When trying to run the ljspeech example, I get the following error, even when the model is moved to the only GPU in the system. I am using Cuda 11.7, Pytorch 1.13.1, and Fairseq 0.12.2.
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper__index_select)
The code used:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
import torch
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0].to(torch.device('cuda'))
models[0] = model
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(models, cfg)
text = "Hello, this is a test run."
sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
ipd.Audio(wav, rate=rate)
Related
I am using IBM's quantum computing lab, and was following a tutorial made by IBM for getting started, and my code is throwing errors. I followed the tutorial exactly. Here is my code:
#-----------Cell 1:
import numpy as np
# Importing standard Qiskit libraries
from qiskit import QuantumCircuit, transpile, Aer, IBMQ
from qiskit.tools.jupyter import *
from qiskit.visualization import *
from ibm_quantum_widgets import *
from qiskit.providers.aer import QasmSimulator
# Loading your IBM Quantum account(s)
provider = IBMQ.load_account()
#-----------Cell 2:
# Build
#------
# Create a Quantum Circuit acting on the q register
circuit = QuantumCircuit(2, 2)
# Add a H gate on qubit 0
circuit.h(0)
# Add a CX (CNOT) gate on control qubit 0 and target qubit 1
circuit.cx(0, 1)
# Map the quantum measurement to the classical bits
circuit.measure([0,1], [0,1])
# END
# Execute
#--------
# Use Aer's qasm_simulator
simulator = Aer.get_backend('qasm_simulator')
# Execute the circuit on the qasm simulator
job = execute(circuit, simulator, shots=1000)
# Grab results from the job
result = job.result()
# Return counts
counts = result.get_counts(circuit)
print("\nTotal count for 00 and 11 are:",counts)
# END
# Visualize
#----------
# Import draw_circuit, then use it to draw the circuit
from ibm_quantum_widgets import draw_circuit
draw_circuit(circuit)
# Analyze
#--------
# Plot a histogram
plot_histogram(counts)
# END
This code throws this error:
Traceback (most recent call last):
File "/tmp/ipykernel_59/1801586149.py", line 26, in <module>
job = execute(circuit, simulator, shots=1000)
NameError: name 'execute' is not defined
Use %tb to get the full traceback.
I am new to IBM and quantum computing, how do I fix this error?
Here is the tutorial I was following if you need it: https://quantum-computing.ibm.com/lab/docs/iql/first-circuit
You did not import execute from qiskit.
Change
from qiskit import QuantumCircuit, transpile, Aer, IBMQ
to
from qiskit import QuantumCircuit, transpile, Aer, IBMQ, execute
I understand reformer is able to handle a large number of tokens. However it does not appear to support the summarization task:
>>> from transformers import ReformerTokenizer, ReformerModel
>>> from transformers import pipeline
>>> summarizer = pipeline("summarization", model="reformer")
404 Client Error: Not Found for url: https://huggingface.co/reformer/resolve/main/config.json
...
How would you construct the pipeline "manually" to use reformer for summarization?
Try this:
summarizer = pipeline("summarization", model="google/reformer-enwik8")
via here.
However, this produces...
/lib/python3.7/site-packages/sentencepiece.py", line 177, in LoadFromFile
return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg)
TypeError: not a string
when i use the st.cache decorator to cash hugging-face transformer model i get
Unhashable TypeError
this is the code
from transformers import pipeline
import streamlit as st
from io import StringIO
#st.cache(hash_funcs={StringIO: StringIO.getvalue})
def model() :
return pipeline("sentiment-analysis", model='akhooli/xlm-r-large-arabic-sent')
after searching in issues section in streamlit repo
i found that hashing argument is not required , just need to pass this argument
allow_output_mutation = True
This worked for me:
from transformers import pipeline
import tokenizers
import streamlit as st
import copy
#st.cache(hash_funcs={tokenizers.Tokenizer: lambda _: None, tokenizers.AddedToken: lambda _: None})
def get_model() :
return pipeline("sentiment-analysis", model='akhooli/xlm-r-large-arabic-sent')
input = st.text_input('Text')
bt = st.button("Get Sentiment Analysis")
if bt and input:
model = copy.deepcopy(get_model())
st.write(model(input))
Note 1:
calling the pipeline with input model(input) changes the model and we shouldn't change a cached value so we need to copy the model and run it on the copy.
Note 2:
First run will load the model using the get_model function next run will use the chace.
Note 3:
You can read more about Advanced caching in stremlit in thier documentation.
Output examples:
from pydotplus import graph_from_dot_data
from sklearn.tree import export_graphviz
from IPython.display import Image
dot_data = export_graphviz(tree,filled=True,rounded=True,class_names=['Setosa','Versicolor','Virginica'],feature_names=['petal length','petal width'],out_file=None)
graph = graph_from_dot_data(dot_data)
Image(graph.create_png())
Program terminated with status:
1. stderr follows: 'C:\Users\En' is not recognized as an internal or external command,
operable program or batch file.
it seems that it split my username into half.How do i overcome this?
I have a very similar example that I'm trying out, it's based on a ML how-to book which is working with a Taiwan Credit Card dataset predicting default risk. My setup is as follows:
from six import StringIO
from sklearn.tree import export_graphviz
from IPython.display import Image
import pydotplus
Then creating the decision tree plot is done in this way:
dot_data = StringIO()
export_graphviz(decision_tree=class_tree,
out_file=dot_data,
filled=True,
rounded=True,
feature_names = X_train.columns,
class_names = ['pay','default'],
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
I think it's all coming from the out_file=dot_data argument but cannot figure out where the file path is created and stored as print(dot_data.getvalue()) did not show any pathname.
In my research I came across sklearn.plot_tree() which seems to do everything that the graphviz does. So I took the above exporet_graphviz arguments and were matching arguments were in the .plot_tree method I added them.
I ended up with the following which created the same image as was found in the text:
from sklearn import tree
plt.figure(figsize=(20, 10))
tree.plot_tree(class_tree,
filled=True, rounded=True,
feature_names = X_train.columns,
class_names = ['pay','default'],
fontsize=12)
plt.show()
I am trying to do a hyperparameter tuning using GridSearchCV on XGBoost.But, I'm getting the following error.
/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.if diff:
This keeps on running forever. Given below is the code.
classifier = xgb.XGBClassifier()
from sklearn.grid_search import GridSearchCV
n_estimators=[10,50,100,150,200,250,300]
max_depth=[2,3,4,5,6,7,8,9,10]
learning_rate=[0.1,0.01,0.09,0.08,0.07,0.001]
colsample_bytree=[0.5,0.6,0.7,0.8,0.9]
min_child_weight=[1,2,3,4,5,6,7,8,9,10]
gamma=[0.001,0.01,0.1,0.2,0.3,0.4,0.5,1]
subsample=[0.5,0.6,0.7,0.8,0.9]
param_grid=dict(n_estimators=n_estimators,max_depth=max_depth,learning_rate=learning_rate,colsample_bytree=colsample_bytree,min_child_weight=min_child_weight,gamma=gamma,subsample=subsample)
grid = GridSearchCV(classifier, param_grid, cv=10, scoring='accuracy')
grid.fit(X, Y)
grid.grid_scores_
print(grid.best_score_)
print(grid.best_params_)
print(grid.best_estimator_)
# Predicting the Test set results
Y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(Y_test, Y_pred)
I am using python3.5, XGBOOT and gridsearch library has already been preloaded. I am running this on google collaboratory.
Please suggest what is going wrong ?