never commit API keys to source control. Use a secrets manager (GCP Secret Manager, AWS Secrets Manager, HashiCorp Vault) in production.

---

Quick Start

import asyncio, os
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.local.audio import LocalAudioTransport
from pipecat_shunyalabs import ShunyalabsSTTService, ShunyalabsTTSService

async def main():
    transport = LocalAudioTransport()

    stt = ShunyalabsSTTService(
        api_key=os.environ["SHUNYALABS_API_KEY"],
        language="en",
    )

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        model="gpt-4o",
    )

    tts = ShunyalabsTTSService(
        api_key=os.environ["SHUNYALABS_API_KEY"],
        voice="Rajesh",
        language="en",
        # style is optional — defaults to <Conversational>
    )

    pipeline = Pipeline([transport.input(), stt, llm, tts, transport.output()])
    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
    await PipelineRunner().run(task)

if __name__ == "__main__":
    asyncio.run(main())

---

STT — ShunyalabsSTTService

Real-time streaming speech-to-text over WebSocket. Maintains a persistent connection for the lifetime of the pipeline. Supports 23 Indian and international languages with automatic language detection.

Parameters

Parameter	Type	Default	Description
api_key	str	None	API key. Falls back to SHUNYALABS_API_KEY env var.
language	str	"auto"	Language code (e.g. "en", "hi") or "auto" for auto-detection.
url	str	wss://asr.shunyalabs.ai/ws	WebSocket endpoint URL.
sample_rate	int	16000	Expected audio sample rate in Hz. Must match transport input.

How It Works

1. On pipeline start, opens a WebSocket connection to the Shunyalabs ASR gateway.
2. Audio chunks from the pipeline input are forwarded via send_audio().
3. The gateway's built-in VAD detects speech boundaries and emits transcription events.
4. Events are mapped to Pipecat frames and pushed into the pipeline.

Frame Mapping

Shunyalabs Event	Pipecat Frame
PARTIAL	InterimTranscriptionFrame — emitted continuously as speech is recognized
FINAL_SEGMENT	TranscriptionFrame — emitted at speech segment boundary
FINAL	TranscriptionFrame — emitted when full utterance is finalized

Auto-Reconnect

If the WebSocket connection drops during audio streaming, the service automatically reconnects and resumes sending audio.

---

TTS — ShunyalabsTTSService

Streaming text-to-speech over WebSocket. Each synthesis request opens a new connection, streams audio chunks back as TTSAudioRawFrame frames. Supports 46 speakers across 23 languages — any speaker can synthesize in any language.

Parameters

Parameter	Type	Default	Required	Description
api_key	str	env SHUNYALABS_API_KEY	✓	API key. Pass directly or via env var.
voice	str	"Rajesh"	✓	Speaker voice. See Available Speakers.
language	str	"en"	✓	ISO 639 language code ("en", "hi", "ta", etc.). Now required by gateway.
model	str	"zero-indic"		TTS model identifier.
style	str	None		Emotion/delivery style tag. If omitted, gateway uses <Conversational>.
url	str	wss://tts.shunyalabs.ai/ws		WebSocket endpoint URL.
output_format	str	"pcm"		Audio encoding. See Output Formats.
speed	float	1.0		Speaking speed multiplier (0.25–4.0).
sample_rate	int	16000		Output sample rate in Hz.

Note for upgraders: The speaker parameter has been removed in 1.0.1 — use voice only. See migration notes.

Output Formats

Format	Value	Recommended Use
PCM (raw 16-bit)	pcm	Real-time pipelines, Pipecat TTSAudioRawFrame
WAV	wav	Uncompressed storage, offline processing
MP3	mp3	Compressed storage, web delivery
OGG Opus	ogg_opus	Compressed web streaming
FLAC	flac	Lossless compressed storage
mu-law	mulaw	Telephony systems (G.711)
A-law	alaw	Telephony systems (G.711 European)

Style Tags

Tag	Description
<Conversational>	Casual, everyday speech — default if style omitted
<Neutral>	Clean read-speech
<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
<Narrative>	Storytelling / audiobook delivery style
<Enthusiastic>	Energetic, passionate tone

Text Formatting

The plugin only prepends the style tag (if you set one). The gateway handles the speaker prefix and default style tag server-side.

tts = ShunyalabsTTSService(voice="Rajesh", style="<Happy>", language="en")
# Plugin sends:    "<Happy> Welcome!"
# Gateway expands: "Rajesh: <Happy> Welcome!"

If you omit style:

tts = ShunyalabsTTSService(voice="Rajesh", language="en")
# Plugin sends:    "Welcome!"
# Gateway expands: "Rajesh: <Conversational> Welcome!"

Available Speakers

46 speakers across 23 languages (1 male + 1 female per language). Every speaker can synthesize in any language.

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

Frame Output

Frame	Description
TTSStartedFrame	Emitted when synthesis begins.
TTSAudioRawFrame	Emitted for each audio chunk (PCM, 16kHz, mono).
TTSStoppedFrame	Emitted when synthesis completes.

Examples

Default (Conversational) — recommended for voice agents:

tts = ShunyalabsTTSService(
    voice="Nisha",
    language="en",
)

Custom emotion + speed:

tts = ShunyalabsTTSService(
    voice="Nisha",
    style="<Enthusiastic>",
    language="en",
    speed=1.1,
    output_format="pcm",
)

Hindi news-style:

tts = ShunyalabsTTSService(
    voice="Rajesh",
    language="hi",
    style="<News>",
)

---

Full Pipeline Example

A complete voice agent using Shunyalabs STT and TTS with OpenAI LLM on the Daily WebRTC transport:

import asyncio, os
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext, OpenAILLMContextAggregator,
)
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat_shunyalabs import ShunyalabsSTTService, ShunyalabsTTSService

async def run_voice_agent(room_url: str, token: str):
    transport = DailyTransport(
        room_url, token, "Shunyalabs Agent",
        DailyParams(audio_out_enabled=True, transcription_enabled=False),
    )

    stt = ShunyalabsSTTService(
        api_key=os.environ["SHUNYALABS_API_KEY"],
        language="auto",
        sample_rate=16000,
    )

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        model="gpt-4o",
    )

    messages = [{
        "role": "system",
        "content": (
            "You are a helpful voice assistant powered by Shunyalabs. "
            "Keep responses concise and natural for voice delivery."
        ),
    }]
    context = OpenAILLMContext(messages)
    context_aggregator = llm.create_context_aggregator(context)

    tts = ShunyalabsTTSService(
        api_key=os.environ["SHUNYALABS_API_KEY"],
        voice="Rajesh",
        language="hi",  # required
    )

    pipeline = Pipeline([
        transport.input(),
        stt,
        context_aggregator.user(),
        llm,
        tts,
        transport.output(),
        context_aggregator.assistant(),
    ])

    task = PipelineTask(
        pipeline,
        PipelineParams(allow_interruptions=True, enable_metrics=True),
    )

    @transport.event_handler("on_first_participant_joined")
    async def on_first_participant_joined(transport, participant):
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    await PipelineRunner().run(task)

if __name__ == "__main__":
    asyncio.run(run_voice_agent(
        room_url=os.environ["DAILY_ROOM_URL"],
        token=os.environ["DAILY_TOKEN"],
    ))

---

Multilingual Examples

# Hindi conversational bot (default style)
tts = ShunyalabsTTSService(voice="Rajesh", language="hi")

# English news-style bot
tts = ShunyalabsTTSService(voice="Varun", language="en", style="<News>")

# Tamil narrative voice
tts = ShunyalabsTTSService(voice="Murugan", language="ta", style="<Narrative>")

---

Error Reference

All Shunyalabs SDK exceptions inherit from ShunyalabsError.

Exception	HTTP Code	Description
AuthenticationError	401	Invalid or missing API key.
PermissionDeniedError	403	API key lacks permission for the resource.
NotFoundError	404	Requested resource not found.
ValidationError	422	Missing required field (e.g. language).
RateLimitError	429	Rate limit exceeded. Implement exponential backoff.
ServerError	5xx	Server-side error. Retried automatically.
TimeoutError	—	Request exceeded timeout (default 60s).
ConnectionError	—	Network connectivity issue.
TranscriptionError	—	ASR-specific failure (e.g. unsupported audio format).
SynthesisError	—	TTS-specific failure (e.g. invalid voice parameter).

from shunyalabs.exceptions import AuthenticationError, RateLimitError, ShunyalabsError

try:
    result = await client.tts.synthesize(text, config=config)
except AuthenticationError:
    print("Invalid API key — check SHUNYALABS_API_KEY")
except RateLimitError as e:
    print(f"Rate limited — retry after {e.retry_after}s")
except ShunyalabsError as e:
    print(f"Unexpected error: {e}")

---

Troubleshooting

Symptom	Resolution
HTTP 422 "language: Field required"	Add language="en" (or another ISO 639 code) to ShunyalabsTTSService(...).
TTS audio sounds wrong / muddied (after upgrade)	Remove speaker=... from the constructor — it's no longer needed and was causing a double-prefix bug in 1.0.0.
AuthenticationError on startup	Verify SHUNYALABS_API_KEY is set and valid.
WebSocket connection refused	Ensure outbound WSS (port 443) is open to asr.shunyalabs.ai and tts.shunyalabs.ai.
No transcription output	Check sample_rate matches your transport input. Verify audio source is active.
TTS audio silent or missing	Ensure output_format=pcm matches transport output. Verify TTSStartedFrame is received.
High latency on first TTS chunk	Deploy closer to the Shunyalabs gateway region (asia-south1).
RateLimitError	Implement exponential backoff. Check e.retry_after.
ImportError: pipecat_shunyalabs	Run pip install pipecat-shunyalabs. Confirm virtual environment is activated.

---

Changelog

1.0.1 (2026-04-11)

• Breaking: language is now required by the gateway. Always pass an ISO 639 code.
• Breaking: Removed speaker parameter (was a duplicate of voice).
• Bug fix: _format_text no longer prepends the speaker name on top of the gateway's server-side prefix. Old 1.0.0 behaviour produced "Rajesh: Rajesh: <Neutral> ..." in the model prompt and degraded output quality.
• style is now optional — defaults to <Conversational> server-side.

1.0.0

• Initial public release.

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License

MIT