<jupyter_start><jupyter_text>Training and testing the Tacotron Model<jupyter_code>import os
from os.path import exists, join, expanduser

# Setting directory as root
os.chdir(join(expanduser("~"),""))

# Clone repository
if not exists("tacotron"):
  ! git clone https://github.com/GrassDinosaur/tacotron
    
# Change working directory to Tacotron 
os.chdir(join(expanduser("~"), "tacotron"))
# Install dependencices
! pip install -r requirements.txt
# Download MLAILABS en_UK dataset
! curl http://data.m-ailabs.bayern/data/Training/stt_tts/en_UK.tgz | tar xz
# Preprocess the data
! python preprocess.py --dataset en_UK
# Train the data
! python train.py
# Evaluate the model
! python eval.py --checkpoint ~/tacotron/logs-tacotron/model.ckpt-7000<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11825192802056556, 'fraction_numerical': 0.005141388174807198, 'mean_word_length': 5.282258064516129, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '329746', 'license': 'MIT', 'n_tokens_mistral': 282, 'n_tokens_neox': 274, 'n_words': 66, 'path': 'notebooks/Training and testing the model.ipynb', 'repo_name': 'GrassDinosaur/tacotron', 'size': '778'}