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Merge pull request #3349 from eRuaro/feat/camb-tts-integration

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Add Camb.ai TTS integration with MARS models
This commit is contained in:
Mark Backman
2026-01-15 14:43:12 -05:00
committed by GitHub
parent 24082b84f2 26ddb2de2f
commit 30a3f42255
8 changed files with 516 additions and 2 deletions
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2
README.md
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@@ -75,7 +75,7 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Camb AI](https://docs.pipecat.ai/server/services/tts/camb), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [Grok Voice Agent](https://docs.pipecat.ai/server/services/s2s/grok), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai), [Ultravox](https://docs.pipecat.ai/server/services/s2s/ultravox), |
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
| Serializers | [Exotel](https://docs.pipecat.ai/server/utilities/serializers/exotel), [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx), [Vonage](https://docs.pipecat.ai/server/utilities/serializers/vonage) |

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changelog/3349.added.md Normal file
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@@ -0,0 +1 @@
- Added `CambTTSService`, using Camb.ai's TTS integration with MARS models (mars-flash, mars-pro, mars-instruct) for high-quality text-to-speech synthesis.

3
env.example
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@@ -31,6 +31,9 @@ AZURE_DALLE_API_KEY=...
AZURE_DALLE_ENDPOINT=https://...
AZURE_DALLE_MODEL=...
# Camb.ai
CAMB_API_KEY=...
# Cartesia
CARTESIA_API_KEY=...
CARTESIA_VOICE_ID=...

138
examples/foundational/07zg-interruptible-camb.py Normal file
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@@ -0,0 +1,138 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
LLMContextAggregatorPair,
LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.camb.tts import CambTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.turns.user_stop import TurnAnalyzerUserTurnStopStrategy
from pipecat.turns.user_turn_strategies import UserTurnStrategies
load_dotenv(override=True)
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
),
}
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info("Starting Camb AI TTS bot")
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CambTTSService(
api_key=os.getenv("CAMB_API_KEY"),
model="mars-flash",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
"content": "You are a helpful voice assistant powered by Camb AI text-to-speech. "
"Keep your responses concise and conversational since they will be spoken aloud. "
"Avoid special characters, emojis, or bullet points.",
},
]
context = LLMContext(messages)
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(
user_turn_strategies=UserTurnStrategies(
stop=[TurnAnalyzerUserTurnStopStrategy(turn_analyzer=LocalSmartTurnAnalyzerV3())]
),
),
)
pipeline = Pipeline(
[
transport.input(),
stt,
user_aggregator,
llm,
tts,
transport.output(),
assistant_aggregator,
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
audio_out_sample_rate=22050,
),
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info("Client connected")
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info("Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = await create_transport(runner_args, transport_params)
await run_bot(transport, runner_args)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

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pyproject.toml
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@@ -53,6 +53,7 @@ aws = [ "aioboto3~=15.5.0", "pipecat-ai[websockets-base]" ]
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.2.0; python_version>='3.12'" ]
azure = [ "azure-cognitiveservices-speech~=1.44.0"]
cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
camb = [ "camb-sdk>=1.5.4" ]
cerebras = []
daily = [ "daily-python~=0.23.0" ]
deepgram = [ "deepgram-sdk~=4.7.0", "pipecat-ai[websockets-base]" ]

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src/pipecat/services/camb/__init__.py Normal file
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@@ -0,0 +1,5 @@
#
# Copyright (c) 2024–2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

331
src/pipecat/services/camb/tts.py Normal file
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@@ -0,0 +1,331 @@
#
# Copyright (c) 2024–2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Camb.ai MARS text-to-speech service implementation.
This module provides TTS functionality using Camb.ai's MARS model family,
offering high-quality text-to-speech synthesis with streaming support.
Features:
- MARS models: mars-flash (fast), mars-pro (high quality)
- 140+ languages supported
- Real-time streaming via official SDK
- Model-specific sample rates: mars-pro (48kHz), mars-flash (22.05kHz)
"""
from typing import Any, AsyncGenerator, Dict, Optional
from camb import StreamTtsOutputConfiguration
from camb.client import AsyncCambAI
from loguru import logger
from pydantic import BaseModel, Field
from pipecat.frames.frames import (
ErrorFrame,
Frame,
StartFrame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame,
)
from pipecat.services.tts_service import TTSService
from pipecat.transcriptions.language import Language, resolve_language
from pipecat.utils.tracing.service_decorators import traced_tts
# Model-specific sample rates
MODEL_SAMPLE_RATES: Dict[str, int] = {
"mars-flash": 22050, # 22.05kHz
"mars-pro": 48000, # 48kHz
"mars-instruct": 22050, # 22.05kHz
}
def language_to_camb_language(language: Language) -> Optional[str]:
"""Convert a Pipecat Language enum to Camb.ai language code.
Args:
language: The Language enum value to convert.
Returns:
The corresponding Camb.ai language code (BCP-47 format), or None if not supported.
"""
LANGUAGE_MAP = {
Language.EN: "en-us",
Language.EN_US: "en-us",
Language.EN_GB: "en-gb",
Language.EN_AU: "en-au",
Language.ES: "es-es",
Language.ES_ES: "es-es",
Language.ES_MX: "es-mx",
Language.FR: "fr-fr",
Language.FR_FR: "fr-fr",
Language.FR_CA: "fr-ca",
Language.DE: "de-de",
Language.DE_DE: "de-de",
Language.IT: "it-it",
Language.PT: "pt-pt",
Language.PT_BR: "pt-br",
Language.PT_PT: "pt-pt",
Language.NL: "nl-nl",
Language.PL: "pl-pl",
Language.RU: "ru-ru",
Language.JA: "ja-jp",
Language.KO: "ko-kr",
Language.ZH: "zh-cn",
Language.ZH_CN: "zh-cn",
Language.ZH_TW: "zh-tw",
Language.AR: "ar-sa",
Language.HI: "hi-in",
Language.TR: "tr-tr",
Language.VI: "vi-vn",
Language.TH: "th-th",
Language.ID: "id-id",
Language.MS: "ms-my",
Language.SV: "sv-se",
Language.DA: "da-dk",
Language.NO: "no-no",
Language.FI: "fi-fi",
Language.CS: "cs-cz",
Language.EL: "el-gr",
Language.HE: "he-il",
Language.HU: "hu-hu",
Language.RO: "ro-ro",
Language.SK: "sk-sk",
Language.UK: "uk-ua",
Language.BG: "bg-bg",
Language.HR: "hr-hr",
Language.SR: "sr-rs",
Language.SL: "sl-si",
Language.CA: "ca-es",
Language.EU: "eu-es",
Language.GL: "gl-es",
Language.AF: "af-za",
Language.SW: "sw-ke",
Language.TA: "ta-in",
Language.TE: "te-in",
Language.BN: "bn-in",
Language.MR: "mr-in",
Language.GU: "gu-in",
Language.KN: "kn-in",
Language.ML: "ml-in",
Language.PA: "pa-in",
Language.UR: "ur-pk",
Language.FA: "fa-ir",
Language.TL: "tl-ph",
}
return resolve_language(language, LANGUAGE_MAP, use_base_code=True)
def _get_aligned_audio(buffer: bytes) -> tuple[bytes, bytes]:
"""Split buffer into aligned audio (2-byte samples) and remainder.
Args:
buffer: Raw audio bytes to align.
Returns:
Tuple of (aligned audio bytes, remaining bytes).
"""
aligned_size = (len(buffer) // 2) * 2
return buffer[:aligned_size], buffer[aligned_size:]
class CambTTSService(TTSService):
"""Camb.ai MARS text-to-speech service using the official SDK.
Converts text to speech using Camb.ai's MARS TTS models with support for
multiple languages.
Models:
- mars-flash: Fast inference, 22.05kHz output (default)
- mars-pro: High quality, 48kHz output
Example::
# Basic usage with mars-flash (fast)
tts = CambTTSService(api_key="your-api-key", model="mars-flash")
# High quality with mars-pro
tts = CambTTSService(
api_key="your-api-key",
voice_id=12345,
model="mars-pro",
)
"""
class InputParams(BaseModel):
"""Input parameters for Camb.ai TTS configuration.
Parameters:
language: Language for synthesis (BCP-47 format). Defaults to English.
user_instructions: Custom instructions for mars-instruct model only.
Ignored for other models. Max 1000 characters.
"""
language: Optional[Language] = Language.EN
user_instructions: Optional[str] = Field(
default=None,
max_length=1000,
description="Custom instructions for mars-instruct model only. "
"Use to control tone, style, or pronunciation. Max 1000 characters.",
)
def __init__(
self,
*,
api_key: str,
voice_id: int = 147320,
model: str = "mars-flash",
timeout: float = 60.0,
sample_rate: Optional[int] = None,
params: Optional[InputParams] = None,
**kwargs,
):
"""Initialize the Camb.ai TTS service.
Args:
api_key: Camb.ai API key for authentication.
voice_id: Voice ID to use. Defaults to 147320.
model: TTS model to use. Options: "mars-flash" (fast), "mars-pro" (high quality).
Defaults to "mars-flash".
timeout: Request timeout in seconds. Defaults to 60.0 (minimum recommended
by Camb.ai).
sample_rate: Audio sample rate in Hz. If None, uses model-specific default.
params: Additional voice parameters. If None, uses defaults.
**kwargs: Additional arguments passed to parent TTSService.
"""
super().__init__(sample_rate=sample_rate, **kwargs)
params = params or CambTTSService.InputParams()
self._client = AsyncCambAI(api_key=api_key, timeout=timeout)
# Warn if sample rate doesn't match model's supported rate
if sample_rate and sample_rate != MODEL_SAMPLE_RATES.get(model):
logger.warning(
f"Camb.ai's {model} model only supports {MODEL_SAMPLE_RATES.get(model)}Hz "
f"sample rate. Current rate of {sample_rate}Hz may cause issues."
)
# Build settings
self._settings = {
"language": (
self.language_to_service_language(params.language) if params.language else "en-us"
),
"user_instructions": params.user_instructions,
}
self.set_model_name(model)
self.set_voice(str(voice_id))
self._voice_id = voice_id
def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics.
Returns:
True, as Camb.ai service supports metrics generation.
"""
return True
def language_to_service_language(self, language: Language) -> Optional[str]:
"""Convert a Language enum to Camb.ai language format.
Args:
language: The language to convert.
Returns:
The Camb.ai-specific language code, or None if not supported.
"""
return language_to_camb_language(language)
async def start(self, frame: StartFrame):
"""Start the Camb.ai TTS service.
Args:
frame: The start frame containing initialization parameters.
"""
await super().start(frame)
# Use model-specific sample rate if not explicitly specified
if not self._init_sample_rate:
self._sample_rate = MODEL_SAMPLE_RATES.get(self._model_name, 22050)
self._settings["sample_rate"] = self._sample_rate
# Warn if sample rate doesn't match model's supported rate
if self._sample_rate != MODEL_SAMPLE_RATES.get(self._model_name):
logger.warning(
f"Camb.ai's {self._model_name} model requires "
f"{MODEL_SAMPLE_RATES.get(self._model_name)}Hz sample rate. "
f"Current rate of {self._sample_rate}Hz may cause issues."
)
@traced_tts
async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
"""Generate speech from text using Camb.ai's TTS API.
Args:
text: The text to synthesize into speech (max 3000 characters).
Yields:
Frame: Audio frames containing the synthesized speech.
"""
logger.debug(f"{self}: Generating TTS [{text}]")
# Validate text length
if len(text) > 3000:
logger.warning("Text too long for Camb.ai TTS (max 3000 chars), truncating")
text = text[:3000]
try:
await self.start_ttfb_metrics()
# Build SDK parameters
tts_kwargs: Dict[str, Any] = {
"text": text,
"voice_id": self._voice_id,
"language": self._settings["language"],
"speech_model": self.model_name,
"output_configuration": StreamTtsOutputConfiguration(format="pcm_s16le"),
}
# Add user instructions if using mars-instruct model
if self._model_name == "mars-instruct" and self._settings.get("user_instructions"):
tts_kwargs["user_instructions"] = self._settings["user_instructions"]
await self.start_tts_usage_metrics(text)
yield TTSStartedFrame()
# Buffer for aligning chunks to 2-byte boundaries (16-bit PCM)
audio_buffer = b""
# Stream audio chunks from SDK
async for chunk in self._client.text_to_speech.tts(**tts_kwargs):
if chunk:
await self.stop_ttfb_metrics()
audio_buffer += chunk
# Only yield complete 16-bit samples (2 bytes per sample)
aligned_audio, audio_buffer = _get_aligned_audio(audio_buffer)
if aligned_audio:
yield TTSAudioRawFrame(
audio=aligned_audio,
sample_rate=self.sample_rate,
num_channels=1,
)
# Yield any remaining complete samples
if len(audio_buffer) >= 2:
aligned_audio, _ = _get_aligned_audio(audio_buffer)
if aligned_audio:
yield TTSAudioRawFrame(
audio=aligned_audio,
sample_rate=self.sample_rate,
num_channels=1,
)
except Exception as e:
yield ErrorFrame(error=f"Camb.ai TTS error: {e}")
finally:
yield TTSStoppedFrame()

37
uv.lock generated
View File

@@ -629,6 +629,22 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/84/c2/80633736cd183ee4a62107413def345f7e6e3c01563dbca1417363cf957e/build-1.2.2.post1-py3-none-any.whl", hash = "sha256:1d61c0887fa860c01971625baae8bdd338e517b836a2f70dd1f7aa3a6b2fc5b5", size = 22950, upload-time = "2024-10-06T17:22:23.299Z" },
]
[[package]]
name = "camb-sdk"
version = "1.5.4"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "httpx" },
{ name = "pydantic" },
{ name = "typing-extensions" },
{ name = "websocket-client" },
{ name = "websockets" },
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