file_path stringlengths 3 280 | file_language stringclasses 66
values | content stringlengths 1 1.04M | repo_name stringlengths 5 92 | repo_stars int64 0 154k | repo_description stringlengths 0 402 | repo_primary_language stringclasses 108
values | developer_username stringlengths 1 25 | developer_name stringlengths 0 30 | developer_company stringlengths 0 82 |
|---|---|---|---|---|---|---|---|---|---|
docs/examples/name-property/index.html | HTML |
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title><my-element> ⌲ Examples ⌲ Name Property</title>
<link rel="stylesheet" href="../../docs.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
docs/index.html | HTML |
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title><my-element> ⌲ Home</title>
<link rel="stylesheet" href="docs.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Open+Sans:300,400,... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
docs/install/index.html | HTML |
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title><my-element> ⌲ Install</title>
<link rel="stylesheet" href="../docs.css">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Open+Sans:30... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
docs/my-element.bundled.js | JavaScript | /**
* @license
* Copyright 2017 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
var t,i,s,e;const o=globalThis.trustedTypes,n=o?o.createPolicy("lit-html",{createHTML:t=>t}):void 0,h=`lit$${(Math.random()+"").slice(9)}$`,l="?"+h,r=`<${l}>`,u=document,d=(t="")=>u.createComment(t),c=t=>null===t||"object"!=typeof... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
docs/prism-okaidia.css | CSS | /**
* okaidia theme for JavaScript, CSS and HTML
* Loosely based on Monokai textmate theme by http://www.monokai.nl/
* @author ocodia
*/
code[class*="language-"],
pre[class*="language-"] {
color: #f8f8f2;
background: none;
text-shadow: 0 1px rgba(0, 0, 0, 0.3);
font-family: Consolas, Monaco, 'Andale Mono', 'Ub... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
rollup.config.js | JavaScript | /**
* @license
* Copyright 2018 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
import summary from 'rollup-plugin-summary';
import {terser} from 'rollup-plugin-terser';
import resolve from '@rollup/plugin-node-resolve';
import replace from '@rollup/plugin-replace';
export default {
input: 'my-element.js'... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
src/my-element.ts | TypeScript | /**
* @license
* Copyright 2019 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
import {LitElement, html, css} from 'lit';
import {customElement, property} from 'lit/decorators.js';
/**
* An example element.
*
* @slot - This element has a slot
* @csspart button - The button
*/
@customElement('my-elemen... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
src/test/my-element_test.ts | TypeScript | /**
* @license
* Copyright 2021 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
import {MyElement} from '../my-element.js';
import {fixture, html} from '@open-wc/testing';
const assert = chai.assert;
suite('my-element', () => {
test('is defined', () => {
const el = document.createElement('my-element... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
web-dev-server.config.js | JavaScript | /**
* @license
* Copyright 2021 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
import {legacyPlugin} from '@web/dev-server-legacy';
export default {
nodeResolve: true,
preserveSymlinks: true,
plugins: [
legacyPlugin({
polyfills: {
// Manually imported in index.html file
webc... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
web-test-runner.config.js | JavaScript | /**
* @license
* Copyright 2021 Google LLC
* SPDX-License-Identifier: BSD-3-Clause
*/
import {legacyPlugin} from '@web/dev-server-legacy';
import {playwrightLauncher} from '@web/test-runner-playwright';
// Uncomment for testing on Sauce Labs
// Must run `npm i --save-dev @web/test-runner-saucelabs` and set
// SAU... | xiekw2010/lit-component-play | 0 | lit component play | JavaScript | xiekw2010 | David Tse | Alipay |
controllers/api/looks.js | JavaScript | /*!
* mojing - controllers/task.js
* Copyright(c) 2014 ju.taobao.com
* Author: jianhui.fjh <jianhui.fjh@alibaba-inc.com>
*/
'use strict';
exports.allLooks = function* () {
this.body = {
"hi": 'xiekw'
}
};
| xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
crawler.js | JavaScript | /**
* Created by xiekaiwei on 16/6/23.
*/
"use strict";
const Crawler = require('simplecrawler');
const cheerio = require('cheerio');
const fs = require('fs');
const path = require('path');
const redis = require('./storage/redis');
const TEMP_HOT_PATH = 'http://www.wxcha.com/biaoqing/hot_';
const TEMP_RECENT_PATH ... | xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
index.js | JavaScript | 'use strict';
const koa = require('koa');
const logger = require('koa-logger');
const onerror = require('koa-onerror');
const routes = require('./routes');
const crawler = require('./crawler');
require('./storage/redis');
const app = koa();
// middlewares
app.use(logger());
onerror(app);
routes(app);
// listen
app.l... | xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
localStart.sh | Shell | #!/usr/bin/env bash
/Users/xiekaiwei/Downloads/redis-3.2.1/src/redis-server
| xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
routes.js | JavaScript | /*!
* mojing - routes.js
* Copyright(c) 2014 ju.taobao.com
* Author: jianhui.fjh <jianhui.fjh@alibaba-inc.com>
*/
'use strict';
/**
* Module dependencies.
*/
const route = require('koa-route');
const looks = require('./controllers/api/looks');
module.exports = function (app) {
app.use(route.get('/api/allLook... | xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
storage/redis.js | JavaScript | /**
* Created by xiekaiwei on 16/6/24.
*/
"use strict";
var redis = require("redis");
// if you'd like to select database 3, instead of 0 (default), call
// client.select(3, function() { /* ... */ });
var client = redis.createClient();
client.on("error", function (err) {
console.log("Error " + err);
});
//clien... | xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
test/index-spec.js | JavaScript | import expect from 'expect.js';
describe('index', () => {
it('normal', () => {
expect(1).be.equal(1);
});
});
| xiekw2010/wechatlook | 0 | A koa crawler for wechat look | JavaScript | xiekw2010 | David Tse | Alipay |
datafeeder.py | Python | import difflib,os
import numpy as np
import scipy.io.wavfile as wav
from tqdm import tqdm
from scipy.fftpack import fft
from random import shuffle
from keras import backend as K
from utils import audio
from utils.mylogger import log
from hparams import hparams as hp
import librosa
class DataFeeder():
'''
属性：
... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
hparams.py | Python | from tensorflow.contrib.training.python.training.hparam import HParams
import os
# Default hyperparameters:
hparams = HParams(
# TODO: audio
# num_freq=2048, # 使用librosa默认值 n-fft 256 一般等于窗长（一个窗口有多少个采样点）,也就是16000/1000*25ms
num_mels=40, # 通常设为 20-40
num_mfccs = 26,#一般至少39啊
# 如果使用快速傅里叶变换（fft）需要保证窗长是... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/ASR_DFCNN.py | Python | import tensorflow as tf
from .modules import Conv2dBlockWithMaxPool,post_net
from utils.mylogger import log
from keras import backend as K
from tensorflow.python.ops import ctc_ops as ctc
from .base_model import _learning_rate_decay,batch_wer,Base_Model
class ASR(Base_Model):
def __init__(self,hparams,name='ASR'):... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/ASR_transformer.py | Python | import tensorflow as tf
from keras import regularizers
from keras.layers import Softmax
# noinspection PyPep8Naming
from keras import backend as K
from utils.mylogger import log
from .base_model import Base_Model,_learning_rate_decay
from modulesLib.extras import ReusableEmbedding, TiedOutputEmbedding
from modulesLib.... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/ASR_transformer2.py | Python | from utils.mylogger import log
from .base_model import Base_Model,_learning_rate_decay
from .modules import normalize,embedding,multihead_attention,feedforward,label_smoothing
import tensorflow as tf
class ASR_transformer2(Base_Model):
def __init__(self,hparams,name='ASR_transformer2',is_training = True):
... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/ASR_transformer_encoder.py | Python | from utils.mylogger import log
from .base_model import Base_Model,_learning_rate_decay
from .modules import normalize,embedding,multihead_attention,feedforward,label_smoothing
import tensorflow as tf
class ASR_transformer_encoder(Base_Model):
def __init__(self,hparams,name='ASR_transformer_encoder',is_training = ... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/ASR_wavnet.py | Python | import tensorflow as tf
from .modules import casual_layer,post,res_block,dilated_stack
from utils.mylogger import log
from keras import backend as K
from .base_model import _learning_rate_decay,batch_wer
from tensorflow.python.ops import ctc_ops as ctc
class ASR_wavnet(object):
def __init__(self,hparams,name=None)... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/__init__.py | Python | from .ASR_DFCNN import ASR
from .ASR_wavnet import ASR_wavnet
from .ASR_transformer2 import ASR_transformer2
from .ASR_transformer_encoder import ASR_transformer_encoder
def create_model(name, hparams,is_training =True):
if name == 'ASR':
return ASR(hparams,name=name)
elif name == 'ASR_wavnet':
return ASR... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/base_model.py | Python | import tensorflow as tf
from keras import backend as K
class Base_Model(object):
def __init__(self,hparams,name=None):
super().__init__()
self._hparams = hparams
self.name = name
def build_graph(self):
raise NotImplementedError()
def add_loss(self):
raise NotImplem... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modules.py | Python | import tensorflow as tf
from six.moves import xrange
from hparams import hparams
################################ DFCnn ###################################################
class Conv2dBlockWithMaxPool(object):
"""Conv2d Block
The output is max_pooled along time.
"""
def __init__(self,num_conv,activatio... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modulesLib/attention.py | Python | import numpy as np
# noinspection PyPep8Naming
from keras import backend as K
from keras.engine import Layer
from keras.utils import get_custom_objects
class _BaseMultiHeadAttention(Layer):
"""
Base class for two types of Multi-head attention layers:
Self-attention and its more general form used in decode... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modulesLib/bert.py | Python | """
BERT stands for Bidirectional Encoder Representations from Transformers.
It's a way of pre-training Transformer to model a language, described in
paper [BERT: Pre-training of Deep Bidirectional Transformers for
Language Understanding](https://arxiv.org/abs/1810.04805). A quote from it:
> BERT is designed to pre-t... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modulesLib/extras.py | Python | """
Tools that are not necessary for the Transformer by itself, but might be
useful in building models with it.
"""
import math
from keras import activations, regularizers
# noinspection PyPep8Naming
from keras import backend as K
from keras.engine import Layer
from keras.layers import Embedding
from keras.utils impor... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modulesLib/position.py | Python | import numpy as np
# noinspection PyPep8Naming
from keras import backend as K
from keras.engine import Layer
from keras.utils import get_custom_objects
def positional_signal(d_model: int, length: int,
min_timescale: float = 1.0, max_timescale: float = 1e4):
"""
Helper function, constructi... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
models/modulesLib/transformer.py | Python | """
Contains implementation of the Transformer model described in papers
"Attention is all you need" (https://arxiv.org/abs/1706.03762) and
"Universal Transformer" (https://arxiv.org/abs/1807.03819)
"""
import math
from typing import Union, Callable, Optional
from keras.layers import Layer, Add, activations, Dropout
f... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
test.py | Python | import tensorflow as tf
import argparse
import os,math,copy,shutil
from utils.mylogger import *
from utils import ValueWindow , plot
from hparams import hparams as hp
from hparams import hparams_debug_string
from datafeeder import DataFeeder,DataFeeder_wavnet
from models import create_model
import datetime,time
import ... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
test/serving_client.py | Python | # import tensorflow as tf # import 严重影响速度
import numpy as np
# Communication to TensorFlow server via gRPC
from grpc.beta import implementations
from tensorflow.contrib.util import make_tensor_proto
# TensorFlow serving stuff to send messages
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
test/test_am.py | Python | from hparams import hparams as hp
from models import create_model
from utils.mylogger import *
import os
import tensorflow as tf
import audio,librosa
import numpy as np
from tensorflow.python import pywrap_tensorflow
def compute_mfcc2(file):
wav = audio.load_wav(file)
# mfcc = p.mfcc(wav,numcep=hp.num_mfccs) ... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
train_ASR_DFCNN.py | Python | import tensorflow as tf
import argparse
import os,math,copy
from utils.mylogger import *
from utils import ValueWindow , plot
from hparams import hparams as hp
from hparams import hparams_debug_string
from datafeeder import DataFeeder,GetEditDistance
from models import create_model
import datetime,time
import traceback... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
train_ASR_transformer_encoder.py | Python | import tensorflow as tf
import argparse
import os,math,copy
from utils.mylogger import *
from utils import ValueWindow , plot
from hparams import hparams as hp
from hparams import hparams_debug_string
from datafeeder import DataFeeder,DataFeeder_wavnet,DataFeeder_transformer
from models import create_model
import datet... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
train_ASR_wavnet.py | Python | import tensorflow as tf
import argparse
import os,math,copy,shutil
from utils.mylogger import *
from utils import ValueWindow , plot
from hparams import hparams as hp
from hparams import hparams_debug_string
from datafeeder import DataFeeder,DataFeeder_wavnet
from models import create_model
import datetime,time
import ... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
train_win.py | Python | import tensorflow as tf
import argparse
import os,math
from utils.mylogger import *
from utils import ValueWindow , plot
from hparams import hparams as hp
from datafeeder import DataFeeder,GetEditDistance
from models import create_model
import datetime,time
import traceback,random
import numpy as np
from keras import b... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
utils/__init__.py | Python | from .mylogger import *
from .valuewindow import *
class ValueWindow():
def __init__(self, window_size=100):
self._window_size = window_size
self._values = []
def append(self, x):
self._values = self._values[-(self._window_size - 1):] + [x]
@property
def sum(self):
return sum(self._values)
... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
utils/audio.py | Python | import librosa
import librosa.filters
import numpy as np
import tensorflow as tf
import scipy
from hparams import hparams
def load_wav(path):
return librosa.core.load(path, sr=hparams.sample_rate)[0]
def save_wav(wav, path):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
scipy.io.wavfile.write(path, hparams.sa... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
utils/mylogger.py | Python | import logging
from termcolor import colored
from datetime import datetime
import sys
# set up logger
class _MyFormatter(logging.Formatter):
def format(self, record):
date = colored('[%(asctime)s @%(filename)s:%(lineno)d]', 'green')
msg = '%(message)s'
if record.levelno == logging.WARNING:... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
utils/plot.py | Python | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
def plot_alignment(true_labels, pred_labels, info=None):
lens = len(true_labels)
matrix = np.zeros(shape=[lens,lens],dtype=np.int32)
for j in range(lens):
for i in rang... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
utils/valuewindow.py | Python | class ValueWindow():
def __init__(self, window_size=100):
self._window_size = window_size
self._values = []
def append(self, x):
self._values = self._values[-(self._window_size - 1):] + [x]
@property
def sum(self):
return sum(self._values)
@property
def count(self):
return len(self._v... | xingchensong/ASR-Wavnet | 5 | some ASR-system implementations （via tensorflow 1.x） | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
setup.py | Python | import setuptools
PACKAGE_NAME = "cosyvoice_ttsfrd"
# Include package data in `setup.cfg` or `setup.py`
# setuptools will automatically handle configurations in pyproject.toml, but we need to ensure files are included
setuptools.setup(
# Ensure specific files in the bundled_files directory are included, but excl... | xingchensong/CosyVoice-ttsfrd | 25 | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) | |
src/cosyvoice_ttsfrd/__init__.py | Python | __version__ = "0.4.2"
import pathlib
# Provide a helper function or variable to assist users in finding the decompressed resources.
# The resources are expected to be in the parent directory of this file.
PACKAGE_ROOT = pathlib.Path(__file__).parent
RESOURCE_PATH = PACKAGE_ROOT / "resource" # Assume the decompressed... | xingchensong/CosyVoice-ttsfrd | 25 | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) | |
src/cosyvoice_ttsfrd/post_install.py | Python | import sys
import subprocess
import pathlib
import zipfile
import urllib.request
import hashlib
def download_with_progress(url, dest_path, expected_sha256=None):
print(f"Downloading {url}...")
def progress_hook(block_num, block_size, total_size):
if total_size > 0:
percent = min(100, (blo... | xingchensong/CosyVoice-ttsfrd | 25 | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) | |
flashcosyvoice/cli.py | Python | # Copyright (c) 2025 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/config.py | Python | import os
from dataclasses import dataclass, field
import torch
from transformers import AutoConfig
@dataclass
class CosyVoice2LLMConfig:
architectures: list[str] = field(default_factory=lambda: ["Qwen2ForCausalLM"])
attention_dropout: float = 0.0
bos_token_id: int = 151643
eos_token_id: int = 6561 ... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/cosyvoice2.py | Python | # Copyright (c) 2025 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/cosyvoice3.py | Python | # TODO(xcsong): Implement CosyVoice3 when it is released
| xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/engine/block_manager.py | Python | from collections import deque
import numpy as np
import xxhash
from flashcosyvoice.engine.sequence import Sequence
class Block:
def __init__(self, block_id):
self.block_id = block_id
self.ref_count = 0
self.hash = -1
self.token_ids = []
def update(self, hash: int, token_ids... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/engine/llm_engine.py | Python | import atexit
from dataclasses import fields
from time import perf_counter
import torch.multiprocessing as mp
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from flashcosyvoice.config import Config, SamplingParams
from flashcosyvoice.engine.model_runner import ModelRunner
from flashcosyvoice.engine... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/engine/model_runner.py | Python | import pickle
from multiprocessing.shared_memory import SharedMemory
from multiprocessing.synchronize import Event
import torch
import torch.distributed as dist
from flashcosyvoice.config import Config
from flashcosyvoice.engine.sequence import Sequence
from flashcosyvoice.modules.qwen2 import Qwen2ForCausalLM
from f... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/engine/scheduler.py | Python | from collections import deque
from flashcosyvoice.config import Config
from flashcosyvoice.engine.block_manager import BlockManager
from flashcosyvoice.engine.sequence import Sequence, SequenceStatus
class Scheduler:
def __init__(self, config: Config):
self.max_num_seqs = config.max_num_seqs
sel... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/engine/sequence.py | Python | from copy import copy
from enum import Enum, auto
from itertools import count
from flashcosyvoice.config import SamplingParams
class SequenceStatus(Enum):
WAITING = auto()
RUNNING = auto()
FINISHED = auto()
class Sequence:
block_size = 256
counter = count()
def __init__(self, token_ids: li... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/flow.py | Python | from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from flashcosyvoice.modules.flow_components.estimator import \
CausalConditionalDecoder
from flashcosyvoice.modules.flow_components.upsample_encoder import (
UpsampleConformerEncoder, make_pad_mask)
# TODO(x... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/flow_components/estimator.py | Python | import math
from typing import Any, Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.attention import (GEGLU, GELU, AdaLayerNorm,
AdaLayerNormZero, ApproximateGELU)
from diffusers.models.attention_processor import Att... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/flow_components/upsample_encoder.py | Python | import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
def subsequent_chunk_mask(
size: int,
chunk_size: int,
num_left_chunks: int = -1,
device: torch.device = torch.device("cpu"),
) -> torch.Tensor:
"""Create mask ... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/hifigan.py | Python | # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/hifigan_components/layers.py | Python | from typing import List
import numpy as np
import torch
import torch.nn as nn
from torch.distributions.uniform import Uniform
from torch.nn import Conv1d
from torch.nn.utils import remove_weight_norm
try:
from torch.nn.utils.parametrizations import weight_norm
except ImportError:
from torch.nn.utils import we... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/qwen2.py | Python | # Copyright (c) 2025 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/qwen2_components/layers.py | Python | from functools import lru_cache
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import triton
import triton.language as tl
from flash_attn import flash_attn_varlen_func, flash_attn_with_kvcache
from flashcosyvoice.config import CosyVoice2LLMConfig
from flashcosyvoic... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/modules/sampler.py | Python | import torch
from torch import nn
class Sampler(nn.Module):
"""
Optimized sampler implementation using vectorized operations instead of loops, significantly improving performance
Performance optimizations:
1. Using batch processing instead of sequence loops, reducing Python loop overhead
2. Using... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/utils/audio.py | Python | import numpy as np
import torch
from librosa.filters import mel as librosa_mel_fn
from scipy.io.wavfile import read
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data, sampling_rate
def dynamic_range_compression(x, C=1, clip_val=1e-5):
return np.log(np.cl... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/utils/context.py | Python | from dataclasses import dataclass
import torch
@dataclass
class Context:
is_prefill: bool = False
cu_seqlens_q: torch.Tensor | None = None
cu_seqlens_k: torch.Tensor | None = None
max_seqlen_q: int = 0
max_seqlen_k: int = 0
slot_mapping: torch.Tensor | None = None
context_lens: torch.Tens... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/utils/loader.py | Python | import os
from glob import glob
import torch
from safetensors import safe_open
from torch import nn
from flashcosyvoice.config import CosyVoice2LLMConfig
def default_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor):
param.data.copy_(loaded_weight)
def load_text_llm(model: nn.Module, path: str):... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
flashcosyvoice/utils/memory.py | Python | import os
import torch
from pynvml import * # noqa
def get_gpu_memory():
torch.cuda.synchronize()
nvmlInit()
visible_device = list(map(int, os.getenv("CUDA_VISIBLE_DEVICES", "0,1,2,3,4,5,6,7").split(',')))
cuda_device_idx = torch.cuda.current_device()
cuda_device_idx = visible_device[cuda_device... | xingchensong/FlashCosyVoice | 242 | FlashCosyVoice: A lightweight vLLM implementation built from scratch for CosyVoice. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/__init__.py | Python | # Copyright (c) 2023 OpenAI. (authors: Whisper Team)
# 2024 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/cli.py | Python | # Copyright (c) 2024 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/model.py | Python | # Copyright (c) 2023 OpenAI. (authors: Whisper Team)
# 2024 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/model_v2.py | Python | # Copyright (c) (Mddct: Dinghao Zhou)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/model_v3.py | Python | # Copyright (c) (Mddct: Dinghao Zhou)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
s3tokenizer/utils.py | Python | # Copyright (c) 2023 OpenAI. (authors: Whisper Team)
# 2024 Tsinghua Univ. (authors: Xingchen Song)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
setup.py | Python | from pathlib import Path
from setuptools import find_packages, setup
def parse_requirements(filename):
"""Load requirements from a pip requirements file."""
with open(filename, 'r') as file:
lines = (line.strip() for line in file)
return [line for line in lines if line and not line.startswith... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
test/test_batch_efficiency.py | Python | #!/usr/bin/env python3
"""
Batch processing efficiency test
Test the efficiency improvement of new batch processing functionality for mixed long and short audio
"""
import time
import pytest
import s3tokenizer
import torch
def create_test_audio(duration_seconds=20, sample_rate=16000):
"""Create test audio"""
... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
test/test_onnx.py | Python | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright [2024-09-27] <sxc19@mails.tsinghua.edu.cn, Xingchen Song>
import os
import time
from typing import Any, Dict
import numpy as np
import onnxruntime
import pytest
import s3tokenizer
import torch
def create_test_audio(duration_seconds: float = 20,
... | xingchensong/S3Tokenizer | 505 | Reverse Engineering of Supervised Semantic Speech Tokenizer (S3Tokenizer) proposed in CosyVoice | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/pretrain/emilia/path.sh | Shell | cuda_prefix=/usr/local
cache_prefix=/mnt/user-ssd/songxingchen/share
. ./parse_options.sh || exit 1;
if [ ! -d "${cuda_prefix}/cuda" ]; then
echo "Error: CUDA_HOME directory does not exist: ${cuda_prefix}/cuda"
exit 1
fi
if [ ! -d "${cache_prefix}" ]; then
echo "Error: cache_prefix directory does not exis... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/pretrain/wenetspeech/parse_options.sh | Shell | #!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/pretrain/wenetspeech/path.sh | Shell | cuda_prefix=/usr/local
cache_prefix=/mnt/user-ssd/songxingchen/share
. ./parse_options.sh || exit 1;
if [ ! -d "${cuda_prefix}/cuda" ]; then
echo "Error: CUDA_HOME directory does not exist: ${cuda_prefix}/cuda"
exit 1
fi
if [ ! -d "${cache_prefix}" ]; then
echo "Error: cache_prefix directory does not exis... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/pretrain/wenetspeech/run.sh | Shell | #!/bin/bash
# NOTE(xcsong): change xx_prefix and xx_version to ur setup
cache_prefix=/mnt/user-ssd/songxingchen/share
cuda_prefix=/usr/local
pretrained_weight_dir="" # for fromscratch training
# pretrained_weight_dir="/mnt/user-ssd/songxingchen/share/modelscope/Llama-3.2-1B-Instruct" # for continue pretrain
pretrain... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/sft/asr/wenetspeech/local/extract_trans_and_pred.py | Python | import argparse
import json
import os
from tqdm import tqdm
def main(jsonl_path):
out_dir = os.path.dirname(jsonl_path)
trans_path = os.path.join(out_dir, 'trans.txt')
raw_rec_path = os.path.join(out_dir, 'raw_rec.txt')
with open(jsonl_path, 'r', encoding='utf-8') as fin, \
open(trans_path,... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/sft/asr/wenetspeech/parse_options.sh | Shell | #!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/sft/asr/wenetspeech/path.sh | Shell | cuda_prefix=/usr/local
cache_prefix=/mnt/user-ssd/songxingchen/share
. ./parse_options.sh || exit 1;
if [ ! -d "${cuda_prefix}/cuda" ]; then
echo "Error: CUDA_HOME directory does not exist: ${cuda_prefix}/cuda"
exit 1
fi
if [ ! -d "${cache_prefix}" ]; then
echo "Error: cache_prefix directory does not exis... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/audio/sft/asr/wenetspeech/run.sh | Shell | #!/bin/bash
# NOTE(xcsong): change xx_prefix and xx_version to ur setup
cache_prefix=/mnt/user-ssd/songxingchen/share
cuda_prefix=/usr/local
# NOTE(xcsong): Qwen2-Audio-7B https://modelscope.cn/models/Qwen/Qwen2-Audio-7B
# pretrained_weight_dir="${cache_prefix}/modelscope/Qwen2-Audio-7B" # for fintuning
pretrained_... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/allenai_c4/download_c4.py | Python | import os
from datasets import DownloadConfig, load_dataset
hf_data_repo = "allenai/c4"
hf_data_name = "en"
download_config = DownloadConfig(
num_proc=12,
max_retries=1200,
)
# English only
signal = 1
while signal:
try:
# 305GB, 156B tokens
# ref: https://mp.weixin.qq.com/s?__biz=MjM5ODE... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/allenai_c4/parse_options.sh | Shell | #!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/allenai_c4/path.sh | Shell | cuda_prefix=/usr/local
cache_prefix=/mnt/user-ssd/songxingchen/share
. ./parse_options.sh || exit 1;
if [ ! -d "${cuda_prefix}/cuda" ]; then
echo "Error: CUDA_HOME directory does not exist: ${cuda_prefix}/cuda"
exit 1
fi
if [ ! -d "${cache_prefix}" ]; then
echo "Error: cache_prefix directory does not exis... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/allenai_c4/run.sh | Shell | #!/bin/bash
# NOTE(xcsong): change xx_prefix and xx_version to ur setup
cache_prefix=/mnt/user-ssd/songxingchen/share
cuda_prefix=/usr/local
pretrained_weight_dir="" # for fromscratch training
# pretrained_weight_dir="/bucket/output/jfs-hdfs/user/xingchen.song/share/modelscope/Llama-3.2-1B-Instruct" # for continue p... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/fineweb-edu/download_fineweb-edu.py | Python | import os
from datasets import DownloadConfig, load_dataset
hf_data_repo = "HuggingFaceFW/fineweb-edu"
hf_data_name = "default"
download_config = DownloadConfig(
num_proc=12,
max_retries=1200,
)
# English only
signal = 1
while signal:
try:
# 9.74TB, 1.3T tokens
data = load_dataset(
... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/fineweb-edu/parse_options.sh | Shell | #!/bin/bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.ap... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/fineweb-edu/path.sh | Shell | cuda_prefix=/usr/local
cache_prefix=/mnt/user-ssd/songxingchen/share
. ./parse_options.sh || exit 1;
if [ ! -d "${cuda_prefix}/cuda" ]; then
echo "Error: CUDA_HOME directory does not exist: ${cuda_prefix}/cuda"
exit 1
fi
if [ ! -d "${cache_prefix}" ]; then
echo "Error: cache_prefix directory does not exis... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
examples/text/pretrain/fineweb-edu/run.sh | Shell | #!/bin/bash
# NOTE(xcsong): change xx_prefix and xx_version to ur setup
cache_prefix=/mnt/user-ssd/songxingchen/share
cuda_prefix=/usr/local
pretrained_weight_dir="" # for fromscratch training
# pretrained_weight_dir="/bucket/output/jfs-hdfs/user/xingchen.song/share/modelscope/Llama-3.2-1B-Instruct" # for continue p... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
install_cuda_cudnn.sh | Shell | #!/bin/bash
# Copyright [2024-04-09] <sxc19@mails.tsinghua.edu.cn, Xingchen Song>
cuda_version=12.6.3
driver_version=560.35.05
cudnn_version=9.5.1.17
prefix=/bucket/output/jfs-hdfs/user/xingchen.song/tools/cuda
echo "start download cuda ${cuda_version} & cudnn ${cudnn_version}"
wget https://developer.download.nvidia.... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/bin/test_make_data.py | Python | import json
import subprocess
import pytest
import torch
import torchaudio
from touchnet.data import DataConfig
from touchnet.data.datapipe import LowLevelTouchDatapipe
@pytest.fixture
def run_shell():
def _run(cmd, check=True):
return subprocess.run(
cmd,
shell=True,
... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/data/test_dataloader.py | Python | import os
import numpy
import pytest
import torch
from touchnet.bin.make_data import DataBuilder
from touchnet.data import DataConfig
from touchnet.data.dataloader import ParallelAwareDataloader
from touchnet.data.datapipe import LowLevelTouchDatapipe
def build_fake_data(nnodes, nproc_per_node, max_epoch):
tota... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/models/test_llama.py | Python | import os
import subprocess
from multiprocessing import Manager
import pytest
import torch
import torch.distributed.checkpoint as dcp
from torch import distributed as dist
from transformers import AutoConfig, AutoModelForCausalLM
from touchnet.bin import TrainConfig
from touchnet.utils.distributed import ParallelDims... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/utils/distributed_cpu.py | Python | import os
import torch
from transformers.hf_argparser import HfArgumentParser
from touchnet.bin import TrainConfig
from touchnet.utils.distributed import ParallelDims
from touchnet.utils.logging import init_logger
init_logger()
parser = HfArgumentParser(TrainConfig)
job_config = parser.parse_args_into_dataclasses()... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/utils/test_distributed_cpu.py | Python | import subprocess
import time
import pytest
def is_port_open(host, port):
import socket
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.settimeout(2)
return s.connect_ex((host, port)) == 0
# @pytest.mark.parametrize("master_port, nnodes, nproc_per_node, dp_shard, dp_replicate... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
tests/touchnet/utils/test_pack_loss.py | Python | from multiprocessing import Manager
import pytest
import torch
import torch.nn as nn
from torch import distributed as dist
from torch.distributed.nn.functional import all_gather
def calc_batch_dp_loss(batch_input_ids=None, batch_labels=None):
"""
Calculate loss using data parallelism (batch splitting).
... | xingchensong/TouchNet | 224 | A native-PyTorch library for large scale M-LLM (text/audio) training with tp/cp/dp. | Python | xingchensong | Xingchen Song(宋星辰) | Tsinghua University (2019-2022), WeNet Community (2021-now) |
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