feat(neutts): add backend #6404
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,23 @@ | ||
| .PHONY: neutts | ||
| neutts: | ||
| bash install.sh | ||
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| .PHONY: run | ||
| run: neutts | ||
| @echo "Running neutts..." | ||
| bash run.sh | ||
| @echo "neutts run." | ||
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| .PHONY: test | ||
| test: neutts | ||
| @echo "Testing neutts..." | ||
| bash test.sh | ||
| @echo "neutts tested." | ||
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| .PHONY: protogen-clean | ||
| protogen-clean: | ||
| $(RM) backend_pb2_grpc.py backend_pb2.py | ||
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| .PHONY: clean | ||
| clean: protogen-clean | ||
| rm -rf venv __pycache__ |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,162 @@ | ||
| #!/usr/bin/env python3 | ||
| """ | ||
| This is an extra gRPC server of LocalAI for NeuTTSAir | ||
| """ | ||
| from concurrent import futures | ||
| import time | ||
| import argparse | ||
| import signal | ||
| import sys | ||
| import os | ||
| import backend_pb2 | ||
| import backend_pb2_grpc | ||
| import torch | ||
| from neuttsair.neutts import NeuTTSAir | ||
| import soundfile as sf | ||
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| import grpc | ||
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| 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 | ||
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| _ONE_DAY_IN_SECONDS = 60 * 60 * 24 | ||
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| # 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')) | ||
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| # 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): | ||
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| # 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") | ||
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| options = request.Options | ||
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| # empty dict | ||
| self.options = {} | ||
| self.ref_text = None | ||
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| # 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 | ||
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| codec_repo = "neuphonic/neucodec" | ||
| if "codec_repo" in self.options: | ||
| codec_repo = self.options["codec_repo"] | ||
| del self.options["codec_repo"] | ||
| if "ref_text" in self.options: | ||
| self.ref_text = self.options["ref_text"] | ||
| del self.options["ref_text"] | ||
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| self.AudioPath = None | ||
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| 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) | ||
| self.model = NeuTTSAir(backbone_repo=request.Model, backbone_device=device, codec_repo=codec_repo, codec_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) | ||
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| def TTS(self, request, context): | ||
| try: | ||
| kwargs = {} | ||
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| # add options to kwargs | ||
| kwargs.update(self.options) | ||
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| ref_codes = self.model.encode_reference(self.AudioPath) | ||
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| wav = self.model.infer(request.text, ref_codes, self.ref_text) | ||
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| sf.write(request.dst, wav, 24000) | ||
| except Exception as err: | ||
| return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}") | ||
| return backend_pb2.Result(success=True) | ||
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| 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) | ||
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| # Define the signal handler function | ||
| def signal_handler(sig, frame): | ||
| print("Received termination signal. Shutting down...") | ||
| server.stop(0) | ||
| sys.exit(0) | ||
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| # Set the signal handlers for SIGINT and SIGTERM | ||
| signal.signal(signal.SIGINT, signal_handler) | ||
| signal.signal(signal.SIGTERM, signal_handler) | ||
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| try: | ||
| while True: | ||
| time.sleep(_ONE_DAY_IN_SECONDS) | ||
| except KeyboardInterrupt: | ||
| server.stop(0) | ||
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| 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() | ||
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| serve(args.addr) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| #!/bin/bash | ||
| set -e | ||
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| backend_dir=$(dirname $0) | ||
| if [ -d $backend_dir/common ]; then | ||
| source $backend_dir/common/libbackend.sh | ||
| else | ||
| source $backend_dir/../common/libbackend.sh | ||
| fi | ||
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| # This is here because the Intel pip index is broken and returns 200 status codes for every package name, it just doesn't return any package links. | ||
| # This makes uv think that the package exists in the Intel pip index, and by default it stops looking at other pip indexes once it finds a match. | ||
| # We need uv to continue falling through to the pypi default index to find optimum[openvino] in the pypi index | ||
| # the --upgrade actually allows us to *downgrade* torch to the version provided in the Intel pip index | ||
| if [ "x${BUILD_PROFILE}" == "xintel" ]; then | ||
| EXTRA_PIP_INSTALL_FLAGS+=" --upgrade --index-strategy=unsafe-first-match" | ||
| fi | ||
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| if [ "x${BUILD_TYPE}" == "xcublas" ] || [ "x${BUILD_TYPE}" == "xl4t" ]; then | ||
| export CMAKE_ARGS="-DGGML_CUDA=on" | ||
| export LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/compat:${LD_LIBRARY_PATH:-}" | ||
| fi | ||
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| if [ "x${BUILD_TYPE}" == "xhipblas" ]; then | ||
| export CMAKE_ARGS="-DGGML_HIPBLAS=on" | ||
| fi | ||
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| EXTRA_PIP_INSTALL_FLAGS+=" --no-build-isolation" | ||
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| git clone https://github.com/neuphonic/neutts-air neutts-air | ||
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| cp -rfv neutts-air/neuttsair ./ | ||
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| installRequirements | ||
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