mirror of
https://github.com/mudler/LocalAI.git
synced 2024-12-19 20:57:54 +00:00
feat: add initial Bark backend implementation
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
parent
a843e64fc2
commit
433605e282
1
Makefile
1
Makefile
@ -337,6 +337,7 @@ protogen-go:
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protogen-python:
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/huggingface/ --grpc_python_out=extra/grpc/huggingface/ pkg/grpc/proto/backend.proto
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/autogptq/ --grpc_python_out=extra/grpc/autogptq/ pkg/grpc/proto/backend.proto
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python3 -m grpc_tools.protoc -Ipkg/grpc/proto/ --python_out=extra/grpc/bark/ --grpc_python_out=extra/grpc/bark/ pkg/grpc/proto/backend.proto
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## GRPC
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@ -28,10 +28,14 @@ func generateUniqueFileName(dir, baseName, ext string) string {
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}
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}
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func ModelTTS(text, modelFile string, loader *model.ModelLoader, o *options.Option) (string, *proto.Result, error) {
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func ModelTTS(backend, text, modelFile string, loader *model.ModelLoader, o *options.Option) (string, *proto.Result, error) {
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bb := backend
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if bb == "" {
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bb = model.PiperBackend
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}
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opts := []model.Option{
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model.WithBackendString(model.PiperBackend),
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model.WithModelFile(modelFile),
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model.WithBackendString(bb),
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model.WithModel(modelFile),
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model.WithContext(o.Context),
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model.WithAssetDir(o.AssetsDestination),
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}
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@ -56,10 +60,13 @@ func ModelTTS(text, modelFile string, loader *model.ModelLoader, o *options.Opti
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fileName := generateUniqueFileName(o.AudioDir, "piper", ".wav")
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filePath := filepath.Join(o.AudioDir, fileName)
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modelPath := filepath.Join(o.Loader.ModelPath, modelFile)
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if err := utils.VerifyPath(modelPath, o.Loader.ModelPath); err != nil {
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return "", nil, err
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// If the model file is not empty, we pass it joined with the model path
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modelPath := ""
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if modelFile != "" {
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modelPath = filepath.Join(o.Loader.ModelPath, modelFile)
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if err := utils.VerifyPath(modelPath, o.Loader.ModelPath); err != nil {
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return "", nil, err
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}
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}
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res, err := piperModel.TTS(context.Background(), &proto.TTSRequest{
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@ -9,8 +9,9 @@ import (
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)
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type TTSRequest struct {
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Model string `json:"model" yaml:"model"`
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Input string `json:"input" yaml:"input"`
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Model string `json:"model" yaml:"model"`
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Input string `json:"input" yaml:"input"`
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Backend string `json:"backend" yaml:"backend"`
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}
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func TTSEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx) error {
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@ -22,7 +23,7 @@ func TTSEndpoint(cm *config.ConfigLoader, o *options.Option) func(c *fiber.Ctx)
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return err
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}
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filePath, _, err := backend.ModelTTS(input.Input, input.Model, o.Loader, o)
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filePath, _, err := backend.ModelTTS(input.Backend, input.Input, input.Model, o.Loader, o)
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if err != nil {
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return err
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}
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49
extra/grpc/bark/backend_pb2.py
Normal file
49
extra/grpc/bark/backend_pb2.py
Normal file
@ -0,0 +1,49 @@
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# -*- coding: utf-8 -*-
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# Generated by the protocol buffer compiler. DO NOT EDIT!
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# source: backend.proto
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"""Generated protocol buffer code."""
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from google.protobuf import descriptor as _descriptor
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from google.protobuf import descriptor_pool as _descriptor_pool
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from google.protobuf import symbol_database as _symbol_database
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from google.protobuf.internal import builder as _builder
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# @@protoc_insertion_point(imports)
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_sym_db = _symbol_database.Default()
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DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\rbackend.proto\x12\x07\x62\x61\x63kend\"\x0f\n\rHealthMessage\"\x86\x06\n\x0ePredictOptions\x12\x0e\n\x06Prompt\x18\x01 \x01(\t\x12\x0c\n\x04Seed\x18\x02 \x01(\x05\x12\x0f\n\x07Threads\x18\x03 \x01(\x05\x12\x0e\n\x06Tokens\x18\x04 \x01(\x05\x12\x0c\n\x04TopK\x18\x05 \x01(\x05\x12\x0e\n\x06Repeat\x18\x06 \x01(\x05\x12\r\n\x05\x42\x61tch\x18\x07 \x01(\x05\x12\r\n\x05NKeep\x18\x08 \x01(\x05\x12\x13\n\x0bTemperature\x18\t \x01(\x02\x12\x0f\n\x07Penalty\x18\n \x01(\x02\x12\r\n\x05\x46\x31\x36KV\x18\x0b \x01(\x08\x12\x11\n\tDebugMode\x18\x0c \x01(\x08\x12\x13\n\x0bStopPrompts\x18\r \x03(\t\x12\x11\n\tIgnoreEOS\x18\x0e \x01(\x08\x12\x19\n\x11TailFreeSamplingZ\x18\x0f \x01(\x02\x12\x10\n\x08TypicalP\x18\x10 \x01(\x02\x12\x18\n\x10\x46requencyPenalty\x18\x11 \x01(\x02\x12\x17\n\x0fPresencePenalty\x18\x12 \x01(\x02\x12\x10\n\x08Mirostat\x18\x13 \x01(\x05\x12\x13\n\x0bMirostatETA\x18\x14 \x01(\x02\x12\x13\n\x0bMirostatTAU\x18\x15 \x01(\x02\x12\x12\n\nPenalizeNL\x18\x16 \x01(\x08\x12\x11\n\tLogitBias\x18\x17 \x01(\t\x12\r\n\x05MLock\x18\x19 \x01(\x08\x12\x0c\n\x04MMap\x18\x1a \x01(\x08\x12\x16\n\x0ePromptCacheAll\x18\x1b \x01(\x08\x12\x15\n\rPromptCacheRO\x18\x1c \x01(\x08\x12\x0f\n\x07Grammar\x18\x1d \x01(\t\x12\x0f\n\x07MainGPU\x18\x1e \x01(\t\x12\x13\n\x0bTensorSplit\x18\x1f \x01(\t\x12\x0c\n\x04TopP\x18 \x01(\x02\x12\x17\n\x0fPromptCachePath\x18! \x01(\t\x12\r\n\x05\x44\x65\x62ug\x18\" \x01(\x08\x12\x17\n\x0f\x45mbeddingTokens\x18# \x03(\x05\x12\x12\n\nEmbeddings\x18$ \x01(\t\x12\x14\n\x0cRopeFreqBase\x18% \x01(\x02\x12\x15\n\rRopeFreqScale\x18& \x01(\x02\x12\x1b\n\x13NegativePromptScale\x18\' \x01(\x02\x12\x16\n\x0eNegativePrompt\x18( \x01(\t\"\x18\n\x05Reply\x12\x0f\n\x07message\x18\x01 \x01(\x0c\"\xc8\x03\n\x0cModelOptions\x12\r\n\x05Model\x18\x01 \x01(\t\x12\x13\n\x0b\x43ontextSize\x18\x02 \x01(\x05\x12\x0c\n\x04Seed\x18\x03 \x01(\x05\x12\x0e\n\x06NBatch\x18\x04 \x01(\x05\x12\x11\n\tF16Memory\x18\x05 \x01(\x08\x12\r\n\x05MLock\x18\x06 \x01(\x08\x12\x0c\n\x04MMap\x18\x07 \x01(\x08\x12\x11\n\tVocabOnly\x18\x08 \x01(\x08\x12\x0f\n\x07LowVRAM\x18\t \x01(\x08\x12\x12\n\nEmbeddings\x18\n \x01(\x08\x12\x0c\n\x04NUMA\x18\x0b \x01(\x08\x12\x12\n\nNGPULayers\x18\x0c \x01(\x05\x12\x0f\n\x07MainGPU\x18\r \x01(\t\x12\x13\n\x0bTensorSplit\x18\x0e \x01(\t\x12\x0f\n\x07Threads\x18\x0f \x01(\x05\x12\x19\n\x11LibrarySearchPath\x18\x10 \x01(\t\x12\x14\n\x0cRopeFreqBase\x18\x11 \x01(\x02\x12\x15\n\rRopeFreqScale\x18\x12 \x01(\x02\x12\x12\n\nRMSNormEps\x18\x13 \x01(\x02\x12\x0c\n\x04NGQA\x18\x14 \x01(\x05\x12\x11\n\tModelFile\x18\x15 \x01(\t\x12\x0e\n\x06\x44\x65vice\x18\x16 \x01(\t\x12\x11\n\tUseTriton\x18\x17 \x01(\x08\x12\x15\n\rModelBaseName\x18\x18 \x01(\t\"*\n\x06Result\x12\x0f\n\x07message\x18\x01 \x01(\t\x12\x0f\n\x07success\x18\x02 \x01(\x08\"%\n\x0f\x45mbeddingResult\x12\x12\n\nembeddings\x18\x01 \x03(\x02\"C\n\x11TranscriptRequest\x12\x0b\n\x03\x64st\x18\x02 \x01(\t\x12\x10\n\x08language\x18\x03 \x01(\t\x12\x0f\n\x07threads\x18\x04 \x01(\r\"N\n\x10TranscriptResult\x12,\n\x08segments\x18\x01 \x03(\x0b\x32\x1a.backend.TranscriptSegment\x12\x0c\n\x04text\x18\x02 \x01(\t\"Y\n\x11TranscriptSegment\x12\n\n\x02id\x18\x01 \x01(\x05\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12\x0c\n\x04text\x18\x04 \x01(\t\x12\x0e\n\x06tokens\x18\x05 \x03(\x05\"\x9e\x01\n\x14GenerateImageRequest\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x0c\n\x04mode\x18\x03 \x01(\x05\x12\x0c\n\x04step\x18\x04 \x01(\x05\x12\x0c\n\x04seed\x18\x05 \x01(\x05\x12\x17\n\x0fpositive_prompt\x18\x06 \x01(\t\x12\x17\n\x0fnegative_prompt\x18\x07 \x01(\t\x12\x0b\n\x03\x64st\x18\x08 \x01(\t\"6\n\nTTSRequest\x12\x0c\n\x04text\x18\x01 \x01(\t\x12\r\n\x05model\x18\x02 \x01(\t\x12\x0b\n\x03\x64st\x18\x03 \x01(\t2\xeb\x03\n\x07\x42\x61\x63kend\x12\x32\n\x06Health\x12\x16.backend.HealthMessage\x1a\x0e.backend.Reply\"\x00\x12\x34\n\x07Predict\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x12\x35\n\tLoadModel\x12\x15.backend.ModelOptions\x1a\x0f.backend.Result\"\x00\x12<\n\rPredictStream\x12\x17.backend.PredictOptions\x1a\x0e.backend.Reply\"\x00\x30\x01\x12@\n\tEmbedding\x12\x17.backend.PredictOptions\x1a\x18.backend.EmbeddingResult\"\x00\x12\x41\n\rGenerateImage\x12\x1d.backend.GenerateImageRequest\x1a\x0f.backend.Result\"\x00\x12M\n\x12\x41udioTranscription\x12\x1a.backend.TranscriptRequest\x1a\x19.backend.TranscriptResult\"\x00\x12-\n\x03TTS\x12\x13.backend.TTSRequest\x1a\x0f.backend.Result\"\x00\x42Z\n\x19io.skynet.localai.backendB\x0eLocalAIBackendP\x01Z+github.com/go-skynet/LocalAI/pkg/grpc/protob\x06proto3')
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_globals = globals()
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_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
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_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'backend_pb2', _globals)
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if _descriptor._USE_C_DESCRIPTORS == False:
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DESCRIPTOR._options = None
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DESCRIPTOR._serialized_options = b'\n\031io.skynet.localai.backendB\016LocalAIBackendP\001Z+github.com/go-skynet/LocalAI/pkg/grpc/proto'
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_globals['_HEALTHMESSAGE']._serialized_start=26
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_globals['_HEALTHMESSAGE']._serialized_end=41
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_globals['_PREDICTOPTIONS']._serialized_start=44
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_globals['_PREDICTOPTIONS']._serialized_end=818
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_globals['_REPLY']._serialized_start=820
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_globals['_REPLY']._serialized_end=844
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_globals['_MODELOPTIONS']._serialized_start=847
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_globals['_MODELOPTIONS']._serialized_end=1303
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_globals['_RESULT']._serialized_start=1305
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_globals['_RESULT']._serialized_end=1347
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_globals['_EMBEDDINGRESULT']._serialized_start=1349
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_globals['_EMBEDDINGRESULT']._serialized_end=1386
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_globals['_TRANSCRIPTREQUEST']._serialized_start=1388
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_globals['_TRANSCRIPTREQUEST']._serialized_end=1455
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_globals['_TRANSCRIPTRESULT']._serialized_start=1457
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_globals['_TRANSCRIPTRESULT']._serialized_end=1535
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_globals['_TRANSCRIPTSEGMENT']._serialized_start=1537
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_globals['_TRANSCRIPTSEGMENT']._serialized_end=1626
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_globals['_GENERATEIMAGEREQUEST']._serialized_start=1629
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_globals['_GENERATEIMAGEREQUEST']._serialized_end=1787
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_globals['_TTSREQUEST']._serialized_start=1789
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_globals['_TTSREQUEST']._serialized_end=1843
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_globals['_BACKEND']._serialized_start=1846
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_globals['_BACKEND']._serialized_end=2337
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# @@protoc_insertion_point(module_scope)
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297
extra/grpc/bark/backend_pb2_grpc.py
Normal file
297
extra/grpc/bark/backend_pb2_grpc.py
Normal file
@ -0,0 +1,297 @@
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# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
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"""Client and server classes corresponding to protobuf-defined services."""
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import grpc
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import backend_pb2 as backend__pb2
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class BackendStub(object):
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"""Missing associated documentation comment in .proto file."""
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def __init__(self, channel):
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"""Constructor.
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Args:
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channel: A grpc.Channel.
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"""
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self.Health = channel.unary_unary(
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'/backend.Backend/Health',
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request_serializer=backend__pb2.HealthMessage.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.Predict = channel.unary_unary(
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'/backend.Backend/Predict',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.LoadModel = channel.unary_unary(
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'/backend.Backend/LoadModel',
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request_serializer=backend__pb2.ModelOptions.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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self.PredictStream = channel.unary_stream(
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'/backend.Backend/PredictStream',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.Reply.FromString,
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)
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self.Embedding = channel.unary_unary(
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'/backend.Backend/Embedding',
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request_serializer=backend__pb2.PredictOptions.SerializeToString,
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response_deserializer=backend__pb2.EmbeddingResult.FromString,
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)
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self.GenerateImage = channel.unary_unary(
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'/backend.Backend/GenerateImage',
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request_serializer=backend__pb2.GenerateImageRequest.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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self.AudioTranscription = channel.unary_unary(
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'/backend.Backend/AudioTranscription',
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request_serializer=backend__pb2.TranscriptRequest.SerializeToString,
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response_deserializer=backend__pb2.TranscriptResult.FromString,
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)
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self.TTS = channel.unary_unary(
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'/backend.Backend/TTS',
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request_serializer=backend__pb2.TTSRequest.SerializeToString,
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response_deserializer=backend__pb2.Result.FromString,
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)
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class BackendServicer(object):
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"""Missing associated documentation comment in .proto file."""
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def Health(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def Predict(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def LoadModel(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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||||
context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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def PredictStream(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
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||||
|
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def Embedding(self, request, context):
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"""Missing associated documentation comment in .proto file."""
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context.set_code(grpc.StatusCode.UNIMPLEMENTED)
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||||
context.set_details('Method not implemented!')
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raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def GenerateImage(self, request, context):
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||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def AudioTranscription(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
|
||||
raise NotImplementedError('Method not implemented!')
|
||||
|
||||
def TTS(self, request, context):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
|
||||
context.set_details('Method not implemented!')
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||||
raise NotImplementedError('Method not implemented!')
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||||
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||||
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||||
def add_BackendServicer_to_server(servicer, server):
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rpc_method_handlers = {
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||||
'Health': grpc.unary_unary_rpc_method_handler(
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||||
servicer.Health,
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request_deserializer=backend__pb2.HealthMessage.FromString,
|
||||
response_serializer=backend__pb2.Reply.SerializeToString,
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),
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||||
'Predict': grpc.unary_unary_rpc_method_handler(
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servicer.Predict,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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response_serializer=backend__pb2.Reply.SerializeToString,
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),
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||||
'LoadModel': grpc.unary_unary_rpc_method_handler(
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servicer.LoadModel,
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request_deserializer=backend__pb2.ModelOptions.FromString,
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response_serializer=backend__pb2.Result.SerializeToString,
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),
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'PredictStream': grpc.unary_stream_rpc_method_handler(
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servicer.PredictStream,
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request_deserializer=backend__pb2.PredictOptions.FromString,
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||||
response_serializer=backend__pb2.Reply.SerializeToString,
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||||
),
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||||
'Embedding': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.Embedding,
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||||
request_deserializer=backend__pb2.PredictOptions.FromString,
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||||
response_serializer=backend__pb2.EmbeddingResult.SerializeToString,
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||||
),
|
||||
'GenerateImage': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.GenerateImage,
|
||||
request_deserializer=backend__pb2.GenerateImageRequest.FromString,
|
||||
response_serializer=backend__pb2.Result.SerializeToString,
|
||||
),
|
||||
'AudioTranscription': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.AudioTranscription,
|
||||
request_deserializer=backend__pb2.TranscriptRequest.FromString,
|
||||
response_serializer=backend__pb2.TranscriptResult.SerializeToString,
|
||||
),
|
||||
'TTS': grpc.unary_unary_rpc_method_handler(
|
||||
servicer.TTS,
|
||||
request_deserializer=backend__pb2.TTSRequest.FromString,
|
||||
response_serializer=backend__pb2.Result.SerializeToString,
|
||||
),
|
||||
}
|
||||
generic_handler = grpc.method_handlers_generic_handler(
|
||||
'backend.Backend', rpc_method_handlers)
|
||||
server.add_generic_rpc_handlers((generic_handler,))
|
||||
|
||||
|
||||
# This class is part of an EXPERIMENTAL API.
|
||||
class Backend(object):
|
||||
"""Missing associated documentation comment in .proto file."""
|
||||
|
||||
@staticmethod
|
||||
def Health(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Health',
|
||||
backend__pb2.HealthMessage.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def Predict(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Predict',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def LoadModel(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/LoadModel',
|
||||
backend__pb2.ModelOptions.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def PredictStream(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_stream(request, target, '/backend.Backend/PredictStream',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.Reply.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def Embedding(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/Embedding',
|
||||
backend__pb2.PredictOptions.SerializeToString,
|
||||
backend__pb2.EmbeddingResult.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def GenerateImage(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/GenerateImage',
|
||||
backend__pb2.GenerateImageRequest.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def AudioTranscription(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/AudioTranscription',
|
||||
backend__pb2.TranscriptRequest.SerializeToString,
|
||||
backend__pb2.TranscriptResult.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
|
||||
@staticmethod
|
||||
def TTS(request,
|
||||
target,
|
||||
options=(),
|
||||
channel_credentials=None,
|
||||
call_credentials=None,
|
||||
insecure=False,
|
||||
compression=None,
|
||||
wait_for_ready=None,
|
||||
timeout=None,
|
||||
metadata=None):
|
||||
return grpc.experimental.unary_unary(request, target, '/backend.Backend/TTS',
|
||||
backend__pb2.TTSRequest.SerializeToString,
|
||||
backend__pb2.Result.FromString,
|
||||
options, channel_credentials,
|
||||
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
|
||||
83
extra/grpc/bark/ttsbark.py
Normal file
83
extra/grpc/bark/ttsbark.py
Normal file
@ -0,0 +1,83 @@
|
||||
#!/usr/bin/env python3
|
||||
import grpc
|
||||
from concurrent import futures
|
||||
import time
|
||||
import backend_pb2
|
||||
import backend_pb2_grpc
|
||||
import argparse
|
||||
import signal
|
||||
import sys
|
||||
import os
|
||||
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
|
||||
from pathlib import Path
|
||||
from bark import SAMPLE_RATE, generate_audio, preload_models
|
||||
from scipy.io.wavfile import write as write_wav
|
||||
|
||||
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
|
||||
|
||||
# Implement the BackendServicer class with the service methods
|
||||
class BackendServicer(backend_pb2_grpc.BackendServicer):
|
||||
def Health(self, request, context):
|
||||
return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
|
||||
def LoadModel(self, request, context):
|
||||
model_name = request.Model
|
||||
try:
|
||||
print("Preparing models, please wait", file=sys.stderr)
|
||||
# download and load all models
|
||||
preload_models()
|
||||
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):
|
||||
model = request.model
|
||||
print(request, file=sys.stderr)
|
||||
try:
|
||||
audio_array = None
|
||||
if model != "":
|
||||
audio_array = generate_audio(request.text, history_prompt=model)
|
||||
else:
|
||||
audio_array = generate_audio(request.text)
|
||||
print("saving to", request.dst, file=sys.stderr)
|
||||
# save audio to disk
|
||||
write_wav(request.dst, SAMPLE_RATE, audio_array)
|
||||
print("saved to", request.dst, file=sys.stderr)
|
||||
print("tts for", file=sys.stderr)
|
||||
print(request, file=sys.stderr)
|
||||
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=10))
|
||||
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)
|
||||
@ -4,3 +4,4 @@ google
|
||||
protobuf
|
||||
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.3.0/auto_gptq-0.3.0+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
|
||||
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.3.0/auto_gptq-0.3.0+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
|
||||
git+https://github.com/suno-ai/bark.git
|
||||
Loading…
Reference in New Issue
Block a user