import json
import math
import random
import os
import streamlit as st
import lyricsgenius
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
st.set_page_config(page_title="HuggingArtists")
st.title("HuggingArtists")
st.sidebar.markdown(
"""
""",
unsafe_allow_html=True,
)
st.sidebar.markdown(
"""
GitHub | Project Report
""",
unsafe_allow_html=True,
)
st.sidebar.header("Generation settings:")
num_sequences = st.sidebar.number_input(
"Number of sequences to generate",
min_value=1,
value=5,
help="The amount of generated texts",
)
min_length = st.sidebar.number_input(
"Minimum length of the sequence",
min_value=1,
value=100,
help="The minimum length of the sequence to be generated",
)
max_length= st.sidebar.number_input(
"Maximum length of the sequence",
min_value=1,
value=160,
help="The maximum length of the sequence to be generated",
)
temperature = st.sidebar.slider(
"Temperature",
min_value=0.0,
max_value=3.0,
step=0.01,
value=1.0,
help="The value used to module the next token probabilities",
)
top_p = st.sidebar.slider(
"Top-P",
min_value=0.0,
max_value=1.0,
step=0.01,
value=0.95,
help="If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
)
top_k= st.sidebar.number_input(
"Top-K",
min_value=0,
value=50,
step=1,
help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
)
caption = (
"In [HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist), we can generate lyrics by a specific artist. This was made by fine-tuning a pre-trained [HuggingFace Transformer](https://huggingface.co) on parsed datasets from [Genius](https://genius.com)."
)
st.markdown("[HuggingArtists](https://github.com/AlekseyKorshuk/huggingartist) - Train a model to generate lyrics 🎵")
st.markdown(caption)
st.subheader("Settings:")
artist_name = st.text_input("Artist name:", "Eminem")
start = st.text_input("Beginning of the song:", "But for me to rap like a computer")
TOKEN = "q_JK_BFy9OMiG7fGTzL-nUto9JDv3iXI24aYRrQnkOvjSCSbY4BuFIindweRsr5I"
genius = lyricsgenius.Genius(TOKEN)
model_html = """
"""
def post_process(output_sequences):
predictions = []
generated_sequences = []
max_repeat = 2
# decode prediction
for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
generated_sequence = generated_sequence.tolist()
text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True, skip_special_tokens=True)
generated_sequences.append(text.strip())
for i, g in enumerate(generated_sequences):
res = str(g).replace('\n\n\n', '\n').replace('\n\n', '\n')
lines = res.split('\n')
# print(lines)
# i = max_repeat
# while i != len(lines):
# remove_count = 0
# for index in range(0, max_repeat):
# # print(i - index - 1, i - index)
# if lines[i - index - 1] == lines[i - index]:
# remove_count += 1
# if remove_count == max_repeat:
# lines.pop(i)
# i -= 1
# else:
# i += 1
predictions.append('\n'.join(lines))
return predictions
if st.button("Run"):
model_name = None
with st.spinner(text=f"Searching for {artist_name } in Genius..."):
artist = genius.search_artist(artist_name, max_songs=0, get_full_info=False)
if artist is not None:
artist_dict = genius.artist(artist.id)['artist']
artist_url = str(artist_dict['url'])
model_name = artist_url[artist_url.rfind('/') + 1:].lower()
st.markdown(model_html.replace("USER_PROFILE",artist.image_url).replace("USER_NAME",artist.name).replace("USER_HANDLE",model_name), unsafe_allow_html=True)
else:
st.markdown(f"Could not find {artist_name}! Be sure that he/she exists in [Genius](https://genius.com/).")
if model_name is not None:
with st.spinner(text=f"Downloading the model of {artist_name }..."):
model = None
tokenizer = None
try:
tokenizer = AutoTokenizer.from_pretrained(f"huggingartists/{model_name}")
model = AutoModelForCausalLM.from_pretrained(f"huggingartists/{model_name}")
except Exception as ex:
# st.markdown(ex)
st.markdown(f"Model for this artist does not exist yet. Create it in just 5 min with [Colab Notebook](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb):")
st.markdown(
"""
""",
unsafe_allow_html=True,
)
if model is not None:
with st.spinner(text=f"Generating lyrics..."):
encoded_prompt = tokenizer(start, add_special_tokens=False, return_tensors="pt").input_ids
encoded_prompt = encoded_prompt.to(model.device)
# prediction
output_sequences = model.generate(
input_ids=encoded_prompt,
max_length=max_length,
min_length=min_length,
temperature=float(temperature),
top_p=float(top_p),
top_k=int(top_k),
do_sample=True,
repetition_penalty=1.0,
num_return_sequences=num_sequences
)
# Post-processing
predictions = post_process(output_sequences)
st.subheader("Results")
for prediction in predictions:
st.text(prediction)
st.subheader("Please star this repository and join my Telegram Channel:")
st.markdown(
"""
""",
unsafe_allow_html=True,
)