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#!/usr/bin/env python3
"""
This is an extra gRPC server of LocalAI for Chatterbox TTS
"""
from concurrent import futures
import time
import argparse
import signal
import sys
import os
import backend_pb2
import backend_pb2_grpc
import torch
import torchaudio as ta
from chatterbox.tts import ChatterboxTTS
from chatterbox.mtl_tts import ChatterboxMultilingualTTS
import grpc
import tempfile
def is_float(s):
"""Check if a string can be converted to float."""
try:
float(s)
return True
except ValueError:
return False
def is_int(s):
"""Check if a string can be converted to int."""
try:
int(s)
return True
except ValueError:
return False
def split_text_at_word_boundary(text, max_length=250):
"""
Split text at word boundaries without truncating words.
Returns a list of text chunks.
"""
if not text or len(text) <= max_length:
return [text]
chunks = []
words = text.split()
current_chunk = ""
for word in words:
# Check if adding this word would exceed the limit
if len(current_chunk) + len(word) + 1 <= max_length:
if current_chunk:
current_chunk += " " + word
else:
current_chunk = word
else:
# If current chunk is not empty, add it to chunks
if current_chunk:
chunks.append(current_chunk)
current_chunk = word
else:
# If a single word is longer than max_length, we have to include it anyway
chunks.append(word)
current_chunk = ""
# Add the last chunk if it's not empty
if current_chunk:
chunks.append(current_chunk)
return chunks
def merge_audio_files(audio_files, output_path, sample_rate):
"""
Merge multiple audio files into a single audio file.
"""
if not audio_files:
return
if len(audio_files) == 1:
# If only one file, just copy it
import shutil
shutil.copy2(audio_files[0], output_path)
return
# Load all audio files
waveforms = []
for audio_file in audio_files:
waveform, sr = ta.load(audio_file)
if sr != sample_rate:
# Resample if necessary
resampler = ta.transforms.Resample(sr, sample_rate)
waveform = resampler(waveform)
waveforms.append(waveform)
# Concatenate all waveforms
merged_waveform = torch.cat(waveforms, dim=1)
# Save the merged audio
ta.save(output_path, merged_waveform, sample_rate)
# Clean up temporary files
for audio_file in audio_files:
if os.path.exists(audio_file):
os.remove(audio_file)
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
# If MAX_WORKERS are specified in the environment use it, otherwise default to 1
MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
# Implement the BackendServicer class with the service methods
class BackendServicer(backend_pb2_grpc.BackendServicer):
"""
BackendServicer is the class that implements the gRPC service
"""
def Health(self, request, context):
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
def LoadModel(self, request, context):
# Get device
# device = "cuda" if request.CUDA else "cpu"
if torch.cuda.is_available():
print("CUDA is available", file=sys.stderr)
device = "cuda"
else:
print("CUDA is not available", file=sys.stderr)
device = "cpu"
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
if mps_available:
device = "mps"
if not torch.cuda.is_available() and request.CUDA:
return backend_pb2.Result(success=False, message="CUDA is not available")
options = request.Options
# empty dict
self.options = {}
# The options are a list of strings in this form optname:optvalue
# We are storing all the options in a dict so we can use it later when
# generating the images
for opt in options:
if ":" not in opt:
continue
key, value = opt.split(":")
# if value is a number, convert it to the appropriate type
if is_float(value):
value = float(value)
elif is_int(value):
value = int(value)
elif value.lower() in ["true", "false"]:
value = value.lower() == "true"
self.options[key] = value
self.AudioPath = None
if os.path.isabs(request.AudioPath):
self.AudioPath = request.AudioPath
elif request.AudioPath and request.ModelFile != "" and not os.path.isabs(request.AudioPath):
# get base path of modelFile
modelFileBase = os.path.dirname(request.ModelFile)
# modify LoraAdapter to be relative to modelFileBase
self.AudioPath = os.path.join(modelFileBase, request.AudioPath)
try:
print("Preparing models, please wait", file=sys.stderr)
if "multilingual" in self.options:
# remove key from options
del self.options["multilingual"]
self.model = ChatterboxMultilingualTTS.from_pretrained(device=device)
else:
self.model = ChatterboxTTS.from_pretrained(device=device)
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
# Implement your logic here for the LoadModel service
# Replace this with your desired response
return backend_pb2.Result(message="Model loaded successfully", success=True)
def TTS(self, request, context):
try:
kwargs = {}
if "language" in self.options:
kwargs["language_id"] = self.options["language"]
if self.AudioPath is not None:
kwargs["audio_prompt_path"] = self.AudioPath
# add options to kwargs
kwargs.update(self.options)
# Check if text exceeds 250 characters
# (chatterbox does not support long text)
# https://github.com/resemble-ai/chatterbox/issues/60
# https://github.com/resemble-ai/chatterbox/issues/110
if len(request.text) > 250:
# Split text at word boundaries
text_chunks = split_text_at_word_boundary(request.text, max_length=250)
print(f"Splitting text into chunks of 250 characters: {len(text_chunks)}", file=sys.stderr)
# Generate audio for each chunk
temp_audio_files = []
for i, chunk in enumerate(text_chunks):
# Generate audio for this chunk
wav = self.model.generate(chunk, **kwargs)
# Create temporary file for this chunk
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.wav')
temp_file.close()
ta.save(temp_file.name, wav, self.model.sr)
temp_audio_files.append(temp_file.name)
# Merge all audio files
merge_audio_files(temp_audio_files, request.dst, self.model.sr)
else:
# Generate audio using ChatterboxTTS for short text
wav = self.model.generate(request.text, **kwargs)
# Save the generated audio
ta.save(request.dst, wav, self.model.sr)
except Exception as err:
return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
return backend_pb2.Result(success=True)
def serve(address):
server = grpc.server(futures.ThreadPoolExecutor(max_workers=MAX_WORKERS),
options=[
('grpc.max_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_send_message_length', 50 * 1024 * 1024), # 50MB
('grpc.max_receive_message_length', 50 * 1024 * 1024), # 50MB
])
backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
server.add_insecure_port(address)
server.start()
print("Server started. Listening on: " + address, file=sys.stderr)
# Define the signal handler function
def signal_handler(sig, frame):
print("Received termination signal. Shutting down...")
server.stop(0)
sys.exit(0)
# Set the signal handlers for SIGINT and SIGTERM
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run the gRPC server.")
parser.add_argument(
"--addr", default="localhost:50051", help="The address to bind the server to."
)
args = parser.parse_args()
serve(args.addr)
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