examples: added 07i-interruptible-xtts

CHANGELOG.md

@@ -9,6 +9,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Added `XTTSService`. This is a local Text-To-Speech service.
+  See https://github.com/coqui-ai/TTS
+
 - It is now possible to specify a Silero VAD version when using `SileroVADAnalyzer`
   or `SileroVAD`.
 

examples/foundational/07i-interruptible-xtts.py

@@ -0,0 +1,96 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import aiohttp
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
+from pipecat.services.openai import OpenAILLMService
+from pipecat.services.xtts import XTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Respond bot",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer(),
+            )
+        )
+
+        tts = XTTSService(
+            aiohttp_session=session,
+            voice_id="Claribel Dervla",
+            language="en",
+            base_url="http://localhost:8000"
+        )
+
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o")
+
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+            },
+        ]
+
+        tma_in = LLMUserResponseAggregator(messages)
+        tma_out = LLMAssistantResponseAggregator(messages)
+
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            tma_in,              # User responses
+            llm,                 # LLM
+            tts,                 # TTS
+            transport.output(),  # Transport bot output
+            tma_out              # Assistant spoken responses
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append(
+                {"role": "system", "content": "Please introduce yourself to the user."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))

src/pipecat/services/xtts.py

@@ -24,13 +24,14 @@ except ModuleNotFoundError as e:
     logger.error("In order to use XTTS, you need to `pip install pipecat-ai[xtts]`.")
     raise Exception(f"Missing module: {e}")
 
-#####
-## The server below can connect to XTTS through a local running docker
-##
-## Docker command: $ docker run --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest-cuda121
-## 
-## You can find more information on the official repo: https://github.com/coqui-ai/xtts-streaming-server
-####
+
+# The server below can connect to XTTS through a local running docker
+#
+# Docker command: $ docker run --gpus=all -e COQUI_TOS_AGREED=1 --rm -p 8000:80 ghcr.io/coqui-ai/xtts-streaming-server:latest-cuda121
+#
+# You can find more information on the official repo:
+# https://github.com/coqui-ai/xtts-streaming-server
+
 
 class XTTSService(TTSService):
 
@@ -40,7 +41,7 @@ class XTTSService(TTSService):
             aiohttp_session: aiohttp.ClientSession,
             voice_id: str,
             language: str,
-            base_url:str,
+            base_url: str,
             **kwargs):
         super().__init__(**kwargs)
 
@@ -58,9 +59,9 @@ class XTTSService(TTSService):
         embeddings = self._studio_speakers[self._voice_id]
 
         url = self._base_url + "/tts_stream"
-        
-        payload={
-            "text": text.replace('.','').replace('*',''),
+
+        payload = {
+            "text": text.replace('.', '').replace('*', ''),
             "language": self._language,
             "speaker_embedding": embeddings["speaker_embedding"],
             "gpt_cond_latent": embeddings["gpt_cond_latent"],
@@ -76,7 +77,7 @@ class XTTSService(TTSService):
                 logger.error(f"{self} error getting audio (status: {r.status}, error: {text})")
                 yield ErrorFrame(f"Error getting audio (status: {r.status}, error: {text})")
                 return
-            
+
             buffer = bytearray()
 
             async for chunk in r.content.iter_chunked(1024):
@@ -84,14 +85,14 @@ class XTTSService(TTSService):
                     await self.stop_ttfb_metrics()
                     # Append new chunk to the buffer
                     buffer.extend(chunk)
-                    
+
                     # Check if buffer has enough data for processing
                     while len(buffer) >= 48000:  # Assuming at least 0.5 seconds of audio data at 24000 Hz
                         # Process the buffer up to a safe size for resampling
                         process_data = buffer[:48000]
                         # Remove processed data from buffer
                         buffer = buffer[48000:]
-                        
+
                         # Convert the byte data to numpy array for resampling
                         audio_np = np.frombuffer(process_data, dtype=np.int16)
                         # Resample the audio from 24000 Hz to 16000 Hz
@@ -108,4 +109,4 @@ class XTTSService(TTSService):
                 resampled_audio = resampy.resample(audio_np, 24000, 16000)
                 resampled_audio_bytes = resampled_audio.astype(np.int16).tobytes()
                 frame = AudioRawFrame(resampled_audio_bytes, 16000, 1)
-                yield frame
+                yield frame