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feat(kokoro): complete kokoro integration (#5978)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
This commit is contained in:
Ettore Di Giacinto
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20a70e1244
commit
9c2840ac38
@@ -1,9 +1,18 @@
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.DEFAULT_GOAL := install
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.PHONY: install
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install:
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.PHONY: kokoro
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kokoro: protogen
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bash install.sh
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$(MAKE) protogen
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.PHONY: run
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run: protogen
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@echo "Running kokoro..."
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bash run.sh
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@echo "kokoro run."
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.PHONY: test
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test: protogen
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@echo "Testing kokoro..."
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bash test.sh
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@echo "kokoro tested."
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.PHONY: protogen
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protogen: backend_pb2_grpc.py backend_pb2.py
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@@ -13,7 +22,7 @@ protogen-clean:
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$(RM) backend_pb2_grpc.py backend_pb2.py
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backend_pb2_grpc.py backend_pb2.py:
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bash protogen.sh
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python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto
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.PHONY: clean
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clean: protogen-clean
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23
backend/python/kokoro/README.md
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23
backend/python/kokoro/README.md
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@@ -0,0 +1,23 @@
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# Kokoro TTS Backend for LocalAI
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This is a gRPC server backend for LocalAI that uses the Kokoro TTS pipeline.
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## Creating a separate environment for kokoro project
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```bash
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make kokoro
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```
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## Testing the gRPC server
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```bash
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make test
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```
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## Features
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- Lightweight TTS model with 82 million parameters
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- Apache-licensed weights
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- Fast and cost-efficient
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- Multi-language support
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- Multiple voice options
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115
backend/python/kokoro/backend.py
Executable file → Normal file
115
backend/python/kokoro/backend.py
Executable file → Normal file
@@ -1,101 +1,92 @@
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#!/usr/bin/env python3
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"""
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Extra gRPC server for Kokoro models.
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This is an extra gRPC server of LocalAI for Kokoro TTS
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"""
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from concurrent import futures
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import time
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import argparse
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import signal
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import sys
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import os
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import time
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import backend_pb2
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import backend_pb2_grpc
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import torch
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from kokoro import KPipeline
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import soundfile as sf
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import grpc
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from models import build_model
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from kokoro import generate
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import torch
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SAMPLE_RATE = 22050
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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
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MAX_WORKERS = int(os.environ.get('PYTHON_GRPC_MAX_WORKERS', '1'))
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KOKORO_LANG_CODE = os.environ.get('KOKORO_LANG_CODE', 'a')
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# Implement the BackendServicer class with the service methods
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class BackendServicer(backend_pb2_grpc.BackendServicer):
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"""
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A gRPC servicer for the backend service.
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This class implements the gRPC methods for the backend service, including Health, LoadModel, and Embedding.
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BackendServicer is the class that implements the gRPC service
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"""
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def Health(self, request, context):
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"""
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A gRPC method that returns the health status of the backend service.
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Args:
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request: A HealthRequest object that contains the request parameters.
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context: A grpc.ServicerContext object that provides information about the RPC.
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Returns:
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A Reply object that contains the health status of the backend service.
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"""
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return backend_pb2.Reply(message=bytes("OK", 'utf-8'))
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def LoadModel(self, request, context):
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"""
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A gRPC method that loads a model into memory.
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# Get device
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if torch.cuda.is_available():
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print("CUDA is available", file=sys.stderr)
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device = "cuda"
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else:
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print("CUDA is not available", file=sys.stderr)
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device = "cpu"
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Args:
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request: A LoadModelRequest object that contains the request parameters.
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context: A grpc.ServicerContext object that provides information about the RPC.
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if not torch.cuda.is_available() and request.CUDA:
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return backend_pb2.Result(success=False, message="CUDA is not available")
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Returns:
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A Result object that contains the result of the LoadModel operation.
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"""
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model_name = request.Model
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try:
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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self.MODEL = build_model(request.ModelFile, device)
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print("Preparing Kokoro TTS pipeline, please wait", file=sys.stderr)
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# empty dict
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self.options = {}
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options = request.Options
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# Find the voice from the options, options are a list of strings in this form optname:optvalue:
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VOICE_NAME = None
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# The options are a list of strings in this form optname:optvalue
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# We are storing all the options in a dict so we can use it later when
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# generating the images
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for opt in options:
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if opt.startswith("voice:"):
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VOICE_NAME = opt.split(":")[1]
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break
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if VOICE_NAME is None:
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return backend_pb2.Result(success=False, message=f"No voice specified in options")
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MODELPATH = request.ModelPath
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# If voice name contains a plus, split it and load the two models and combine them
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if "+" in VOICE_NAME:
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voice1, voice2 = VOICE_NAME.split("+")
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voice1 = torch.load(f'{MODELPATH}/{voice1}.pt', weights_only=True).to(device)
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voice2 = torch.load(f'{MODELPATH}/{voice2}.pt', weights_only=True).to(device)
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self.VOICEPACK = torch.mean(torch.stack([voice1, voice2]), dim=0)
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else:
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self.VOICEPACK = torch.load(f'{MODELPATH}/{VOICE_NAME}.pt', weights_only=True).to(device)
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if ":" not in opt:
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continue
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key, value = opt.split(":")
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self.options[key] = value
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self.VOICE_NAME = VOICE_NAME
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print(f'Loaded voice: {VOICE_NAME}')
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# Initialize Kokoro pipeline with language code
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lang_code = self.options.get("lang_code", KOKORO_LANG_CODE)
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self.pipeline = KPipeline(lang_code=lang_code)
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print(f"Kokoro TTS pipeline loaded with language code: {lang_code}", file=sys.stderr)
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(message="Model loaded successfully", success=True)
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return backend_pb2.Result(message="Kokoro TTS pipeline loaded successfully", success=True)
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def TTS(self, request, context):
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model_name = request.model
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if model_name == "":
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return backend_pb2.Result(success=False, message="request.model is required")
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try:
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audio, out_ps = generate(self.MODEL, request.text, self.VOICEPACK, lang=self.VOICE_NAME)
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print(out_ps)
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sf.write(request.dst, audio, SAMPLE_RATE)
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# Get voice from request, default to 'af_heart' if not specified
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voice = request.voice if request.voice else 'af_heart'
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# Generate audio using Kokoro pipeline
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generator = self.pipeline(request.text, voice=voice)
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# Get the first (and typically only) audio segment
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for i, (gs, ps, audio) in enumerate(generator):
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# Save audio to the destination file
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sf.write(request.dst, audio, 24000)
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print(f"Generated audio segment {i}: gs={gs}, ps={ps}", file=sys.stderr)
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# For now, we only process the first segment
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# If you need to handle multiple segments, you might want to modify this
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break
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except Exception as err:
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return backend_pb2.Result(success=False, message=f"Unexpected {err=}, {type(err)=}")
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return backend_pb2.Result(success=True)
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def serve(address):
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@@ -108,11 +99,11 @@ def serve(address):
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backend_pb2_grpc.add_BackendServicer_to_server(BackendServicer(), server)
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server.add_insecure_port(address)
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server.start()
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print("[Kokoro] Server started. Listening on: " + address, file=sys.stderr)
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print("Server started. Listening on: " + address, file=sys.stderr)
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# Define the signal handler function
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def signal_handler(sig, frame):
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print("[Kokoro] Received termination signal. Shutting down...")
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print("Received termination signal. Shutting down...")
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server.stop(0)
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sys.exit(0)
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@@ -132,5 +123,5 @@ if __name__ == "__main__":
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"--addr", default="localhost:50051", help="The address to bind the server to."
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)
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args = parser.parse_args()
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print(f"[Kokoro] startup: {args}", file=sys.stderr)
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serve(args.addr)
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@@ -1,524 +0,0 @@
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# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/istftnet.py
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# https://github.com/yl4579/StyleTTS2/blob/main/Modules/istftnet.py
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from scipy.signal import get_window
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from torch.nn import Conv1d, ConvTranspose1d
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from torch.nn.utils import weight_norm, remove_weight_norm
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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# https://github.com/yl4579/StyleTTS2/blob/main/Modules/utils.py
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size*dilation - dilation)/2)
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LRELU_SLOPE = 0.1
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class AdaIN1d(nn.Module):
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def __init__(self, style_dim, num_features):
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super().__init__()
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self.norm = nn.InstanceNorm1d(num_features, affine=False)
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self.fc = nn.Linear(style_dim, num_features*2)
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def forward(self, x, s):
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h = self.fc(s)
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h = h.view(h.size(0), h.size(1), 1)
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gamma, beta = torch.chunk(h, chunks=2, dim=1)
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return (1 + gamma) * self.norm(x) + beta
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class AdaINResBlock1(torch.nn.Module):
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def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), style_dim=64):
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super(AdaINResBlock1, self).__init__()
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self.convs1 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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])
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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])
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self.convs2.apply(init_weights)
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self.adain1 = nn.ModuleList([
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AdaIN1d(style_dim, channels),
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AdaIN1d(style_dim, channels),
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AdaIN1d(style_dim, channels),
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])
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self.adain2 = nn.ModuleList([
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AdaIN1d(style_dim, channels),
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AdaIN1d(style_dim, channels),
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AdaIN1d(style_dim, channels),
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])
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self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))])
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self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))])
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def forward(self, x, s):
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for c1, c2, n1, n2, a1, a2 in zip(self.convs1, self.convs2, self.adain1, self.adain2, self.alpha1, self.alpha2):
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xt = n1(x, s)
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xt = xt + (1 / a1) * (torch.sin(a1 * xt) ** 2) # Snake1D
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xt = c1(xt)
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xt = n2(xt, s)
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xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2) # Snake1D
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class TorchSTFT(torch.nn.Module):
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def __init__(self, filter_length=800, hop_length=200, win_length=800, window='hann'):
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super().__init__()
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self.filter_length = filter_length
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self.hop_length = hop_length
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self.win_length = win_length
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self.window = torch.from_numpy(get_window(window, win_length, fftbins=True).astype(np.float32))
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def transform(self, input_data):
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forward_transform = torch.stft(
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input_data,
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self.filter_length, self.hop_length, self.win_length, window=self.window.to(input_data.device),
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return_complex=True)
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return torch.abs(forward_transform), torch.angle(forward_transform)
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def inverse(self, magnitude, phase):
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inverse_transform = torch.istft(
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magnitude * torch.exp(phase * 1j),
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self.filter_length, self.hop_length, self.win_length, window=self.window.to(magnitude.device))
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return inverse_transform.unsqueeze(-2) # unsqueeze to stay consistent with conv_transpose1d implementation
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def forward(self, input_data):
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self.magnitude, self.phase = self.transform(input_data)
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reconstruction = self.inverse(self.magnitude, self.phase)
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return reconstruction
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class SineGen(torch.nn.Module):
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""" Definition of sine generator
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SineGen(samp_rate, harmonic_num = 0,
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sine_amp = 0.1, noise_std = 0.003,
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voiced_threshold = 0,
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flag_for_pulse=False)
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samp_rate: sampling rate in Hz
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harmonic_num: number of harmonic overtones (default 0)
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sine_amp: amplitude of sine-wavefrom (default 0.1)
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noise_std: std of Gaussian noise (default 0.003)
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voiced_thoreshold: F0 threshold for U/V classification (default 0)
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flag_for_pulse: this SinGen is used inside PulseGen (default False)
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Note: when flag_for_pulse is True, the first time step of a voiced
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segment is always sin(np.pi) or cos(0)
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"""
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def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
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sine_amp=0.1, noise_std=0.003,
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voiced_threshold=0,
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flag_for_pulse=False):
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super(SineGen, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = noise_std
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self.harmonic_num = harmonic_num
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self.dim = self.harmonic_num + 1
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self.sampling_rate = samp_rate
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self.voiced_threshold = voiced_threshold
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self.flag_for_pulse = flag_for_pulse
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self.upsample_scale = upsample_scale
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def _f02uv(self, f0):
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# generate uv signal
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uv = (f0 > self.voiced_threshold).type(torch.float32)
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return uv
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def _f02sine(self, f0_values):
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""" f0_values: (batchsize, length, dim)
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where dim indicates fundamental tone and overtones
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"""
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# convert to F0 in rad. The integer part n can be ignored
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# because 2 * np.pi * n doesn't affect phase
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rad_values = (f0_values / self.sampling_rate) % 1
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# initial phase noise (no noise for fundamental component)
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rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
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device=f0_values.device)
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rand_ini[:, 0] = 0
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rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
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# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
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if not self.flag_for_pulse:
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# # for normal case
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# # To prevent torch.cumsum numerical overflow,
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# # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
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# # Buffer tmp_over_one_idx indicates the time step to add -1.
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# # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
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# tmp_over_one = torch.cumsum(rad_values, 1) % 1
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# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
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# cumsum_shift = torch.zeros_like(rad_values)
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# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
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# phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
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rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2),
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scale_factor=1/self.upsample_scale,
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mode="linear").transpose(1, 2)
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# tmp_over_one = torch.cumsum(rad_values, 1) % 1
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# tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
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# cumsum_shift = torch.zeros_like(rad_values)
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# cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
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phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
|
||||
phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale,
|
||||
scale_factor=self.upsample_scale, mode="linear").transpose(1, 2)
|
||||
sines = torch.sin(phase)
|
||||
|
||||
else:
|
||||
# If necessary, make sure that the first time step of every
|
||||
# voiced segments is sin(pi) or cos(0)
|
||||
# This is used for pulse-train generation
|
||||
|
||||
# identify the last time step in unvoiced segments
|
||||
uv = self._f02uv(f0_values)
|
||||
uv_1 = torch.roll(uv, shifts=-1, dims=1)
|
||||
uv_1[:, -1, :] = 1
|
||||
u_loc = (uv < 1) * (uv_1 > 0)
|
||||
|
||||
# get the instantanouse phase
|
||||
tmp_cumsum = torch.cumsum(rad_values, dim=1)
|
||||
# different batch needs to be processed differently
|
||||
for idx in range(f0_values.shape[0]):
|
||||
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
|
||||
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
|
||||
# stores the accumulation of i.phase within
|
||||
# each voiced segments
|
||||
tmp_cumsum[idx, :, :] = 0
|
||||
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
|
||||
|
||||
# rad_values - tmp_cumsum: remove the accumulation of i.phase
|
||||
# within the previous voiced segment.
|
||||
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
|
||||
|
||||
# get the sines
|
||||
sines = torch.cos(i_phase * 2 * np.pi)
|
||||
return sines
|
||||
|
||||
def forward(self, f0):
|
||||
""" sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
|
||||
device=f0.device)
|
||||
# fundamental component
|
||||
fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self._f02sine(fn) * self.sine_amp
|
||||
|
||||
# generate uv signal
|
||||
# uv = torch.ones(f0.shape)
|
||||
# uv = uv * (f0 > self.voiced_threshold)
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
""" SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num,
|
||||
sine_amp, add_noise_std, voiced_threshod)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
with torch.no_grad():
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
def padDiff(x):
|
||||
return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(self, style_dim, resblock_kernel_sizes, upsample_rates, upsample_initial_channel, resblock_dilation_sizes, upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size):
|
||||
super(Generator, self).__init__()
|
||||
|
||||
self.num_kernels = len(resblock_kernel_sizes)
|
||||
self.num_upsamples = len(upsample_rates)
|
||||
resblock = AdaINResBlock1
|
||||
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=24000,
|
||||
upsample_scale=np.prod(upsample_rates) * gen_istft_hop_size,
|
||||
harmonic_num=8, voiced_threshod=10)
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates) * gen_istft_hop_size)
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.noise_res = nn.ModuleList()
|
||||
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
|
||||
self.ups.append(weight_norm(
|
||||
ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
|
||||
k, u, padding=(k-u)//2)))
|
||||
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = upsample_initial_channel//(2**(i+1))
|
||||
for j, (k, d) in enumerate(zip(resblock_kernel_sizes,resblock_dilation_sizes)):
|
||||
self.resblocks.append(resblock(ch, k, d, style_dim))
|
||||
|
||||
c_cur = upsample_initial_channel // (2 ** (i + 1))
|
||||
|
||||
if i + 1 < len(upsample_rates): #
|
||||
stride_f0 = np.prod(upsample_rates[i + 1:])
|
||||
self.noise_convs.append(Conv1d(
|
||||
gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2))
|
||||
self.noise_res.append(resblock(c_cur, 7, [1,3,5], style_dim))
|
||||
else:
|
||||
self.noise_convs.append(Conv1d(gen_istft_n_fft + 2, c_cur, kernel_size=1))
|
||||
self.noise_res.append(resblock(c_cur, 11, [1,3,5], style_dim))
|
||||
|
||||
|
||||
self.post_n_fft = gen_istft_n_fft
|
||||
self.conv_post = weight_norm(Conv1d(ch, self.post_n_fft + 2, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
|
||||
self.stft = TorchSTFT(filter_length=gen_istft_n_fft, hop_length=gen_istft_hop_size, win_length=gen_istft_n_fft)
|
||||
|
||||
|
||||
def forward(self, x, s, f0):
|
||||
with torch.no_grad():
|
||||
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
|
||||
har_source, noi_source, uv = self.m_source(f0)
|
||||
har_source = har_source.transpose(1, 2).squeeze(1)
|
||||
har_spec, har_phase = self.stft.transform(har_source)
|
||||
har = torch.cat([har_spec, har_phase], dim=1)
|
||||
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
x_source = self.noise_convs[i](har)
|
||||
x_source = self.noise_res[i](x_source, s)
|
||||
|
||||
x = self.ups[i](x)
|
||||
if i == self.num_upsamples - 1:
|
||||
x = self.reflection_pad(x)
|
||||
|
||||
x = x + x_source
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i*self.num_kernels+j](x, s)
|
||||
else:
|
||||
xs += self.resblocks[i*self.num_kernels+j](x, s)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
|
||||
phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
|
||||
return self.stft.inverse(spec, phase)
|
||||
|
||||
def fw_phase(self, x, s):
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
x = self.ups[i](x)
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i*self.num_kernels+j](x, s)
|
||||
else:
|
||||
xs += self.resblocks[i*self.num_kernels+j](x, s)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.reflection_pad(x)
|
||||
x = self.conv_post(x)
|
||||
spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
|
||||
phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])
|
||||
return spec, phase
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
|
||||
|
||||
class AdainResBlk1d(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
|
||||
upsample='none', dropout_p=0.0):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.upsample_type = upsample
|
||||
self.upsample = UpSample1d(upsample)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out, style_dim)
|
||||
self.dropout = nn.Dropout(dropout_p)
|
||||
|
||||
if upsample == 'none':
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
|
||||
|
||||
|
||||
def _build_weights(self, dim_in, dim_out, style_dim):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
|
||||
self.norm1 = AdaIN1d(style_dim, dim_in)
|
||||
self.norm2 = AdaIN1d(style_dim, dim_out)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def _shortcut(self, x):
|
||||
x = self.upsample(x)
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x, s):
|
||||
x = self.norm1(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.pool(x)
|
||||
x = self.conv1(self.dropout(x))
|
||||
x = self.norm2(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(self.dropout(x))
|
||||
return x
|
||||
|
||||
def forward(self, x, s):
|
||||
out = self._residual(x, s)
|
||||
out = (out + self._shortcut(x)) / np.sqrt(2)
|
||||
return out
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == 'none':
|
||||
return x
|
||||
else:
|
||||
return F.interpolate(x, scale_factor=2, mode='nearest')
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, dim_in=512, F0_channel=512, style_dim=64, dim_out=80,
|
||||
resblock_kernel_sizes = [3,7,11],
|
||||
upsample_rates = [10, 6],
|
||||
upsample_initial_channel=512,
|
||||
resblock_dilation_sizes=[[1,3,5], [1,3,5], [1,3,5]],
|
||||
upsample_kernel_sizes=[20, 12],
|
||||
gen_istft_n_fft=20, gen_istft_hop_size=5):
|
||||
super().__init__()
|
||||
|
||||
self.decode = nn.ModuleList()
|
||||
|
||||
self.encode = AdainResBlk1d(dim_in + 2, 1024, style_dim)
|
||||
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 1024, style_dim))
|
||||
self.decode.append(AdainResBlk1d(1024 + 2 + 64, 512, style_dim, upsample=True))
|
||||
|
||||
self.F0_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
|
||||
|
||||
self.N_conv = weight_norm(nn.Conv1d(1, 1, kernel_size=3, stride=2, groups=1, padding=1))
|
||||
|
||||
self.asr_res = nn.Sequential(
|
||||
weight_norm(nn.Conv1d(512, 64, kernel_size=1)),
|
||||
)
|
||||
|
||||
|
||||
self.generator = Generator(style_dim, resblock_kernel_sizes, upsample_rates,
|
||||
upsample_initial_channel, resblock_dilation_sizes,
|
||||
upsample_kernel_sizes, gen_istft_n_fft, gen_istft_hop_size)
|
||||
|
||||
def forward(self, asr, F0_curve, N, s):
|
||||
F0 = self.F0_conv(F0_curve.unsqueeze(1))
|
||||
N = self.N_conv(N.unsqueeze(1))
|
||||
|
||||
x = torch.cat([asr, F0, N], axis=1)
|
||||
x = self.encode(x, s)
|
||||
|
||||
asr_res = self.asr_res(asr)
|
||||
|
||||
res = True
|
||||
for block in self.decode:
|
||||
if res:
|
||||
x = torch.cat([x, asr_res, F0, N], axis=1)
|
||||
x = block(x, s)
|
||||
if block.upsample_type != "none":
|
||||
res = False
|
||||
|
||||
x = self.generator(x, s, F0_curve)
|
||||
return x
|
||||
@@ -1,166 +0,0 @@
|
||||
# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/kokoro.py
|
||||
import phonemizer
|
||||
import re
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
def split_num(num):
|
||||
num = num.group()
|
||||
if '.' in num:
|
||||
return num
|
||||
elif ':' in num:
|
||||
h, m = [int(n) for n in num.split(':')]
|
||||
if m == 0:
|
||||
return f"{h} o'clock"
|
||||
elif m < 10:
|
||||
return f'{h} oh {m}'
|
||||
return f'{h} {m}'
|
||||
year = int(num[:4])
|
||||
if year < 1100 or year % 1000 < 10:
|
||||
return num
|
||||
left, right = num[:2], int(num[2:4])
|
||||
s = 's' if num.endswith('s') else ''
|
||||
if 100 <= year % 1000 <= 999:
|
||||
if right == 0:
|
||||
return f'{left} hundred{s}'
|
||||
elif right < 10:
|
||||
return f'{left} oh {right}{s}'
|
||||
return f'{left} {right}{s}'
|
||||
|
||||
def flip_money(m):
|
||||
m = m.group()
|
||||
bill = 'dollar' if m[0] == '$' else 'pound'
|
||||
if m[-1].isalpha():
|
||||
return f'{m[1:]} {bill}s'
|
||||
elif '.' not in m:
|
||||
s = '' if m[1:] == '1' else 's'
|
||||
return f'{m[1:]} {bill}{s}'
|
||||
b, c = m[1:].split('.')
|
||||
s = '' if b == '1' else 's'
|
||||
c = int(c.ljust(2, '0'))
|
||||
coins = f"cent{'' if c == 1 else 's'}" if m[0] == '$' else ('penny' if c == 1 else 'pence')
|
||||
return f'{b} {bill}{s} and {c} {coins}'
|
||||
|
||||
def point_num(num):
|
||||
a, b = num.group().split('.')
|
||||
return ' point '.join([a, ' '.join(b)])
|
||||
|
||||
def normalize_text(text):
|
||||
text = text.replace(chr(8216), "'").replace(chr(8217), "'")
|
||||
text = text.replace('«', chr(8220)).replace('»', chr(8221))
|
||||
text = text.replace(chr(8220), '"').replace(chr(8221), '"')
|
||||
text = text.replace('(', '«').replace(')', '»')
|
||||
for a, b in zip('、。！，：；？', ',.!,:;?'):
|
||||
text = text.replace(a, b+' ')
|
||||
text = re.sub(r'[^\S \n]', ' ', text)
|
||||
text = re.sub(r' +', ' ', text)
|
||||
text = re.sub(r'(?<=\n) +(?=\n)', '', text)
|
||||
text = re.sub(r'\bD[Rr]\.(?= [A-Z])', 'Doctor', text)
|
||||
text = re.sub(r'\b(?:Mr\.|MR\.(?= [A-Z]))', 'Mister', text)
|
||||
text = re.sub(r'\b(?:Ms\.|MS\.(?= [A-Z]))', 'Miss', text)
|
||||
text = re.sub(r'\b(?:Mrs\.|MRS\.(?= [A-Z]))', 'Mrs', text)
|
||||
text = re.sub(r'\betc\.(?! [A-Z])', 'etc', text)
|
||||
text = re.sub(r'(?i)\b(y)eah?\b', r"\1e'a", text)
|
||||
text = re.sub(r'\d*\.\d+|\b\d{4}s?\b|(?<!:)\b(?:[1-9]|1[0-2]):[0-5]\d\b(?!:)', split_num, text)
|
||||
text = re.sub(r'(?<=\d),(?=\d)', '', text)
|
||||
text = re.sub(r'(?i)[$£]\d+(?:\.\d+)?(?: hundred| thousand| (?:[bm]|tr)illion)*\b|[$£]\d+\.\d\d?\b', flip_money, text)
|
||||
text = re.sub(r'\d*\.\d+', point_num, text)
|
||||
text = re.sub(r'(?<=\d)-(?=\d)', ' to ', text)
|
||||
text = re.sub(r'(?<=\d)S', ' S', text)
|
||||
text = re.sub(r"(?<=[BCDFGHJ-NP-TV-Z])'?s\b", "'S", text)
|
||||
text = re.sub(r"(?<=X')S\b", 's', text)
|
||||
text = re.sub(r'(?:[A-Za-z]\.){2,} [a-z]', lambda m: m.group().replace('.', '-'), text)
|
||||
text = re.sub(r'(?i)(?<=[A-Z])\.(?=[A-Z])', '-', text)
|
||||
return text.strip()
|
||||
|
||||
def get_vocab():
|
||||
_pad = "$"
|
||||
_punctuation = ';:,.!?¡¿—…"«»“” '
|
||||
_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
|
||||
_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
|
||||
symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
|
||||
dicts = {}
|
||||
for i in range(len((symbols))):
|
||||
dicts[symbols[i]] = i
|
||||
return dicts
|
||||
|
||||
VOCAB = get_vocab()
|
||||
def tokenize(ps):
|
||||
return [i for i in map(VOCAB.get, ps) if i is not None]
|
||||
|
||||
phonemizers = dict(
|
||||
a=phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True),
|
||||
b=phonemizer.backend.EspeakBackend(language='en-gb', preserve_punctuation=True, with_stress=True),
|
||||
)
|
||||
def phonemize(text, lang, norm=True):
|
||||
if norm:
|
||||
text = normalize_text(text)
|
||||
ps = phonemizers[lang].phonemize([text])
|
||||
ps = ps[0] if ps else ''
|
||||
# https://en.wiktionary.org/wiki/kokoro#English
|
||||
ps = ps.replace('kəkˈoːɹoʊ', 'kˈoʊkəɹoʊ').replace('kəkˈɔːɹəʊ', 'kˈəʊkəɹəʊ')
|
||||
ps = ps.replace('ʲ', 'j').replace('r', 'ɹ').replace('x', 'k').replace('ɬ', 'l')
|
||||
ps = re.sub(r'(?<=[a-zɹː])(?=hˈʌndɹɪd)', ' ', ps)
|
||||
ps = re.sub(r' z(?=[;:,.!?¡¿—…"«»“” ]|$)', 'z', ps)
|
||||
if lang == 'a':
|
||||
ps = re.sub(r'(?<=nˈaɪn)ti(?!ː)', 'di', ps)
|
||||
ps = ''.join(filter(lambda p: p in VOCAB, ps))
|
||||
return ps.strip()
|
||||
|
||||
def length_to_mask(lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(model, tokens, ref_s, speed):
|
||||
device = ref_s.device
|
||||
tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)
|
||||
input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
|
||||
text_mask = length_to_mask(input_lengths).to(device)
|
||||
bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
|
||||
d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
|
||||
s = ref_s[:, 128:]
|
||||
d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask)
|
||||
x, _ = model.predictor.lstm(d)
|
||||
duration = model.predictor.duration_proj(x)
|
||||
duration = torch.sigmoid(duration).sum(axis=-1) / speed
|
||||
pred_dur = torch.round(duration).clamp(min=1).long()
|
||||
pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())
|
||||
c_frame = 0
|
||||
for i in range(pred_aln_trg.size(0)):
|
||||
pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1
|
||||
c_frame += pred_dur[0,i].item()
|
||||
en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)
|
||||
F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
|
||||
t_en = model.text_encoder(tokens, input_lengths, text_mask)
|
||||
asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)
|
||||
return model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().cpu().numpy()
|
||||
|
||||
def generate(model, text, voicepack, lang='a', speed=1, ps=None):
|
||||
ps = ps or phonemize(text, lang)
|
||||
tokens = tokenize(ps)
|
||||
if not tokens:
|
||||
return None
|
||||
elif len(tokens) > 510:
|
||||
tokens = tokens[:510]
|
||||
print('Truncated to 510 tokens')
|
||||
ref_s = voicepack[len(tokens)]
|
||||
out = forward(model, tokens, ref_s, speed)
|
||||
ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
|
||||
return out, ps
|
||||
|
||||
def generate_full(model, text, voicepack, lang='a', speed=1, ps=None):
|
||||
ps = ps or phonemize(text, lang)
|
||||
tokens = tokenize(ps)
|
||||
if not tokens:
|
||||
return None
|
||||
outs = []
|
||||
loop_count = len(tokens)//510 + (1 if len(tokens) % 510 != 0 else 0)
|
||||
for i in range(loop_count):
|
||||
ref_s = voicepack[len(tokens[i*510:(i+1)*510])]
|
||||
out = forward(model, tokens[i*510:(i+1)*510], ref_s, speed)
|
||||
outs.append(out)
|
||||
outs = np.concatenate(outs)
|
||||
ps = ''.join(next(k for k, v in VOCAB.items() if i == v) for i in tokens)
|
||||
return outs, ps
|
||||
@@ -1,373 +0,0 @@
|
||||
# https://github.com/yl4579/StyleTTS2/blob/main/models.py
|
||||
# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/models.py
|
||||
from istftnet import AdaIN1d, Decoder
|
||||
from munch import Munch
|
||||
from pathlib import Path
|
||||
from plbert import load_plbert
|
||||
from torch.nn.utils import weight_norm, spectral_norm
|
||||
import json
|
||||
import numpy as np
|
||||
import os
|
||||
import os.path as osp
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
class LinearNorm(torch.nn.Module):
|
||||
def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
|
||||
super(LinearNorm, self).__init__()
|
||||
self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
|
||||
|
||||
torch.nn.init.xavier_uniform_(
|
||||
self.linear_layer.weight,
|
||||
gain=torch.nn.init.calculate_gain(w_init_gain))
|
||||
|
||||
def forward(self, x):
|
||||
return self.linear_layer(x)
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self, channels, kernel_size, depth, n_symbols, actv=nn.LeakyReLU(0.2)):
|
||||
super().__init__()
|
||||
self.embedding = nn.Embedding(n_symbols, channels)
|
||||
|
||||
padding = (kernel_size - 1) // 2
|
||||
self.cnn = nn.ModuleList()
|
||||
for _ in range(depth):
|
||||
self.cnn.append(nn.Sequential(
|
||||
weight_norm(nn.Conv1d(channels, channels, kernel_size=kernel_size, padding=padding)),
|
||||
LayerNorm(channels),
|
||||
actv,
|
||||
nn.Dropout(0.2),
|
||||
))
|
||||
# self.cnn = nn.Sequential(*self.cnn)
|
||||
|
||||
self.lstm = nn.LSTM(channels, channels//2, 1, batch_first=True, bidirectional=True)
|
||||
|
||||
def forward(self, x, input_lengths, m):
|
||||
x = self.embedding(x) # [B, T, emb]
|
||||
x = x.transpose(1, 2) # [B, emb, T]
|
||||
m = m.to(input_lengths.device).unsqueeze(1)
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
for c in self.cnn:
|
||||
x = c(x)
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
x = x.transpose(1, 2) # [B, T, chn]
|
||||
|
||||
input_lengths = input_lengths.cpu().numpy()
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
x, input_lengths, batch_first=True, enforce_sorted=False)
|
||||
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(
|
||||
x, batch_first=True)
|
||||
|
||||
x = x.transpose(-1, -2)
|
||||
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
|
||||
|
||||
x_pad[:, :, :x.shape[-1]] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
x.masked_fill_(m, 0.0)
|
||||
|
||||
return x
|
||||
|
||||
def inference(self, x):
|
||||
x = self.embedding(x)
|
||||
x = x.transpose(1, 2)
|
||||
x = self.cnn(x)
|
||||
x = x.transpose(1, 2)
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
return x
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
|
||||
class UpSample1d(nn.Module):
|
||||
def __init__(self, layer_type):
|
||||
super().__init__()
|
||||
self.layer_type = layer_type
|
||||
|
||||
def forward(self, x):
|
||||
if self.layer_type == 'none':
|
||||
return x
|
||||
else:
|
||||
return F.interpolate(x, scale_factor=2, mode='nearest')
|
||||
|
||||
class AdainResBlk1d(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, style_dim=64, actv=nn.LeakyReLU(0.2),
|
||||
upsample='none', dropout_p=0.0):
|
||||
super().__init__()
|
||||
self.actv = actv
|
||||
self.upsample_type = upsample
|
||||
self.upsample = UpSample1d(upsample)
|
||||
self.learned_sc = dim_in != dim_out
|
||||
self._build_weights(dim_in, dim_out, style_dim)
|
||||
self.dropout = nn.Dropout(dropout_p)
|
||||
|
||||
if upsample == 'none':
|
||||
self.pool = nn.Identity()
|
||||
else:
|
||||
self.pool = weight_norm(nn.ConvTranspose1d(dim_in, dim_in, kernel_size=3, stride=2, groups=dim_in, padding=1, output_padding=1))
|
||||
|
||||
|
||||
def _build_weights(self, dim_in, dim_out, style_dim):
|
||||
self.conv1 = weight_norm(nn.Conv1d(dim_in, dim_out, 3, 1, 1))
|
||||
self.conv2 = weight_norm(nn.Conv1d(dim_out, dim_out, 3, 1, 1))
|
||||
self.norm1 = AdaIN1d(style_dim, dim_in)
|
||||
self.norm2 = AdaIN1d(style_dim, dim_out)
|
||||
if self.learned_sc:
|
||||
self.conv1x1 = weight_norm(nn.Conv1d(dim_in, dim_out, 1, 1, 0, bias=False))
|
||||
|
||||
def _shortcut(self, x):
|
||||
x = self.upsample(x)
|
||||
if self.learned_sc:
|
||||
x = self.conv1x1(x)
|
||||
return x
|
||||
|
||||
def _residual(self, x, s):
|
||||
x = self.norm1(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.pool(x)
|
||||
x = self.conv1(self.dropout(x))
|
||||
x = self.norm2(x, s)
|
||||
x = self.actv(x)
|
||||
x = self.conv2(self.dropout(x))
|
||||
return x
|
||||
|
||||
def forward(self, x, s):
|
||||
out = self._residual(x, s)
|
||||
out = (out + self._shortcut(x)) / np.sqrt(2)
|
||||
return out
|
||||
|
||||
class AdaLayerNorm(nn.Module):
|
||||
def __init__(self, style_dim, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.fc = nn.Linear(style_dim, channels*2)
|
||||
|
||||
def forward(self, x, s):
|
||||
x = x.transpose(-1, -2)
|
||||
x = x.transpose(1, -1)
|
||||
|
||||
h = self.fc(s)
|
||||
h = h.view(h.size(0), h.size(1), 1)
|
||||
gamma, beta = torch.chunk(h, chunks=2, dim=1)
|
||||
gamma, beta = gamma.transpose(1, -1), beta.transpose(1, -1)
|
||||
|
||||
|
||||
x = F.layer_norm(x, (self.channels,), eps=self.eps)
|
||||
x = (1 + gamma) * x + beta
|
||||
return x.transpose(1, -1).transpose(-1, -2)
|
||||
|
||||
class ProsodyPredictor(nn.Module):
|
||||
|
||||
def __init__(self, style_dim, d_hid, nlayers, max_dur=50, dropout=0.1):
|
||||
super().__init__()
|
||||
|
||||
self.text_encoder = DurationEncoder(sty_dim=style_dim,
|
||||
d_model=d_hid,
|
||||
nlayers=nlayers,
|
||||
dropout=dropout)
|
||||
|
||||
self.lstm = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
|
||||
self.duration_proj = LinearNorm(d_hid, max_dur)
|
||||
|
||||
self.shared = nn.LSTM(d_hid + style_dim, d_hid // 2, 1, batch_first=True, bidirectional=True)
|
||||
self.F0 = nn.ModuleList()
|
||||
self.F0.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
|
||||
self.F0.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
|
||||
self.F0.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
|
||||
|
||||
self.N = nn.ModuleList()
|
||||
self.N.append(AdainResBlk1d(d_hid, d_hid, style_dim, dropout_p=dropout))
|
||||
self.N.append(AdainResBlk1d(d_hid, d_hid // 2, style_dim, upsample=True, dropout_p=dropout))
|
||||
self.N.append(AdainResBlk1d(d_hid // 2, d_hid // 2, style_dim, dropout_p=dropout))
|
||||
|
||||
self.F0_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
|
||||
self.N_proj = nn.Conv1d(d_hid // 2, 1, 1, 1, 0)
|
||||
|
||||
|
||||
def forward(self, texts, style, text_lengths, alignment, m):
|
||||
d = self.text_encoder(texts, style, text_lengths, m)
|
||||
|
||||
batch_size = d.shape[0]
|
||||
text_size = d.shape[1]
|
||||
|
||||
# predict duration
|
||||
input_lengths = text_lengths.cpu().numpy()
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
d, input_lengths, batch_first=True, enforce_sorted=False)
|
||||
|
||||
m = m.to(text_lengths.device).unsqueeze(1)
|
||||
|
||||
self.lstm.flatten_parameters()
|
||||
x, _ = self.lstm(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(
|
||||
x, batch_first=True)
|
||||
|
||||
x_pad = torch.zeros([x.shape[0], m.shape[-1], x.shape[-1]])
|
||||
|
||||
x_pad[:, :x.shape[1], :] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
duration = self.duration_proj(nn.functional.dropout(x, 0.5, training=self.training))
|
||||
|
||||
en = (d.transpose(-1, -2) @ alignment)
|
||||
|
||||
return duration.squeeze(-1), en
|
||||
|
||||
def F0Ntrain(self, x, s):
|
||||
x, _ = self.shared(x.transpose(-1, -2))
|
||||
|
||||
F0 = x.transpose(-1, -2)
|
||||
for block in self.F0:
|
||||
F0 = block(F0, s)
|
||||
F0 = self.F0_proj(F0)
|
||||
|
||||
N = x.transpose(-1, -2)
|
||||
for block in self.N:
|
||||
N = block(N, s)
|
||||
N = self.N_proj(N)
|
||||
|
||||
return F0.squeeze(1), N.squeeze(1)
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
class DurationEncoder(nn.Module):
|
||||
|
||||
def __init__(self, sty_dim, d_model, nlayers, dropout=0.1):
|
||||
super().__init__()
|
||||
self.lstms = nn.ModuleList()
|
||||
for _ in range(nlayers):
|
||||
self.lstms.append(nn.LSTM(d_model + sty_dim,
|
||||
d_model // 2,
|
||||
num_layers=1,
|
||||
batch_first=True,
|
||||
bidirectional=True,
|
||||
dropout=dropout))
|
||||
self.lstms.append(AdaLayerNorm(sty_dim, d_model))
|
||||
|
||||
|
||||
self.dropout = dropout
|
||||
self.d_model = d_model
|
||||
self.sty_dim = sty_dim
|
||||
|
||||
def forward(self, x, style, text_lengths, m):
|
||||
masks = m.to(text_lengths.device)
|
||||
|
||||
x = x.permute(2, 0, 1)
|
||||
s = style.expand(x.shape[0], x.shape[1], -1)
|
||||
x = torch.cat([x, s], axis=-1)
|
||||
x.masked_fill_(masks.unsqueeze(-1).transpose(0, 1), 0.0)
|
||||
|
||||
x = x.transpose(0, 1)
|
||||
input_lengths = text_lengths.cpu().numpy()
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
for block in self.lstms:
|
||||
if isinstance(block, AdaLayerNorm):
|
||||
x = block(x.transpose(-1, -2), style).transpose(-1, -2)
|
||||
x = torch.cat([x, s.permute(1, -1, 0)], axis=1)
|
||||
x.masked_fill_(masks.unsqueeze(-1).transpose(-1, -2), 0.0)
|
||||
else:
|
||||
x = x.transpose(-1, -2)
|
||||
x = nn.utils.rnn.pack_padded_sequence(
|
||||
x, input_lengths, batch_first=True, enforce_sorted=False)
|
||||
block.flatten_parameters()
|
||||
x, _ = block(x)
|
||||
x, _ = nn.utils.rnn.pad_packed_sequence(
|
||||
x, batch_first=True)
|
||||
x = F.dropout(x, p=self.dropout, training=self.training)
|
||||
x = x.transpose(-1, -2)
|
||||
|
||||
x_pad = torch.zeros([x.shape[0], x.shape[1], m.shape[-1]])
|
||||
|
||||
x_pad[:, :, :x.shape[-1]] = x
|
||||
x = x_pad.to(x.device)
|
||||
|
||||
return x.transpose(-1, -2)
|
||||
|
||||
def inference(self, x, style):
|
||||
x = self.embedding(x.transpose(-1, -2)) * np.sqrt(self.d_model)
|
||||
style = style.expand(x.shape[0], x.shape[1], -1)
|
||||
x = torch.cat([x, style], axis=-1)
|
||||
src = self.pos_encoder(x)
|
||||
output = self.transformer_encoder(src).transpose(0, 1)
|
||||
return output
|
||||
|
||||
def length_to_mask(self, lengths):
|
||||
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
|
||||
mask = torch.gt(mask+1, lengths.unsqueeze(1))
|
||||
return mask
|
||||
|
||||
# https://github.com/yl4579/StyleTTS2/blob/main/utils.py
|
||||
def recursive_munch(d):
|
||||
if isinstance(d, dict):
|
||||
return Munch((k, recursive_munch(v)) for k, v in d.items())
|
||||
elif isinstance(d, list):
|
||||
return [recursive_munch(v) for v in d]
|
||||
else:
|
||||
return d
|
||||
|
||||
def build_model(path, device):
|
||||
config = Path(__file__).parent / 'config.json'
|
||||
assert config.exists(), f'Config path incorrect: config.json not found at {config}'
|
||||
with open(config, 'r') as r:
|
||||
args = recursive_munch(json.load(r))
|
||||
assert args.decoder.type == 'istftnet', f'Unknown decoder type: {args.decoder.type}'
|
||||
decoder = Decoder(dim_in=args.hidden_dim, style_dim=args.style_dim, dim_out=args.n_mels,
|
||||
resblock_kernel_sizes = args.decoder.resblock_kernel_sizes,
|
||||
upsample_rates = args.decoder.upsample_rates,
|
||||
upsample_initial_channel=args.decoder.upsample_initial_channel,
|
||||
resblock_dilation_sizes=args.decoder.resblock_dilation_sizes,
|
||||
upsample_kernel_sizes=args.decoder.upsample_kernel_sizes,
|
||||
gen_istft_n_fft=args.decoder.gen_istft_n_fft, gen_istft_hop_size=args.decoder.gen_istft_hop_size)
|
||||
text_encoder = TextEncoder(channels=args.hidden_dim, kernel_size=5, depth=args.n_layer, n_symbols=args.n_token)
|
||||
predictor = ProsodyPredictor(style_dim=args.style_dim, d_hid=args.hidden_dim, nlayers=args.n_layer, max_dur=args.max_dur, dropout=args.dropout)
|
||||
bert = load_plbert()
|
||||
bert_encoder = nn.Linear(bert.config.hidden_size, args.hidden_dim)
|
||||
for parent in [bert, bert_encoder, predictor, decoder, text_encoder]:
|
||||
for child in parent.children():
|
||||
if isinstance(child, nn.RNNBase):
|
||||
child.flatten_parameters()
|
||||
model = Munch(
|
||||
bert=bert.to(device).eval(),
|
||||
bert_encoder=bert_encoder.to(device).eval(),
|
||||
predictor=predictor.to(device).eval(),
|
||||
decoder=decoder.to(device).eval(),
|
||||
text_encoder=text_encoder.to(device).eval(),
|
||||
)
|
||||
for key, state_dict in torch.load(path, map_location='cpu', weights_only=True)['net'].items():
|
||||
assert key in model, key
|
||||
try:
|
||||
model[key].load_state_dict(state_dict)
|
||||
except:
|
||||
state_dict = {k[7:]: v for k, v in state_dict.items()}
|
||||
model[key].load_state_dict(state_dict, strict=False)
|
||||
return model
|
||||
@@ -1,16 +0,0 @@
|
||||
# https://huggingface.co/hexgrad/Kokoro-82M/blob/main/plbert.py
|
||||
# https://github.com/yl4579/StyleTTS2/blob/main/Utils/PLBERT/util.py
|
||||
from transformers import AlbertConfig, AlbertModel
|
||||
|
||||
class CustomAlbert(AlbertModel):
|
||||
def forward(self, *args, **kwargs):
|
||||
# Call the original forward method
|
||||
outputs = super().forward(*args, **kwargs)
|
||||
# Only return the last_hidden_state
|
||||
return outputs.last_hidden_state
|
||||
|
||||
def load_plbert():
|
||||
plbert_config = {'vocab_size': 178, 'hidden_size': 768, 'num_attention_heads': 12, 'intermediate_size': 2048, 'max_position_embeddings': 512, 'num_hidden_layers': 12, 'dropout': 0.1}
|
||||
albert_base_configuration = AlbertConfig(**plbert_config)
|
||||
bert = CustomAlbert(albert_base_configuration)
|
||||
return bert
|
||||
@@ -1,11 +0,0 @@
|
||||
#!/bin/bash
|
||||
set -e
|
||||
|
||||
backend_dir=$(dirname $0)
|
||||
if [ -d $backend_dir/common ]; then
|
||||
source $backend_dir/common/libbackend.sh
|
||||
else
|
||||
source $backend_dir/../common/libbackend.sh
|
||||
fi
|
||||
|
||||
python3 -m grpc_tools.protoc -I../.. -I./ --python_out=. --grpc_python_out=. backend.proto
|
||||
@@ -1,2 +1,6 @@
|
||||
torch==2.4.1
|
||||
transformers
|
||||
--extra-index-url https://download.pytorch.org/whl/cpu
|
||||
transformers
|
||||
accelerate
|
||||
torch
|
||||
kokoro
|
||||
soundfile
|
||||
@@ -1,3 +1,7 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/cu118
|
||||
torch==2.4.1+cu118
|
||||
transformers
|
||||
torch==2.7.1+cu118
|
||||
torchaudio==2.7.1+cu118
|
||||
transformers
|
||||
accelerate
|
||||
kokoro
|
||||
soundfile
|
||||
@@ -1,2 +1,6 @@
|
||||
torch==2.4.1
|
||||
transformers
|
||||
torch==2.7.1
|
||||
torchaudio==2.7.1
|
||||
transformers
|
||||
accelerate
|
||||
kokoro
|
||||
soundfile
|
||||
@@ -1,3 +1,7 @@
|
||||
--extra-index-url https://download.pytorch.org/whl/rocm6.0
|
||||
torch==2.4.1+rocm6.0
|
||||
transformers
|
||||
--extra-index-url https://download.pytorch.org/whl/rocm6.3
|
||||
torch==2.7.1+rocm6.3
|
||||
torchaudio==2.7.1+rocm6.3
|
||||
transformers
|
||||
accelerate
|
||||
kokoro
|
||||
soundfile
|
||||
@@ -1,5 +1,11 @@
|
||||
--extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
|
||||
intel-extension-for-pytorch==2.3.110+xpu
|
||||
torch==2.3.1+cxx11.abi
|
||||
oneccl_bind_pt==2.3.100+xpu
|
||||
transformers
|
||||
intel-extension-for-pytorch==2.8.10+xpu
|
||||
torch==2.5.1+cxx11.abi
|
||||
oneccl_bind_pt==2.8.0+xpu
|
||||
torchaudio==2.5.1+cxx11.abi
|
||||
optimum[openvino]
|
||||
setuptools
|
||||
transformers==4.48.3
|
||||
accelerate
|
||||
kokoro
|
||||
soundfile
|
||||
@@ -1,7 +1,6 @@
|
||||
grpcio==1.71.0
|
||||
protobuf
|
||||
phonemizer
|
||||
scipy
|
||||
munch
|
||||
setuptools
|
||||
soundfile
|
||||
certifi
|
||||
packaging==24.1
|
||||
pip
|
||||
chardet
|
||||
87
backend/python/kokoro/test.py
Normal file
87
backend/python/kokoro/test.py
Normal file
@@ -0,0 +1,87 @@
|
||||
"""
|
||||
A test script to test the gRPC service
|
||||
"""
|
||||
import unittest
|
||||
import subprocess
|
||||
import time
|
||||
import backend_pb2
|
||||
import backend_pb2_grpc
|
||||
|
||||
import grpc
|
||||
|
||||
|
||||
class TestBackendServicer(unittest.TestCase):
|
||||
"""
|
||||
TestBackendServicer is the class that tests the gRPC service
|
||||
"""
|
||||
def setUp(self):
|
||||
"""
|
||||
This method sets up the gRPC service by starting the server
|
||||
"""
|
||||
self.service = subprocess.Popen(["python3", "backend.py", "--addr", "localhost:50051"])
|
||||
time.sleep(30)
|
||||
|
||||
def tearDown(self) -> None:
|
||||
"""
|
||||
This method tears down the gRPC service by terminating the server
|
||||
"""
|
||||
self.service.terminate()
|
||||
self.service.wait()
|
||||
|
||||
def test_server_startup(self):
|
||||
"""
|
||||
This method tests if the server starts up successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.Health(backend_pb2.HealthMessage())
|
||||
self.assertEqual(response.message, b'OK')
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("Server failed to start")
|
||||
finally:
|
||||
self.tearDown()
|
||||
|
||||
def test_load_model(self):
|
||||
"""
|
||||
This method tests if the Kokoro pipeline is loaded successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.LoadModel(backend_pb2.ModelOptions(language="a"))
|
||||
print(response)
|
||||
self.assertTrue(response.success)
|
||||
self.assertEqual(response.message, "Kokoro TTS pipeline loaded successfully")
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("LoadModel service failed")
|
||||
finally:
|
||||
self.tearDown()
|
||||
|
||||
def test_tts(self):
|
||||
"""
|
||||
This method tests if the TTS generation works successfully
|
||||
"""
|
||||
try:
|
||||
self.setUp()
|
||||
with grpc.insecure_channel("localhost:50051") as channel:
|
||||
stub = backend_pb2_grpc.BackendStub(channel)
|
||||
response = stub.LoadModel(backend_pb2.ModelOptions(language="a"))
|
||||
self.assertTrue(response.success)
|
||||
tts_request = backend_pb2.TTSRequest(
|
||||
text="Kokoro is an open-weight TTS model with 82 million parameters.",
|
||||
voice="af_heart",
|
||||
dst="test_output.wav"
|
||||
)
|
||||
tts_response = stub.TTS(tts_request)
|
||||
self.assertIsNotNone(tts_response)
|
||||
self.assertTrue(tts_response.success)
|
||||
except Exception as err:
|
||||
print(err)
|
||||
self.fail("TTS service failed")
|
||||
finally:
|
||||
self.tearDown()
|
||||
Reference in New Issue
Block a user