Combine two TTS outputs in a single mp3 file not working - text-to-speech

I want to combine two requests to the Google cloud text-to-speech API in a single mp3 output. The reason I need to combine two requests is that the output should contain two different languages.
Below code works fine for many language pair combinations, but unfortunately not for all. If I request e.g. a sentence in English and one in German and combine them everything works. If I request one in English and one in Japanes I can't combine the two files in a single output. The output only contains the first sentence and instead of the second sentence, it outputs silence.
I tried now multiple ways to combine the two outputs but the result stays the same. The code below should show the issue.
Please run the code first with:
python synthesize_bug.py --t1 'Hallo' --code1 de-De --t2 'August' --code2 de-De
This works perfectly.
python synthesize_bug.py --t1 'Hallo' --code1 de-De --t2 'こんにちは' --code2 ja-JP
This doesn't work. The single files are ok, but the combined files contain silence instead of the Japanese part.
Also, if used with two Japanes sentences everything works.
I already filed a bug report at Google with no response yet, but maybe it's just me who is doing something wrong here with encoding assumptions. Hope someone has an idea.
#!/usr/bin/env python
import argparse
# [START tts_synthesize_text_file]
def synthesize_text_file(text1, text2, code1, code2):
"""Synthesizes speech from the input file of text."""
from apiclient.discovery import build
import base64
service = build('texttospeech', 'v1beta1')
collection = service.text()
data1 = {}
data1['input'] = {}
data1['input']['ssml'] = '<speak><break time="2s"/></speak>'
data1['voice'] = {}
data1['voice']['ssmlGender'] = 'FEMALE'
data1['voice']['languageCode'] = code1
data1['audioConfig'] = {}
data1['audioConfig']['speakingRate'] = 0.8
data1['audioConfig']['audioEncoding'] = 'MP3'
request = collection.synthesize(body=data1)
response = request.execute()
audio_pause = base64.b64decode(response['audioContent'].decode('UTF-8'))
raw_pause = response['audioContent']
ssmlLine = '<speak>' + text1 + '</speak>'
data1 = {}
data1['input'] = {}
data1['input']['ssml'] = ssmlLine
data1['voice'] = {}
data1['voice']['ssmlGender'] = 'FEMALE'
data1['voice']['languageCode'] = code1
data1['audioConfig'] = {}
data1['audioConfig']['speakingRate'] = 0.8
data1['audioConfig']['audioEncoding'] = 'MP3'
request = collection.synthesize(body=data1)
response = request.execute()
# The response's audio_content is binary.
with open('output1.mp3', 'wb') as out:
out.write(base64.b64decode(response['audioContent'].decode('UTF-8')))
print('Audio content written to file "output1.mp3"')
audio_text1 = base64.b64decode(response['audioContent'].decode('UTF-8'))
raw_text1 = response['audioContent']
ssmlLine = '<speak>' + text2 + '</speak>'
data2 = {}
data2['input'] = {}
data2['input']['ssml'] = ssmlLine
data2['voice'] = {}
data2['voice']['ssmlGender'] = 'MALE'
data2['voice']['languageCode'] = code2 #'ko-KR'
data2['audioConfig'] = {}
data2['audioConfig']['speakingRate'] = 0.8
data2['audioConfig']['audioEncoding'] = 'MP3'
request = collection.synthesize(body=data2)
response = request.execute()
# The response's audio_content is binary.
with open('output2.mp3', 'wb') as out:
out.write(base64.b64decode(response['audioContent'].decode('UTF-8')))
print('Audio content written to file "output2.mp3"')
audio_text2 = base64.b64decode(response['audioContent'].decode('UTF-8'))
raw_text2 = response['audioContent']
result = audio_text1 + audio_pause + audio_text2
with open('result.mp3', 'wb') as out:
out.write(result)
print('Audio content written to file "result.mp3"')
raw_result = raw_text1 + raw_pause + raw_text2
with open('raw_result.mp3', 'wb') as out:
out.write(base64.b64decode(raw_result.decode('UTF-8')))
print('Audio content written to file "raw_result.mp3"')
# [END tts_synthesize_text_file]ls
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--t1')
parser.add_argument('--code1')
parser.add_argument('--t2')
parser.add_argument('--code2')
args = parser.parse_args()
synthesize_text_file(args.t1, args.t2, args.code1, args.code2)

You can find the answer here:
https://issuetracker.google.com/issues/120687867
Short answer: It's not clear why it is not working, but Google suggests a workaround to first write the files as .wav, combine and then re-encode the result to mp3.

I have managed to do this in NodeJS with just one function (idk how optimal is it, but at least it works). Maybe you could take inspiration from it
I have used memory-streams dependency from npm
var streams = require('memory-streams');
function mergeAudios(audios) {
var reader = new streams.ReadableStream();
var writer = new streams.WritableStream();
audios.forEach(element => {
if (element instanceof streams.ReadableStream) {
element.pipe(writer)
}
else {
writer.write(element)
}
});
reader.append(writer.toBuffer())
return reader
}
Input parameter is a list which contain ReadableStream or responce.audioContent from synthesizeSpeech operation. If it is readablestream, it uses pipe operation, if it is audiocontent, it uses write method. At the end all content is passed into an readabblestream.

Related

How to extract the [Documentation] text from Robot framework test case

I am trying to extract the content of the [Documentation] section as a string for comparision with other part in a Python script.
I was told to use Robot framework API https://robot-framework.readthedocs.io/en/stable/
to extract but I have no idea how.
However, I am required to work with version 3.1.2
Example:
*** Test Cases ***
ATC Verify that Sensor Battery can enable and disable manufacturing mode
[Documentation] E1: This is the description of the test 1
... E2: This is the description of the test 2
[Tags] E1 TRACE{Trace_of_E1}
... E2 TRACE{Trace_of_E2}
Extract the string as
E1: This is the description of the test 1
E2: This is the description of the test 2
Have a look at these examples. I did something similar to generate testplans descritio. I tried to adapt my code to your requirements and this could maybe work for you.
import os
import re
from robot.api.parsing import (
get_model, get_tokens, Documentation, EmptyLine, KeywordCall,
ModelVisitor, Token
)
class RobotParser(ModelVisitor):
def __init__(self):
# Create object with remarkup_text to store formated documentation
self.text = ''
def get_text(self):
return self.text
def visit_TestCase(self, node):
# The matched `TestCase` node is a block with `header` and
# `body` attributes. `header` is a statement with familiar
# `get_token` and `get_value` methods for getting certain
# tokens or their value.
for keyword in node.body:
# skip empty lines
if keyword.get_value(Token.DOCUMENTATION) == None:
continue
self.text += keyword.get_value(Token.ARGUMENT)
def visit_Documentation(self,node):
# The matched "Documentation" node with value
self.remarkup_text += node.value + self.new_line
def visit_File(self, node):
# Call `generic_visit` to visit also child nodes.
return self.generic_visit(node)
if __name__ == "__main__":
path = "../tests"
for filename in os.listdir(path):
if re.match(".*\.robot", filename):
model = get_model(os.path.join(path, filename))
robot_parser = RobotParser()
robot_parser.visit(model)
text=robot_parser._text()
The code marked as best answer didn't quite work for me and has a lot of redundancy but it inspired me enough to get into the parsing and write it in a much readable and efficient way that actually works as is. You just have to have your own way of generating & iterating through filesystem where you call the get_robot_metadata(filepath) function.
from robot.api.parsing import (get_model, ModelVisitor, Token)
class RobotParser(ModelVisitor):
def __init__(self):
self.testcases = {}
def visit_TestCase(self, node):
testcasename = (node.header.name)
self.testcases[testcasename] = {}
for section in node.body:
if section.get_value(Token.DOCUMENTATION) != None:
documentation = section.value
self.testcases[testcasename]['Documentation'] = documentation
elif section.get_value(Token.TAGS) != None:
tags = section.values
self.testcases[testcasename]['Tags'] = tags
def get_testcases(self):
return self.testcases
def get_robot_metadata(filepath):
if filepath.endswith('.robot'):
robot_parser = RobotParser()
model = get_model(filepath)
robot_parser.visit(model)
metadata = robot_parser.get_testcases()
return metadata
This function will be able to extract the [Documentation] section from the testcase:
def documentation_extractor(testcase):
documentation = []
for setting in testcase.settings:
if len(setting) > 2 and setting[1].lower() == "[documentation]":
for doc in setting[2:]:
if doc.startswith("#"):
# the start of a comment, so skip rest of the line
break
documentation.append(doc)
break
return "\n".join(documentation)

Scrapy crawl spider only crawls as if DEPTH = 1 and stops with reason = finished

I've got a rather simple spider, that loads URLs from files (working) and should then start crawling and archive the HTML response.
It was working before nicely and since days, I can't figure out anymore, what I've changed to make it stop working.
Now, the spider only crawls the first page of every URL and stops then:
'finish_reason': 'finished',
Spider:
class TesterSpider(CrawlSpider):
name = 'tester'
allowed_domains = []
rules = (
Rule(LinkExtractor(allow=(), deny=(r'.*Zahlung.*', r'.*Cookies.*', r'.*Login.*', r'.*Datenschutz.*', r'.*Registrieren.*', r'.*Kontaktformular.*', )),callback='parse_item'),
)
def __init__(self, *a, **kw):
super(CrawlSpider, self).__init__(*a, **kw)
def start_requests(self):
logging.log(logging.INFO, "======== Starting with start_requests")
self._compile_rules()
smgt = Sourcemanagement()
rootdir = smgt.get_root_dir()
file_list = smgt.list_all_files ( rootdir + "/sources" )
links = smgt.get_all_domains()
links = list(set(links))
request_list = []
for link in links:
o = urlparse(link)
result = '{uri.netloc}'.format(uri=o)
self.allowed_domains.append(result)
request_list.append ( Request(url=link, callback=self.parse_item) )
return ( request_list )
def parse_item(self, response):
item = {}
self.write_html_file ( response )
return item
And the settings:
BOT_NAME = 'crawlerscrapy'
SPIDER_MODULES = ['crawlerscrapy.spiders']
NEWSPIDER_MODULE = 'crawlerscrapy.spiders'
USER_AGENT_LIST = "useragents.txt"
ROBOTSTXT_OBEY = True
CONCURRENT_REQUESTS = 150
DOWNLOAD_DELAY = 43
CONCURRENT_REQUESTS_PER_DOMAIN = 1
COOKIES_ENABLED = False
DEFAULT_REQUEST_HEADERS = {
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
'Accept-Encoding':'gzip, deflate, sdch',
'Connection':'keep-alive',
'Cache-Control':'max-age=0',
'Accept-Language': 'de',
}
DOWNLOADER_MIDDLEWARES = {
'scrapy.downloadermiddlewares.useragent.UserAgentMiddleware':None,
'random_useragent.RandomUserAgentMiddleware': 400
}
AUTOTHROTTLE_ENABLED = False
SCHEDULER_PRIORITY_QUEUE = 'scrapy.pqueues.DownloaderAwarePriorityQueue'
REACTOR_THREADPOOL_MAXSIZE = 20
LOG_LEVEL = 'DEBUG'
DEPTH_LIMIT = 0
DOWNLOAD_TIMEOUT = 15
DEPTH_PRIORITY = 1
SCHEDULER_DISK_QUEUE = 'scrapy.squeues.PickleFifoDiskQueue'
SCHEDULER_MEMORY_QUEUE = 'scrapy.squeues.FifoMemoryQueue'
Any idea, what I'm doing wrong?
EDIT:
I found out the answer:
request_list.append ( Request(url=link, callback=self.parse_item) )
# to be replaced by:
request_list.append ( Request(url=link, callback=self.parse) )
But I don't really understand why.
https://doc.scrapy.org/en/latest/topics/spiders.html#scrapy.spiders.Spider.parse
So I can return an empty dict in parse_item but I shouldn't because it would break the flow of things?
CrawlSpider.parse is the method that takes care of applying your rules to a response. Only responses you send to CrawlSpider.parse will get your rules applied, generating additional responses.
By yielding a request with a different callback, you are specifying that you don’t want rules to be applied to the response to that request.
The right place to put your parse_item callback when using a CrawlSpider subclass (as opposed to a Spider) is your rules. You already did that.
If what you want is to have responses to your start requests be handled both by rules and by a different callback, you might be better off using a regular spider. CrawlSpider is a very specialized spider, with a limited set of use cases; as soon as you need to do something it doesn’t support, you need to switch to a regular spider.

Django local server: Atomic Database from a text file

I made a Web app that takes in a text file, reads each line, takes the 11th character and saves it to SQLite3 db. How do I lock the database or have two or more separate tables while multiple requests are running?
I have added adding ATOMIC_REQUESTS': True to the settings.py in Django.
and I tried creating temporary tables for each request, but can't figure it out. I am pretty fresh to Django 2.2
My View.py
def home(request):
if request.method == 'GET':
return render(request, 'home.html')
if request.method == 'POST':
form = DocumentForm(data=request.POST, files=request.FILES)
print(form.errors)
if form.is_valid():
try:
f = request.FILES['fileToUpload']
except:
print('\033[0;93m'+ "No File uploaded, Redirecting" +'\033[0m')
return HttpResponseRedirect('/tryagain')
print('\033[32m'+ "Success" +'\033[0m')
print('Working...')
line = f.readline()
while line:
#print(line)
mst = message_typer.messages.create_message(str(line)[11])
line = f.readline()
else:
print('\033[0;91m'+ "Failed to validate Form" +'\033[0m')
return HttpResponseRedirect('/output')
return HttpResponse('Failure')
def output(request):
s = message_typer.messages.filter(message='s').count()
A = message_typer.messages.filter(message='A').count()
d = message_typer.messages.filter(message='d').count()
E = message_typer.messages.filter(message='E').count()
X = message_typer.messages.filter(message='X').count()
P = message_typer.messages.filter(message='P').count()
r = message_typer.messages.filter(message='r').count()
B = message_typer.messages.filter(message='B').count()
H = message_typer.messages.filter(message='H').count()
I = message_typer.messages.filter(message='I').count()
J = message_typer.messages.filter(message='J').count()
R = message_typer.messages.filter(message='R').count()
message_types = {'s':s, 'A':A, 'd':d, 'E':E, 'X':X, 'P':P,\
'r':r, 'B':B, 'H':H, 'I':I, 'J':J, 'R':R }
output = {'output':message_types}
#return HttpResponse('Received')
message_typer.messages.all().delete()
return render(request, 'output.html',output)
When the web page loads, it should display a simple break down each character in the 11th position of the uploaded text file.
However, if two requests are running concurrently, the first page that makes the request gets an Operation Error; Db is locked.
Traceback to here:
message_typer.messages.all().delete()
The second page will sum the total of the two files that were uploaded.
I do want to wipe the table after so that the next user will have an empty table to populate and perform a count on.
Is there a better way?

How to pass list/array of request objects to tensorflow serving in one server call?

After loading the wide and deep model, i was able to make prediction for one request object using the map of features and then serializing it to string for predictions as shown below-
is there a way we can create a batch of requests objects and send them for prediction to tensorflow server?
Code for single prediction looks like this-
for (each feature in feature list) {
Feature feature = null;
feature = Feature.newBuilder().setBytesList(BytesList.newBuilder().addValue(ByteString.copyFromUtf8("dummy string"))).build();
if (feature != null) {
inputFeatureMap.put(name, feature);
}
}
//Converting features(in inputFeatureMap) corresponding to one request into 'Features' Proto object
Features features = Features.newBuilder().putAllFeature(inputFeatureMap).build();
inputStr = Example.newBuilder().setFeatures(features).build().toByteString();
}
TensorProto proto = TensorProto.newBuilder()
.addStringVal(inputStr)
.setTensorShape(TensorShapeProto.newBuilder().addDim(TensorShapeProto.Dim.newBuilder().setSize(1).build()).build())
.setDtype(DataType.DT_STRING)
.build();
PredictRequest req = PredictRequest.newBuilder()
.setModelSpec(ModelSpec.newBuilder()
.setName("your serving model name")
.setSignatureName("serving_default")
.setVersion(Int64Value.newBuilder().setValue(modelVer)))
.putAllInputs(ImmutableMap.of("inputs", proto))
.build();
PredictResponse response = stub.predict(req);
System.out.println(response.getOutputsMap());
Is there a way we can send the list of Features Object for predictions, something similar to this-
List<Features> = {someway to create array/list of inputFeatureMap's which can be converted to serialized string.}
For anyone stumbling here, I found a simple workaround with Example proto to do batch request. I will borrow code from this question and modify it for the batch.
Features features =
Features.newBuilder()
.putFeature("Attribute1", feature("A12"))
.putFeature("Attribute2", feature(12))
.putFeature("Attribute3", feature("A32"))
.putFeature("Attribute4", feature("A40"))
.putFeature("Attribute5", feature(7472))
.putFeature("Attribute6", feature("A65"))
.putFeature("Attribute7", feature("A71"))
.putFeature("Attribute8", feature(1))
.putFeature("Attribute9", feature("A92"))
.putFeature("Attribute10", feature("A101"))
.putFeature("Attribute11", feature(2))
.putFeature("Attribute12", feature("A121"))
.putFeature("Attribute13", feature(24))
.putFeature("Attribute14", feature("A143"))
.putFeature("Attribute15", feature("A151"))
.putFeature("Attribute16", feature(1))
.putFeature("Attribute17", feature("A171"))
.putFeature("Attribute18", feature(1))
.putFeature("Attribute19", feature("A191"))
.putFeature("Attribute20", feature("A201"))
.build();
Example example = Example.newBuilder().setFeatures(features).build();
String pfad = System.getProperty("user.dir") + "\\1511523781";
try (SavedModelBundle model = SavedModelBundle.load(pfad, "serve")) {
Session session = model.session();
final String xName = "input_example_tensor";
final String scoresName = "dnn/head/predictions/probabilities:0";
try (Tensor<String> inputBatch = Tensors.create(new byte[][] {example.toByteArray(), example.toByteArray(), example.toByteArray(), example.toByteArray()});
Tensor<Float> output =
session
.runner()
.feed(xName, inputBatch)
.fetch(scoresName)
.run()
.get(0)
.expect(Float.class)) {
System.out.println(Arrays.deepToString(output.copyTo(new float[4][2])));
}
}
Essentially you can pass each example as an object in byte[4][] and you will receive the result in the same shape float[4][2]

Sending form data with an HTTP PUT request using Grinder API

I'm trying to replicate the following successful cURL operation with Grinder.
curl -X PUT -d "title=Here%27s+the+title&content=Here%27s+the+content&signature=myusername%3A3ad1117dab0ade17bdbd47cc8efd5b08" http://www.mysite.com/api
Here's my script:
from net.grinder.script import Test
from net.grinder.script.Grinder import grinder
from net.grinder.plugin.http import HTTPRequest
from HTTPClient import NVPair
import hashlib
test1 = Test(1, "Request resource")
request1 = HTTPRequest(url="http://www.mysite.com/api")
test1.record(request1)
log = grinder.logger.info
test1.record(log)
m = hashlib.md5()
class TestRunner:
def __call__(self):
params = [NVPair("title","Here's the title"),NVPair("content", "Here's the content")]
params.sort(key=lambda param: param.getName())
ps = ""
for param in params:
ps = ps + param.getValue() + ":"
ps = ps + "myapikey"
m.update(ps)
params.append(NVPair("signature", ("myusername:" + m.hexdigest())))
request1.setFormData(tuple(params))
result = request1.PUT()
The test runs okay, but it seems that my script doesn't actually send any of the params data to the API, and I can't work out why. There are no errors generated, but I get a 401 Unauthorized response from the API, indicating that a successful PUT request reached it, but obviously without a signature the request was rejected.
This isn't exactly an answer, more of a workaround that I came up with, that I've decided to post since this question hasn't yet received any responses, and it may help anyone else trying to achieve the same thing.
The workaround is basically to use the httplib and urllib modules to build and make the PUT request instead of the HTTPClient module.
import hashlib
import httplib, urllib
....
params = [("title", "Here's the title"),("content", "Here's the content")]
params.sort(key=lambda param: param[0])
ps = ""
for param in params:
ps = ps + param[1] + ":"
ps = ps + "myapikey"
m = hashlib.md5()
m.update(ps)
params.append(("signature", "myusername:" + m.hexdigest()))
params = urllib.urlencode(params)
print params
headers = {"Content-type": "application/x-www-form-urlencoded"}
conn = httplib.HTTPConnection("www.mysite.com:80")
conn.request("PUT", "/api", params, headers)
response = conn.getresponse()
print response.status, response.reason
print response.read()
conn.close()
(Based on the example at the bottom of this documentation page.)
You have to refer to the multi-form posting example in Grinder script gallery, but changing the Post to Put. It works for me.
files = ( NVPair("self", "form.py"), )
parameters = ( NVPair("run number", str(grinder.runNumber)), )
# This is the Jython way of creating an NVPair[] Java array
# with one element.
headers = zeros(1, NVPair)
# Create a multi-part form encoded byte array.
data = Codecs.mpFormDataEncode(parameters, files, headers)
grinder.logger.output("Content type set to %s" % headers[0].value)
# Call the version of POST that takes a byte array.
result = request1.PUT("/upload", data, headers)