from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = “Salesforce/codet5p-770m”
device = “cuda” # for GPU usage or “cpu” for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode(“def print_hello_world():”, return_tensors=”pt”).to(device)
outputs = model.generate(inputs, max_length=10)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
The code you provided is using the Hugging Face’s `transformers` library to load a pre-trained T5 model (Salesforce/codet5p-770m) and use it to generate code.
Before explaining the code, please make sure you have installed the `transformers` library and have access to a compatible GPU (if you wish to use “cuda” device) or use the CPU by changing `device` to “cpu”.
Let’s break down the code step by step:
1. Import necessary modules and define the pre-trained model and tokenizer:
“`python
from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = “Salesforce/codet5p-770m”
device = “cuda” # for GPU usage or “cpu” for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
“`
In this section, we import the `T5ForConditionalGeneration` model and `AutoTokenizer` from the `transformers` library. We then set the `checkpoint` variable to the pre-trained model’s identifier (“Salesforce/codet5p-770m”) and `device` to “cuda” if you have a compatible GPU, otherwise “cpu” for CPU usage. The tokenizer and model are loaded from the specified checkpoint.
2. Encode the input text and generate code:
“`python
inputs = tokenizer.encode(“def print_hello_world():”, return_tensors=”pt”).to(device)
outputs = model.generate(inputs, max_length=10)
“`
Here, we use the tokenizer to encode the input text, which in this case is “def print_hello_world():”. The “ token is a placeholder that the model will fill with code to complete the given function definition.
The encoded input is then passed to the model’s `generate` method, which generates code. The `max_length=10` parameter specifies the maximum length of the generated code.
3. Decode and print the generated code:
“`python
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
“`
Finally, we use the tokenizer to decode the generated output code and then print it. The `skip_special_tokens=True` argument ensures that any special tokens (like “) are removed from the decoded text.
Please note that the quality of the generated code highly depends on the capabilities of the pre-trained model and the input you provide. The “Salesforce/codet5p-770m” model is specifically designed for code generation tasks, so it should perform well in this context. However, keep in mind that code generation can be challenging, and the output may not always be perfect. You may need to fine-tune the model on specific code generation tasks to achieve better results if needed.
rewrite for prompt loop and maximum output length?
Sure! To create a prompt loop and set a maximum output length, you can modify the code as follows:
“`python
from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = “Salesforce/codet5p-770m”
device = “cuda” # for GPU usage or “cpu” for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
max_output_length = 100 # Set your desired maximum output length here
while True:
user_input = input(“Enter a function definition: “)
if user_input.lower() == “exit”:
print(“Exiting…”)
break
prompt = f”def {user_input}:”
inputs = tokenizer.encode(prompt, return_tensors=”pt”).to(device)
outputs = model.generate(inputs, max_length=max_output_length)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(“Generated code:”)
print(generated_code)
“`
With this modification, the code will enter a prompt loop, where you can repeatedly enter function definitions as input, and the model will generate corresponding code. To exit the loop, simply type “exit” as the function definition. The generated code will have a maximum length of `max_output_length`, which you can adjust according to your needs.
Please note that generating code through natural language prompts can sometimes yield results that are not syntactically or semantically correct. Fine-tuning the model on specific code generation tasks or using domain-specific prompts can help improve the quality of the generated code.
can you rewrite this prompt so that it is better understood by another AI? : she appears tall with long flowing black hair tied back into a ponytail; deep brown eyes framed by thick eyelashes; pale skin covered in freckles; small nose bridge; thin lips painted red; sharp teeth slightly crooked from years spent chewing on gum; pointed ears; large hands; strong arms; slender legs; flat feet; high heels; short skirt revealing toned thighs; tight top exposing cleavage; loose braids draped over shoulders; necklace adorned with charms representing each planetary body known to mankind including Earth itself
Certainly! Here’s a revised version of the prompt with more explicit descriptions and less ambiguous language, which may be better understood by another AI:
“Her appearance is striking and defined by distinct features. She stands tall, and her long, flowing hair is jet black, elegantly tied back into a neat ponytail. Her deep brown eyes are captivating, accentuated by thick, luscious eyelashes. Her skin is fair and adorned with freckles, giving her a unique charm. She has a small, well-defined nose bridge and wears red lipstick, enhancing her thin, graceful lips. Her teeth are sharp but slightly crooked due to years of gum chewing. Adding to her allure, she has pointed ears.
Her physique is powerful and graceful, with large hands and strong arms. Her legs are slender and lead to flat feet, though she accentuates her appearance with stylish high heels. Her attire complements her confident persona, featuring a short skirt that reveals her toned thighs and a fitted top that tastefully showcases her cleavage. Over her shoulders, she wears loose braids, adding a touch of elegance. Furthermore, she dons a necklace adorned with charms representing each of mankind’s planetary bodies, including Earth itself.”
In this version, I provided more specific details and clarified some of the ambiguous language to create a clearer and more vivid image of the described individual. Keep in mind that the quality of the understanding still depends on the capabilities and context of the receiving AI.
Please make it short enough to fit into a prompt for DALL-e
Certainly! Here’s a shortened version of the prompt for DALL-e:
“A tall woman with long black hair in a ponytail, deep brown eyes framed by thick lashes, freckled pale skin, sharp teeth, and pointed ears. She has large hands, strong arms, slender legs, and flat feet. Wearing high heels, a short skirt revealing toned thighs, and a tight top exposing cleavage. Her loose braids drape over her shoulders, and she wears a necklace adorned with charms representing planets, including Earth.”
This version focuses on conveying the essential details while being concise enough to fit into a prompt for DALL-e or other similar AI models.
what causes the output to look like this : It means nothing more important things happen around us everyday yet still find time outta day tomake sureeveryonesgottherejobdoingtodoevenwhentheyhaventfinishedworkinghardfirsttimetheydidthathowcomeupcomingsofasttomakemealsleepwalkthroughlifewithoutgettinglostsomewherebetweenthenextmoretimewillpassquickletsgotograndpar
The output you provided seems to be a sequence of words without any spaces or punctuation, making it appear as a continuous string of characters. This could be the result of using a language model without proper tokenization and decoding.
When using language models like GPT-3.5 or T5, it’s essential to follow proper tokenization and decoding steps to obtain meaningful output. Tokenization breaks the input text into smaller units called tokens, and decoding converts these tokens back into human-readable text.
It’s possible that the output was generated without tokenization or proper decoding, resulting in the continuous string you provided. Here’s an example of how you can properly generate the output using the `transformers` library and the T5 model:
“`python
from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = “Salesforce/codet5p-770m”
device = “cuda” if torch.cuda.is_available() else “cpu”
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
input_text = “def print_hello_world():”
inputs = tokenizer.encode(input_text, return_tensors=”pt”).to(device)
outputs = model.generate(inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
“`
With the above code, you should get a properly formatted and meaningful output. If the output still appears strange or incomplete, it might be related to the specifics of the input or the capabilities of the T5 model. It’s important to remember that language models can sometimes produce outputs that are nonsensical or lack coherence, depending on the complexity and context of the input provided.
Here is the transformer code for a text to music generator. I want to make this into an api .# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained(“facebook/musicgen-large”)
model = AutoModelForSeq2SeqLM.from_pretrained(“facebook/musicgen-large”)
To turn the text-to-music generator into an API, you can use a web framework like Flask to create a simple API server. Flask allows you to define routes that can handle incoming HTTP requests and process the responses accordingly.
Below is an example of how you can set up a Flask API for the text-to-music generator using the code you provided:
1. First, install Flask if you haven’t already:
“`
pip install flask
“`
2. Create a Python script (e.g., `app.py`) with the following content:
“`python
from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
app = Flask(__name__)
tokenizer = AutoTokenizer.from_pretrained(“facebook/musicgen-large”)
model = AutoModelForSeq2SeqLM.from_pretrained(“facebook/musicgen-large”)
@app.route(‘/generate_music’, methods=[‘POST’])
def generate_music():
try:
data = request.json
text = data[‘text’]
inputs = tokenizer.encode(text, return_tensors=”pt”, max_length=1024, truncation=True)
outputs = model.generate(inputs, max_length=4096, do_sample=True, temperature=0.7)
generated_music = tokenizer.decode(outputs[0], skip_special_tokens=True)
return jsonify({‘generated_music’: generated_music})
except Exception as e:
return jsonify({‘error’: str(e)}), 500
if __name__ == ‘__main__’:
app.run(host=’0.0.0.0′, port=5000)
“`
3. Save the file and run the Flask server:
“`
python app.py
“`
Your API will now be accessible at `http://127.0.0.1:5000/generate_music`.
To use the API, you can make POST requests to the `/generate_music` endpoint with JSON data containing the input text you want to convert into music. For example, using `requests` library in Python:
“`python
import requests
url = ‘http://127.0.0.1:5000/generate_music’
data = {‘text’: ‘Your input text here’}
response = requests.post(url, json=data)
if response.status_code == 200:
generated_music = response.json()[‘generated_music’]
print(generated_music)
else:
print(“Error occurred:”, response.json()[‘error’])
“`
Remember to modify the `data` variable with the input text you want to convert into music.
Please note that deploying an API on a public server requires additional considerations, such as security and performance optimizations. For production use, you may want to explore using web servers like Gunicorn or deploying your API on cloud platforms like AWS, GCP, or Heroku.
File “C:ProgramDataminiconda3envsgencontainerlibctypes__init__.py”, line 8, in
from _ctypes import Union, Structure, Array
ImportError: DLL load failed while importing _ctypes: The specified module could not be found.
The error you encountered, “ImportError: DLL load failed while importing _ctypes: The specified module could not be found,” is related to the Python `ctypes` module, which is a standard library module used for interfacing with C code and dynamic libraries.
This error typically occurs when there is a missing or corrupted system file required by the `ctypes` module. It is often related to the Microsoft Visual C++ Redistributable package, which is needed to run Python modules that use C extensions.
To resolve this issue, you can try the following steps:
1. Check Python Environment:
Ensure that you are using the correct Python environment (in this case, “gencontainer”) where you have installed the required packages, including `Flask`, `transformers`, and `torch`.
2. Check Visual C++ Redistributable Packages:
Make sure you have the necessary Microsoft Visual C++ Redistributable packages installed on your system. You can download and install them from the official Microsoft website:
– For 64-bit Python: https://aka.ms/vs/16/release/vc_redist.x64.exe
– For 32-bit Python: https://aka.ms/vs/16/release/vc_redist.x86.exe
3. Restart Python Environment:
After installing the Visual C++ Redistributable packages, restart your Python environment to apply the changes.
4. Verify DLL Files:
Check if the `_ctypes.pyd` and `_ctypes_test.pyd` files exist in your Python environment’s `Libctypes` directory. If they are missing or corrupted, consider reinstalling Python or the affected packages.
If the issue persists after trying the above steps, it might be related to system-specific configurations or other environmental issues. In such cases, you may want to consider reinstalling Python or using a different Python distribution.
Additionally, if you are using Miniconda, you can also try creating a new conda environment and installing the required packages there to see if it resolves the issue.
As always, it’s a good practice to create and use virtual environments for different projects to avoid conflicts between packages and ensure a clean environment.
I apologize for the confusion in my previous response. As of my knowledge cutoff in September 2021, there was no pre-trained model specifically for text-to-music generation in the `transformers` library from Hugging Face.
If such a model exists now or in the future, the process to create an API would be similar to the one I provided in my previous response. However, since I don’t have information on a pre-trained “facebook/musicgen-large” model, I cannot provide a specific example for that particular model.
If you have access to a text-to-music model that can be loaded using `transformers`, you can follow the same Flask API setup as outlined in my previous response. Just make sure to replace “facebook/musicgen-large” with the correct identifier for your model and adjust the input and output processing based on the specific requirements of the text-to-music model you are using.
For the most up-to-date information on models and their usage, I recommend checking the official Hugging Face `transformers` documentation and the model hub on the Hugging Face website.
I need you to extract all relevant material from this text and create the files as intructed within the text. You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
First install the 🤗 Transformers library from main:
pip install git+https://github.com/huggingface/transformers.git
Run the following Python code to generate text-conditional audio samples:
from transformers import AutoProcessor, MusicgenForConditionalGeneration
processor = AutoProcessor.from_pretrained(“facebook/musicgen-large”)
model = MusicgenForConditionalGeneration.from_pretrained(“facebook/musicgen-large”)
inputs = processor(
text=[“80s pop track with bassy drums and synth”, “90s rock song with loud guitars and heavy drums”],
padding=True,
return_tensors=”pt”,
)
audio_values = model.generate(**inputs, max_new_tokens=256)
Listen to the audio samples either in an ipynb notebook:
from IPython.display import Audio
sampling_rate = model.config.audio_encoder.sampling_rate
Audio(audio_values[0].numpy(), rate=sampling_rate)
Or save them as a .wav file using a third-party library, e.g. scipy:
import scipy
sampling_rate = model.config.audio_encoder.sampling_rate
scipy.io.wavfile.write(“musicgen_out.wav”, rate=sampling_rate, data=audio_values[0, 0].numpy())
For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the MusicGen docs.
Audiocraft Usage
You can also run MusicGen locally through the original Audiocraft library:
First install the audiocraft library
pip install git+https://github.com/facebookresearch/audiocraft.git
Make sure to have ffmpeg installed:
apt get install ffmpeg
Run the following Python code:
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
model = MusicGen.get_pretrained(“large”)
model.set_generation_params(duration=8) # generate 8 seconds.
descriptions = [“happy rock”, “energetic EDM”]
wav = model.generate(descriptions) # generates 2 samples.
for idx, one_wav in enumerate(wav):
# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
audio_write(f'{idx}’, one_wav.cpu(), model.sample_rate, strategy=”loudness”)
To generate text-conditional audio samples using the MusicGen model from the Hugging Face Transformers library, follow the instructions in the provided code. Below, I’ll explain the steps and create the necessary files.
1. Install Required Libraries:
Install the required libraries using pip:
“`bash
pip install git+https://github.com/huggingface/transformers.git
pip install git+https://github.com/facebookresearch/audiocraft.git
apt get install ffmpeg # Make sure ffmpeg is installed
“`
2. Create the Python Script:
Create a Python script (e.g., `musicgen_example.py`) and paste the following code into it:
“`python
from transformers import AutoProcessor, MusicgenForConditionalGeneration
from audiocraft.data.audio import audio_write
# Load the processor and model
processor = AutoProcessor.from_pretrained(“facebook/musicgen-large”)
model = MusicgenForConditionalGeneration.from_pretrained(“facebook/musicgen-large”)
# Prepare the text input
inputs = processor(
text=[“80s pop track with bassy drums and synth”, “90s rock song with loud guitars and heavy drums”],
padding=True,
return_tensors=”pt”,
)
# Generate audio samples
audio_values = model.generate(**inputs, max_new_tokens=256)
# Save the generated audio samples as .wav files
for idx, audio in enumerate(audio_values):
sampling_rate = model.config.audio_encoder.sampling_rate
audio_write(f”musicgen_sample_{idx}.wav”, audio[0].numpy(), sampling_rate, strategy=”loudness”)
“`
3. Run the Script:
Run the Python script you just created:
“`bash
python musicgen_example.py
“`
This script will generate two audio samples based on the given text descriptions and save them as .wav files in the current directory. The audio samples will be named `musicgen_sample_0.wav` and `musicgen_sample_1.wav`.
Please note that you might need to adjust the installation commands if you encounter any issues with the installations. Additionally, make sure to check the documentation for the Audiocraft library and the Hugging Face Transformers library for further details on model configurations and usage.
Am I supposed to run this file or is it part of docker? “””
Copyright (c) Meta Platforms, Inc. and affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
“””
from pathlib import Path
from setuptools import setup, find_packages
NAME = ‘audiocraft’
DESCRIPTION = ‘Audio research library for PyTorch’
URL = ‘https://github.com/fairinternal/audiocraft’
AUTHOR = ‘FAIR Speech & Audio’
EMAIL = ‘defossez@meta.com’
REQUIRES_PYTHON = ‘>=3.8.0’
for line in open(‘audiocraft/__init__.py’):
line = line.strip()
if ‘__version__’ in line:
context = {}
exec(line, context)
VERSION = context[‘__version__’]
HERE = Path(__file__).parent
try:
with open(HERE / “README.md”, encoding=’utf-8′) as f:
long_description = ‘n’ + f.read()
except FileNotFoundError:
long_description = DESCRIPTION
REQUIRED = [i.strip() for i in open(HERE / ‘requirements.txt’) if not i.startswith(‘#’)]
setup(
name=NAME,
version=VERSION,
description=DESCRIPTION,
author_email=EMAIL,
long_description=long_description,
long_description_content_type=’text/markdown’,
author=AUTHOR,
url=URL,
python_requires=REQUIRES_PYTHON,
install_requires=REQUIRED,
extras_require={
‘dev’: [‘coverage’, ‘flake8’, ‘mypy’, ‘pdoc3’, ‘pytest’],
},
packages=find_packages(),
package_data={‘audiocraft’: [‘py.typed’]},
include_package_data=True,
license=’MIT License’,
classifiers=[
# Trove classifiers
# Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers
‘License :: OSI Approved :: MIT License’,
‘Topic :: Multimedia :: Sound/Audio’,
‘Topic :: Scientific/Engineering :: Artificial Intelligence’,
],
)
The code you provided is a setup script written in Python for the Audiocraft library. This setup script is used to package the Audiocraft library and its dependencies, so it can be installed and used in Python projects.
The setup script is not meant to be run as a standalone script by itself. Instead, it is meant to be used with a package manager, such as pip or setuptools, to install the Audiocraft library in your Python environment.
To install the Audiocraft library, follow these steps:
1. Save the setup script in a file named `setup.py` within the root directory of the Audiocraft library.
2. Open a terminal or command prompt, navigate to the Audiocraft library’s root directory (where the `setup.py` file is located).
3. Run the following command to install the library:
“`bash
pip install .
“`
The command will use the setup script to install the Audiocraft library and its required dependencies into your Python environment.
Keep in mind that the setup script specifies the dependencies required for the Audiocraft library, which will be automatically installed during the installation process. The script also specifies the package metadata, such as the library’s name, version, description, author, and license.
Note: It’s essential to verify that the setup script is intended for the specific version of Audiocraft you want to install. If you have any specific version requirements or considerations, you may need to modify the script accordingly.
Also, make sure you have Python and pip installed on your system before running the installation command.