mirror of
https://github.com/leon-ai/leon.git
synced 2024-08-17 06:00:33 +03:00
feat(python tcp server): embed new text-to-speech engine in TCP server binary
This commit is contained in:
parent
e455a9d96b
commit
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@ -54,7 +54,7 @@
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"build:nodejs-bridge": "tsx scripts/build-binaries.js nodejs-bridge",
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"build:python-bridge": "tsx scripts/build-binaries.js python-bridge",
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"build:tcp-server": "tsx scripts/build-binaries.js tcp-server",
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"start:tcp-server": "cross-env PIPENV_PIPFILE=tcp_server/src/Pipfile pipenv run python tcp_server/src/main.py",
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"start:tcp-server": "cross-env HF_HUB_VERBOSITY=debug PIPENV_PIPFILE=tcp_server/src/Pipfile pipenv run python tcp_server/src/main.py",
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"start": "cross-env LEON_NODE_ENV=production node server/dist/pre-check.js && node server/dist/index.js",
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"python-bridge": "cross-env PIPENV_PIPFILE=bridges/python/src/Pipfile pipenv run python bridges/python/src/main.py server/src/intent-object.sample.json",
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"train": "tsx scripts/train/run-train.js",
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@ -19,7 +19,6 @@ import {
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} from '@/constants'
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import { OSTypes } from '@/types'
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import { LogHelper } from '@/helpers/log-helper'
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import { LoaderHelper } from '@/helpers/loader-helper'
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import { SystemHelper } from '@/helpers/system-helper'
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/**
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@ -57,8 +56,6 @@ BUILD_TARGETS.set('tcp-server', {
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dotVenvPath: path.join(PYTHON_TCP_SERVER_SRC_PATH, '.venv')
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})
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;(async () => {
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LoaderHelper.start()
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const { argv } = process
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const givenBuildTarget = argv[2].toLowerCase()
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@ -117,8 +114,12 @@ BUILD_TARGETS.set('tcp-server', {
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process.env.PIPENV_PIPFILE = pipfilePath
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process.env.PIPENV_VENV_IN_PROJECT = true
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/**
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* cx_Freeze usage
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* @see https://cx-freeze.readthedocs.io/en/latest/setup_script.html#build-exe
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*/
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await command(
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`pipenv run python ${setupFilePath} build --build-exe ${buildPath}`,
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`pipenv run python ${setupFilePath} build_exe --build-exe ${buildPath}`,
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{
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shell: true,
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stdio: 'inherit'
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@ -131,26 +131,37 @@ SPACY_MODELS.set('fr', {
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const hasDotVenv = fs.existsSync(dotVenvPath)
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const { type: osType, cpuArchitecture } = SystemHelper.getInformation()
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/**
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* Install PyTorch nightly to support CUDA 12.4
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* as it is required by the latest NVIDIA drivers for CUDA runtime APIs
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* Install PyTorch with CUDA support
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* as it is required by the latest NVIDIA drivers for CUDA runtime APIs.
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* PyTorch will automatically download nvidia-* packages and bundle them.
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*
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* It is important to specify the "--ignore-installed" flag to make sure the
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* "~/.pyenv/versions/3.9.10/lib/python3.9/site-packages" is not used in case
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* NVIDIA deps are already installed. Otherwise, it won't install it in our
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* TCP server .venv as it is already installed (satisfied) in
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* the path mentioned above
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*
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* @see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix
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* @see https://pytorch.org/get-started/locally/
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* @see https://stackoverflow.com/a/76972265/1768162
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* @see https://docs.nvidia.com/deeplearning/cudnn/latest/reference/support-matrix.html
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*/
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const installPytorch = async () => {
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LogHelper.info('Installing PyTorch nightly with CUDA support...')
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LogHelper.info('Installing PyTorch with CUDA support...')
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try {
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await command(
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'pipenv run pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu124',
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{
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shell: true,
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stdio: 'inherit'
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}
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)
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LogHelper.success('PyTorch nightly with CUDA support installed')
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// There is no CUDA support on macOS
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const commandToExecute =
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osType === OSTypes.MacOS
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? 'pipenv run pip install --ignore-installed torch==2.3.0'
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: 'pipenv run pip install --ignore-installed torch==2.3.0 --index-url https://download.pytorch.org/whl/cu121'
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await command(commandToExecute, {
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shell: true,
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stdio: 'inherit'
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})
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LogHelper.success('PyTorch with CUDA support installed')
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} catch (e) {
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LogHelper.error(
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`Failed to install PyTorch nightly with CUDA support: ${e}`
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)
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LogHelper.error(`Failed to install PyTorch with CUDA support: ${e}`)
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process.exit(1)
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}
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}
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@ -10,21 +10,16 @@ python_version = "3.9.10"
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# Setup
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setuptools = "*"
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wheel = "*"
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# Build
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cx-freeze = "==6.11.1"
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cx-freeze = "==7.0.0"
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# Common
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python-dotenv = "==0.19.2"
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# TCP server
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spacy = "==3.5.4"
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geonamescache = "==1.6.0"
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# TCP server; TTS
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# PyTorch is installed via the setup script
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# torch = "*"
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# TTS
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transformers = "==4.27.4"
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g2p-en = "==2.1.0"
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@ -1,6 +1,9 @@
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import os
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import sys
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SRC_PATH = os.path.join(os.getcwd(), 'tcp_server', 'src')
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IS_RAN_FROM_BINARY = getattr(sys, 'frozen', False)
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SRC_PATH = os.path.join(os.getcwd(), 'tcp_server', 'src') if not IS_RAN_FROM_BINARY else '.'
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# TTS
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TTS_MODEL_VERSION = 'V1'
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@ -1,6 +1,7 @@
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import copy
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from sys import argv
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import spacy
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import time
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from geonamescache import GeonamesCache
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lang = argv[1] or 'en'
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@ -41,9 +42,12 @@ def load_spacy_model() -> None:
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model = spacy_model_mapping[lang]['model']
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exclude = spacy_model_mapping[lang]['exclude']
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tic = time.perf_counter()
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log(f'Loading {model} spaCy model...')
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spacy_nlp = spacy.load(model, exclude=exclude)
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log('spaCy model loaded')
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toc = time.perf_counter()
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log(f"Time taken to load spaCy model: {toc - tic:0.4f} seconds")
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def delete_unneeded_country_data(data: dict) -> None:
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@ -2,6 +2,7 @@ import socket
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import json
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import os
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from typing import Union
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import time
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import lib.nlp as nlp
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from .tts.api import TTS
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@ -47,7 +48,11 @@ class TCPServer:
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output_path = 'output.wav'
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speed = 1.0
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tic = time.perf_counter()
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self.tts.tts_to_file(text, speaker_ids['EN-Leon-V1'], output_path, speed=speed)
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toc = time.perf_counter()
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self.log(f"Time taken to generate audio: {toc - tic:0.4f} seconds")
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def init(self):
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# Make sure to establish TCP connection by reusing the address so it does not conflict with port already in use
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@ -4,11 +4,14 @@ import numpy as np
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import torch.nn as nn
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from tqdm import tqdm
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import torch
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import time
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from . import utils
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from .models import SynthesizerTrn
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from .split_utils import split_sentence
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# torch.backends.cudnn.enabled = False
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class TTS(nn.Module):
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def __init__(self,
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language,
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@ -18,6 +21,7 @@ class TTS(nn.Module):
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ckpt_path=None):
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super().__init__()
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tic = time.perf_counter()
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self.log('Loading model...')
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if device == 'auto':
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@ -62,6 +66,9 @@ class TTS(nn.Module):
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self.language = 'ZH_MIX_EN' if language == 'ZH' else language # we support a ZH_MIX_EN model
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self.log('Model loaded')
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toc = time.perf_counter()
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self.log(f"Time taken to load model: {toc - tic:0.4f} seconds")
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@staticmethod
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def audio_numpy_concat(segment_data_list, sr, speed=1.):
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@ -24,16 +24,6 @@ class LayerNorm(nn.Module):
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return x.transpose(1, -1)
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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class Encoder(nn.Module):
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def __init__(
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self,
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return mask
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a + input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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def convert_pad_shape(pad_shape):
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layer = pad_shape[::-1]
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pad_shape = [item for sublist in layer for item in sublist]
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
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self.res_skip_layers.append(res_skip_layer)
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def forward(self, x, x_mask, g=None, **kwargs):
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output = torch.zeros_like(x)
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n_channels_tensor = torch.IntTensor([self.hidden_channels])
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if g is not None:
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g = self.cond_layer(g)
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for i in range(self.n_layers):
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x_in = self.in_layers[i](x)
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if g is not None:
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cond_offset = i * 2 * self.hidden_channels
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g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
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else:
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g_l = torch.zeros_like(x_in)
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acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
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acts = self.drop(acts)
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res_skip_acts = self.res_skip_layers[i](acts)
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if i < self.n_layers - 1:
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res_acts = res_skip_acts[:, : self.hidden_channels, :]
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x = (x + res_acts) * x_mask
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output = output + res_skip_acts[:, self.hidden_channels :, :]
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else:
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output = output + res_skip_acts
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return output * x_mask
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def remove_weight_norm(self):
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if self.gin_channels != 0:
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torch.nn.utils.remove_weight_norm(self.cond_layer)
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import os
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import re
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from g2p_en import G2p
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from transformers import AutoTokenizer
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from . import symbols
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@ -9,7 +10,6 @@ from .english_utils.abbreviations import expand_abbreviations
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from .english_utils.time_norm import expand_time_english
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from .english_utils.number_norm import normalize_numbers
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from transformers import AutoTokenizer
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current_file_path = os.path.dirname(__file__)
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CMU_DICT_PATH = os.path.join(current_file_path, "cmudict.rep")
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from transformers import AutoTokenizer, AutoModelForMaskedLM
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import sys
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model_id = 'bert-base-uncased'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = None
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from transformers import AutoTokenizer
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from .fr_phonemizer import cleaner as fr_cleaner
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from .fr_phonemizer import fr_to_ipa
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from transformers import AutoTokenizer
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def distribute_phone(n_phone, n_word):
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from cx_Freeze import setup, Executable
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import sysconfig
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import sys
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from version import __version__
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"""
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Increase the recursion limit to avoid RecursionError
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@see: https://github.com/marcelotduarte/cx_Freeze/issues/2240
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"""
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sys.setrecursionlimit(sys.getrecursionlimit() * 10)
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"""
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Instead of injecting everything from a package,
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it's recommended to only include the necessary files via the
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"include_files" property.
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"""
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options = {
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'build_exe': {
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'packages': [
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'spacy',
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'torch',
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'en_core_web_trf',
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'fr_core_news_md'
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'fr_core_news_md',
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'pycrfsuite'
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],
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'includes': [
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'srsly.msgpack.util',
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'blis',
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'cymem'
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],
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'include_files': [
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('tcp_server/src/.venv/lib/python3.9/site-packages/nvidia/cudnn/lib', 'lib/nvidia/cudnn/lib')
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]
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}
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}
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# Include private libraries from the tokenizers package for Linux
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if 'linux' in sysconfig.get_platform():
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options['build_exe']['include_files'] = [('tcp_server/src/.venv/lib/python3.9/site-packages/tokenizers.libs', 'lib/tokenizers.libs')]
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options['build_exe']['include_files'] = [
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*options['build_exe']['include_files'],
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('tcp_server/src/.venv/lib/python3.9/site-packages/tokenizers.libs', 'lib/tokenizers.libs')
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]
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executables = [
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Executable(
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