Resemblyzer | python package to analyze and compare voices | Speech library
kandi X-RAY | Resemblyzer Summary
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- Preprocess a wav file
- Trim the end of the audio waveform
- Normalize a wav
- Plot the diagization of the similarity plot
- Play a WAV file
- Embed the utterance of the utterance
- Computes the split utterances for two partial utterances
- Embed a list of speakers
- Embed the utterance of a given utterance
- Plot projections of speakers
- Plot similarity matrix
- Plot histogram of samples
Resemblyzer Key Features
Resemblyzer Examples and Code Snippets
Trending Discussions on Speech
Trending Discussions on Speech
QUESTION
I'm following a tutorial https://docs.openfaas.com/tutorials/first-python-function/,
currently, I have the right image
$ docker images | grep hello-openfaas
wm/hello-openfaas latest bd08d01ce09b 34 minutes ago 65.2MB
$ faas-cli deploy -f ./hello-openfaas.yml
Deploying: hello-openfaas.
WARNING! You are not using an encrypted connection to the gateway, consider using HTTPS.
Deployed. 202 Accepted.
URL: http://IP:8099/function/hello-openfaas
there is a step that forewarns me to do some setup(My case is I'm using Kubernetes and minikube and don't want to push to a remote container registry, I should enable the use of images from the local library on Kubernetes.), I see the hints
see the helm chart for how to set the ImagePullPolicy
I'm not sure how to configure it correctly. the final result indicates I failed.
Unsurprisingly, I couldn't access the function service, I find some clues in https://docs.openfaas.com/deployment/troubleshooting/#openfaas-didnt-start which might help to diagnose the problem.
$ kubectl logs -n openfaas-fn deploy/hello-openfaas
Error from server (BadRequest): container "hello-openfaas" in pod "hello-openfaas-558f99477f-wd697" is waiting to start: trying and failing to pull image
$ kubectl describe -n openfaas-fn deploy/hello-openfaas
Name: hello-openfaas
Namespace: openfaas-fn
CreationTimestamp: Wed, 16 Mar 2022 14:59:49 +0800
Labels: faas_function=hello-openfaas
Annotations: deployment.kubernetes.io/revision: 1
prometheus.io.scrape: false
Selector: faas_function=hello-openfaas
Replicas: 1 desired | 1 updated | 1 total | 0 available | 1 unavailable
StrategyType: RollingUpdate
MinReadySeconds: 0
RollingUpdateStrategy: 0 max unavailable, 1 max surge
Pod Template:
Labels: faas_function=hello-openfaas
Annotations: prometheus.io.scrape: false
Containers:
hello-openfaas:
Image: wm/hello-openfaas:latest
Port: 8080/TCP
Host Port: 0/TCP
Liveness: http-get http://:8080/_/health delay=2s timeout=1s period=2s #success=1 #failure=3
Readiness: http-get http://:8080/_/health delay=2s timeout=1s period=2s #success=1 #failure=3
Environment:
fprocess: python3 index.py
Mounts:
Volumes:
Conditions:
Type Status Reason
---- ------ ------
Available False MinimumReplicasUnavailable
Progressing False ProgressDeadlineExceeded
OldReplicaSets:
NewReplicaSet: hello-openfaas-558f99477f (1/1 replicas created)
Events:
Type Reason Age From Message
---- ------ ---- ---- -------
Normal ScalingReplicaSet 29m deployment-controller Scaled up replica set hello-openfaas-558f99477f to 1
hello-openfaas.yml
version: 1.0
provider:
name: openfaas
gateway: http://IP:8099
functions:
hello-openfaas:
lang: python3
handler: ./hello-openfaas
image: wm/hello-openfaas:latest
imagePullPolicy: Never
I create a new project hello-openfaas2 to reproduce this error
$ faas-cli new --lang python3 hello-openfaas2 --prefix="wm"
Folder: hello-openfaas2 created.
# I add `imagePullPolicy: Never` to `hello-openfaas2.yml`
$ faas-cli build -f ./hello-openfaas2.yml
$ faas-cli deploy -f ./hello-openfaas2.yml
Deploying: hello-openfaas2.
WARNING! You are not using an encrypted connection to the gateway, consider using HTTPS.
Deployed. 202 Accepted.
URL: http://192.168.1.3:8099/function/hello-openfaas2
$ kubectl logs -n openfaas-fn deploy/hello-openfaas2
Error from server (BadRequest): container "hello-openfaas2" in pod "hello-openfaas2-7c67488865-7d7vm" is waiting to start: image can't be pulled
$ kubectl get pods --all-namespaces
NAMESPACE NAME READY STATUS RESTARTS AGE
kube-system coredns-64897985d-kp7vf 1/1 Running 0 47h
...
openfaas-fn env-6c79f7b946-bzbtm 1/1 Running 0 4h28m
openfaas-fn figlet-54db496f88-957xl 1/1 Running 0 18h
openfaas-fn hello-openfaas-547857b9d6-z277c 0/1 ImagePullBackOff 0 127m
openfaas-fn hello-openfaas-7b6946b4f9-hcvq4 0/1 ImagePullBackOff 0 165m
openfaas-fn hello-openfaas2-7c67488865-qmrkl 0/1 ImagePullBackOff 0 13m
openfaas-fn hello-openfaas3-65847b8b67-b94kd 0/1 ImagePullBackOff 0 97m
openfaas-fn hello-python-554b464498-zxcdv 0/1 ErrImagePull 0 3h23m
openfaas-fn hello-python-8698bc68bd-62gh9 0/1 ImagePullBackOff 0 3h25m
from https://docs.openfaas.com/reference/yaml/, I know I put the imagePullPolicy in the wrong place, there is no such keyword in its schema.
I also tried eval $(minikube docker-env and still get the same error.
I've a feeling that faas-cli deploy can be replace by helm, they all mean to run the image(whether from remote or local) in Kubernetes cluster, then I can use helm chart to setup the pullPolicy there. Even though the detail is not still clear to me, This discovery inspires me.
So far, after eval $(minikube docker-env)
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
wm/hello-openfaas2 0.1 03c21bd96d5e About an hour ago 65.2MB
python 3-alpine 69fba17b9bae 12 days ago 48.6MB
ghcr.io/openfaas/figlet latest ca5eef0de441 2 weeks ago 14.8MB
ghcr.io/openfaas/alpine latest 35f3d4be6bb8 2 weeks ago 14.2MB
ghcr.io/openfaas/faas-netes 0.14.2 524b510505ec 3 weeks ago 77.3MB
k8s.gcr.io/kube-apiserver v1.23.3 f40be0088a83 7 weeks ago 135MB
k8s.gcr.io/kube-controller-manager v1.23.3 b07520cd7ab7 7 weeks ago 125MB
k8s.gcr.io/kube-scheduler v1.23.3 99a3486be4f2 7 weeks ago 53.5MB
k8s.gcr.io/kube-proxy v1.23.3 9b7cc9982109 7 weeks ago 112MB
ghcr.io/openfaas/gateway 0.21.3 ab4851262cd1 7 weeks ago 30.6MB
ghcr.io/openfaas/basic-auth 0.21.3 16e7168a17a3 7 weeks ago 14.3MB
k8s.gcr.io/etcd 3.5.1-0 25f8c7f3da61 4 months ago 293MB
ghcr.io/openfaas/classic-watchdog 0.2.0 6f97aa96da81 4 months ago 8.18MB
k8s.gcr.io/coredns/coredns v1.8.6 a4ca41631cc7 5 months ago 46.8MB
k8s.gcr.io/pause 3.6 6270bb605e12 6 months ago 683kB
ghcr.io/openfaas/queue-worker 0.12.2 56e7216201bc 7 months ago 7.97MB
kubernetesui/dashboard v2.3.1 e1482a24335a 9 months ago 220MB
kubernetesui/metrics-scraper v1.0.7 7801cfc6d5c0 9 months ago 34.4MB
nats-streaming 0.22.0 12f2d32e0c9a 9 months ago 19.8MB
gcr.io/k8s-minikube/storage-provisioner v5 6e38f40d628d 11 months ago 31.5MB
functions/markdown-render latest 93b5da182216 2 years ago 24.6MB
functions/hubstats latest 01affa91e9e4 2 years ago 29.3MB
functions/nodeinfo latest 2fe8a87bf79c 2 years ago 71.4MB
functions/alpine latest 46c6f6d74471 2 years ago 21.5MB
prom/prometheus v2.11.0 b97ed892eb23 2 years ago 126MB
prom/alertmanager v0.18.0 ce3c87f17369 2 years ago 51.9MB
alexellis2/openfaas-colorization 0.4.1 d36b67b1b5c1 2 years ago 1.84GB
rorpage/text-to-speech latest 5dc20810eb54 2 years ago 86.9MB
stefanprodan/faas-grafana 4.6.3 2a4bd9caea50 4 years ago 284MB
$ kubectl get pods --all-namespaces
NAMESPACE NAME READY STATUS RESTARTS AGE
kube-system coredns-64897985d-kp7vf 1/1 Running 0 6d
kube-system etcd-minikube 1/1 Running 0 6d
kube-system kube-apiserver-minikube 1/1 Running 0 6d
kube-system kube-controller-manager-minikube 1/1 Running 0 6d
kube-system kube-proxy-5m8lr 1/1 Running 0 6d
kube-system kube-scheduler-minikube 1/1 Running 0 6d
kube-system storage-provisioner 1/1 Running 1 (6d ago) 6d
kubernetes-dashboard dashboard-metrics-scraper-58549894f-97tsv 1/1 Running 0 5d7h
kubernetes-dashboard kubernetes-dashboard-ccd587f44-lkwcx 1/1 Running 0 5d7h
openfaas-fn base64-6bdbcdb64c-djz8f 1/1 Running 0 5d1h
openfaas-fn colorise-85c74c686b-2fz66 1/1 Running 0 4d5h
openfaas-fn echoit-5d7df6684c-k6ljn 1/1 Running 0 5d1h
openfaas-fn env-6c79f7b946-bzbtm 1/1 Running 0 4d5h
openfaas-fn figlet-54db496f88-957xl 1/1 Running 0 4d19h
openfaas-fn hello-openfaas-547857b9d6-z277c 0/1 ImagePullBackOff 0 4d3h
openfaas-fn hello-openfaas-7b6946b4f9-hcvq4 0/1 ImagePullBackOff 0 4d3h
openfaas-fn hello-openfaas2-5c6f6cb5d9-24hkz 0/1 ImagePullBackOff 0 9m22s
openfaas-fn hello-openfaas2-8957bb47b-7cgjg 0/1 ImagePullBackOff 0 2d22h
openfaas-fn hello-openfaas3-65847b8b67-b94kd 0/1 ImagePullBackOff 0 4d2h
openfaas-fn hello-python-6d6976845f-cwsln 0/1 ImagePullBackOff 0 3d19h
openfaas-fn hello-python-b577cb8dc-64wf5 0/1 ImagePullBackOff 0 3d9h
openfaas-fn hubstats-b6cd4dccc-z8tvl 1/1 Running 0 5d1h
openfaas-fn markdown-68f69f47c8-w5m47 1/1 Running 0 5d1h
openfaas-fn nodeinfo-d48cbbfcc-hfj79 1/1 Running 0 5d1h
openfaas-fn openfaas2-fun 1/1 Running 0 15s
openfaas-fn text-to-speech-74ffcdfd7-997t4 0/1 CrashLoopBackOff 2235 (3s ago) 4d5h
openfaas-fn wordcount-6489865566-cvfzr 1/1 Running 0 5d1h
openfaas alertmanager-88449c789-fq2rg 1/1 Running 0 3d1h
openfaas basic-auth-plugin-75fd7d69c5-zw4jh 1/1 Running 0 3d2h
openfaas gateway-5c4bb7c5d7-n8h27 2/2 Running 0 3d2h
openfaas grafana 1/1 Running 0 4d8h
openfaas nats-647b476664-hkr7p 1/1 Running 0 3d2h
openfaas prometheus-687648749f-tl8jp 1/1 Running 0 3d1h
openfaas queue-worker-7777ffd7f6-htx6t 1/1 Running 0 3d2h
$ kubectl get -o yaml -n openfaas-fn deploy/hello-openfaas2
apiVersion: apps/v1
kind: Deployment
metadata:
annotations:
deployment.kubernetes.io/revision: "6"
prometheus.io.scrape: "false"
creationTimestamp: "2022-03-17T12:47:35Z"
generation: 6
labels:
faas_function: hello-openfaas2
name: hello-openfaas2
namespace: openfaas-fn
resourceVersion: "400833"
uid: 9c4e9d26-23af-4f93-8538-4e2d96f0d7e0
spec:
progressDeadlineSeconds: 600
replicas: 1
revisionHistoryLimit: 10
selector:
matchLabels:
faas_function: hello-openfaas2
strategy:
rollingUpdate:
maxSurge: 1
maxUnavailable: 0
type: RollingUpdate
template:
metadata:
annotations:
prometheus.io.scrape: "false"
creationTimestamp: null
labels:
faas_function: hello-openfaas2
uid: "969512830"
name: hello-openfaas2
spec:
containers:
- env:
- name: fprocess
value: python3 index.py
image: wm/hello-openfaas2:0.1
imagePullPolicy: Always
livenessProbe:
failureThreshold: 3
httpGet:
path: /_/health
port: 8080
scheme: HTTP
initialDelaySeconds: 2
periodSeconds: 2
successThreshold: 1
timeoutSeconds: 1
name: hello-openfaas2
ports:
- containerPort: 8080
name: http
protocol: TCP
readinessProbe:
failureThreshold: 3
httpGet:
path: /_/health
port: 8080
scheme: HTTP
initialDelaySeconds: 2
periodSeconds: 2
successThreshold: 1
timeoutSeconds: 1
resources: {}
securityContext:
allowPrivilegeEscalation: false
readOnlyRootFilesystem: false
terminationMessagePath: /dev/termination-log
terminationMessagePolicy: File
dnsPolicy: ClusterFirst
enableServiceLinks: false
restartPolicy: Always
schedulerName: default-scheduler
securityContext: {}
terminationGracePeriodSeconds: 30
status:
conditions:
- lastTransitionTime: "2022-03-17T12:47:35Z"
lastUpdateTime: "2022-03-17T12:47:35Z"
message: Deployment does not have minimum availability.
reason: MinimumReplicasUnavailable
status: "False"
type: Available
- lastTransitionTime: "2022-03-20T12:16:56Z"
lastUpdateTime: "2022-03-20T12:16:56Z"
message: ReplicaSet "hello-openfaas2-5d6c7c7fb4" has timed out progressing.
reason: ProgressDeadlineExceeded
status: "False"
type: Progressing
observedGeneration: 6
replicas: 2
unavailableReplicas: 2
updatedReplicas: 1
In one shell,
docker@minikube:~$ docker run --name wm -ti wm/hello-openfaas2:0.1
2022/03/20 13:04:52 Version: 0.2.0 SHA: 56bf6aac54deb3863a690f5fc03a2a38e7d9e6ef
2022/03/20 13:04:52 Timeouts: read: 5s write: 5s hard: 0s health: 5s.
2022/03/20 13:04:52 Listening on port: 8080
...
and another shell
docker@minikube:~$ docker ps | grep wm
d7796286641c wm/hello-openfaas2:0.1 "fwatchdog" 3 minutes ago Up 3 minutes (healthy) 8080/tcp wm
ANSWER
Answered 2022-Mar-16 at 08:10If your image has a latest tag, the Pod's ImagePullPolicy will be automatically set to Always. Each time the pod is created, Kubernetes tries to pull the newest image.
Try not tagging the image as latest or manually setting the Pod's ImagePullPolicy to Never. If you're using static manifest to create a Pod, the setting will be like the following:
containers:
- name: test-container
image: testImage:latest
imagePullPolicy: Never
QUESTION
I have been trying out an open-sourced personal AI assistant script. The script works fine but I want to create an executable so that I can gift the executable to one of my friends. However, when I try to create the executable using the auto-py-to-exe, it states the below error:
Running auto-py-to-exe v2.10.1
Building directory: C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x
Provided command: pyinstaller --noconfirm --onedir --console --no-embed-manifest "C:/Users/Tarun/AppData/Local/Programs/Python/Python310/AI_Ass.py"
Recursion Limit is set to 5000
Executing: pyinstaller --noconfirm --onedir --console --no-embed-manifest C:/Users/Tarun/AppData/Local/Programs/Python/Python310/AI_Ass.py --distpath C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x\application --workpath C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x\build --specpath C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x
42681 INFO: PyInstaller: 4.6
42690 INFO: Python: 3.10.0
42732 INFO: Platform: Windows-10-10.0.19042-SP0
42744 INFO: wrote C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x\AI_Ass.spec
42764 INFO: UPX is not available.
42772 INFO: Extending PYTHONPATH with paths
['C:\\Users\\Tarun\\AppData\\Local\\Programs\\Python\\Python310']
43887 INFO: checking Analysis
43891 INFO: Building Analysis because Analysis-00.toc is non existent
43895 INFO: Initializing module dependency graph...
43915 INFO: Caching module graph hooks...
43975 INFO: Analyzing base_library.zip ...
54298 INFO: Processing pre-find module path hook distutils from 'C:\\Users\\Tarun\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PyInstaller\\hooks\\pre_find_module_path\\hook-distutils.py'.
54306 INFO: distutils: retargeting to non-venv dir 'C:\\Users\\Tarun\\AppData\\Local\\Programs\\Python\\Python310\\lib'
57474 INFO: Caching module dependency graph...
58088 INFO: running Analysis Analysis-00.toc
58132 INFO: Adding Microsoft.Windows.Common-Controls to dependent assemblies of final executable
required by C:\Users\Tarun\AppData\Local\Programs\Python\Python310\python.exe
58365 INFO: Analyzing C:\Users\Tarun\AppData\Local\Programs\Python\Python310\AI_Ass.py
59641 INFO: Processing pre-safe import module hook urllib3.packages.six.moves from 'C:\\Users\\Tarun\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\PyInstaller\\hooks\\pre_safe_import_module\\hook-urllib3.packages.six.moves.py'.
An error occurred while packaging
Traceback (most recent call last):
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\auto_py_to_exe\packaging.py", line 131, in package
run_pyinstaller()
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\__main__.py", line 124, in run
run_build(pyi_config, spec_file, **vars(args))
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\__main__.py", line 58, in run_build
PyInstaller.building.build_main.main(pyi_config, spec_file, **kwargs)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\building\build_main.py", line 782, in main
build(specfile, kw.get('distpath'), kw.get('workpath'), kw.get('clean_build'))
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\building\build_main.py", line 714, in build
exec(code, spec_namespace)
File "C:\Users\Tarun\AppData\Local\Temp\tmpjaw1ky1x\AI_Ass.spec", line 7, in
a = Analysis(['C:/Users/Tarun/AppData/Local/Programs/Python/Python310/AI_Ass.py'],
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\building\build_main.py", line 277, in __init__
self.__postinit__()
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\building\datastruct.py", line 155, in __postinit__
self.assemble()
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\building\build_main.py", line 439, in assemble
priority_scripts.append(self.graph.add_script(script))
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 265, in add_script
self._top_script_node = super().add_script(pathname)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1433, in add_script
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1505, in import_hook
target_package, target_module_partname = self._find_head_package(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1684, in _find_head_package
target_package = self._safe_import_module(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1505, in import_hook
target_package, target_module_partname = self._find_head_package(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1684, in _find_head_package
target_package = self._safe_import_module(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1505, in import_hook
target_package, target_module_partname = self._find_head_package(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1684, in _find_head_package
target_package = self._safe_import_module(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1505, in import_hook
target_package, target_module_partname = self._find_head_package(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1684, in _find_head_package
target_package = self._safe_import_module(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1518, in import_hook
submodule = self._safe_import_module(head, mname, submodule)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1518, in import_hook
submodule = self._safe_import_module(head, mname, submodule)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2062, in _safe_import_module
self._process_imports(n)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2850, in _process_imports
target_module = self._safe_import_hook(*import_info, **kwargs)[0]
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2301, in _safe_import_hook
target_modules = self.import_hook(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 1518, in import_hook
submodule = self._safe_import_module(head, mname, submodule)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\depend\analysis.py", line 387, in _safe_import_module
return super()._safe_import_module(module_basename, module_name, parent_package)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2061, in _safe_import_module
n = self._scan_code(module, co, co_ast)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2645, in _scan_code
self._scan_bytecode(
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\modulegraph.py", line 2749, in _scan_bytecode
for inst in util.iterate_instructions(module_code_object):
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\util.py", line 147, in iterate_instructions
yield from iterate_instructions(constant)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\site-packages\PyInstaller\lib\modulegraph\util.py", line 139, in iterate_instructions
yield from get_instructions(code_object)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\dis.py", line 338, in _get_instructions_bytes
argval, argrepr = _get_const_info(arg, constants)
File "C:\Users\Tarun\AppData\Local\Programs\Python\Python310\lib\dis.py", line 292, in _get_const_info
argval = const_list[const_index]
IndexError: tuple index out of range
Project output will not be moved to output folder
Complete.
I understand that there is a thread already about similar issue but it still doesn't solve the issue. Hence seeking out help
I really have no idea why is the error occurring and how to resolve it. I am pasting the script below for your reference. Can some one please help? Thank you in advance
#importing libraries
import speech_recognition as sr
import pyttsx3
import datetime
import wikipedia
import webbrowser
import os
import time
import subprocess
from ecapture import ecapture as ec
import wolframalpha
import json
import requests
#setting up speech engine
engine=pyttsx3.init('sapi5')
voices=engine.getProperty('voices')
engine.setProperty('voice','voices[1].id')
def speak(text):
engine.say(text)
engine.runAndWait()
#Greet user
def wishMe():
hour=datetime.datetime.now().hour
if hour>=0 and hour<12:
speak("Hello,Good Morning")
print("Hello,Good Morning")
elif hour>=12 and hour<18:
speak("Hello,Good Afternoon")
print("Hello,Good Afternoon")
else:
speak("Hello,Good Evening")
print("Hello,Good Evening")
#Setting up the command function for your AI assistant
def takeCommand():
r=sr.Recognizer()
with sr.Microphone() as source:
print("Listening...")
audio=r.listen(source)
try:
statement=r.recognize_google(audio,language='en-in')
print(f"user said:{statement}\n")
except Exception as e:
speak("Pardon me, please say that again")
return "None"
return statement
print("Loading your AI personal assistant Friday")
speak("Loading your AI personal assistant Friday")
wishMe()
#main function
if __name__=='__main__':
while True:
speak("Tell me how can I help you now?")
statement = takeCommand().lower()
if statement==0:
continue
if "good bye" in statement or "ok bye" in statement or "stop" in statement:
speak('your personal assistant Friday is shutting down,Good bye')
print('your personal assistant Friday is shutting down,Good bye')
break
if 'wikipedia' in statement:
speak('Searching Wikipedia...')
statement =statement.replace("wikipedia", "")
results = wikipedia.summary(statement, sentences=10)
webbrowser.open_new_tab("https://en.wikipedia.org/wiki/"+ statement)
speak("According to Wikipedia")
print(results)
speak(results)
elif 'open youtube' in statement:
webbrowser.register('chrome', None,
webbrowser.BackgroundBrowser("C://Program Files (x86)//Google//Chrome//Application//chrome.exe"))
webbrowser.get('chrome').open_new_tab("https://www.youtube.com")
#webbrowser.open_new_tab("https://www.youtube.com")
speak("youtube is open now")
time.sleep(5)
elif 'open google' in statement:
webbrowser.open_new_tab("https://www.google.com")
speak("Google chrome is open now")
time.sleep(5)
elif 'open gmail' in statement:
webbrowser.open_new_tab("gmail.com")
speak("Google Mail open now")
time.sleep(5)
elif 'time' in statement:
strTime=datetime.datetime.now().strftime("%H:%M:%S")
speak(f"the time is {strTime}")
elif 'news' in statement:
news = webbrowser.open_new_tab("https://timesofindia.indiatimes.com/home/headlines")
speak('Here are some headlines from the Times of India,Happy reading')
time.sleep(6)
elif "camera" in statement or "take a photo" in statement:
ec.capture(0,"robo camera","img.jpg")
elif 'search' in statement:
statement = statement.replace("search", "")
webbrowser.open_new_tab(statement)
time.sleep(5)
elif 'who are you' in statement or 'what can you do' in statement:
speak('I am Friday version 1 point O your personal assistant. I am programmed to minor tasks like'
'opening youtube,google chrome, gmail and stackoverflow ,predict time,take a photo,search wikipedia,predict weather'
'In different cities, get top headline news from times of india and you can ask me computational or geographical questions too!')
elif "who made you" in statement or "who created you" in statement or "who discovered you" in statement:
speak("I was built by Mirthula")
print("I was built by Mirthula")
elif "log off" in statement or "sign out" in statement:
speak("Ok , your pc will log off in 10 sec make sure you exit from all applications")
subprocess.call(["shutdown", "/l"])
time.sleep(3)
ANSWER
Answered 2021-Nov-05 at 02:2042681 INFO: PyInstaller: 4.6
42690 INFO: Python: 3.10.0
There's the issue. Python 3.10.0 has a bug with PyInstaller 4.6. The problem isn't you or PyInstaller. Try converting it using Python 3.9.7 instead. Ironic, considering 3.10.0 was suppose to be a bugfix update.
QUESTION
I'm pulling my hairs here. I have a Google Assistant application that I build with Jovo 4 and Google Actions Builder.
The goal is to create a HelpScene, which shows some options that explain the possibilities/features of the app on selection. This is the response I return from my Webhook. (This is Jovo code, but doesn't matter as this returns a JSON when the Assistant calls the webhook.)
@Handle(GoogleAssistantHandles.onScene('HelpScene'))
showHelpList() {
return this.$send({
platforms: {
googleAssistant: {
nativeResponse: {
scene: {
name: this.jovo.$googleAssistant?.$request.scene?.name,
slots: {},
next: {
name: 'MainScene',
},
},
session: {
id: 'session_id',
languageCode: 'nl-BE',
params: {},
typeOverrides: [
{
name: 'prompt_option',
synonym: {
entries: [
{
name: 'ITEM_1',
synonyms: ['Item 1', 'First item'],
display: {
title: 'Item #1',
description: 'Description of Item #1',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_2',
synonyms: ['Item 2', 'Second item'],
display: {
title: 'Item #2',
description: 'Description of Item #2',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_3',
synonyms: ['Item 3', 'Third item'],
display: {
title: 'Item #3',
description: 'Description of Item #3',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_4',
synonyms: ['Item 4', 'Fourth item'],
display: {
title: 'Item #4',
description: 'Description of Item #4',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
],
},
typeOverrideMode: 'TYPE_REPLACE',
},
],
},
prompt: {
override: false,
content: {
collection: {
items: [
{
key: 'ITEM_1',
},
{
key: 'ITEM_2',
},
{
key: 'ITEM_3',
},
{
key: 'ITEM_4',
},
],
subtitle: 'List subtitle',
title: 'List title',
},
},
firstSimple: {
speech: 'This is a list.',
text: 'This is a list.',
},
},
},
},
},
});
I created a HelpScene which pulls my options from my webhook.
In my slot filling, this is the configuration.
When I use the simulator, the options from my webhook are shown perfectly. But when I click an item in the list, the app just stops working. "YourApp is currently not responding".
At first I thought it had something to do with my webhook, so I changed the behaviour of the "on slot is filled" condition, that it should prompt something, directly from Google Actions Builder, but the behaviour is still not desired: the app just stops working.
Any ideas what I'm doing wrong?
Thanks in advance!
ANSWER
Answered 2022-Feb-23 at 15:32Okay, after days of searching, I finally figured it out. It did have something to do with the Jovo framework/setup and/or the scene parameter in the native response.
This is my component, in which I redirect new users to the HelpScene. This scene should show multiple cards in a list/collection/whatever on which the user can tap to receive more information about the application's features.
@Component()
export class WelcomeComponent extends BaseComponent {
async START(): Promise {
const isNewUser = true;
if (isNewUser && this.$device.supports(Capability.Screen)) {
return this.$send(NextSceneOutput, {
name: 'HelpScene',
message: 'Hi, I noticed you are a new user, let me walk you through some options.',
});
}
return this.$send('Welcome!');
}
@Handle(GoogleAssistantHandles.onScene('HelpScene'))
help() {
const sessionData = this.$request.getSession();
if (sessionData && sessionData.prompt_option) {
return this.$send(NextSceneOutput, {
name: 'MainScene',
message: `You picked option ${sessionData.prompt_option}. This is some info about it ... What do you want to do now?`,
});
}
return this.$send({
platforms: {
googleAssistant: {
nativeResponse: {
session: {
id: 'session_id',
languageCode: '',
params: {},
typeOverrides: [
{
name: 'HelpOptionType',
typeOverrideMode: 'TYPE_REPLACE',
synonym: {
entries: [
{
name: 'ITEM_1',
synonyms: ['Item 1', 'First item'],
display: {
title: 'Item #1',
description: 'Description of Item #1',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_2',
synonyms: ['Item 2', 'Second item'],
display: {
title: 'Item #2',
description: 'Description of Item #2',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_3',
synonyms: ['Item 3', 'Third item'],
display: {
title: 'Item #3',
description: 'Description of Item #3',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
{
name: 'ITEM_4',
synonyms: ['Item 4', 'Fourth item'],
display: {
title: 'Item #4',
description: 'Description of Item #4',
image: {
alt: 'Google Assistant logo',
height: 0,
url: 'https://developers.google.com/assistant/assistant_96.png',
width: 0,
},
},
},
],
},
},
],
},
prompt: {
override: false,
content: {
list: {
items: [
{
key: 'ITEM_1',
},
{
key: 'ITEM_2',
},
{
key: 'ITEM_3',
},
{
key: 'ITEM_4',
},
],
subtitle: 'List subtitle',
title: 'List title',
},
},
firstSimple: {
speech: 'This is a list.',
text: 'This is a list.',
},
},
},
},
},
});
}
// ...other intents...
In AoG I made 2 scenes, 1 MainScene on which a user enters the app and one HelpScene, which looks like this (yaml config). The goal of the HelpScene is only to be used for slot filling on the different options, then a user should go back to the MainScene.
"conditionalEvents":
- "condition": "scene.slots.status == \"FINAL\""
"handler":
"webhookHandler": "Jovo"
"slots":
- "commitBehavior":
"writeSessionParam": "prompt_option"
"name": "prompt_option"
"promptSettings":
"initialPrompt":
"webhookHandler": "Jovo"
"required": true
"type":
"name": "HelpOptionType"
As you can see in my help() method, I just check if the session param is filled out. If it is, I redirect the user to the MainScene, but first give a response about the chosen option.
QUESTION
I want to convert text to speech from a document where multiple languages are included. When I am trying to do the following code, I fetch problems to record each language clearly. How can I save such type mixer text-audio clearly?
from gtts import gTTS
mytext = 'Welcome to gtts! আজ একটি ভাল দিন। tumi kemon acho? ٱلْحَمْدُ لِلَّٰهِ'
language = 'ar' # arabic
myobj = gTTS(text=mytext, tld='co.in', lang=language, slow=False)
myobj.save("audio.mp3")
ANSWER
Answered 2022-Jan-29 at 07:05It's not enough to use just text to speech, since it can work with one language only.
To solve this problem we need to detect language for each part of the sentence.
Then run it through text to speech and append it to our final spoken sentence.
It would be ideal to use some neural network (there are plenty) to do this categorization for You.
Just for a sake of proof of concept I used googletrans to detect language for each part of the sentences and gtts to make a mp3 file from it.
It's not bullet proof, especially with arabic text. googletrans somehow detect different language code, which is not recognized by gtts. For that reason we have to use code_table to pick proper language code that works with gtts.
Here is working example:
from googletrans import Translator
from gtts import gTTS
input_text = "Welcome to gtts! আজ একটি ভাল দিন। tumi kemon acho? ٱلْحَمْدُ لِلَّٰه"
words = input_text.split(" ")
translator = Translator()
language, sentence = None, ""
lang_code_table = {"sd": "ar"}
with open('output.mp3', 'wb') as ff:
for word in words:
if word == " ":
continue
# Detect language of current word
word_language = translator.detect(word).lang
if word_language == language:
# Same language, append word to the sentence
sentence += " " + word
else:
if language is None:
# No language set yet, initialize and continue
language, sentence = word_language, word
continue
if word.endswith(("?", ".", "!")):
# If word endswith one of the punctuation marks, it should be part of previous sentence
sentence += " " + word
continue
# We have whole previous sentence, translate it into speech and append to mp3 file
gTTS(text=sentence, lang=lang_code_table.get(language, language), slow=False).write_to_fp(ff)
# Continue with other language
language, sentence = word_language, word
if language and sentence:
# Append last detected sentence
gTTS(text=sentence, lang=lang_code_table.get(language, language), slow=False).write_to_fp(ff)
It's obviously not fast and won't fit for longer text.
Also it needs better tokenizer and proper error handling.
Again, it's just proof of concept.
QUESTION
My current data-frame is:
|articleID | keywords |
|:-------- |:------------------------------------------------------:|
0 |58b61d1d | ['Second Avenue (Manhattan, NY)'] |
1 |58b6393b | ['Crossword Puzzles'] |
2 |58b6556e | ['Workplace Hazards and Violations', 'Trump, Donald J']|
3 |58b657fa | ['Trump, Donald J', 'Speeches and Statements']. |
I want a data-frame similar to the following, where a column is added based on whether a Trump token, 'Trump, Donald J' is mentioned in the keywords and if so then it is assigned True :
|articleID | keywords | trumpMention |
|:-------- |:------------------------------------------------------:| ------------:|
0 |58b61d1d | ['Second Avenue (Manhattan, NY)'] | False |
1 |58b6393b | ['Crossword Puzzles'] | False |
2 |58b6556e | ['Workplace Hazards and Violations', 'Trump, Donald J']| True |
3 |58b657fa | ['Trump, Donald J', 'Speeches and Statements']. | True |
I have tried multiple ways using df functions. But cannot achieve my wanted results. Some of the ways I've tried are:
df['trumpMention'] = np.where(any(df['keywords']) == 'Trump, Donald J', True, False)
or
df['trumpMention'] = df['keywords'].apply(lambda x: any(token == 'Trump, Donald J') for token in x)
or
lst = ['Trump, Donald J']
df['trumpMention'] = df['keywords'].apply(lambda x: ([ True for token in x if any(token in lst)]))
Raw input:
df = pd.DataFrame({'articleID': ['58b61d1d', '58b6393b', '58b6556e', '58b657fa'],
'keywords': [['Second Avenue (Manhattan, NY)'],
['Crossword Puzzles'],
['Workplace Hazards and Violations', 'Trump, Donald J'],
['Trump, Donald J', 'Speeches and Statements']],
'trumpMention': [False, False, True, True]})
ANSWER
Answered 2022-Jan-06 at 12:13try
df["trumpMention"] = df["keywords"].apply(lambda x: "Trump, Donald J" in x)
QUESTION
I'm trying to use Web Speech API to read text on my web page. But I found that some of the SAPI5 voices installed in my Windows 10 would not show up in the output of speechSynthesis.getVoices(), including the Microsoft Eva Mobile on Windows 10 "unlock"ed by importing a registry file. These voices could work fine in local TTS programs like Balabolka but they just don't show in the browser. Are there any specific rules by which the browser chooses whether to list the voices or not?
ANSWER
Answered 2021-Dec-31 at 08:19OK, I found out what was wrong. I was using Microsoft Edge and it seems that Edge only shows some of Microsoft voices. If I use Firefox, the other installed voices will also show up. So it was Edge's fault.
QUESTION
I am trying to write an object detection + text-to-speech code to detect objects and produce a voice output on the raspberry pi 4. However, as of right now, I am trying to write a simple python script that incorporates both elements into a single .py file and preferably as a function. I will then run this script on the raspberry pi. I want to give credit to Murtaza's Workshop "Object Detection OpenCV Python | Easy and Fast (2020)" and https://pypi.org/project/pyttsx3/ for the Text to speech documentation for pyttsx3. I have attached the code below. I have tried running the program and I always keep getting errors with the Text to speech code (commented lines 33-36 for reference). I believe it is some looping error but I just can't seem to get the program to run continuously. For instance, if I run the code without the TTS part, it works fine. Otherwise, it runs for perhaps 3-5 seconds and suddenly stops. I am a beginner but highly passionate in computer vision, and any help is appreciated!
import cv2
#import pyttsx3
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
classNames = []
classFile = 'coco.names'
with open(classFile,'rt') as f:
classNames = [line.rstrip() for line in f]
configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'
weightsPath = 'frozen_inference_graph.pb'
net = cv2.dnn_DetectionModel(weightsPath, configPath)
net.setInputSize(320, 320)
net.setInputScale(1.0 / 127.5)
net.setInputMean((127.5, 127.5, 127.5))
net.setInputSwapRB(True)
while True:
success, img = cap.read()
classIds, confs, bbox = net.detect(img, confThreshold=0.45)
if len(classIds) != 0:
for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
className = classNames[classId-1]
#engine = pyttsx3.init()
#str1 = str(className)
#engine.say(str1 + "detected")
#engine.runAndWait()
cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
cv2.putText(img, classNames[classId-1].upper(), (box[0]+10, box[1]+30),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
cv2.putText(img, str(round(confidence * 100, 2)), (box[0]+200, box[1]+30),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Output', img)
cv2.waitKey(1)
Here is a screenshot of my code 1
Here is a link to the download files needed to run code as well in case
Here is the error: /Users/venuchannarayappa/PycharmProjects/ObjectDetector/venv/bin/python /Users/venuchannarayappa/PycharmProjects/ObjectDetector/main.py
Traceback (most recent call last): File "/Users/venuchannarayappa/PycharmProjects/ObjectDetector/main.py", line 24, in
classIds, confs, bbox = net.detect(img, confThreshold=0.45)
cv2.error: OpenCV(4.5.4) /Users/runner/work/opencv-python/opencv-python/opencv/modules/imgproc/src/resize.cpp:4051: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
Process finished with exit code 1
Link to video output recorded through iphone: https://www.icloud.com/iclouddrive/03jGfqy7-A9DKfekcu3wjk0rA#IMG_4932
Sorry for such a long post! I was debugging my code for the past few hours and I think I got it to work. I changed the main while loop only and rest of code is the same. The program seems to run continuously for me. I would appreciate any comments if there are any difficulties in running it.
engine = pyttsx3.init()
while True:
success, img = cap.read()
#print(success)
#print(img)
#print(img.shape)
classIds, confs, bbox = net.detect(img, confThreshold=0.45)
if len(classIds) != 0:
for classId, confidence, box in zip(classIds.flatten(), confs.flatten(), bbox):
className = classNames[classId - 1]
#print(len(classIds))
str1 = str(className)
#print(str1)
engine.say(str1 + "detected")
engine.runAndWait()
cv2.rectangle(img, box, color=(0, 255, 0), thickness=2)
cv2.putText(img, classNames[classId-1].upper(), (box[0]+10, box[1]+30),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
cv2.putText(img, str(round(confidence * 100, 2)), (box[0]+200, box[1]+30),
cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
continue
cv2.imshow('Output', img)
cv2.waitKey(1)
I am planning to run this code on the raspberry pi. I am planning on installing opencv using this command: pip3 install opencv-python. However, I am not sure how to install pyttsx3 since I think I need to install from source. Please let me know if there is a simple method to install pyttsx3.
Update: As of December 27th, I have installed all necessary packages and my code is now functional.
ANSWER
Answered 2021-Dec-28 at 16:46I installed pyttsx3 using the two commands in the terminal on the Raspberry Pi:
- sudo apt update && sudo apt install espeak ffmpeg libespeak1
- pip install pyttsx3
I followed the video youtube.com/watch?v=AWhDDl-7Iis&ab_channel=AiPhile to install pyttsx3. My functional code should also be listed above. My question should be resolved but hopefully useful to anyone looking to write a similar program. I have made minor tweaks to my code.
QUESTION
In my scrapy code I'm trying to yield the following figures from parliament's website where all the members of parliament (MPs) are listed. Opening the links for each MP, I'm making parallel requests to get the figures I'm trying to count. I'm intending to yield each three figures below in the company of the name and the party of the MP
Here are the figures I'm trying to scrape
- How many bill proposals that each MP has their signature on
- How many question proposals that each MP has their signature on
- How many times that each MP spoke on the parliament
In order to count and yield out how many bills has each member of parliament has their signature on, I'm trying to write a scraper on the members of parliament which works with 3 layers:
- Starting with the link where all MPs are listed
- From (1) accessing the individual page of each MP where the three information defined above is displayed
- 3a) Requesting the page with bill proposals and counting the number of them by len function 3b) Requesting the page with question proposals and counting the number of them by len function 3c) Requesting the page with speeches and counting the number of them by len function
What I want: I want to yield the inquiries of 3a,3b,3c with the name and the party of the MP in the same raw
Problem 1) When I get an output to csv it only creates fields of speech count, name, part. It doesn't show me the fields of bill proposals and question proposals
Problem 2) There are two empty values for each MP, which I guess corresponds to the values I described above at Problem1
Problem 3) What is the better way of restructuring my code to output the three values in the same line, rather than printing each MP three times for each value that I'm scraping
from scrapy import Spider
from scrapy.http import Request
import logging
class MvSpider(Spider):
name = 'mv2'
allowed_domains = ['tbmm.gov.tr']
start_urls = ['https://www.tbmm.gov.tr/Milletvekilleri/liste']
def parse(self, response):
mv_list = mv_list = response.xpath("//ul[@class='list-group list-group-flush']") #taking all MPs listed
for mv in mv_list:
name = mv.xpath("./li/div/div/a/text()").get() # MP's name taken
party = mv.xpath("./li/div/div[@class='col-md-4 text-right']/text()").get().strip() #MP's party name taken
partial_link = mv.xpath('.//div[@class="col-md-8"]/a/@href').get()
full_link = response.urljoin(partial_link)
yield Request(full_link, callback = self.mv_analysis, meta = {
'name': name,
'party': party
})
def mv_analysis(self, response):
name = response.meta.get('name')
party = response.meta.get('party')
billprop_link_path = response.xpath(".//a[contains(text(),'İmzası Bulunan Kanun Teklifleri')]/@href").get()
billprop_link = response.urljoin(billprop_link_path)
questionprop_link_path = response.xpath(".//a[contains(text(),'Sahibi Olduğu Yazılı Soru Önergeleri')]/@href").get()
questionprop_link = response.urljoin(questionprop_link_path)
speech_link_path = response.xpath(".//a[contains(text(),'Genel Kurul Konuşmaları')]/@href").get()
speech_link = response.urljoin(speech_link_path)
yield Request(billprop_link, callback = self.bill_prop_counter, meta = {
'name': name,
'party': party
}) #number of bill proposals to be requested
yield Request(questionprop_link, callback = self.quest_prop_counter, meta = {
'name': name,
'party': party
}) #number of question propoesals to be requested
yield Request(speech_link, callback = self.speech_counter, meta = {
'name': name,
'party': party
}) #number of speeches to be requested
# COUNTING FUNCTIONS
def bill_prop_counter(self,response):
name = response.meta.get('name')
party = response.meta.get('party')
billproposals = response.xpath("//tr[@valign='TOP']")
yield { 'bill_prop_count': len(billproposals),
'name': name,
'party': party}
def quest_prop_counter(self, response):
name = response.meta.get('name')
party = response.meta.get('party')
researchproposals = response.xpath("//tr[@valign='TOP']")
yield {'res_prop_count': len(researchproposals),
'name': name,
'party': party}
def speech_counter(self, response):
name = response.meta.get('name')
party = response.meta.get('party')
speeches = response.xpath("//tr[@valign='TOP']")
yield { 'speech_count' : len(speeches),
'name': name,
'party': party}
ANSWER
Answered 2021-Dec-18 at 06:26This is happening because you are yielding dicts instead of item objects, so spider engine will not have a guide of fields you want to have as default.
In order to make the csv output fields bill_prop_count and res_prop_count, you should make the following changes in your code:
1 - Create a base item object with all desirable fields - you can create this in the items.py file of your scrapy project:
from scrapy import Item, Field
class MvItem(Item):
name = Field()
party = Field()
bill_prop_count = Field()
res_prop_count = Field()
speech_count = Field()
2 - Import the item object created to the spider code & yield items populated with the dict, instead of single dicts:
from your_project.items import MvItem
...
# COUNTING FUNCTIONS
def bill_prop_counter(self,response):
name = response.meta.get('name')
party = response.meta.get('party')
billproposals = response.xpath("//tr[@valign='TOP']")
yield MvItem(**{ 'bill_prop_count': len(billproposals),
'name': name,
'party': party})
def quest_prop_counter(self, response):
name = response.meta.get('name')
party = response.meta.get('party')
researchproposals = response.xpath("//tr[@valign='TOP']")
yield MvItem(**{'res_prop_count': len(researchproposals),
'name': name,
'party': party})
def speech_counter(self, response):
name = response.meta.get('name')
party = response.meta.get('party')
speeches = response.xpath("//tr[@valign='TOP']")
yield MvItem(**{ 'speech_count' : len(speeches),
'name': name,
'party': party})
The output csv will have all possible columns for the item:
bill_prop_count,name,party,res_prop_count,speech_count
,Abdullah DOĞRU,AK Parti,,11
,Mehmet Şükrü ERDİNÇ,AK Parti,,3
,Muharrem VARLI,MHP,,13
,Muharrem VARLI,MHP,0,
,Jülide SARIEROĞLU,AK Parti,,3
,İbrahim Halil FIRAT,AK Parti,,7
20,Burhanettin BULUT,CHP,,
,Ünal DEMİRTAŞ,CHP,,22
...
Now if you want to have all the three counts in the same row, you'll have to change the design of your spider. Possibly one counting function at the time passing the item in the meta attribute.
QUESTION
I've upgraded my Ruby version from 2.5.x to 2.6.x (and uninstalled the 2.5.x version). And now Puma server stops working when instantiating a client of Google Cloud Text-to-Speech:
client = Google::Cloud::TextToSpeech.text_to_speech
It just exits without giving an error (in Command Prompt). And there is a 'Segmentation fault' message in the 'bash' terminal.
Puma config-file:
max_threads_count = ENV.fetch("RAILS_MAX_THREADS") { 5 }
min_threads_count = ENV.fetch("RAILS_MIN_THREADS") { max_threads_count }
threads min_threads_count, max_threads_count
port ENV.fetch("PORT") { 3000 }
environment ENV.fetch("RAILS_ENV") { ENV['RACK_ENV'] || "development" }
pidfile ENV.fetch("PIDFILE") { "tmp/pids/server.pid" }
# workers ENV.fetch("WEB_CONCURRENCY") { 2 }
preload_app!
plugin :tmp_restart
The method that works with Google Cloud Text-to-Speech:
require "google/cloud/text_to_speech"
# Instantiates a client
client = Google::Cloud::TextToSpeech.text_to_speech
...
Gemfile:
gem 'rails', '6.0.1'
gem 'puma', '3.12.2'
gem 'google-cloud-text_to_speech', '1.1.1'
...
OS Windows 10.
I'm confused. I don't understand why it happens and how to fix it. I've tried to use the last gem-versions for 'puma' and 'google-cloud-text_to_speech', reinstalled the Google SDK, but it continues happening.
Maybe something's wrong with my credentials? I get the nil value when running the configure method (before instantiating the client):
(byebug) Google::Cloud::TextToSpeech.configure
Can somebody please help me?
ANSWER
Answered 2021-Dec-07 at 08:52Try reinstalling ruby-debug
sudo gem uninstall ruby-debug
sudo gem install ruby-debug
And, can you expand your question with including your Gemfile, and Gemfile.lock
Another aproach may be deleting Gemfile.lock then running bundle install
rm -rf Gemfile.lock
bundle install
QUESTION
I need to extract the text from between parentheses if a keyword is inside the parentheses.
So if I have a string that looks like this:
('one', 'CARDINAL'), ('Castro', 'PERSON'), ('Latin America', 'LOC'), ('Somoza', 'PERSON')
And my keyword is "LOC", I just want to extract ('Latin America', 'LOC'), not the others.
Help is appreciated!!
This is a sample of my data set, a csv file:
,speech_id,sentence,date,speaker,file,parsed_text,named_entities
0,950094636,Let me state that the one sure way we can make it easy for Castro to continue to gain converts in Latin America is if we continue to support regimes of the ilk of the Somoza family,19770623,Mr. OBEY,06231977.txt,Let me state that the one sure way we can make it easy for Castro to continue to gain converts in Latin America is if we continue to support regimes of the ilk of the Somoza family,"[('one', 'CARDINAL'), ('Castro', 'PERSON'), ('Latin America', 'LOC'), ('Somoza', 'PERSON')]"
1,950094636,That is how we encourage the growth of communism,19770623,Mr. OBEY,06231977.txt,That is how we encourage the growth of communism,[]
2,950094636,That is how we discourage the growth of democracy in Latin America,19770623,Mr. OBEY,06231977.txt,That is how we discourage the growth of democracy in Latin America,"[('Latin America', 'LOC')]"
3,950094636,Mr Chairman,19770623,Mr. OBEY,06231977.txt,Mr Chairman,[]
4,950094636,given the speeches I have made lately about the press,19770623,Mr. OBEY,06231977.txt,given the speeches I have made lately about the press,[]
5,950094636,I am not one,19770623,Mr. OBEY,06231977.txt,I am not one,[]
6,950094636,I suppose,19770623,Mr. OBEY,06231977.txt,I suppose,[]
I am trying to extract just parentheses with the word LOC:
regex <- "(?=\\().*? \'LOC.*?(?<=\\))"
filtered_df$clean_NE <- str_extract_all(filtered_df$named_entities, regex)
The above regular expression does not work. Thanks!
ANSWER
Answered 2021-Nov-13 at 22:41You can use
str_extract_all(filtered_df$named_entities, "\\([^()]*'LOC'[^()]*\\)")
See the regex demo. Details:
\(- a(char[^()]*- zero or more chars other than(and)'LOC'- a'LOC'string[^()]*- zero or more chars other than(and)\)- a)char.
See the online R demo:
library(stringr)
x <- "[('one', 'CARDINAL'), ('Castro', 'PERSON'), ('Latin America', 'LOC'), ('Somoza', 'PERSON')]"
str_extract_all(x, "\\([^()]*'LOC'[^()]*\\)")
# => [1] "('Latin America', 'LOC')"
As a bonus solution to get Latin America, you can use
str_extract_all(x, "[^']+(?=',\\s*'LOC'\\))")
# => [1] "Latin America"
Here, [^']+(?=',\s*'LOC'\)) matches one or more chars other than ' that are followed with ',, zero or more whitespaces, and then 'LOC') string.
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